code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
class lowerCamelCase__ :
"""simple docstring"""
def __init__(self ):
'''simple docstring'''
lowerCamelCase = 0
lowerCamelCase = 0
lowerCamelCase = {}
def _a (... | 623 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from acceler... | 623 | 1 |
from __future__ import annotations
import os
from collections.abc import Mapping
a_ : Optional[Any] = tuple[int, int]
class lowerCamelCase__ :
"""simple docstring"""
def __init__(self , __a , __a ):
'''simple docstrin... | 623 |
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, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.u... | 623 | 1 |
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class lowerCamelCase__ :
"""simple docstring"""
def __init__(self , __a ):
... | 623 |
from __future__ import annotations
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = 2
lowerCamelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 623 | 1 |
from __future__ import annotations
a_ : str = list[list[int]]
# assigning initial values to the grid
a_ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0... | 623 |
import socket
def __lowercase( ):
"""simple docstring"""
lowerCamelCase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowerCamelCase = socket.gethostname()
lowerCamelCase = 12312
sock.connect((host, port... | 623 | 1 |
from math import isqrt
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
return all(number % divisor != 0 for divisor in range(2 , isqrt(UpperCAmelCase__ ) + 1 ) )
def __lowercase( UpperCAmelCase__ = 10**6 ):
"""simple docstring"""... | 623 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( UpperCAmelCase_):
"""simple docstring"""
_A = (PNDMScheduler,)
_A = (('num_inference_steps', 50),)
... | 623 | 1 |
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 TFModelTesterMixin, floats_tensor... | 623 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
a_ : Tuple = datasets.logging.get_logger(__name__)
a_ : List[str] = '\\n@InProceedings{moosavi2019minimum,\n... | 623 | 1 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('>=', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.c... | 623 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ : Tuple = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
... | 623 | 1 |
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowerCamelCase = mf_knapsack(i... | 623 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner im... | 623 | 1 |
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
lowerCamelCase = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0... | 623 |
from __future__ import annotations
from math import pow, sqrt
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argume... | 623 | 1 |
from __future__ import annotations
import time
a_ : Tuple = list[tuple[int, int]]
a_ : List[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, 0, 0, ... | 623 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
Au... | 623 | 1 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
... | 623 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids... | 623 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
Ra... | 623 |
a_ : Dict = 8.314462 # Unit - J mol-1 K-1
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
... | 623 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Optional[Any] = logging.get_logger(__name__)
a_ : Union[str, Any] = {
'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
# See all XGLM models... | 623 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCA... | 623 | 1 |
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = [[] for _ in range(UpperCAmelCase__ )]
lowerCamelCase = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or... | 623 |
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,
re... | 623 | 1 |
import math
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
lowerCamelCase = F"""Input value of [number={number}] must be an integer"""
raise TypeError(UpperCAmelC... | 623 |
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 is_torch_ava... | 623 | 1 |
import unittest
from transformers import DonutProcessor
a_ : List[Any] = 'naver-clova-ix/donut-base'
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
def _a (self ):
'''simple docstring'''
... | 623 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""... | 623 | 1 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a_ : Dict = False
class lowerCamelCase__ ( unittest.T... | 623 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_mo... | 623 | 1 |
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_configuration_common imp... | 623 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, ... | 623 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ : str = logging.get_logger(__name__)
a_ : List[Any] = {
'andreasmadsen/efficient_mlm_m0.40'... | 623 |
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
if n == 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return 0
elif n == 2:
return 1
else:
lowerCamelCase = [0, 1]
for i i... | 623 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Any = logging.get_logger(__name__)
a_ : int = {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
'google/fnet-large': 'https://huggingface.co/go... | 623 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __lowercase( UpperCAmelCase__ = "laptop" ):
"""simple docstring"""
lowerCamelCase = F"""https://www.amazon.in/laptop/s?k={product}"""
l... | 623 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params im... | 623 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=UpperCAmelCase_):
"""simple docstring"""
_A = ['transformers', 'torch', 'note_seq']
def __init__(self , *__a , **__a ):
... | 623 | 1 |
import numpy as np
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = 1E-12 , UpperCAmelCase__ = 100 , ):
"""simple docstring"""
assert np.shape(UpperCAmelCase__ )[0] == np.shape(UpperCAmelCase__ )[1]
