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"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDi... | 66 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 75 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
UpperCAmelCase = R"""
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the mo... | 141 |
'''simple docstring'''
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
... | 75 | 0 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> str:
UpperCamelCase : str = ("dense.weight", "attention.self.query", "attentio... | 52 |
'''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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 75 | 0 |
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def a ( lowerCamelCase_ , lowerCamelCase_=0 ):
'''simple docstring'''
return sorted(__snake_case , key=lambda l... | 207 |
'''simple docstring'''
from __future__ import annotations
def a_ ( __snake_case : str , __snake_case : list[str] | None = None , __snake_case : dict[str, float] | None = None , __snake_case : bool = False , ) -> tup... | 75 | 0 |
from datetime import datetime as dt
import os
from github import Github
_UpperCamelCase = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def _lowercase ( ):
__lowerCAmelCase : ... | 275 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import ... | 75 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""configuration_data2vec_text""": ... | 340 |
'''simple docstring'''
a_ : Any = [
9_99,
8_00,
7_99,
6_00,
5_99,
5_00,
4_00,
3_99,
3_77,
3_55,
3_33,
3_11,
2_88,
2_66,
2_44,
2_22,
2_00,
1_99,
1_77,
1_55,
1_33,
1_11,
88,
66,
44,
22,... | 75 | 0 |
'''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
A__ : Dict =HfApi()
A__ : Tuple ={}
# fmt: off
A__ : Union[str, Any] =torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.... | 70 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ : Union[str, Any] = {
"""configuration_funnel""": ["""FUNNEL_PRETRAIN... | 75 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __magic_name__ ... | 291 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from ... | 75 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers impo... | 150 |
'''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,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteSchedule... | 75 | 0 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
fro... | 96 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixi... | 75 | 0 |
"""simple docstring"""
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import... | 347 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelin... | 75 | 0 |
"""simple docstring"""
__a = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("3.7"):
raise ImportWarning(
"To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match thi... | 66 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : Optional[int] ... | 75 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenizati... | 141 |
'''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Any = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingf... | 75 | 0 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def A_ ( _lowerCAmelCase ) -> int:
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def A_ ( _lowerCAmelCase ... | 52 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_spe... | 75 | 0 |
from __future__ import annotations
import math
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if not scores:
... | 207 |
'''simple docstring'''
def a_ ( __snake_case : Any , __snake_case : List[str] ) -> str:
"""simple docstring"""
lowerCamelCase_ =''''''
for i in table:
res += inp[i - 1]
return res
def a_ ( ... | 75 | 0 |
from typing import Any
class __lowercase :
def __init__( self , A_ ) ->str:
'''simple docstring'''
__lowerCAmelCase : Any = data
__lowerCAmelCase : Dict = None
def __repr__( self ) ->Any:
... | 275 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_t... | 75 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
a_ = logging.get_logger(__name__)
a_ = {
"""Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
# See all DPT models at https... | 340 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
Ada... | 75 | 0 |
'''simple docstring'''
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
A__ : List[str] =yaml.safe_load(
'''\
name: \"\"
allow_empty: false
all... | 70 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __UpperCamelCase :
def __init__( self ):
"""simple docstring"""
lowerCamelCase_ =''''''
lowerCamelCase_ ... | 75 | 0 |
"""simple docstring"""
def a__ ( snake_case__ ) -> bool:
if not isinstance(__snake_case , __snake_case ):
raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""" )
if len(__snake_case ) == 0:
raise ValueError("""Input list must be a non emp... | 291 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
