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 |
|---|---|---|---|---|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils impo... | 22 |
'''simple docstring'''
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
... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
if not isinstance(UpperCamelCase , UpperCamelCase ):
raise TypeError('''only integers accepted as input''' )
else:
_a = ... | 22 |
'''simple docstring'''
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
... | 22 | 1 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_snake_case : int = logging.getLogger(__name__)
class A :
... | 22 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Any =... | 22 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_snake_case : Any = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
... | 22 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class A :
lowercase_ = 42
lowercase_ = 42
class A ... | 22 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_snake_case : Tuple = ... | 22 |
'''simple docstring'''
from math import pi, sqrt
def snake_case_ (UpperCamelCase : float ):
'''simple docstring'''
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math rang... | 22 | 1 |
'''simple docstring'''
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, re... | 22 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determini... | 22 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_im... | 22 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_snake_case : Any = '\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation ... | 22 | 1 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class A :
def __init__( self : Tuple ) -> str:
"""simple docstring"""
_a = ... | 22 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
_snake_case : Tuple = {
'linear': PIL.Image.Resampling.BILINEAR,
... | 22 | 1 |
'''simple docstring'''
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is... | 22 |
'''simple docstring'''
import requests
def snake_case_ (UpperCamelCase : str , UpperCamelCase : str ):
'''simple docstring'''
_a = {'''Content-Type''': '''application/json'''}
_a = requests.post(UpperCamelCase ,... | 22 | 1 |
'''simple docstring'''
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
... | 22 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
_snake_case : List[str] = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def snake_case_ (UpperCamelCase : list[list[int]] , UpperCamelCase : list[int] , Uppe... | 22 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
... | 22 | 1 |
'''simple docstring'''
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 transform... | 22 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.uti... | 22 | 1 |
'''simple docstring'''
import numpy as np
def snake_case_ (UpperCamelCase : Optional[int] , UpperCamelCase : Union[str, Any] , UpperCamelCase : Optional[Any] , UpperCamelCase : List[str] , UpperCamelCase : Dict ):
'''simple docst... | 22 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , num... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : Any , UpperCamelCase : List[Any] , UpperCamelCase : List[Any]=False ):
'''simple docstring'''
if isinstance(UpperCamelCase , UpperCamelCase ) and isinstance(UpperCamelCase ... | 22 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_snake_case : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_c... | 22 | 1 |
'''simple docstring'''
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_comm... | 22 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_snake_case : Tuple = ... | 22 | 1 |
'''simple docstring'''
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 A ( _a ):
def __i... | 22 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
r... | 22 | 1 |
'''simple docstring'''
from bisect import bisect
from itertools import accumulate
def snake_case_ (UpperCamelCase : Optional[int] , UpperCamelCase : Dict , UpperCamelCase : Any , UpperCamelCase : str ):
'''simple docstring'''
... | 22 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_snake_case : str = {
'configuration_layou... | 22 | 1 |
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
_snake_case : Any = 4
_snake_case : Any = 3
class... | 22 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( _a ):
lowercase_ = (DDPMParallelScheduler,)
def __lowerCAmelCase ( self : ... | 22 | 1 |
'''simple docstring'''
import unittest
from transformers import LiltConfig, 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_mo... | 22 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def snake_case_ (UpperCamelCase : ... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : list ):
'''simple docstring'''
if len(UpperCamelCase ) <= 1:
return lst
_a = 1
while i < len(UpperCamelCase ):
if lst[i - 1] <= lst[i]:
... | 22 |
'''simple docstring'''
import qiskit
def snake_case_ (UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
_a = qiskit.Aer.get_backend('''aer_simulator''' )
_a = qiskit.QuantumCircuit(4 , 2 )... | 22 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Optional[Any] = logging.get_logger(__name__)
_snake_case : str = {
'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
'https://huggingface.co/tanreina... | 22 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def snake_case_ (UpperCamelCase : bytes ):
'''simple docstring'''
if len(UpperCamelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
... | 22 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class A ( _a ):
lowe... | 22 |
'''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_im... | 22 | 1 |