# Ensure proper dimensionality.
a... | 623 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
a_ : Any = logging.get_logger(__n... | 623 | 1 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.p... | 623 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from acceler... | 623 | 1 |
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]" )
if len(UpperCAmelCase__ ) == 0:
raise ValueErro... | 623 |
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, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.u... | 623 | 1 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin... | 623 |
from __future__ import annotations
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = 2
lowerCamelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 623 | 1 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
... | 623 |
import socket
def __lowercase( ):
"""simple docstring"""
lowerCamelCase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowerCamelCase = socket.gethostname()
lowerCamelCase = 12312
sock.connect((host, port... | 623 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvai... | 623 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( UpperCAmelCase_):
"""simple docstring"""
_A = (PNDMScheduler,)
_A = (('num_inference_steps', 50),)
... | 623 | 1 |
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
if num <= 0:
raise ValueError("Input must be a positive integer" )
lowerCamelCase = [True] * (num + 1)
lowerCamelCase = 2
while p * p <= num:
... | 623 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
a_ : Tuple = datasets.logging.get_logger(__name__)
a_ : List[str] = '\\n@InProceedings{moosavi2019minimum,\n... | 623 | 1 |
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 transformers
... | 623 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ : Tuple = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
... | 623 | 1 |
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
return " ".join(
"".join(word[::-1] ) if len(UpperCAmelCase__ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print... | 623 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner im... | 623 | 1 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class lowerCamelCase__ ( tf.keras.optimizers.schedules.LearningRateS... | 623 |
from __future__ import annotations
from math import pow, sqrt
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argume... | 623 | 1 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCas... | 623 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
Au... | 623 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Any = logging.get_logger(__name__)
a_ : Optional[int] = '▁'
a_... | 623 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids... | 623 | 1 |
from scipy.stats import spearmanr
import datasets
a_ : Optional[int] = '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive ... | 623 |
a_ : Dict = 8.314462 # Unit - J mol-1 K-1
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
... | 623 | 1 |
from typing import Any
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
if not input_list:
return []
lowerCamelCase = [input_list.count(UpperCAmelCase__ ) for value in input_list]
lowerCamelCase = max(UpperC... | 623 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCA... | 623 | 1 |
import numpy as np
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod() | 623 |
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,
re... | 623 | 1 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__=1 ):
"""simple docstring"""
if n_shave_prefix_segments >= 0:
return ".".join(path.split(... | 623 |
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 is_torch_ava... | 623 | 1 |
import itertools
import math
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even nu... | 623 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""... | 623 | 1 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
... | 623 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_mo... | 623 | 1 |
a_ : List[Any] = {
0: '0',
1: '1',
2: '2',
3: '3',
4: '4',
5: '5',
6: '6',
7: '7',
8: '8',
9: '9',
1_0: 'a',
1_1: 'b',
1_2: 'c',
1_3: 'd',
1_4: 'e',
1_5: 'f',
}
def __lowercase( UpperCAmelCase__ ):
... | 623 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, ... | 623 | 1 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
a_ : str = 5_0_0_0_0_0
a_ , a_ : Any = os.path.split(__file__)
a_ : Dict = os.path.join(RESULTS_BASEPATH, 'results', RESULTS... | 623 |
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
if n == 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return 0
elif n == 2:
return 1
else:
lowerCamelCase = [0, 1]
for i i... | 623 | 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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcesso... | 623 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __lowercase( UpperCAmelCase__ = "laptop" ):
"""simple docstring"""
lowerCamelCase = F"""https://www.amazon.in/laptop/s?k={product}"""
l... | 623 | 1 |
from collections import Counter
from timeit import timeit
def __lowercase( UpperCAmelCase__ = "" , ):
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def __lowercase( UpperCAmelCase__ = "" )... | 623 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=UpperCAmelCase_):
"""simple docstring"""
_A = ['transformers', 'torch', 'note_seq']
def __init__(self , *__a , **__a ):
... | 623 | 1 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
low... | 623 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
a_ : Any = logging.get_logger(__n... | 623 | 1 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_cons... | 623 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from acceler... | 623 | 1 |
from pathlib import Path
import fire
from tqdm import tqdm
def __lowercase( UpperCAmelCase__="ro" , UpperCAmelCase__="en" , UpperCAmelCase__="wmt16" , UpperCAmelCase__=None ):
"""simple docstring"""
try:
import datasets
except (ModuleNotFoundError, ... | 623 |
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, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.u... | 623 | 1 |
def __lowercase( UpperCAmelCase__ = 1000 ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase = 1, 1
lowerCamelCase = 2
while True:
lowerCamelCase = 0
lowerCamelCase = fa ... | 623 |
from __future__ import annotations
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = 2
lowerCamelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 623 | 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... | 623 |
import socket
def __lowercase( ):
"""simple docstring"""
lowerCamelCase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowerCamelCase = socket.gethostname()
lowerCamelCase = 12312
sock.connect((host, port... | 623 | 1 |
a_ : Dict = 8.314462 # Unit - J mol-1 K-1
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
... | 623 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( UpperCAmelCase_):
"""simple docstring"""
