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 |
|---|---|---|---|---|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Optional[Any] = logging.get_logger(__name__)
a_ : List[str] = {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/con... | 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 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
a_ : List[str] = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowerCamelCase__ ( ... | 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 unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ... | 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 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 |
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 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
... | 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 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 |
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 unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import... | 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 unittest
import numpy as np
import requests
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_to... | 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 os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
a_ : str = get_tests_dir('fixtures/test_sentencepi... | 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 warnings
warnings.warn(
'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '
'`from accelerate import find_executable_batch_size` to avoid this warning.',
FutureWarning,
) | 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 logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
... | 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 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
a_ : Tuple = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesyst... | 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 math
def __lowercase( UpperCAmelCase__ = 100 ):
"""simple docstring"""
lowerCamelCase = sum(i * i for i in range(1 , n + 1 ) )
lowerCamelCase = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - su... | 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
from scipy.special import comb # type: ignore
class lowerCamelCase__ :
"""simple docstring"""
def __init__(self , __a ):
'''simple docstring'''
lowerCamelCase = list_of_points... | 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 os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_... | 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[Any] = logging.get_logger(__name__)
a_ : List[str] = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class l... | 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 |
# 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 b... | 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 collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ : Tuple = logging.get_logger(__name__)
a_ : List[Any] = {
... | 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 unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import ... | 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 |
def __lowercase( UpperCAmelCase__ = 10 , UpperCAmelCase__ = 22 ):
"""simple docstring"""
lowerCamelCase = range(1 , UpperCAmelCase__ )
lowerCamelCase = range(1 , UpperCAmelCase__ )
return sum(
1 for power in powers for ba... | 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
a_ : str = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , ):
"""simple docstring"""... | 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 abc import ABC, abstractmethod
from typing import List, Optional
class lowerCamelCase__ ( UpperCAmelCase_):
"""simple docstring"""
def __init__(self ):
'''simple docstring'''
self.test()
def _a (self ... | 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 argparse
from collections import defaultdict
import yaml
a_ : Union[str, Any] = 'docs/source/en/_toctree.yml'
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = defaultdict(UpperCAmelCase__ )
for d... | 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 os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenizatio... | 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 inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
... | 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 gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
... | 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 argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
fro... | 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ : List[Any] = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptTokeniz... | 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 gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_t... | 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 __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import C... | 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.abc import Iterable
from typing import Generic, TypeVar
a_ : Union[str, Any] = TypeVar('_T')
class lowerCamelCase__ ( Generic[_T]):
"""simple docstring"""
def __init__(self , __a = None ):
'''simple docstring'... | 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 typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ : List[str] = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncod... | 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 tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_availa... | 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__ , UpperCAmelCase__ = False ):
"""simple docstring"""
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
retu... | 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 jiwer import compute_measures
import datasets
a_ : Any = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measu... | 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 json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Any = logging.get_logger(__name__)
a_ : Optional[Any] = {
'vocab_file': 'vocab.json',
'tokenizer_config_f... | 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 json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
a_ : List[Any] = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6:... | 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 argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# 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_ : List[Any] = '.'
# Internal TensorFlow ops... | 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 argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attentio... | 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 Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
f... | 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 argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_swi... | 0 |
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 | 0 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def _A ( _lowercase ) -> str:
"""simple docstring"""
if "model" in orig_key:
__UpperCamelCase = orig_key.replace('model.' , '' )
if "norm1" in orig_key:
... | 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 | 0 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowerCamelCase__ ( _A)... | 2 |
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 | 0 |
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
... | 3 |
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 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCamelCase : ... | 4 |
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 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def A (__lowerCamelCase :Dict ):
_lowerCAmelCase = []
... | 5 |
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 | 0 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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_confi... | 6 |
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 | 0 |
"""simple docstring"""
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
fro... | 7 |
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 | 0 |
'''simple docstring'''
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE (a__ ):
def __init__( self , _UpperCAmelCase , ... | 8 |
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 | 0 |
def A ( ) -> int:
return [
a * b * (1_000 - a - b)
for a in range(1 , 999 )
for b in range(__UpperCamelCase , 999 )
if (a * a + b * b == (1_000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f'{solution() = }')
| 9 |
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 | 0 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = (PNDMScheduler,)
UpperCAmelCase = (("num_inference_steps", 50),)
def UpperCamelCase_... | 10 |
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 | 0 |
'''simple docstring'''
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
lowercase_ = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">": operator.gt,
}
... | 11 |
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 | 0 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTes... | 12 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=UpperCAmelCase_):
"""simple docstring"""
_A = ['transformers', 'torch', 'note_seq']
def __init__(self , *__a , **__a ):
... | 623 | 0 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nested... | 13 |
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 | 0 |
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a=None , _a=None ) -> List[str]:
_a : List[Any] = data
_a : List[str] = previous
_a : ... | 14 |
