code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
... | 370 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __a ( lowerCAmelCase_ : Optional[int] ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase_= [
"""decoder.version"... | 277 | 0 |
import re
def __a ( lowerCAmelCase_ : str ) -> int:
'''simple docstring'''
UpperCAmelCase_= re.compile(r"""^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$""" )
if match := re.search(__lowerCAmelCase ,__lowerCAmelCase ):
return match.string == phone... | 371 |
import warnings
from functools import wraps
from typing import Callable
def __a ( lowerCAmelCase_ : Callable ) -> Callable:
'''simple docstring'''
@wraps(lowerCAmelCase_ )
def _inner_fn(*lowerCAmelCase_ : List[Any] ,**lowerCAmelCase_ : Tuple ... | 277 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A = {
'''configuration_owlvit''': [
'''OWLVIT_PRETRAINE... | 350 |
import pytest
import datasets
# Import fixture modules as plugins
__A = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def __a ( lowerCAmelCase_ : Optional[Any] ,lowerCAmelCase_ : Any ) -> Tuple:
'''simple docstring'''... | 277 | 0 |
def __a ( lowerCAmelCase_ : int ,lowerCAmelCase_ : int ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def __a ( ) -> None:
'''simple docstring'''
assert and_gate(0 ,0 ) == 0
... | 351 |
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase ( snake_case__):
"""simple docstring"""
def __init__( self : ... | 277 | 0 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipeline... | 352 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ... | 277 | 0 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...te... | 353 |
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMixin
from ...te... | 277 | 0 |
import datasets
__A = '''\
@InProceedings{conneau2018xnli,
author = "Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
and Stoyanov,... | 354 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__A = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schu... | 277 | 0 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def __a ( lowerCAmelCase_ : str ,lowerCAmelCase_ : Optional[int] ,lowerCAmelCase_ : Option... | 355 |
from __future__ import annotations
def __a ( lowerCAmelCase_ : list[int] ,lowerCAmelCase_ : int ) -> list[list[int]]:
'''simple docstring'''
UpperCAmelCase_= []
UpperCAmelCase_= []
UpperCAmelCase_= 0
UpperCAmelCase_= s... | 277 | 0 |
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''', '''FunnelConfig'''],
'''conv... | 356 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowercase ( snake_case__):
"""simple docstring"""
def __init__( self : ... | 277 | 0 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
__A = ... | 357 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import... | 277 | 0 |
from __future__ import annotations
__A = tuple[int, int, int]
__A = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
__A = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
# -------------------------- default selection --------------------------
# ro... | 358 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput... | 277 | 0 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_availa... | 359 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
__A = logging.get_logger(__name__)
def __a ( lowerCAmelCase_ : Tuple=None ,lowerCAmelCase_ : Optional[Any]=None ... | 277 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = '''▁'''
__A =... | 360 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Ac... | 277 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
'''configuration_xlm_roberta''': [
'''XLM_... | 361 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
... | 277 | 0 |
from __future__ import annotations
def __a ( lowerCAmelCase_ : int ,lowerCAmelCase_ : int ) -> list[list[int]]:
'''simple docstring'''
UpperCAmelCase_= []
create_all_state(1 ,lowerCAmelCase_ ,lowerCAmelCase_ ,[] ,lowerCAmelCase_... | 362 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A = {
'''configuration_clip''': [
'''CLIP_PRETRAINED_CO... | 277 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__A... | 363 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_mo... | 277 | 0 |
import os
from distutils.util import strtobool
def __a ( lowerCAmelCase_ : Optional[Any] ,lowerCAmelCase_ : Optional[Any] ) -> Any:
'''simple docstring'''
for e in env_keys:
UpperCAmelCase_= int(os.environ.get(lowerCAmelCase_ ,-1 ) ... | 364 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_... | 277 | 0 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def __a ( lowerCAmelCase_ : str ,lowerCAmelCase_ : str ,lowerCAmelCase_ : Optional[str] = None ) -> str:
'''simple docstring'''
if ve... | 365 |
__A = 6_5521
def __a ( lowerCAmelCase_ : str ) -> int:
'''simple docstring'''
UpperCAmelCase_= 1
UpperCAmelCase_= 0
for plain_chr in plain_text:
UpperCAmelCase_= (a + ord(lowerCAmelCase_ )) % MOD_ADLER
U... | 277 | 0 |
from __future__ import annotations
__A = 10
def __a ( lowerCAmelCase_ : list[int] ) -> list[int]:
'''simple docstring'''
UpperCAmelCase_= 1
UpperCAmelCase_= max(lowerCAmelCase_ )
while placement <= max_digit:
# declar... | 366 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, Par... | 277 | 0 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_... | 367 |
def __a ( lowerCAmelCase_ : Dict ) -> Dict:
'''simple docstring'''
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
... | 277 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
f... | 368 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = '''https://openaipublic.azureedge.... | 277 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}
class lowercase ( snake_case__):
"""simple docstring"""
a__... | 369 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, G... | 277 | 0 |
def __a ( lowerCAmelCase_ : Union[str, Any] ) -> List[str]: # noqa: E741
'''simple docstring'''
UpperCAmelCase_= len(lowerCAmelCase_ )
UpperCAmelCase_= 0
UpperCAmelCase_= [0] * n
UpperCAmelCase_= [False] * n
UpperCAmelCase... | 370 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __a ( lowerCAmelCase_ : Optional[int] ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase_= [
"""decoder.version"... | 277 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
__A = l... | 371 |
import warnings
from functools import wraps
from typing import Callable
def __a ( lowerCAmelCase_ : Callable ) -> Callable:
'''simple docstring'''
@wraps(lowerCAmelCase_ )
def _inner_fn(*lowerCAmelCase_ : List[Any] ,**lowerCAmelCase_ : Tuple ... | 277 | 0 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipe... | 350 |
import pytest
import datasets
# Import fixture modules as plugins
__A = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def __a ( lowerCAmelCase_ : Optional[Any] ,lowerCAmelCase_ : Any ) -> Tuple:
'''simple docstring'''... | 277 | 0 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddp... | 351 |
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase ( snake_case__):
"""simple docstring"""
def __init__( self : ... | 277 | 0 |
"""simple docstring"""
def __a ( lowerCAmelCase_ : Dict ,lowerCAmelCase_ : str ,lowerCAmelCase_ : Optional[int] ) -> List[str]:
'''simple docstring'''
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation... | 352 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ... | 277 | 0 |
import heapq
import sys
import numpy as np
__A = tuple[int, int]
class lowercase :
"""simple docstring"""
def __init__( self : Dict ) -> List[Any]:
UpperCAmelCase_= []
UpperCAmelCase_= set()
def _SCREAMING_SNAKE_CASE ( ... | 353 |
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMixin
from ...te... | 277 | 0 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
__A = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
__A = typing.Union[np.floataa, int, float] # noqa: UP007
def __a ( lowerCAmelCase_ : Vector... | 354 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__A = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schu... | 277 | 0 |
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 import ConfigTe... | 355 |
from __future__ import annotations
def __a ( lowerCAmelCase_ : list[int] ,lowerCAmelCase_ : int ) -> list[list[int]]:
'''simple docstring'''
UpperCAmelCase_= []
UpperCAmelCase_= []
UpperCAmelCase_= 0
UpperCAmelCase_= s... | 277 | 0 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __a ( lowerCAmelCase_ : int ) -> List[str]:
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase_= orig_key.replace("""model.""" ,"""""" )
if ... | 356 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowercase ( snake_case__):
"""simple docstring"""
def __init__( self : ... | 277 | 0 |
def __a ( lowerCAmelCase_ : float ,lowerCAmelCase_ : float ,lowerCAmelCase_ : float ,lowerCAmelCase_ : float ,lowerCAmelCase_ : float ,) -> float:
'''simple docstring'''
UpperCAmelCase_= [redshift, radiation_density, matte... | 357 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import... | 277 | 0 |
