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 |
|---|---|---|---|---|
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_util... | 651 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_... | 651 | 1 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impor... | 651 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_torch_... | 651 | 1 |
from manim import *
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> Any:
"""simple docstring"""
__lowerCAmelCase : Dict = Rectan... | 651 |
from math import isqrt
def snake_case_ (__A : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(__A ) + 1 ) )
def snake_case_ (__A : int = 1_0**6 ) -> int:
__lowerCAmelCase : Tuple = 0
__lowerCAmelCase... | 651 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""vocab_file""": """vocab.json""",
"""tokenizer_co... | 651 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__UpperCAmelCase = logging.getLo... | 651 | 1 |
def snake_case_ (__A : str ) -> bool:
__lowerCAmelCase : Optional[Any] = [int(__A ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(__A ) == 4 and all(0 <= int(__A ) <= 2_5_4 for octet in octets )
if __name__ == "__main__":
__UpperCAmelCase = ... | 651 |
import cva
import numpy as np
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase : float , lowerCAmelCase : int ) -> Tuple:
"""simple docstring"""
... | 651 | 1 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .... | 651 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class SCREAMING_SNAKE_CASE ( ... | 651 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_im... | 651 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.con... | 651 | 1 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
lowerCamelCase : int
lowerCamelCase : TreeNode | None =None
lowerCamelCase : ... | 651 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class SCREAMING_SNAKE_CASE ( a_ ... | 651 | 1 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class SCR... | 651 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""camembert-base""": """https://hu... | 651 | 1 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse... | 651 |
def snake_case_ (__A : list[int] , __A : list[int] ) -> None:
__lowerCAmelCase : Union[str, Any] = len(__A )
print("""The following activities are selected:""" )
# The first activity is always selected
__lowerCAmelCase : str = 0
print(__A ... | 651 | 1 |
from functools import reduce
__UpperCAmelCase = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"... | 651 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__UpperCAmelCase = models.Sequential()
# Step 1 -... | 651 | 1 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def snake_case_ (__A : str , __A : str = "cpu" , __A : Union[str, None] = None ) -> None:
__lowerCAmelCase : Union[str, Any] = torch.load(__A , map_location=__A )
for k, v in t... | 651 |
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
__UpperCAmelCase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"""text-classificat... | 651 | 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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImag... | 651 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import... | 651 | 1 |
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 transformers.testing_utils import DUM... | 651 |
#
# 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-distribut... | 651 | 1 |
import unittest
import numpy as np
def snake_case_ (__A : np.ndarray , __A : np.ndarray , __A : np.ndarray , __A : np.ndarray | None = None , ) -> np.ndarray:
__lowerCAmelCase : Tuple = np.shape(__A )
__lowerCAmelCase : int = np.... | 651 |
import math
def snake_case_ (__A : int = 1_0_0 ) -> int:
__lowerCAmelCase : List[str] = sum(i * i for i in range(1 , n + 1 ) )
__lowerCAmelCase : int = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares... | 651 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""distilbert-base-uncased""": """h... | 651 |
from __future__ import annotations
import requests
def snake_case_ (__A : str ) -> dict:
__lowerCAmelCase : Tuple = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(__A ).json()
def snake_case_ ... | 651 | 1 |
from __future__ import annotations
__UpperCAmelCase = list[list[int]]
# assigning initial values to the grid
__UpperCAmelCase = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0... | 651 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy,... | 651 | 1 |
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 transformers import glue_compute_metri... | 651 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate ... | 651 | 1 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Tuple , lowerCAmelCase : list ) -> None:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = set_counts
__lowe... | 651 |
from __future__ import annotations
def snake_case_ (__A : list[int] , __A : list[int] , __A : list[int] , __A : list[list[str]] , __A : int , ) -> None:
__lowerCAmelCase : Any = len(__A )
# If row is equal to the size of the board it means the... | 651 | 1 |
import math
from numpy import inf
from scipy.integrate import quad
def snake_case_ (__A : float ) -> float:
if num <= 0:
raise ValueError("""math domain error""" )
return quad(__A , 0 , __A , args=(__A) )[0]
def snake_case_ (__A : float , ... | 651 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassifi... | 651 | 1 |
from __future__ import annotations
import requests
def snake_case_ (__A : str ) -> dict:
__lowerCAmelCase : Tuple = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(__A ).json()
def snake_case_ ... | 651 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCAmelCase = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__UpperCAmelC... | 651 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy,... | 651 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