a_ : Any = lo... | 75 | 0 |
"""simple docstring"""
import itertools
import math
def lowerCAmelCase__ ( _UpperCamelCase : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or ... | 150 |
'''simple docstring'''
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB... | 75 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
if not nums: # Makes sure that the list is not empty
raise ValueError('List is empty' )
_lowerCamelCase : int = sum(__snake_case ) / len(__snake_case ) # Calculate the average
return sum(... | 96 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints,... | 75 | 0 |
"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql im... | 347 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __UpperCamelCase ( lowerCamelCase__ ):
def lowercase__ ( self, lowerCAmelCase ):
"""si... | 75 | 0 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def A_ ( _lowercase, _lowercase, _lowercase = 10**-10 ):
'''simple docstring'''
snake_case_ :List[str] = a
while True:
... | 66 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 75 | 0 |
'''simple docstring'''
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = """▁... | 141 |
'''simple docstring'''
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
... | 75 | 0 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...f... | 52 |
'''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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 75 | 0 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
A__ : Any = logging.get_logger(__name__)
class _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self : Optional[int], *lowerCamelCase ... | 207 |
'''simple docstring'''
from __future__ import annotations
def a_ ( __snake_case : str , __snake_case : list[str] | None = None , __snake_case : dict[str, float] | None = None , __snake_case : bool = False , ) -> tup... | 75 | 0 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
_UpperCamelCase = logging.getLogger(__name__)
... | 275 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import ... | 75 | 0 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a_ = """src/diffusers""... | 340 |
'''simple docstring'''
a_ : Any = [
9_99,
8_00,
7_99,
6_00,
5_99,
5_00,
4_00,
3_99,
3_77,
3_55,
3_33,
3_11,
2_88,
2_66,
2_44,
2_22,
2_00,
1_99,
1_77,
1_55,
1_33,
1_11,
88,
66,
44,
22,... | 75 | 0 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A__ : List[str] =input('''Enter image url: ''').strip()
print(F"""Downloading image from {url} ...""")
A__ : Tuple =Beaut... | 70 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ : Union[str, Any] = {
"""configuration_funnel""": ["""FUNNEL_PRETRAIN... | 75 | 0 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase : Any = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def a__ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ... | 291 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from ... | 75 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
... | 150 |
'''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,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteSchedule... | 75 | 0 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
_lowerCamelCase : Optional[int] = int(np.ceil((... | 96 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixi... | 75 | 0 |
"""simple docstring"""
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = "x" , _SCREAMING_SNAKE_CASE = 10**-10 , _SCREAMING_SNAKE_CASE = 1 , ) -> complex:
... | 347 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelin... | 75 | 0 |
"""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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
re... | 66 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : Optional[int] ... | 75 | 0 |
'''simple docstring'''
import torch
from diffusers import DiffusionPipeline
class lowerCAmelCase ( lowerCamelCase__ ):
def __init__( self : int , __lowercase : Optional[int] , __lowercase : Any ):
"""simple docstring"""
... | 141 |
'''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Any = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingf... | 75 | 0 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__lowerCamelCase : Tuple = logging.get_logger(__name__)
class A__ :
def __init__( self ... | 52 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_spe... | 75 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Any = logging.get_logger(__name__)
A__ : Optional[Any] = {
"""huggingface/time-series-transformer-tourism-monthly""": (
"""https://huggingface.co/huggingfa... | 207 |
'''simple docstring'''
def a_ ( __snake_case : Any , __snake_case : List[str] ) -> str:
"""simple docstring"""
lowerCamelCase_ =''''''
for i in table:
res += inp[i - 1]
return res