def __a ( lowerCAmelCase_ : list[int] ,lowerCAmelCase_ : list[int] ,lowerCAmelCase_ : int ) -> bool:
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(lowerCAmelCase... | 358 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput... | 277 | 0 |
def __a ( lowerCAmelCase_ : str ,lowerCAmelCase_ : List[str] ,lowerCAmelCase_ : Optional[int] ,lowerCAmelCase_ : str ) -> str:
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - ... | 359 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
__A = logging.get_logger(__name__)
def __a ( lowerCAmelCase_ : Tuple=None ,lowerCAmelCase_ : Optional[Any]=None ... | 277 | 0 |
__A = {str(digit): digit**5 for digit in range(10)}
def __a ( lowerCAmelCase_ : int ) -> int:
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCAmelCase_ ) )
def __a ( ) -> int:
'''simple docstr... | 360 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Ac... | 277 | 0 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def __a ( lowerCAmelCase_ : Union[str... | 361 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
... | 277 | 0 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class lowercase :
"""simple docstring"""
def __init__( self : Tuple , __U... | 362 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A = {
'''configuration_clip''': [
'''CLIP_PRETRAINED_CO... | 277 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch... | 363 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_mo... | 277 | 0 |
def __a ( lowerCAmelCase_ : int ,lowerCAmelCase_ : int ) -> int:
'''simple docstring'''
while b:
UpperCAmelCase_, UpperCAmelCase_= b, a % b
return a
def __a ( lowerCAmelCase_ : int ,lowerCAmelCase_ : int ... | 364 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_... | 277 | 0 |
def __a ( lowerCAmelCase_ : List[Any] ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_= len(lowerCAmelCase_ )
for i in range(length - 1 ):
UpperCAmelCase_= i
for k in range(i + 1 ,lowerCAmelCase_ ):
if ... | 365 |
__A = 6_5521
def __a ( lowerCAmelCase_ : str ) -> int:
'''simple docstring'''
UpperCAmelCase_= 1
UpperCAmelCase_= 0
for plain_chr in plain_text:
UpperCAmelCase_= (a + ord(lowerCAmelCase_ )) % MOD_ADLER
U... | 277 | 0 |
__A = {
'''a''': '''AAAAA''',
'''b''': '''AAAAB''',
'''c''': '''AAABA''',
'''d''': '''AAABB''',
'''e''': '''AABAA''',
'''f''': '''AABAB''',
'''g''': '''AABBA''',
'''h''': '''AABBB''',
'''i''': '''ABAAA''',
'''j''': '''BBBAA''',
'''k''': '''ABAAB''',
'''l... | 366 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, Par... | 277 | 0 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def __a ( lowerCAmelCase_ ... | 367 |
def __a ( lowerCAmelCase_ : Dict ) -> Dict:
'''simple docstring'''
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
... | 277 | 0 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction... | 368 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = '''https://openaipublic.azureedge.... | 277 | 0 |
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
__A = logging.getLogger(__name__)... | 369 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, G... | 277 | 0 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu... | 370 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __a ( lowerCAmelCase_ : Optional[int] ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase_= [
"""decoder.version"... | 277 | 0 |
from math import isqrt
def __a ( lowerCAmelCase_ : int ) -> list[int]:
'''simple docstring'''
UpperCAmelCase_= [True] * max_number
for i in range(2 ,isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 ... | 371 |
import warnings
from functools import wraps
from typing import Callable
def __a ( lowerCAmelCase_ : Callable ) -> Callable:
'''simple docstring'''
@wraps(lowerCAmelCase_ )
def _inner_fn(*lowerCAmelCase_ : List[Any] ,**lowerCAmelCase_ : Tuple ... | 277 | 0 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def __a ( lowerCAmelCase_ : List[str] ) -> Op... | 350 |
import pytest
import datasets
# Import fixture modules as plugins
__A = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def __a ( lowerCAmelCase_ : Optional[Any] ,lowerCAmelCase_ : Any ) -> Tuple:
'''simple docstring'''... | 277 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlipConfig''',
... | 351 |
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase ( snake_case__):
"""simple docstring"""
def __init__( self : ... | 277 | 0 |
"""simple docstring"""
def __a ( lowerCAmelCase_ : str ,lowerCAmelCase_ : int ) -> str:
'''simple docstring'''
UpperCAmelCase_= [[] for _ in range(lowerCAmelCase_ )]