requir... | 651 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {
"""configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Debe... | 651 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_... | 651 | 1 |
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common imp... | 651 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_torch_... | 651 | 1 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, cr... | 651 |
from math import isqrt
def snake_case_ (__A : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(__A ) + 1 ) )
def snake_case_ (__A : int = 1_0**6 ) -> int:
__lowerCAmelCase : Tuple = 0
__lowerCAmelCase... | 651 | 1 |
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_simplify,
require_tf,
r... | 651 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__UpperCAmelCase = logging.getLo... | 651 | 1 |
def snake_case_ (__A : int ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 651 |
import cva
import numpy as np
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase : float , lowerCAmelCase : int ) -> Tuple:
"""simple docstring"""
... | 651 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCAmelCase = {"""processing_wav2vec2_with_lm""": ["""Wav2Vec2ProcessorWithLM"""]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
__UpperCAmelCase = ... | 651 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class SCREAMING_SNAKE_CASE ( ... | 651 | 1 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def snake_case_ (__A : U... | 651 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.con... | 651 | 1 |
from __future__ import annotations
def snake_case_ (__A : list[int] , __A : list[int] , __A : list[int] , __A : list[list[str]] , __A : int , ) -> None:
__lowerCAmelCase : Any = len(__A )
# If row is equal to the size of the board it means the... | 651 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class SCREAMING_SNAKE_CASE ( a_ ... | 651 | 1 |
from __future__ import annotations
from fractions import Fraction
def snake_case_ (__A : int , __A : int ) -> bool:
return (
num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num / den
)
def snake_case_ (__A : int ) -> ... | 651 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""camembert-base""": """https://hu... | 651 | 1 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = R"""
Args:
input_ids (`torch.LongT... | 651 |
def snake_case_ (__A : list[int] , __A : list[int] ) -> None:
__lowerCAmelCase : Union[str, Any] = len(__A )
print("""The following activities are selected:""" )
# The first activity is always selected
__lowerCAmelCase : str = 0
print(__A ... | 651 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if... | 651 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__UpperCAmelCase = models.Sequential()
# Step 1 -... | 651 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_C... | 651 |
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
__UpperCAmelCase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"""text-classificat... | 651 | 1 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate ... | 651 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import... | 651 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""google/bit-50""": """https://h... | 651 |
#
# 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-distribut... | 651 | 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 timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import... | 651 |
import math
def snake_case_ (__A : int = 1_0_0 ) -> int:
__lowerCAmelCase : List[str] = sum(i * i for i in range(1 , n + 1 ) )
__lowerCAmelCase : int = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares... | 651 | 1 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def snake_case_ (__A : str ) -> str:
return "".join(sorted(__A ) )
def snake_case_ (__A : str ) -> list[str]:
return word_by_signature[signature(__A )]
... | 651 |
from __future__ import annotations
import requests
def snake_case_ (__A : str ) -> dict:
__lowerCAmelCase : Tuple = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(__A ).json()
def snake_case_ ... | 651 | 1 |
from ...configuration_utils import PretrainedConfig
__UpperCAmelCase = {
"""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... | 651 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy,... | 651 | 1 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
Effi... | 651 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate ... | 651 | 1 |
import os
def snake_case_ () -> Dict:
with open(os.path.dirname(__A ) + """/p022_names.txt""" ) as file:
__lowerCAmelCase : Tuple = str(file.readlines()[0] )
__lowerCAmelCase : Any = names.replace("""\"""" , """""" ).split(""",""" )
... | 651 |
from __future__ import annotations
def snake_case_ (__A : list[int] , __A : list[int] , __A : list[int] , __A : list[list[str]] , __A : int , ) -> None:
__lowerCAmelCase : Any = len(__A )
# If row is equal to the size of the board it means the... | 651 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}
clas... | 651 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassifi... | 651 | 1 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ... | 651 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCAmelCase = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__UpperCAmelC... | 651 | 1 |
import unittest
from transformers import DebertaConfig, 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_tenso... | 651 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
