code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data i... | 60 |
"""simple docstring"""
import math
def _snake_case ( ):
lowerCAmelCase : Union[str, Any] = input('''Enter message: ''' )
lowerCAmelCase : Optional[int] = int(input(f'''Enter key [2-{len(_snake_case ) - 1}]: ''' ) )
lowerCAmelCase : str = input('''Encr... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
if any(not isinstance(_snake_case , _snake_case ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(_snake_case ) ):
for i, (ro... | 60 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import trans... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
lowerCAmelCase : Optional[int] = ... | 60 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configura... | 60 | 1 |
"""simple docstring"""
def _snake_case ( ):
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
snake_case__ : Optional[Any] = generate_large_matrix()
snake_case__ : List[Any] = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -... | 60 |
"""simple docstring"""
snake_case__ : str = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
snake_case__ ... | 60 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class snake_case_( unittest.TestCase ):
__UpperCamelCase ... | 60 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
def merge(_snake_case : list , _snake_case : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
y... | 60 | 1 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import Ba... | 60 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice... | 60 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.trainin... | 60 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_fla... | 60 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTe... | 60 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavin... | 60 | 1 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
snake_case__ : Dict = datasets.utils.logging.get_logger(__name__)
class snake_case_( folder_based_builder.FolderBasedBui... | 60 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from fla... | 60 | 1 |
"""simple docstring"""
def _snake_case ( ):
lowerCAmelCase : str = []
lowerCAmelCase : List[Any] = 1
while len(_snake_case ) < 1E6:
constant.append(str(_snake_case ) )
i += 1
lowerCAmelCase : int = ''''''.join(_snake_case... | 60 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 60 | 1 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from ... | 60 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
snake_case__ : Optional[Any] = '''src/transfo... | 60 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if... | 60 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class snake_case_:
def __init__( self : str , UpperCamelCase_ : int=None , UpperCamelCase_ : List[str]=None ):
# Input as list
lowerCAmelCase : str = li... | 60 | 1 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 60 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_f... | 60 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case__ : int = logging.get_logger(__name__)
snake_case__ :... | 60 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
if not isinstance(_snake_case , _snake_case ):
raise TypeError('''only integers accepted as input''' )
else:
lowerCAmelCase : List[str] = str(abs(_snake_case ) )
lowerCAmelC... | 60 | 1 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_... | 60 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
snake_cas... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : list , _snake_case : list ):
_validate_point(_snake_case )
_validate_point(_snake_case )
if len(_snake_case ) != len(_snake_case ):
raise ValueError('''Both points must be in the same n-dimensional space''' )
... | 60 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_... | 60 | 1 |
"""simple docstring"""
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def _snake_case ( _snake_case : str = "" ):
lowerCAmelCase : List[Any] = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
lowerCAmelCase : Any = ... | 60 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _snake_case ( _snake_case : int ):
for param in module.parameters():
lowerCAmelCase : Optional[int] = False
def _snake_case ( ):
lowerCAmelCase : List[str]... | 60 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class snake_case_( a__ ):
__UpperCamelCas... | 60 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (... | 60 | 1 |
"""simple docstring"""
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def _sna... | 60 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformer... | 60 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
snake_case__ : ... | 60 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( _snake_case : list[list[float]] ):
lowerCAmelCase : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implement... | 60 | 1 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_at... | 60 |
"""simple docstring"""
import numpy as np
def _snake_case ( _snake_case : np.array ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : list[list[int | float]] ):
lowerCAmelCase : Union[str, Any] = len(_snake_case )
lowerCAmelCase : List[Any] = len(matrix[0] )
lowerCAmelCase : str = min(_snake_case , _snake_case )
... | 60 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( _snake_case : np.ndarray , _snake_case : np.ndarray ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(_snake_case , _snak... | 60 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.n... | 60 |
"""simple docstring"""
import math
def _snake_case ( ):
lowerCAmelCase : Union[str, Any] = input('''Enter message: ''' )
lowerCAmelCase : Optional[int] = int(input(f'''Enter key [2-{len(_snake_case ) - 1}]: ''' ) )
lowerCAmelCase : str = input('''Encr... | 60 | 1 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_com... | 60 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import trans... | 60 | 1 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.da... | 60 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configura... | 60 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
snake_case__ : Optional[Any] = logging.get_logger(__name__)
def _snake_case ( _snake_case : Union[tf.Tensor, np.ndarray] ):
if isinstance(_snake_case ... | 60 |
"""simple docstring"""
snake_case__ : str = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
snake_case__ ... | 60 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case__ : Optional[int] = logging.get_logger(__name__)
snake_case__ : List[Any] = {
... | 60 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
def merge(_snake_case : list , _snake_case : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
y... | 60 | 1 |
"""simple docstring"""
import sys
snake_case__ : str = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
... | 60 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice... | 60 | 1 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice... | 60 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_fla... | 60 | 1 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def _snake_case ( _snake_case : Union[str, Any] ):
# encoder.embeddings are double... | 60 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavin... | 60 | 1 |
"""simple docstring"""
import math
def _snake_case ( _snake_case : int ):
lowerCAmelCase : Dict = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_snake_case )
def _snake_case ( _snake_case : float = 1 / 12345 ):
lowerCAmelCase ... | 60 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from fla... | 60 | 1 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def _snake_case ( _snake_case : Namespace ):
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_ou... | 60 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 60 | 1 |
"""simple docstring"""
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('''Googling.....''')