UpperCAmelCase_= key - 1
if key <= 0:
raise ValueErro... | 352 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ... | 277 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case__)
class lowercase ( snake_case__):
"""simple docstring"""
a__ : str = fiel... | 353 |
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMixin
from ...te... | 277 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__A = logging.get_logger(__name__)
__A = OrderedDict(
[
# Base model mapping... | 354 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__A = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schu... | 277 | 0 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def __a ( lowerCAmelCase_ : int ) -> str:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ ,lowerCAmelCase_ ):
raise TypeError("""Undefined for non-integers""" )
... | 355 |
from __future__ import annotations
def __a ( lowerCAmelCase_ : list[int] ,lowerCAmelCase_ : int ) -> list[list[int]]:
'''simple docstring'''
UpperCAmelCase_= []
UpperCAmelCase_= []
UpperCAmelCase_= 0
UpperCAmelCase_= s... | 277 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
St... | 356 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowercase ( snake_case__):
"""simple docstring"""
def __init__( self : ... | 277 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A = {
'''configuration_clip''': [
'''CLIP_PRETRAINED_CO... | 357 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import... | 277 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class lowercase ( snake_case__):
"""simple docstring"""
def __init__( self : List[Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : Tuple ) ->... | 358 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput... | 277 | 0 |
import requests
__A = '''YOUR API KEY'''
def __a ( lowerCAmelCase_ : str ,lowerCAmelCase_ : str = giphy_api_key ) -> list:
'''simple docstring'''
UpperCAmelCase_= """+""".join(query.split() )
UpperCAmelCase_= F"""https://api.gi... | 359 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
__A = logging.get_logger(__name__)
def __a ( lowerCAmelCase_ : Tuple=None ,lowerCAmelCase_ : Optional[Any]=None ... | 277 | 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... | 360 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Ac... | 277 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''facebook/data2vec-text-base''': '''https://huggingface.co/data2ve... | 361 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
... | 277 | 0 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class lowercase :
"""simple docstring"""
a__ : int
a__ : TreeNode | None = None
a__ : TreeNode | None = None
__A = namedtuple(... | 362 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A = {
'''configuration_clip''': [
'''CLIP_PRETRAINED_CO... | 277 | 0 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__A = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': 1000,
... | 363 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_mo... | 277 | 0 |
from __future__ import annotations
def __a ( lowerCAmelCase_ : list[float] ) -> bool:
'''simple docstring'''
if len(lowerCAmelCase_ ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
if any(i <= 0 for i in ... | 364 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_... | 277 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wa... | 365 |
__A = 6_5521
def __a ( lowerCAmelCase_ : str ) -> int:
'''simple docstring'''
UpperCAmelCase_= 1
UpperCAmelCase_= 0
for plain_chr in plain_text:
UpperCAmelCase_= (a + ord(lowerCAmelCase_ )) % MOD_ADLER
U... | 277 | 0 |
def __a ( lowerCAmelCase_ : int = 2_00_00_00 ) -> int:
'''simple docstring'''
UpperCAmelCase_= [0 for i in range(n + 1 )]
UpperCAmelCase_= 1
UpperCAmelCase_= 1
for i in range(2 ,int(n**0.5 ) + 1 ):
if primali... | 366 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, Par... | 277 | 0 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ... | 367 |
def __a ( lowerCAmelCase_ : Dict ) -> Dict:
'''simple docstring'''
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
... | 277 | 0 |
"""simple docstring"""
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... | 368 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = '''https://openaipublic.azureedge.... | 277 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
U... | 369 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, G... | 277 | 0 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_p... | 370 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __a ( lowerCAmelCase_ : Optional[int] ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase_= [