'''simple docstring'''
from collections.abc import Sequence
def snake_case_ (UpperCamelCase : Sequence[int] | None = None ):
'''simple docstring'''
if nums is None or not nums:
raise ValueError('''Input sequence should not be empty''' )
... | 22 |
'''simple docstring'''
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
... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class A :
lowercase_ = 42
lowercase_ = No... | 22 |
'''simple docstring'''
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
... | 22 | 1 |
'''simple docstring'''
_snake_case : Any = 9.8_0665
def snake_case_ (UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float = g ):
'''simple docstring'''
if fluid_density <= 0:
raise ValueError(... | 22 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Any =... | 22 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
r... | 22 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class A :
lowercase_ = 42
lowercase_ = 42
class A ... | 22 | 1 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def snake_case_ (UpperCamelCase : bytes ):
'''simple docstring'''
if len(UpperCamelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
... | 22 |
'''simple docstring'''
from math import pi, sqrt
def snake_case_ (UpperCamelCase : float ):
'''simple docstring'''
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math rang... | 22 | 1 |
'''simple docstring'''
import math
def snake_case_ (UpperCamelCase : int = 100 ):
'''simple docstring'''
_a = sum(i * i for i in range(1 , n + 1 ) )
_a = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
ret... | 22 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determini... | 22 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Dict = {
'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolv... | 22 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_snake_case : Any = '\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation ... | 22 | 1 |
'''simple docstring'''
import copy
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
from ..auto import CONFIG_MAPPING
_snake_case : U... | 22 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
_snake_case : Tuple = {
'linear': PIL.Image.Resampling.BILINEAR,
... | 22 | 1 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested... | 22 |
'''simple docstring'''
import requests
def snake_case_ (UpperCamelCase : str , UpperCamelCase : str ):
'''simple docstring'''
_a = {'''Content-Type''': '''application/json'''}
_a = requests.post(UpperCamelCase ,... | 22 | 1 |
'''simple docstring'''
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_c... | 22 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,... | 22 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class A ( metaclass=_a ):
lowercase_ = ['flax']
def __init__( self : str , *lowerCAmelCase_ : str , **lowerCAmelCase_ : List[An... | 22 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
... | 22 | 1 |
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextMode... | 22 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.uti... | 22 | 1 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
_snake_case : Dict = '\\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 ... | 22 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , num... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def snake_case_ (UpperCamelCase : Callable[[int | float], int | float] , UpperCamelCase : int | float , UpperCamelCase : int | float , UpperCamelCase : int = 1... | 22 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_snake_case : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_c... | 22 | 1 |
'''simple docstring'''
print((lambda quine: quine % quine)('print((lambda quine: quine %% quine)(%r))'))
| 22 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_snake_case : Tuple = ... | 22 | 1 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, 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_mod... | 22 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
r... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTeste... | 22 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_snake_case : str = {
'configuration_layou... | 22 | 1 |
'''simple docstring'''
import requests
def snake_case_ (UpperCamelCase : str , UpperCamelCase : str ):
'''simple docstring'''
_a = {'''Content-Type''': '''application/json'''}
_a = requests.post(UpperCamelCase ,... | 22 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( _a ):
lowercase_ = (DDPMParallelScheduler,)
def __lowerCAmelCase ( self : ... | 22 | 1 |
'''simple docstring'''
_snake_case : int = '0.21.0'
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
fro... | 22 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def snake_case_ (UpperCamelCase : ... | 22 | 1 |
'''simple docstring'''
class A :
def __init__( self : int , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : List[str] ) -> Dict:
"""simple docstring"""
... | 22 |
'''simple docstring'''
import qiskit
def snake_case_ (UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
_a = qiskit.Aer.get_backend('''aer_simulator''' )
_a = qiskit.QuantumCircuit(4 , 2 )... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
if not isinstance(UpperCamelCase , UpperCamelCase ):
_a = f'Input value of [number={number}] must be an integer'
raise TypeE... | 22 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def snake_case_ (UpperCamelCase : bytes ):
'''simple docstring'''
if len(UpperCamelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
if num <= 0:
_a = f'{num}: Invalid input, please enter a positive integer.'
