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 |
|---|---|---|---|---|
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since ... | 277 |
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 | 1 |
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 ... | 277 |
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 | 1 |
def __a ( lowerCAmelCase_ : int ) -> None:
'''simple docstring'''
UpperCAmelCase_= generate_pascal_triangle(lowerCAmelCase_ )
for row_idx in range(lowerCAmelCase_ ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
... | 277 |
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 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLN... | 277 |
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 | 1 |
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... | 277 |
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 | 1 |
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 ... | 277 |
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 | 1 |
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 ... | 277 |
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 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class... | 277 |
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 | 1 |
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''': '... | 277 |
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 | 1 |
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... | 277 |
__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 | 1 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase ( snake_case__... | 277 |
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 | 1 |
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
... | 277 |
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 | 1 |
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... | 277 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Optio... | 277 |
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 | 1 |
from ..utils import DummyObject, requires_backends
class lowercase ( metaclass=snake_case__):
"""simple docstring"""
a__ : Tuple = ["torch"]
def __init__( self : Optional[int] , *__UpperCAmelCase : Any , **__UpperCAmelCase : L... | 277 |
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 | 1 |
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 |
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 | 1 |
from ...configuration_utils import PretrainedConfig
__A = {
'''google/tapas-base-finetuned-sqa''': (
'''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wtq''': (
'''https://huggingface.co/google/tapas-base-... | 277 |
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 | 1 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowercase ( tf.keras.layers.Layer):
"""simple docstring"""
def __init__... | 277 |
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 | 1 |
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] ,lowerCAmelCase_ ... | 277 |
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 | 1 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
__A = 3
def __a ( lowerCAmelCase_ : int ) -> int:
'''simple docstring'''
print("""Generating primitive root of p""" )
while True:
... | 277 |
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 | 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
... | 277 |
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 | 1 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
__A = Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertoo... | 277 |
from __future__ import annotations
def __a ( lowerCAmelCase_ : list[int] ,lowerCAmelCase_ : int ) -> list[list[int]]:
'''simple docstring'''
UpperCAmelCase_= []
UpperCAmelCase_= []
UpperCAmelCase_= 0
UpperCAmelCase_= s... | 277 | 1 |
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... | 277 |
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 | 1 |
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 ) ->... | 277 |
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 | 1 |
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 = l... | 277 |
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 | 1 |
#
# 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... | 277 |
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 | 1 |
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__)... | 277 |
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 | 1 |
import torch
def __a ( ) -> Union[str, Any]:
'''simple docstring'''
if torch.cuda.is_available():
UpperCAmelCase_= torch.cuda.device_count()
else:
UpperCAmelCase_= 0
print(F"""Successfully ran on {num_gpus} GPUs""" )
if __name_... | 277 |
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 | 1 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __a ( lowerCAmelCase_ : Optional[int] ,lowerCAmelCase_ : Optional[Any] ... | 277 |
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 | 1 |
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... | 277 |
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 | 1 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class lowercase ( unittest.TestCase):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : int ) -> List[str]:
... | 277 |
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 | 1 |
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 __a ( lowerCAmelCase_ : Dict[str, torch.Tensor] ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
UpperCAmelCase_= ... | 277 |
__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 | 1 |
__A = {
'''a''': '''AAAAA''',
'''b''': '''AAAAB''',
'''c''': '''AAABA''',
'''d''': '''AAABB''',
'''e''': '''AABAA''',
'''f''': '''AABAB''',
'''g''': '''AABBA''',
'''h''': '''AABBB''',
'''i''': '''ABAAA''',
'''j''': '''BBBAA''',
'''k''': '''ABAAB''',
'''l'''... | 277 |
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 | 1 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = '''https://openaipublic.azureedge.... | 277 |
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 | 1 |
def __a ( lowerCAmelCase_ : int ) -> int:
'''simple docstring'''
UpperCAmelCase_= [1]
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_= 0, 0, 0
UpperCAmelCase_= ugly_nums[ia] * 2
UpperCAmelCase_= ugly_nums[ia] * 3
... | 277 |
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 | 1 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
__A = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'''text-classification''',
'''langu... | 277 |
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 | 1 |
def __a ( lowerCAmelCase_ : int = 1_00_00_00 ) -> int:
'''simple docstring'''
UpperCAmelCase_= [i - 1 for i in range(limit + 1 )]
for i in range(2 ,limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i ,limit + 1 ... | 277 |
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 | 1 |
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 |
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 | 1 |
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(lowerCAmelCase_ ,n - ... | 277 |
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 | 1 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class lowercase ( snake_case__):
"""simple docstring"""
a__ : List[str] = ... | 277 |
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 | 1 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __a ( lowerCAmelCase_ : Union[str, Any] ,lowerCAmelCase_ : Dict ... | 277 |
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 | 1 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effic... | 277 |
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 | 1 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def __a ( lowerCAmelCase_ : Dict ,lowerCAmelCase_ : List[str] ,lowerCAmelCase_ : int ,lowerCAmelCase_ : str ) -> List[str]:
'''simple do... | 277 |
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 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import... | 277 |
from __future__ import annotations
def __a ( lowerCAmelCase_ : list[int] ,lowerCAmelCase_ : int ) -> list[list[int]]:
'''simple docstring'''
UpperCAmelCase_= []
UpperCAmelCase_= []
UpperCAmelCase_= 0
UpperCAmelCase_= s... | 277 | 1 |
def __a ( lowerCAmelCase_ : int ,lowerCAmelCase_ : int ) -> int:
'''simple docstring'''
while b:
UpperCAmelCase_, UpperCAmelCase_= b, a % b
return a
def __a ( lowerCAmelCase_ : int ,lowerCAmelCase_ : int ... | 277 |
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 | 1 |
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 |
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 | 1 |
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 |
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 | 1 |
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 upper... | 277 |
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 | 1 |