snake_case__ : Dict = '''https://www.google.com/search?q=''' + ''' '''.join(sys.argv[1:])
snake_case__ ... | 60 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
snake_case__ : Optional[Any] = '''src/transfo... | 60 | 1 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
f... | 60 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class snake_case_:
def __init__( self : str , UpperCamelCase_ : int=None , UpperCamelCase_ : List[str]=None ):
# Input as list
lowerCAmelCase : str = li... | 60 | 1 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, pr... | 60 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_f... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : str , _snake_case : int , _snake_case : str , _snake_case : Dict ):
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowerCAmelCase : Union[str, ... | 60 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
if not isinstance(_snake_case , _snake_case ):
raise TypeError('''only integers accepted as input''' )
else:
lowerCAmelCase : List[str] = str(abs(_snake_case ) )
lowerCAmelC... | 60 | 1 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_f... | 60 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
snake_cas... | 60 | 1 |
"""simple docstring"""
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchE... | 60 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
if len(_snake_case ) <= 1:
return lst
lowerCAmelCase : Any = 1
while i < len(_snake_case ):
if lst[i - 1] <= lst[i]:
i += 1
else:
lowerCAmel... | 60 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _snake_case ( _snake_case : int ):
for param in module.parameters():
lowerCAmelCase : Optional[int] = False
def _snake_case ( ):
lowerCAmelCase : List[str]... | 60 | 1 |
"""simple docstring"""
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def _snake_case ( _snake_case :... | 60 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (... | 60 | 1 |
"""simple docstring"""
from math import asin, atan, cos, radians, sin, sqrt, tan
snake_case__ : Optional[Any] = 6_3_7_8_1_3_7.0
snake_case__ : Optional[Any] = 6_3_5_6_7_5_2.3_1_4_2_4_5
snake_case__ : Tuple = 6_378_137
def _snake_case ( _snake_case : float , _snak... | 60 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformer... | 60 | 1 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
snake_case__ : Optional[Any] = '''src/transfo... | 60 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( _snake_case : list[list[float]] ):
lowerCAmelCase : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implement... | 60 | 1 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _snake_case ( _snake_case : List[Any] , _snake_case : Union[str, Any] , _snake_case : Union[str, Any] ):
lowerCAmelCase : List[str] = 0
if start < end... | 60 |
"""simple docstring"""
import numpy as np
def _snake_case ( _snake_case : np.array ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.uti... | 60 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( _snake_case : np.ndarray , _snake_case : np.ndarray ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(_snake_case , _snak... | 60 | 1 |
"""simple docstring"""
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_... | 60 |
"""simple docstring"""
import math
def _snake_case ( ):
lowerCAmelCase : Union[str, Any] = input('''Enter message: ''' )
lowerCAmelCase : Optional[int] = int(input(f'''Enter key [2-{len(_snake_case ) - 1}]: ''' ) )
lowerCAmelCase : str = input('''Encr... | 60 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
snake_case__ : Optional[int] = {
'''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
... | 60 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import trans... | 60 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Dict = logging.get_logger(__name__)
snake_case__ : Optional[Any] = {}
class snake_case_( a__ ):
__UpperCamelCase = '''llama'''
__UpperCamelCa... | 60 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configura... | 60 | 1 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def _snake_case ( _snake_case : str = "https://www.worldometers.info/coronavirus" ):
lowerCAmelCase : Any = BeautifulSoup(requests.get(_snake_case ).text , '''html.parser''' )
lowerCAmelCase : Opti... | 60 |
"""simple docstring"""
snake_case__ : str = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
snake_case__ ... | 60 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (... | 60 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
def merge(_snake_case : list , _snake_case : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
y... | 60 | 1 |
"""simple docstring"""
snake_case__ : Optional[Any] = {str(digit): digit**5 for digit in range(10)}
def _snake_case ( _snake_case : int ):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_snake_case ) )
def _snake_case ( ):
return sum(
number
for num... | 60 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice... | 60 | 1 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def _snake_case ( _snake_case : int ):
if not isinstance(_snake_case , _snake_case ):
raise TypeError('''Undefined for non-integers''' )
elif precision < 1:
rais... | 60 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_fla... | 60 | 1 |
"""simple docstring"""
from string import ascii_uppercase
snake_case__ : List[Any] = {char: i for i, char in enumerate(ascii_uppercase)}
snake_case__ : Optional[Any] = dict(enumerate(ascii_uppercase))
def _snake_case ( _snake_case : str , _snake_case : str ):
lowerCA... | 60 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavin... | 60 | 1 |
"""simple docstring"""
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class snake_case_( a__ ):
def __init__( self : List[str] , UpperCamelCase_ : List[Any] , Upp... | 60 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from fla... | 60 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.mode... | 60 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 60 | 1 |