def a_ ( ... | 75 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,
is_tor... | 275 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_t... | 75 | 0 |
import os
import time
import numpy as np
import onnxruntime as ort
a_ = """1"""
a_ = """0"""
a_ = """1"""
a_ = ort.SessionOptions()
a_ = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
a_ = ["""TensorrtExecutionProvider""", "... | 340 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
Ada... | 75 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transforme... | 70 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __UpperCamelCase :
def __init__( self ):
"""simple docstring"""
lowerCamelCase_ =''''''
lowerCamelCase_ ... | 75 | 0 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
a_ = logging.get_logger(__name__)
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : List[str] , *a : Any , **... | 76 |
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_... | 76 | 1 |
import os
def lowerCamelCase__ ( ):
with open(os.path.dirname(_a) + "/p022_names.txt") as file:
SCREAMING_SNAKE_CASE : List[str] = str(file.readlines()[0])
SCREAMING_SNAKE_CASE : List[Any] = names.replace("\"" , "").split(",")
names.sort()
SCREAMING_SNAKE_... | 76 |
import os
def lowerCamelCase__ ( ):
with open(os.path.dirname(_a) + "/p022_names.txt") as file:
SCREAMING_SNAKE_CASE : List[str] = str(file.readlines()[0])
SCREAMING_SNAKE_CASE : List[Any] = names.replace("\"" , "").split(",")
names.sort()
SCREAMING_SNAKE_... | 76 | 1 |
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
a_ = version.parse(version.parse(torch.__version__).base_version)... | 76 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( _a , _a , _a , _a , _a):
SCREAMING_SNAKE_CASE : Dict = int(np.ceil((x_end - xa) / step_size))
SCREAMING_SNAKE_CASE : Tuple = np.zeros((n + 1,))
SCREAMING_SNAKE_CASE... | 76 | 1 |
from __future__ import annotations
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , a : int = 0 ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = key
def ... | 76 |
def lowerCamelCase__ ( _a , _a):
return int((input_a, input_a).count(1) != 0)
def lowerCamelCase__ ( ):
assert or_gate(0 , 0) == 0
assert or_gate(0 , 1) == 1
assert or_gate(1 , 0) == 1
assert or_gate(1 , 1) == 1
if __name__ == "__main__":
print(... | 76 | 1 |
a_ = {str(digit): digit**5 for digit in range(10)}
def lowerCamelCase__ ( _a):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_a))
def lowerCamelCase__ ( ):
return sum(
number
for number in range(1000 , 1000000)
if number == digits_fifth_powers_sum... | 76 |
a_ = 8.314_4598
def lowerCamelCase__ ( _a , _a):
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K")
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than or equal to 0 kg/mol")
else:
return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_ma... | 76 | 1 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class _UpperCamelCase ( __A , __A ):
'''simple docstring'''
... | 76 |
a_ = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : int = set()
# keep track of all the paths to... | 76 | 1 |
# 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-2.0
#
# Unless required by ap... | 76 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
... | 76 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImage... | 76 |
from math import factorial
def lowerCamelCase__ ( _a , _a , _a):
if successes > trials:
raise ValueError("successes must be lower or equal to trials")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(_a , ... | 76 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( _a , _a):
# Checks if the entire collection has been sorted
if len(_a) <= 1 or n <= 1:
return
insert_next(_a , n - 1)
rec_insertion_sort(_a , n - 1)
def lowerCamelCase__ ( _a , _a):
# Checks ord... | 76 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =CustomTokenizer
pass | 76 | 1 |
a_ = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0',
'huggingface-hub': 'huggingface-hub>=0.13.2',
... | 76 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
a_ = logging.getLogger(__name__)
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Any... | 76 | 1 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils... | 76 |
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : Any ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = data
SCREAMING_SNAKE_CASE : int = None
... | 76 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_time_series_transformer': [
'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TimeSeriesTransformerConfig',
],
}
try:
if not is_torch... | 76 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a_ = get_logger(__name__)
class _UpperCamelCase ( enum.Enum ):
'''simple docstring'''
lowerCamelCase__ ='all_checks'
lowerCamelCase__... | 76 | 1 |
import os
from collections.abc import Iterator