def __a ( lowerCAmelCase_ : str ) -> List[str]:
'''simple docstring'''
UpperCAmelCase_= len(lowerCAmelCase_ )
while cur > 1:
# Find the maximum number in arr
UpperCAmelCase_= arr.index(max(arr[0:cur] ) )
# Rever... | 277 |
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 | 1 |
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 ... | 277 |
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 | 1 |
import math
def __a ( lowerCAmelCase_ : int ) -> bool:
'''simple docstring'''
assert isinstance(lowerCAmelCase_ ,lowerCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are pr... | 277 |
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 | 1 |
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... | 277 |
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 | 1 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and ... | 277 |
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 | 1 |
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... | 277 |
__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 | 1 |
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__ , unittest.TestCase):
... | 277 |
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 | 1 |
from collections import Counter
from timeit import timeit
def __a ( lowerCAmelCase_ : str = "" ,) -> bool:
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(""" """ ,"""""" ).lower() ).values() ) < 2
def __a ( lowerCAmel... | 277 |
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 | 1 |
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 |
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 | 1 |
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:
if not is_torch_available(... | 277 |
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 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ... | 277 |
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 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltF... | 277 |
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 | 1 |
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_visi... | 277 |
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 | 1 |
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... | 277 |
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 | 1 |
def __a ( lowerCAmelCase_ : int ,lowerCAmelCase_ : int ) -> float:
'''simple docstring'''
return base * power(lowerCAmelCase_ ,(exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion..... | 277 |
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 | 1 |
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 |
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 | 1 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''snap-research/efficientformer-l1-300''': (
'''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.j... | 277 |
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 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTe... | 277 |
from __future__ import annotations
def __a ( lowerCAmelCase_ : list[int] ,lowerCAmelCase_ : int ) -> list[list[int]]:
'''simple docstring'''
UpperCAmelCase_= []
UpperCAmelCase_= []
UpperCAmelCase_= 0
UpperCAmelCase_= s... | 277 | 1 |
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... | 277 |
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 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass... | 277 |
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 | 1 |
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... | 277 |
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 | 1 |
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_common import SequenceFeat... | 277 |
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 | 1 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
__A = logging.get_logger(__name__)
@add_end_docs... | 277 |
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 | 1 |
def __a ( lowerCAmelCase_ : str ,lowerCAmelCase_ : int ) -> str:
'''simple docstring'''
UpperCAmelCase_= [[] for _ in range(lowerCAmelCase_ )]
UpperCAmelCase_= key - 1
if key <= 0:
raise ValueError("""Height of grid can't b... | 277 |
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 | 1 |
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 |
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 | 1 |
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... | 277 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'''tokenization_mvp''': ['''MvpTokeni... | 277 |
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 | 1 |
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 =... | 277 |
__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 | 1 |
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""" )
... | 277 |
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 | 1 |
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,
... | 277 |
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 | 1 |
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 primality_... | 277 |
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 | 1 |
__A = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__A ... | 277 |
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 | 1 |
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_... | 277 |
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 | 1 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor,... | 277 |
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 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A = logging.get_logger(__name__)
__A = {
'''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/s... | 277 |
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 | 1 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
... | 277 |
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 | 1 |
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... | 277 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A = {'''configuration_yolos''': ['''YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YolosConfig''', '''YolosOnnxConfig''']}
try:
if not is_vision_available():
rai... | 277 |
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 | 1 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
c... | 277 |
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 | 1 |
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... | 277 |
from __future__ import annotations
def __a ( lowerCAmelCase_ : list[int] ,lowerCAmelCase_ : int ) -> list[list[int]]:
'''simple docstring'''
UpperCAmelCase_= []
UpperCAmelCase_= []
UpperCAmelCase_= 0
UpperCAmelCase_= s... | 277 | 1 |
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 (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
Seg... | 277 |
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 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set... | 277 |
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 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = '''▁'''
__A = {'''vocab... | 277 |
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 | 1 |
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 |
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 | 1 |
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 unittest.mock import patch
import pyarrow as pa... | 277 |
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 | 1 |
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 |
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 | 1 |
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_ ):
... | 277 |
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 | 1 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __a ( lowerCAmelCase_ : Union[str, Any] ) -> str:
'''simple docstring'''
for param in module.parameters():
UpperCAmelCase_= False
def __a ( ) -> Optional[Any]... | 277 |
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 | 1 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
__A = datasets.logging.get_logger(__name__)
__A = '''\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for Text Generation},
author={Thibault Sellam and Dipanja... | 277 |
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 | 1 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" ,[
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.md""", """dataset_infos.json"""... | 277 |
__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 | 1 |
def __a ( lowerCAmelCase_ : int ) -> bool:
'''simple docstring'''
return sum(i for i in range(1 ,number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('''Program to check whether a number is a Perfect number or not...''')
__A ... | 277 |
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 | 1 |
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... | 277 |
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 | 1 |
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