"""simple docstring"""
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
snake_case__ : Tuple = logging.get_logger(__name__)
snake_case__ : Tuple = {name: getattr(transforme... | 60 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
snake_case__ : Optional[Any] = '''src/transfo... | 60 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 60 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class snake_case_:
def __init__( self : str , UpperCamelCase_ : int=None , UpperCamelCase_ : List[str]=None ):
# Input as list
lowerCAmelCase : str = li... | 60 | 1 |
"""simple docstring"""
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_p... | 60 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_f... | 60 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Dict = logging.get_logger(__name__)
snake_case__ : List[str] = {
'''BridgeTower/bridgetower-base''': '''https://huggin... | 60 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
if not isinstance(_snake_case , _snake_case ):
raise TypeError('''only integers accepted as input''' )
else:
lowerCAmelCase : List[str] = str(abs(_snake_case ) )
lowerCAmelC... | 60 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
snake_case__ : int = logging.get_logger(__name__)
... | 60 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
snake_cas... | 60 | 1 |
"""simple docstring"""
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.s... | 60 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_... | 60 | 1 |
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def _snake_case ( _snake_case : str , _snake_case : str , _snake_case : Optional[str] = None ):
if version.parse(hfh.__version__ ).rel... | 60 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _snake_case ( _snake_case : int ):
for param in module.parameters():
lowerCAmelCase : Optional[int] = False
def _snake_case ( ):
lowerCAmelCase : List[str]... | 60 | 1 |
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_V... | 60 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (... | 60 | 1 |
"""simple docstring"""
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nno... | 60 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformer... | 60 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from tran... | 60 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( _snake_case : list[list[float]] ):
lowerCAmelCase : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implement... | 60 | 1 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def _snake_case ( _snake_case : int = 1000000 , _snake_case : int = 10 ):
lowerCAmelCase : defaultdict = defaultdict(_snake_case )
for outer_width in range(3 , (t_limit //... | 60 |
"""simple docstring"""
import numpy as np
def _snake_case ( _snake_case : np.array ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpo... | 60 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( _snake_case : np.ndarray , _snake_case : np.ndarray ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(_snake_case , _snak... | 60 | 1 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
snake_case__ : Optional[int] = logging.get_logger(__name__)
snake_case__ : List[Any] = {
'''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
'''https://huggin... | 60 |
"""simple docstring"""
import math
def _snake_case ( ):
lowerCAmelCase : Union[str, Any] = input('''Enter message: ''' )
lowerCAmelCase : Optional[int] = int(input(f'''Enter key [2-{len(_snake_case ) - 1}]: ''' ) )
lowerCAmelCase : str = input('''Encr... | 60 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | 60 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import trans... | 60 | 1 |
"""simple docstring"""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_ava... | 60 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configura... | 60 | 1 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : int = logging.get_logger(__name__)
snake_case__ : Dict = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
... | 60 |
"""simple docstring"""
snake_case__ : str = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
snake_case__ ... | 60 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class snake_case_( a__ ):
__Upp... | 60 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
def merge(_snake_case : list , _snake_case : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
y... | 60 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _snake_case ( _snake_case : str ):
lowerCAmelCase, lowerCAmelCase : List[str] = analyze_text(_snake_case )
lowerCAmelCase : Dict ... | 60 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice... | 60 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=a__ )
class snake_case_( a__ ):
# `task` is not a ClassVar since we want it to be part of the `asd... | 60 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_fla... | 60 | 1 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vis... | 60 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavin... | 60 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def _snake_case ( _snake_case : int , _snake_case : int , _snake_case : bool , _snake_case : list[int] , _snake_case : float ):
if depth < 0:
raise ValueError('''Depth can... | 60 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from fla... | 60 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_cha... | 60 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 60 | 1 |
"""simple docstring"""
from ....utils import logging
snake_case__ : Dict = logging.get_logger(__name__)
class snake_case_( a__ ):
def __init__( self : List[Any] , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : List[Any]=None , UpperCamelCa... | 60 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
snake_case__ : Optional[Any] = '''src/transfo... | 60 | 1 |
"""simple docstring"""
class snake_case_:
def __init__( self : str , UpperCamelCase_ : Union[str, Any] ):