ra... | 22 |
'''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_im... | 22 | 1 |
'''simple docstring'''
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_avai... | 22 |
'''simple docstring'''
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
... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : int = 200_0000 ):
'''simple docstring'''
_a = [0 for i in range(n + 1 )]
_a = 1
_a = 1
for i in range(2 , int(n**0.5 ) + 1 ):
... | 22 |
'''simple docstring'''
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
... | 22 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
_snake_case : Dict = logging.get_logger(__name__)
_snake_case : int = {
'Intel/dpt-large': 'https://huggingface.co/Intel/dp... | 22 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Any =... | 22 | 1 |
'''simple docstring'''
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def snake_case_ (UpperCamelCase : Dict[str, torch.Tensor] ):
'''simple docstring'''
... | 22 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class A :
lowercase_ = 42
lowercase_ = 42
class A ... | 22 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Any =... | 22 |
'''simple docstring'''
from math import pi, sqrt
def snake_case_ (UpperCamelCase : float ):
'''simple docstring'''
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math rang... | 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
_snake_case : str = {
'configuration_sp... | 22 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determini... | 22 | 1 |
'''simple docstring'''
from math import pi, sqrt
def snake_case_ (UpperCamelCase : float ):
'''simple docstring'''
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math rang... | 22 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_snake_case : Any = '\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation ... | 22 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaP... | 22 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
_snake_case : Tuple = {
'linear': PIL.Image.Resampling.BILINEAR,
... | 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_snake_case : str = {
'configuration_layou... | 22 |
'''simple docstring'''
import requests
def snake_case_ (UpperCamelCase : str , UpperCamelCase : str ):
'''simple docstring'''
_a = {'''Content-Type''': '''application/json'''}
_a = requests.post(UpperCamelCase ,... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float , ):
'''simple docstring'''
_a = [reds... | 22 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,... | 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case : List[Any] = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 22 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
... | 22 | 1 |
'''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionMod... | 22 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.uti... | 22 | 1 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from tran... | 22 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , num... | 22 | 1 |
'''simple docstring'''
import numpy
# List of input, output pairs
_snake_case : Tuple = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
_snake_case : str = (((515, 22, 13), 555), ((61, 35, 49), 150))
_snake_... | 22 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_snake_case : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_c... | 22 | 1 |
'''simple docstring'''
import baseaa
def snake_case_ (UpperCamelCase : str ):
'''simple docstring'''
return baseaa.baaencode(string.encode('''utf-8''' ) )
def snake_case_ (UpperCamelCase : bytes ):
'''simple docstring'''
... | 22 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_snake_case : Tuple = ... | 22 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : str = {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook... | 22 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
r... | 22 | 1 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing impor... | 22 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_snake_case : str = {
'configuration_layou... | 22 | 1 |
'''simple docstring'''
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 AutoToken... | 22 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( _a ):
lowercase_ = (DDPMParallelScheduler,)
def __lowerCAmelCase ( self : ... | 22 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
_snake_case : int = 'docs/source/en/_toctree.yml'
def snake_case_ (UpperCamelCase : List[Any] ):
'''simple docstring'''
_a = defaultdict(Up... | 22 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def snake_case_ (UpperCamelCase : ... | 22 | 1 |
'''simple docstring'''
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
_snake_case : str = logging.get_logger(__name__)
def ... | 22 |
'''simple docstring'''
import qiskit
def snake_case_ (UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
_a = qiskit.Aer.get_backend('''aer_simulator''' )
_a = qiskit.QuantumCircuit(4 , 2 )... | 22 | 1 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def snake_case_ (UpperCamelCase : str = "AAPL" ):
'''simple docstring'''
_a = f'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
_a = BeautifulSoup(... | 22 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def snake_case_ (UpperCamelCase : bytes ):
'''simple docstring'''
if len(UpperCamelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
... | 22 | 1 |
'''simple docstring'''
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class A ( _a ):
def __init__( self : Union[str, Any] , low... | 22 |
'''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_im... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : Optional[int] , UpperCamelCase : Optional[Any] ):
'''simple docstring'''
_a = (boundary[1] - boundary[0]) / steps
_a = boundary[0]
_a = bou... | 22 |
'''simple docstring'''
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
... | 22 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determini... | 22 |
'''simple docstring'''
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
... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : list[int] , UpperCamelCase : int ):
'''simple docstring'''
_a = len(UpperCamelCase )
_a = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
... | 22 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Any =... | 22 | 1 |
'''simple docstring'''
import requests
_snake_case : Union[str, Any] = 'https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='
def snake_case_ (UpperCamelCase : str ):
'''simple docstring'''
_a = requests.get(_NEWS_API ... | 22 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class A :
lowercase_ = 42
lowercase_ = 42
class A ... | 22 | 1 |
'''simple docstring'''
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from un... | 22 |
'''simple docstring'''
from math import pi, sqrt
def snake_case_ (UpperCamelCase : float ):
'''simple docstring'''