_A = (PNDMScheduler,)
_A = (('num_inference_steps', 50),)
... | 623 | 1 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_mo... | 623 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
a_ : Tuple = datasets.logging.get_logger(__name__)
a_ : List[str] = '\\n@InProceedings{moosavi2019minimum,\n... | 623 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : int = logging.get_logger(__name__)
a_ : Union[str, Any] = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json... | 623 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ : Tuple = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
... | 623 | 1 |
import unittest
from transformers import BigBirdConfig, 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
from transformers.mode... | 623 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner im... | 623 | 1 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a_ : Dict = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Au... | 623 |
from __future__ import annotations
from math import pow, sqrt
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argume... | 623 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ : int = {
'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCH... | 623 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
Au... | 623 | 1 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCamelCase__ :
"""simple docstring"""
_A = 42
_A = 42
class lowerCamelCase__ :
... | 623 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids... | 623 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
a_ : Union[str, Any] = logging.get_logger(__name__)
a_ : Optional[int] = {
'Visual-Attention-Network/van-base': (
'https://huggingface.co/Visual-Attention-Network/van-base/blob/main/... | 623 |
a_ : Dict = 8.314462 # Unit - J mol-1 K-1
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
... | 623 | 1 |
import socket
def __lowercase( ):
"""simple docstring"""
lowerCamelCase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowerCamelCase = socket.gethostname()
lowerCamelCase = 12312
sock.connect((host, port... | 623 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCA... | 623 | 1 |
import math
import sys
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = ""
try:
with open(UpperCAmelCase__ , "rb" ) as binary_file:
lowerCamelCase = binary_file.read()
... | 623 |
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,
re... | 623 | 1 |
def __lowercase( UpperCAmelCase__ = 50 ):
"""simple docstring"""
lowerCamelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_len... | 623 |
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 is_torch_ava... | 623 | 1 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers i... | 623 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""... | 623 | 1 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class lowerCamelCase__ ( enu... | 623 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_mo... | 623 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Optional[Any] = logging.get_logger(__name__)
a_ : Dict = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models... | 623 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, ... | 623 | 1 |
import warnings
from .generation import TFGenerationMixin
class lowerCamelCase__ ( UpperCAmelCase_):
"""simple docstring"""
warnings.warn(
'Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '
'be removed in... | 623 |
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
if n == 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return 0
elif n == 2:
return 1
else:
lowerCamelCase = [0, 1]
for i i... | 623 | 1 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowerCamelCase__ ( UpperCAmelCase_):
"""simple docstring"""
_A = ['image_processor', 'tokenizer']
_A = 'AutoImageProcessor'
_... | 623 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __lowercase( UpperCAmelCase__ = "laptop" ):
"""simple docstring"""
lowerCamelCase = F"""https://www.amazon.in/laptop/s?k={product}"""
l... | 623 | 1 |
import argparse
import os
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_task_guides.py
a_ : int = 'src/transformers'
a_ : Any = 'docs/sou... | 623 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=UpperCAmelCase_):
"""simple docstring"""
_A = ['transformers', 'torch', 'note_seq']
def __init__(self , *__a , **__a ):
... | 623 | 1 |
from scipy.stats import pearsonr
import datasets
a_ : List[Any] = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption th... | 623 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
a_ : Any = logging.get_logger(__n... | 623 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ : Union[str, Any] = {
'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}
try:
if not is_torch_availab... | 623 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from acceler... | 623 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
a_ : int = {
'configuration_speecht5': [
'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SPEECHT5_PRET... | 623 |
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, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.u... | 623 | 1 |
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = 0
lowerCamelC... | 623 |
from __future__ import annotations
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = 2
lowerCamelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 623 | 1 |
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
while a != 0:
lowerCamelCase , lowerCamelCase = b % a, a
return b
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ ):
... | 623 |
import socket
def __lowercase( ):
"""simple docstring"""
lowerCamelCase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowerCamelCase = socket.gethostname()
lowerCamelCase = 12312
sock.connect((host, port... | 623 | 1 |
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
a_ : Tuple = [
'kernels/rwkv/wkv_cuda.cu',
'kernels/rwkv/wkv_op.cpp',
'kernels/deformable_detr/ms_deform_attn.h',
'kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh... | 623 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( UpperCAmelCase_):
"""simple docstring"""
_A = (PNDMScheduler,)
_A = (('num_inference_steps', 50),)
... | 623 | 1 |
def __lowercase( UpperCAmelCase__ = 2000000 ):
"""simple docstring"""
lowerCamelCase = [0 for i in range(n + 1 )]
lowerCamelCase = 1
lowerCamelCase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if p... | 623 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
a_ : Tuple = datasets.logging.get_logger(__name__)
a_ : List[str] = '\\n@InProceedings{moosavi2019minimum,\n... | 623 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : int = logging.get_logger(__name__)
a_ : str = {
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
... | 623 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ : Tuple = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
... | 623 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a_ : Any = {