"""decoder.version"... | 277 | 0 |
from timeit import timeit
def __a ( lowerCAmelCase_ : int ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCAmelCase_= 0
while number:
number &= number - 1
... | 371 |
import warnings
from functools import wraps
from typing import Callable
def __a ( lowerCAmelCase_ : Callable ) -> Callable:
'''simple docstring'''
@wraps(lowerCAmelCase_ )
def _inner_fn(*lowerCAmelCase_ : List[Any] ,**lowerCAmelCase_ : Tuple ... | 277 | 0 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput... | 350 |
import pytest
import datasets
# Import fixture modules as plugins
__A = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def __a ( lowerCAmelCase_ : Optional[Any] ,lowerCAmelCase_ : Any ) -> Tuple:
'''simple docstring'''... | 277 | 0 |
from scipy.stats import spearmanr
import datasets
__A = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as ... | 351 |
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase ( snake_case__):
"""simple docstring"""
def __init__( self : ... | 277 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase ( snake_case__ , ... | 352 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ... | 277 | 0 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
__A = HfArgumentParser(InitializationArguments)
__A = parser.parse_args()
# Load codeparrot tokenizer trained for Python code tokenizatio... | 353 |
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMixin
from ...te... | 277 | 0 |
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 = logging.get_logger(_... | 354 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__A = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schu... | 277 | 0 |
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMixin
from ...te... | 355 |
from __future__ import annotations
def __a ( lowerCAmelCase_ : list[int] ,lowerCAmelCase_ : int ) -> list[list[int]]:
'''simple docstring'''
UpperCAmelCase_= []
UpperCAmelCase_= []
UpperCAmelCase_= 0
UpperCAmelCase_= s... | 277 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A = {
'''configuration_squeezebert''': [
'''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SqueezeBertConfig''',
'''SqueezeBert... | 356 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowercase ( snake_case__):
"""simple docstring"""
def __init__( self : ... | 277 | 0 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''google/efficientnet-b7''': '... | 357 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import... | 277 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set... | 358 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput... | 277 | 0 |
from __future__ import annotations
from collections.abc import Generator
def __a ( ) -> Generator[int, None, None]:
'''simple docstring'''
UpperCAmelCase_= {}
UpperCAmelCase_= 2
while True:
UpperCAmelCase_= factor_map.pop(lowerCAmelCase_... | 359 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
__A = logging.get_logger(__name__)
def __a ( lowerCAmelCase_ : Tuple=None ,lowerCAmelCase_ : Optional[Any]=None ... | 277 | 0 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__A = 1.0_5457_1817E-34 # unit of ℏ : J * s
__A = 3E8 # unit of c : m * s^-1
def __a ( lowerCAmelCase_ : float ,lowe... | 360 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Ac... | 277 | 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, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import... | 361 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
... | 277 | 0 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class lowercase :
""... | 362 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A = {
'''configuration_clip''': [
'''CLIP_PRETRAINED_CO... | 277 | 0 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__A = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__A = [file for file in filepaths if file != file.lower()]
if upp... | 363 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_mo... | 277 | 0 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_co... | 364 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_... | 277 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if ... | 365 |
__A = 6_5521
def __a ( lowerCAmelCase_ : str ) -> int:
'''simple docstring'''
UpperCAmelCase_= 1
UpperCAmelCase_= 0
for plain_chr in plain_text:
UpperCAmelCase_= (a + ord(lowerCAmelCase_ )) % MOD_ADLER
U... | 277 | 0 |
from collections import deque