def lowerCamelCase__ ( _a = "."):
for dir_path, dir_names, filenames in os.walk(_a):
SCREAMING_SNAKE_CASE : Dict = [d for d in dir_names if d != "scripts" and d[0] not in "._"]
for filename in filenames:
if filename == "__init__.py... | 76 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( _a , _a):
# Load checkpoint
SCREAMING_SNAKE_CA... | 76 | 1 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
tem... | 76 |
def lowerCamelCase__ ( _a , _a):
_validate_point(_a)
_validate_point(_a)
if len(_a) != len(_a):
raise ValueError("Both points must be in the same n-dimensional space")
return float(sum(abs(a - b) for a, b in zip(_a , _a)))
def lowerCamelCase__ ( _a):
if point:
if... | 76 | 1 |
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : Any ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = data
SCREAMING_SNAKE_CASE : int = None
... | 76 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
... | 76 | 1 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __vers... | 76 |
import baseaa
def lowerCamelCase__ ( _a):
return baseaa.aaaencode(string.encode("utf-8"))
def lowerCamelCase__ ( _a):
return baseaa.aaadecode(_a).decode("utf-8")
if __name__ == "__main__":
import doctest
doctest.testmod() | 76 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a_ = {
'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}
try:
if not is_torch_available():
raise OptionalDepende... | 76 |
from datetime import datetime as dt
import os
from github import Github
a_ = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : int = Github(os.en... | 76 | 1 |
import comet # From: unbabel-comet
import torch
import datasets
a_ = datasets.logging.get_logger(__name__)
a_ = '\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n title = {Unbabel\'s Participation in the WMT20 Metr... | 76 |
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 (
center_crop,
get_res... | 76 | 1 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCamel... | 76 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.txt'}
... | 76 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( _a = 4):
SCREAMING_SNAKE_CASE : Optional[Any] = abs(_a) or 4
return [[1 + x + y * row_size for x in range(_a)] for y in range(_a)]
def lowerCamelCase__ ( _a):
return reverse_row(transpose(_a))
# OR.. transpo... | 76 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkout... | 76 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, ... | 76 |
import unittest
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 ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
... | 76 | 1 |
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : str = 0
# if input_string is "aba" than new_input_string become "a|b|a"
SCREAMING_SNAKE_CASE : Any = ""
SCREAMING_SNAKE_CASE : Any = ""
# append each character + "|" in new_string for range(0, length... | 76 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : int = {}
SCREAMING_SNAKE_CASE : Any = token... | 76 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from tra... | 76 |
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_... | 76 | 1 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def lowerCamelCase__ ( _a , _a):
return math.sqrt(sum(pow(a - b , 2) for a, b in zip(_a , _a)))
def lowerCamelCase__ ( _a , _a):
if dataset.ndim != value_array... | 76 |
import os
def lowerCamelCase__ ( ):
with open(os.path.dirname(_a) + "/p022_names.txt") as file:
SCREAMING_SNAKE_CASE : List[str] = str(file.readlines()[0])
SCREAMING_SNAKE_CASE : List[Any] = names.replace("\"" , "").split(",")
names.sort()
SCREAMING_SNAKE_... | 76 | 1 |
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.spectrogram_diffusion import S... | 76 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( _a , _a , _a , _a , _a):
SCREAMING_SNAKE_CASE : Dict = int(np.ceil((x_end - xa) / step_size))
SCREAMING_SNAKE_CASE : Tuple = np.zeros((n + 1,))
SCREAMING_SNAKE_CASE... | 76 | 1 |
from timeit import timeit
a_ = {
'MALAYALAM': True,
'String': False,
'rotor': True,
'level': True,
'A': True,
'BB': True,
'ABC': False,
'amanaplanacanalpanama': True, # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for ke... | 76 |
def lowerCamelCase__ ( _a , _a):
return int((input_a, input_a).count(1) != 0)
def lowerCamelCase__ ( ):
assert or_gate(0 , 0) == 0
assert or_gate(0 , 1) == 1
assert or_gate(1 , 0) == 1
assert or_gate(1 , 1) == 1
if __name__ == "__main__":
print(... | 76 | 1 |
from __future__ import annotations
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Tuple , a : str , a : str ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : str... | 76 |
a_ = 8.314_4598
def lowerCamelCase__ ( _a , _a):
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K")
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than or equal to 0 kg/mol")
else:
return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_ma... | 76 | 1 |
from collections import Counter
from timeit import timeit
def lowerCamelCase__ ( _a = "" , ):
return sum(c % 2 for c in Counter(input_str.replace(" " , "").lower()).values()) < 2
def lowerCamelCase__ ( _a = ""):
if len(_a) == 0:
return True
SCREAMING_SNAKE_CASE ... | 76 |
a_ = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : int = set()
# keep track of all the paths to... | 76 | 1 |
a_ = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o': 'ABBAB',
'p': 'ABBBA',
'q': 'ABBBB',... | 76 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
... | 76 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a_ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['BartphoTokenize... | 76 |
from math import factorial
def lowerCamelCase__ ( _a , _a , _a):
if successes > trials:
raise ValueError("successes must be lower or equal to trials")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(_a , ... | 76 | 1 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelT... | 76 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =CustomTokenizer
pass | 76 | 1 |
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Optional[int] = len(_a)
for i in range(1 , _a):
SCREAMING_SNAKE_CASE : Optional[int] = collection[i]
SCREAMING_SNAKE_CASE : Dict = 0
SCREAMING_SNAKE_CASE : Tuple = i - 1
whil... | 76 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
a_ = logging.getLogger(__name__)
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Any... | 76 | 1 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acce... | 76 |
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : Any ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = data
SCREAMING_SNAKE_CASE : int = None
... | 76 | 1 |
a_ = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : int = set()
# keep track of all the paths to... | 76 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a_ = get_logger(__name__)
class _UpperCamelCase ( enum.Enum ):
'''simple docstring'''
lowerCamelCase__ ='all_checks'
lowerCamelCase__... | 76 | 1 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
a... | 76 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( _a , _a):
# Load checkpoint
SCREAMING_SNAKE_CA... | 76 | 1 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = 0
SCREAMING_SNAKE_CASE : Any = 0
SCREAMING_SNAKE_CASE : Optional... | 76 |
def lowerCamelCase__ ( _a , _a):
_validate_point(_a)
_validate_point(_a)
if len(_a) != len(_a):
raise ValueError("Both points must be in the same n-dimensional space")
return float(sum(abs(a - b) for a, b in zip(_a , _a)))
def lowerCamelCase__ ( _a):
if point:
if... | 76 | 1 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a_ = ... | 76 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
... | 76 | 1 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
A... | 76 |
import baseaa
def lowerCamelCase__ ( _a):
return baseaa.aaaencode(string.encode("utf-8"))
def lowerCamelCase__ ( _a):
return baseaa.aaadecode(_a).decode("utf-8")
if __name__ == "__main__":
import doctest
doctest.testmod() | 76 | 1 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_u... | 76 |
from datetime import datetime as dt
import os
from github import Github
a_ = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : int = Github(os.en... | 76 | 1 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenizer... | 76 |
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 (
center_crop,
get_res... | 76 | 1 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ... | 76 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.txt'}
... | 76 | 1 |
from collections.abc import Sequence
def lowerCamelCase__ ( _a , _a = False):
if not arr:
return 0
SCREAMING_SNAKE_CASE : int = 0 if allow_empty_subarrays else float("-inf")
SCREAMING_SNAKE_CASE : Optional[int] = 0.0
for num in arr:
SCREAMING_SNAKE_CASE ... | 76 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkout... | 76 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ ... | 76 |
import unittest
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 ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
... | 76 | 1 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
a_ = logging.get_logger(__name__)
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : Dict , *a : Dict , **a ... | 76 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : int = {}
SCREAMING_SNAKE_CASE : Any = token... | 76 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 76 |
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_... | 76 | 1 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
a_ = logging.getLogger(__name__)
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self ... | 76 |
import os
def lowerCamelCase__ ( ):
with open(os.path.dirname(_a) + "/p022_names.txt") as file:
SCREAMING_SNAKE_CASE : List[str] = str(file.readlines()[0])
SCREAMING_SNAKE_CASE : List[Any] = names.replace("\"" , "").split(",")
names.sort()
SCREAMING_SNAKE_... | 76 | 1 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
a_ = 'https://openaipublic.azureedge.net/jukebox/mode... | 76 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( _a , _a , _a , _a , _a):
SCREAMING_SNAKE_CASE : Dict = int(np.ceil((x_end - xa) / step_size))
SCREAMING_SNAKE_CASE : Tuple = np.zeros((n + 1,))
SCREAMING_SNAKE_CASE... | 76 | 1 |
def lowerCamelCase__ ( _a , _a , _a):
return round(float(moles / volume) * nfactor)
def lowerCamelCase__ ( _a , _a , _a):
return round(float((moles * 0.0821 * temperature) / (volume)))
def lowerCamelCase__ ( _a , _a , _a):
return... | 76 |
def lowerCamelCase__ ( _a , _a):
return int((input_a, input_a).count(1) != 0)
def lowerCamelCase__ ( ):
assert or_gate(0 , 0) == 0
assert or_gate(0 , 1) == 1
assert or_gate(1 , 0) == 1
assert or_gate(1 , 1) == 1
if __name__ == "__main__":
print(... | 76 | 1 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
a_ = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul Arora\n and Steven ... | 76 |
a_ = 8.314_4598
def lowerCamelCase__ ( _a , _a):
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K")
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than or equal to 0 kg/mol")
else:
return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_ma... | 76 | 1 |
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,
transpose,
)
if is_flax_available(... | 76 |
a_ = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : int = set()
# keep track of all the paths to... | 76 | 1 |
import requests
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Dict = {"Content-Type": "application/json"}
SCREAMING_SNAKE_CASE : Optional[int] = requests.post(_a , json={"text": message_body} , headers=_a)
if response.status_code != 200:
... | 76 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
... | 76 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.txt'}
... | 76 |
from math import factorial
def lowerCamelCase__ ( _a , _a , _a):
if successes > trials:
raise ValueError("successes must be lower or equal to trials")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(_a , ... | 76 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a_ = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Opti... | 76 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =CustomTokenizer
pass | 76 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( _a , _a , _a):
if days_between_payments <= 0:
raise ValueError("days_between_payments must be > 0")
if daily_interest_rate < 0:
raise ValueError("daily_interest_rate must be >= 0")
if principal <= 0:
raise ValueError("prin... | 76 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
a_ = logging.getLogger(__name__)
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Any... | 76 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
... | 76 |
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : Any ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = data
SCREAMING_SNAKE_CASE : int = None
... | 76 | 1 |
def lowerCamelCase__ ( _a , _a):
return int(input_a == input_a == 0)
def lowerCamelCase__ ( ):
print("Truth Table of NOR Gate:")
print("| Input 1 | Input 2 | Output |")
print(f"| 0 | 0 | {nor_gate(0 , 0)} |")
print(f"| 0 | 1 ... | 76 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a_ = get_logger(__name__)
class _UpperCamelCase ( enum.Enum ):
'''simple docstring'''
lowerCamelCase__ ='all_checks'
lowerCamelCase__... | 76 | 1 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
a_ = datasets.utils.logging.get_logger(__name__)
class _UpperCamelCase ( folder_based_builder.FolderBasedBuilderConfig ):
'''simple docstri... | 76 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( _a , _a):
# Load checkpoint
SCREAMING_SNAKE_CA... | 76 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.util... | 76 |
def lowerCamelCase__ ( _a , _a):
_validate_point(_a)
_validate_point(_a)
if len(_a) != len(_a):
raise ValueError("Both points must be in the same n-dimensional space")
return float(sum(abs(a - b) for a, b in zip(_a , _a)))
def lowerCamelCase__ ( _a):
if point:
if... | 76 | 1 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( _a , _a):
# Load checkpoint
SCREAMING_SNAKE_CA... | 76 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
... | 76 | 1 |
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_... | 76 |
import baseaa
def lowerCamelCase__ ( _a):
return baseaa.aaaencode(string.encode("utf-8"))
def lowerCamelCase__ ( _a):
return baseaa.aaadecode(_a).decode("utf-8")
if __name__ == "__main__":
import doctest
doctest.testmod() | 76 | 1 |
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