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 | 0 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
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
... | 15 |
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 | 0 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__A : str = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must ... | 16 |
from __future__ import annotations
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = 2
lowerCamelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 623 | 0 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __SCREAMING_SNAKE_CASE ( a__ : int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(a__ ,a__ ):
raise TypeError("""number of qubits must be... | 17 |
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 | 0 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"t5-small": "https://huggi... | 18 |
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 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_imag... | 19 |
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 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase: Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase: Any ... | 20 |
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 | 0 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_... | 21 |
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 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@data... | 22 |
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 | 0 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
snake_case__ : int = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Syste... | 23 |
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 | 0 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
class lowerCAmelCase ( __lowerCAmelCase):
def __init__( self , __SCREAMING_SNAKE_CASE=None , **__SCREAMING_SNAKE_CAS... | 24 |
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 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_clap': [
'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ClapAudioConfig',
'ClapConfig',
'ClapTextConfig',
],
'processing_clap': ['... | 25 |
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 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
__UpperCamelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
... | 26 |
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 | 0 |
from __future__ import annotations
class lowerCamelCase:
'''simple docstring'''
def __init__( self , snake_case_=None ):
_A = data
_A = None
def __repr__( self ):
_A = []
... | 27 |
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 | 0 |
'''simple docstring'''
import numpy as np
UpperCamelCase_ = [
["a", "b", "c", "d", "e"],
["f", "g", "h", "i", "k"],
["l", "m", "n", "o", "p"],
["q", "r", "s", "t", "u"],
["v", "w", "x", "y", "z"],
]
class _a :
'''simple docstr... | 28 |
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 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {
"""configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Funne... | 29 |
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 | 0 |
from ... import PretrainedConfig
__a = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class __a( _a ):
"""simple docstring"""
lowerCAmelCase = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
lowerCAmelCase ... | 30 |
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 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : Optional[int] = logging.get_logger(__name__)
lowerCamelCase__ : Any = {
'microsoft/git-base': 'https://huggingface.co/mi... | 31 |
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 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_ctrl": ["CTRLTokenizer"],
}
try:
if not is_t... | 32 |
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 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ..... | 33 |
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 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICE... | 34 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=UpperCAmelCase_):
"""simple docstring"""
_A = ['transformers', 'torch', 'note_seq']
def __init__(self , *__a , **__a ):
... | 623 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import ... | 35 |
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 | 0 |
import os
import numpy
import onnx
def lowercase ( __A : str , __A : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Any = a.name
snake_case : int = b.name
snake_case : Tuple = """"""
snak... | 36 |
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 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def UpperCamelCase_ ( __a , __a , __a , ... | 37 |
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 | 0 |
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class __snake_case ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( ... | 38 |
from __future__ import annotations
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = 2
lowerCamelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 623 | 0 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , S... | 39 |
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 | 0 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_... | 40 |
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 | 0 |
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _A ( A__ ):
"""simple docstring"""
for param in module.parameters():
__lowercase = False
def _A ( ):
"""simple docstring"""
__lowercase = ... | 41 |
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 | 0 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> int:
return int((input_a, input_a).count(0 ) != 0 )
def _UpperCamelCase ( ) -> None:
assert nand_gate(0 ,0 ) == 1
assert nand_gate(0 ,1 ) == 1
assert nand_gate(1 ,0 ) ... | 42 |
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 | 0 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class _a ( datasets.BuilderConfig ):
_lowercase : Optional[datasets.Features] ... | 43 |
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 | 0 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def A_ ( _lowerCAmelCase : Optional[Any] ):
... | 44 |
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 | 0 |
def A ( lowercase__ : int , lowercase__ : int ) -> int:
UpperCamelCase__ :List[str] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
UpperCamelCase__ :Any = n - k
# Calculate C(n,k)
for i in range(lowercase__ ):
result *= n - i
result... | 45 |
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 | 0 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=5 ) -> Tuple:
'''simple docstring'''
assert masked_input.count("<mask... | 46 |
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 | 0 |
SCREAMING_SNAKE_CASE__ = '''Tobias Carryer'''
from time import time
class _UpperCamelCase:
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CAS... | 47 |
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 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 48 |
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 | 0 |
"""simple docstring"""
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowercase__ ( snake_case_ :List[str] , snake_case_ :... | 49 |
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 | 0 |
'''simple docstring'''
from __future__ import annotations
UpperCamelCase : Optional[int] = 10
def A__ ( __lowerCAmelCase : list[int] ):
lowerCamelCase__ = 1
lowerCamelCase__ = max(__lowerCAmelCase )
while placement <= max_digit:
... | 50 |
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 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCAmelCase__ :
'''simple docstring'''
_lowerCamelCase =42
_lowerCamelCase =42
c... | 51 |
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 | 0 |
"""simple docstring"""
import string
from math import logaa
def __A ( a_ :str , a_ :str) -> int:
__a : Optional[int] = document.translate(
str.maketrans('''''' , '''''' , string.punctuation)).replace('''\n''' , '''''... | 52 |
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 | 0 |
from __future__ import annotations
def a_ ( lowerCAmelCase_ : list[float] ):
if len(lowerCAmelCase_ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise ValueError('All values must be g... | 53 |
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 | 0 |
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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STAN... | 54 |
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 | 0 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_tor... | 55 |
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 | 0 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
_a : List[str] = logging.get_logger(__name__)
_a : Tuple = "... | 56 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=UpperCAmelCase_):
"""simple docstring"""
_A = ['transformers', 'torch', 'note_seq']
def __init__(self , *__a , **__a ):
... | 623 | 0 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_... | 57 |
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 | 0 |
"""simple docstring"""
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata... | 58 |
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 | 0 |
def lowerCAmelCase_ ( __a , __a ) -> Union[str, Any]:
"""simple docstring"""
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def lowerCAmelCase_ ( __a , __a=0 ) -> Union[str, Any]:
"""simple docstring"""
... | 59 |
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 | 0 |
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