from .hash_table import HashTable
class lowercase ( snake_case__):
"""simple docstring"""
def __init__( self : Any , *__UpperCAmelCase : List[Any] , **__UpperCAmelCase : Tuple ) -> Any:
super().__... | 366 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, Par... | 277 | 0 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__A = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
if not is_... | 367 |
def __a ( lowerCAmelCase_ : Dict ) -> Dict:
'''simple docstring'''
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
... | 277 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __a ( lowerCAmelCase_ : Dict ,lowerCAmelCase_ : List[Any] ... | 368 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = '''https://openaipublic.azureedge.... | 277 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ... | 369 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, G... | 277 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased... | 370 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __a ( lowerCAmelCase_ : Optional[int] ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase_= [
"""decoder.version"... | 277 | 0 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataL... | 371 |
import warnings
from functools import wraps
from typing import Callable
def __a ( lowerCAmelCase_ : Callable ) -> Callable:
'''simple docstring'''
@wraps(lowerCAmelCase_ )
def _inner_fn(*lowerCAmelCase_ : List[Any] ,**lowerCAmelCase_ : Tuple ... | 277 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, G... | 350 |
import pytest
import datasets
# Import fixture modules as plugins
__A = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def __a ( lowerCAmelCase_ : Optional[Any] ,lowerCAmelCase_ : Any ) -> Tuple:
'''simple docstring'''... | 277 | 0 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_mo... | 351 |
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase ( snake_case__):
"""simple docstring"""
def __init__( self : ... | 277 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A = {
'''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}
try:
... | 352 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ... | 277 | 0 |
import argparse
from collections import defaultdict
import yaml
__A = '''docs/source/en/_toctree.yml'''
def __a ( lowerCAmelCase_ : Optional[Any] ) -> Dict:
'''simple docstring'''
UpperCAmelCase_= defaultdict(lowerCAmelCase_ )
for doc in mode... | 353 |
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMixin
from ...te... | 277 | 0 |
from math import factorial, pi
def __a ( lowerCAmelCase_ : float ,lowerCAmelCase_ : int = 30 ) -> float:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ ,(int, float) ):
raise ValueError("""maclaurin_sin() requires either an int or... | 354 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__A = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schu... | 277 | 0 |
from __future__ import annotations
from random import random
class lowercase :
"""simple docstring"""
def __init__( self : Tuple , __UpperCAmelCase : int | None = None ) -> Optional[Any]:
UpperCAmelCase_= value
UpperCAmelCase_= ... | 355 |
from __future__ import annotations
def __a ( lowerCAmelCase_ : list[int] ,lowerCAmelCase_ : int ) -> list[list[int]]:
'''simple docstring'''
UpperCAmelCase_= []
UpperCAmelCase_= []
UpperCAmelCase_= 0
UpperCAmelCase_= s... | 277 | 0 |
from __future__ import annotations
from math import ceil, floor, sqrt
def __a ( lowerCAmelCase_ : int = 2_00_00_00 ) -> int:
'''simple docstring'''
UpperCAmelCase_= [0]
UpperCAmelCase_= 42
for idx in range(1 ,ceil(sqrt(target * 2 ) * 1.1... | 356 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowercase ( snake_case__):
"""simple docstring"""
def __init__( self : ... | 277 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowercase ( snak... | 357 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import... | 277 | 0 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import A... | 358 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput... | 277 | 0 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
__A = '''
import os
'''
__A = '''
def foo():
import os
return False
'''
__A = '''
def foo():
def bar():
if True:
import os
return False
return bar()
''... | 359 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
__A = logging.get_logger(__name__)
def __a ( lowerCAmelCase_ : Tuple=None ,lowerCAmelCase_ : Optional[Any]=None ... | 277 | 0 |
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