lowerCAmelCase : List[Any] = val
lowerCAmelCase : Dict = None
lowerCAmelCase : Optional[int] = None
... | 60 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class snake_case_:
def __init__( self : str , UpperCamelCase_ : int=None , UpperCamelCase_ : List[str]=None ):
# Input as list
lowerCAmelCase : str = li... | 60 | 1 |
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
snake_case__ : Tuple = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. ... | 60 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_f... | 60 | 1 |
"""simple docstring"""
import math
def _snake_case ( _snake_case : int ):
lowerCAmelCase : str = [True] * n
lowerCAmelCase : List[str] = False
lowerCAmelCase : Dict = False
lowerCAmelCase : List[Any] = True
for i in range(3 ... | 60 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
if not isinstance(_snake_case , _snake_case ):
raise TypeError('''only integers accepted as input''' )
else:
lowerCAmelCase : List[str] = str(abs(_snake_case ) )
lowerCAmelC... | 60 | 1 |
"""simple docstring"""
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common imp... | 60 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
snake_cas... | 60 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 60 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : str , _snake_case : str ):
lowerCAmelCase : Dict = len(_snake_case )
lowerCAmelCase : Union[str, Any] = len(_snake_case )
lowerCAmelCase : Tuple = [[False for _ in range(m + 1 )] for _ in... | 60 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _snake_case ( _snake_case : int ):
for param in module.parameters():
lowerCAmelCase : Optional[int] = False
def _snake_case ( ):
lowerCAmelCase : List[str]... | 60 | 1 |
"""simple docstring"""
from math import log
from scipy.constants import Boltzmann, physical_constants
snake_case__ : Optional[Any] = 300 # TEMPERATURE (unit = K)
def _snake_case ( _snake_case : float , _snake_case : float , _snake_case : float , ):
if ... | 60 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (... | 60 | 1 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
snake_cas... | 60 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformer... | 60 | 1 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from ... | 60 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( _snake_case : list[list[float]] ):
lowerCAmelCase : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implement... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : list[int] ):
if not nums: # Makes sure that the list is not empty
raise ValueError('''List is empty''' )
lowerCAmelCase : Tuple = sum(_snake_case ) / len(_snake_case ) # Calculate the average
return sum(abs(... | 60 |
"""simple docstring"""
import numpy as np
def _snake_case ( _snake_case : np.array ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 1 |
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_log... | 60 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( _snake_case : np.ndarray , _snake_case : np.ndarray ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(_snake_case , _snak... | 60 | 1 |
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermark... | 60 |
"""simple docstring"""
import math
def _snake_case ( ):
lowerCAmelCase : Union[str, Any] = input('''Enter message: ''' )
lowerCAmelCase : Optional[int] = int(input(f'''Enter key [2-{len(_snake_case ) - 1}]: ''' ) )
lowerCAmelCase : str = input('''Encr... | 60 | 1 |
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def _snake_case ( _snake_case : Iterable[str] , _snake_case : int ):
lowerCAmelCase : Optional[int] = iter(_snake_case )
while True:
lowerCAmelCase : Tu... | 60 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import trans... | 60 | 1 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMSchedul... | 60 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configura... | 60 | 1 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class snake_case_( a__ ):
__UpperCamelCase =... | 60 |
"""simple docstring"""
snake_case__ : str = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
snake_case__ ... | 60 | 1 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
snake_case__ : str = '''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "Snover, Matthew and
... | 60 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
def merge(_snake_case : list , _snake_case : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
y... | 60 | 1 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 60 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice... | 60 | 1 |
"""simple docstring"""
from __future__ import annotations
snake_case__ : Any = list[tuple[int, int]]
snake_case__ : Any = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1,... | 60 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_fla... | 60 | 1 |
"""simple docstring"""
snake_case__ : str = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
snake_case__ ... | 60 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavin... | 60 | 1 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _snake_case ( _snake_case : int , _snake_case : int , _snake_case : int , _snake_case : int , _snake_case : int , _snak... | 60 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from fla... | 60 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSp... | 60 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 60 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils i... | 60 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
snake_case__ : Optional[Any] = '''src/transfo... | 60 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 60 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class snake_case_:
def __init__( self : str , UpperCamelCase_ : int=None , UpperCamelCase_ : List[str]=None ):
# Input as list
lowerCAmelCase : str = li... | 60 | 1 |
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