'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}
try:
if not is_torch_... | 623 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner im... | 623 | 1 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
a_ : Optional[int] = numpy.array([0, 0])
a_ : int = numpy.array([0.5, 0.8660254])
a_ : Dict = numpy.array([1, 0])
a_ : Optional[... | 623 |
from __future__ import annotations
from math import pow, sqrt
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argume... | 623 | 1 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
a_ : Optional[Any] = collections.namedtuple('_Datasets', ... | 623 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
Au... | 623 | 1 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
Au... | 623 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids... | 623 | 1 |
import numpy as np
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
return np.where(vector > 0 , UpperCAmelCase__ , (alpha * (np.exp(UpperCAmelCase__ ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 623 |
a_ : Dict = 8.314462 # Unit - J mol-1 K-1
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
... | 623 | 1 |
import os
import sys
import unittest
a_ : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_map... | 623 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCA... | 623 | 1 |
a_ : Union[str, Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
a_ : Optional[int] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
lowerC... | 623 |
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,
re... | 623 | 1 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, ... | 623 |
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 is_torch_ava... | 623 | 1 |
from __future__ import annotations
from random import random
class lowerCamelCase__ :
"""simple docstring"""
def __init__(self , __a = None ):
'''simple docstring'''
lowerCamelCase = value
lowerCamel... | 623 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""... | 623 | 1 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=UpperCAmelCase_):
"""simple docstring"""
_A = ['transformers', 'torch', 'note_seq']
def __init__(self , *__a , **__a ):
... | 623 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_mo... | 623 | 1 |
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = len(UpperCAmelCase__ )
lowerCamelCase = len(UpperCAmelCase__ )
lowerCamelCase = [[False for _ in range(m + 1 )] for _ in ... | 623 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, ... | 623 | 1 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
a_ : Optional[int] = Lock()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__... | 623 |
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
if n == 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return 0
elif n == 2:
return 1
else:
lowerCamelCase = [0, 1]
for i i... | 623 | 1 |
import mpmath # for roots of unity
import numpy as np
class lowerCamelCase__ :
"""simple docstring"""
def __init__(self , __a=None , __a=None ):
'''simple docstring'''
lowerCamelCase = list(poly_a or [0]... | 623 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __lowercase( UpperCAmelCase__ = "laptop" ):
"""simple docstring"""
lowerCamelCase = F"""https://www.amazon.in/laptop/s?k={product}"""
l... | 623 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids... | 623 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=UpperCAmelCase_):
"""simple docstring"""
_A = ['transformers', 'torch', 'note_seq']
def __init__(self , *__a , **__a ):
... | 623 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : Optional[Any] = {
'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve... | 623 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
a_ : Any = logging.get_logger(__n... | 623 | 1 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common i... | 623 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from acceler... | 623 | 1 |
from __future__ import annotations
from math import pow, sqrt
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argume... | 623 |
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, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.u... | 623 | 1 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise TypeError("Undefined for non-integers" )
elif precis... | 623 |
from __future__ import annotations
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = 2
lowerCamelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 623 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : int = logging.get_logger(__name__)
a_ : List[Any] = {
'BAAI/AltCLIP': 'https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json',
... | 623 |
import socket
def __lowercase( ):
"""simple docstring"""
lowerCamelCase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowerCamelCase = socket.gethostname()
lowerCamelCase = 12312
sock.connect((host, port... | 623 | 1 |
from __future__ import annotations
from collections.abc import MutableSequence
class lowerCamelCase__ :
"""simple docstring"""
def __init__(self , __a , __a ):
'''simple docstring'''
if len(__a ) != degree + 1:
... | 623 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( UpperCAmelCase_):
"""simple docstring"""
_A = (PNDMScheduler,)
_A = (('num_inference_steps', 50),)
... | 623 | 1 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a_ : int = {
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D': 4.25,
'L': 4.03,
'C': 2.78,
'... | 623 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
a_ : Tuple = datasets.logging.get_logger(__name__)
a_ : List[str] = '\\n@InProceedings{moosavi2019minimum,\n... | 623 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMix... | 623 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ : Tuple = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
... | 623 | 1 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( UpperCAmelCase_):
"""simple docstring"""
_A = (PNDMScheduler,)
_A = (('num_inference_steps', 50),)
... | 623 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner im... | 623 | 1 |
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, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.u... | 623 |
from __future__ import annotations
from math import pow, sqrt
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argume... | 623 | 1 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_ava... | 623 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
Au... | 623 | 1 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
a_ : Optional[Any] = '__DUMMY_TRANSFORMERS_USER__'
a_ : List[Any] = 'Dummy User'
a_ : Optional[Any] = 'hf_... | 623 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids... | 623 | 1 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
a_ : Optional[An... | 623 |
a_ : Dict = 8.314462 # Unit - J mol-1 K-1
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
... | 623 | 1 |
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