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math rang... | 22 | 1 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from tra... | 22 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determini... | 22 | 1 |
'''simple docstring'''
# 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 u... | 22 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_snake_case : Any = '\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation ... | 22 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case : str = logging.get_logger(__name__)
_snake_case : Tu... | 22 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
_snake_case : Tuple = {
'linear': PIL.Image.Resampling.BILINEAR,
... | 22 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__na... | 22 |
'''simple docstring'''
import requests
def snake_case_ (UpperCamelCase : str , UpperCamelCase : str ):
'''simple docstring'''
_a = {'''Content-Type''': '''application/json'''}
_a = requests.post(UpperCamelCase ,... | 22 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from tr... | 22 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,... | 22 | 1 |
'''simple docstring'''
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 diffu... | 22 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : list , UpperCamelCase : list , UpperCamelCase : int ):
'''simple docstring'''
_a = len(UpperCamelCase )
_a = [[0] * n for i in range(UpperCamelCase )... | 22 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.uti... | 22 | 1 |
'''simple docstring'''
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
... | 22 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , num... | 22 | 1 |
'''simple docstring'''
import re
def snake_case_ (UpperCamelCase : str ):
'''simple docstring'''
return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]''' , str_ )]
def snake_case_ (UpperCamelCase : str ):
... | 22 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_snake_case : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_c... | 22 | 1 |
'''simple docstring'''
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def snake_case_ (UpperCamelCase : List[str] , UpperCamelCase : str , UpperCamelCase : int , UpperCamelCase : Optional[int]=5 ):
'''sim... | 22 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_snake_case : Tuple = ... | 22 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
... | 22 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
r... | 22 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,... | 22 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_snake_case : str = {
'configuration_layou... | 22 | 1 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , num... | 22 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( _a ):
lowercase_ = (DDPMParallelScheduler,)
def __lowerCAmelCase ( self : ... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ (UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float ):
'''simple docstring'''
if days_between_payments <= 0:
raise ValueError(... | 22 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def snake_case_ (UpperCamelCase : ... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : int = 10**9 ):
'''simple docstring'''
_a = 1
_a = 2
_a = 0
_a = 0
_a = 0
while perimeter <= max_per... | 22 |
'''simple docstring'''
import qiskit
def snake_case_ (UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
_a = qiskit.Aer.get_backend('''aer_simulator''' )
_a = qiskit.QuantumCircuit(4 , 2 )... | 22 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class A ( unittest.T... | 22 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def snake_case_ (UpperCamelCase : bytes ):
'''simple docstring'''
if len(UpperCamelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
... | 22 | 1 |
'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from... | 22 |
'''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_im... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : list , UpperCamelCase : list ):
'''simple docstring'''
_validate_point(UpperCamelCase )
_validate_point(UpperCamelCase )
if len(UpperCamelCase ) != len(UpperCamelCase ):
... | 22 |
'''simple docstring'''
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
... | 22 | 1 |
'''simple docstring'''
from math import factorial
_snake_case : Optional[int] = {str(d): factorial(d) for d in range(10)}
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
return sum(DIGIT_FACTORIAL[d] for d in str(UpperCamelCase ... | 22 |
'''simple docstring'''
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
... | 22 | 1 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
... | 22 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Any =... | 22 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class A ( _a ):
lowercase_ = 'WhisperFeatureExtractor'
lowercase_ = 'WhisperTokenizer'
def __init__( self : Union[str, Any] , ... | 22 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class A :
lowercase_ = 42
lowercase_ = 42
class A ... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ... | 22 |
'''simple docstring'''
from math import pi, sqrt
def snake_case_ (UpperCamelCase : float ):
'''simple docstring'''
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math rang... | 22 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def snake_case_ ():
'''simple docstring'''
_a = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
_a... | 22 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determini... | 22 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/... | 22 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_snake_case : Any = '\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation ... | 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_snake_case : Dict = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Option... | 22 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
_snake_case : Tuple = {
'linear': PIL.Image.Resampling.BILINEAR,
... | 22 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def snake_case_ (UpperCa... | 22 |
'''simple docstring'''
import requests
def snake_case_ (UpperCamelCase : str , UpperCamelCase : str ):
'''simple docstring'''
_a = {'''Content-Type''': '''application/json'''}
_a = requests.post(UpperCamelCase ,... | 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_snake_case : str = {
'configuration_perceiver': ['PERCEIVER_PRE... | 22 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,... | 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_snake_case : Optional[int] = {
'configuration_roberta_prelayernorm': [
... | 22 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
... | 22 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobert... | 22 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.uti... | 22 | 1 |
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