requir... | 651 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCAmelCase = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__UpperCAmelC... | 651 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_... | 651 | 1 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, requ... | 651 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_torch_... | 651 | 1 |
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
f... | 651 |
from math import isqrt
def snake_case_ (__A : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(__A ) + 1 ) )
def snake_case_ (__A : int = 1_0**6 ) -> int:
__lowerCAmelCase : Tuple = 0
__lowerCAmelCase... | 651 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multi... | 651 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__UpperCAmelCase = logging.getLo... | 651 | 1 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
__UpperCAmelCase = {
"""text_branch""": """text_model""",
"""audio_branch""": """audio_model.audio_encoder""",
"""attn""": """attention.self"... | 651 |
import cva
import numpy as np
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase : float , lowerCAmelCase : int ) -> Tuple:
"""simple docstring"""
... | 651 | 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.schedul... | 651 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class SCREAMING_SNAKE_CASE ( ... | 651 | 1 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__... | 651 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.con... | 651 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCAmelCase = {
"""configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
"""configuration_maskfo... | 651 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class SCREAMING_SNAKE_CASE ( a_ ... | 651 | 1 |
def snake_case_ (__A : Dict ) -> Optional[int]:
__lowerCAmelCase : Optional[int] = len(__A )
for i in range(length - 1 ):
__lowerCAmelCase : Dict = i
for k in range(i + 1 , __A ):
if collection[k] < collection[least]:
... | 651 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""camembert-base""": """https://hu... | 651 | 1 |
from __future__ import annotations
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Tuple , lowerCAmelCase : Any=None ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase ... | 651 |
def snake_case_ (__A : list[int] , __A : list[int] ) -> None:
__lowerCAmelCase : Union[str, Any] = len(__A )
print("""The following activities are selected:""" )
# The first activity is always selected
__lowerCAmelCase : str = 0
print(__A ... | 651 | 1 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : str ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = 0
__lowerCAmelCase : Tuple = 0... | 651 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__UpperCAmelCase = models.Sequential()
# Step 1 -... | 651 | 1 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path impo... | 651 |
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
__UpperCAmelCase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"""text-classificat... | 651 | 1 |
from math import ceil, sqrt
def snake_case_ (__A : int = 1_0_0_0_0_0_0 ) -> int:
__lowerCAmelCase : Tuple = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
__lowerCAmelCase : List[str] = max(ceil... | 651 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import... | 651 | 1 |
from itertools import product
def snake_case_ (__A : int , __A : int ) -> list[int]:
__lowerCAmelCase : Optional[Any] = sides_number
__lowerCAmelCase : Union[str, Any] = max_face_number * dice_number
__lowerCAmelCase : Optional[int]... | 651 |
#
# 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-distribut... | 651 | 1 |
__UpperCAmelCase = range(2, 20 + 1)
__UpperCAmelCase = [10**k for k in range(ks[-1] + 1)]
__UpperCAmelCase = {}
def snake_case_ (__A : List[Any] , __A : Optional[Any] , __A : Union[str, Any] , __A : int ) -> Union[str, Any]:
__lowerCAmelCase ... | 651 |
import math
def snake_case_ (__A : int = 1_0_0 ) -> int:
__lowerCAmelCase : List[str] = sum(i * i for i in range(1 , n + 1 ) )
__lowerCAmelCase : int = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares... | 651 | 1 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def snake_case_ (__A : str ) -> int:
# encoder.embeddings are double copied in origin... | 651 |
from __future__ import annotations
import requests
def snake_case_ (__A : str ) -> dict:
__lowerCAmelCase : Tuple = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(__A ).json()
def snake_case_ ... | 651 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase )
class lowerCamelCase_ ( lowerCamelCase ):
# `task` is not a ClassVar since we want it to be part of the ... | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy,... | 651 | 0 |
import unittest
from typing import Dict, List, Optional, Union
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, prepa... | 1 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate ... | 651 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase__... | 2 |
from __future__ import annotations
def snake_case_ (__A : list[int] , __A : list[int] , __A : list[int] , __A : list[list[str]] , __A : int , ) -> None:
__lowerCAmelCase : Any = len(__A )
# If row is equal to the size of the board it means the... | 651 | 0 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatm... | 3 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassifi... | 651 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
lowerCAmelCase ,lowerCAmelCase = analyze_text(_UpperCAmelCase )
lowerCAmelCase = list(' '... | 4 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCAmelCase = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__UpperCAmelC... | 651 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""caidas/swin2sr-classicalsr-x2-64""": (
"""https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/mai... | 5 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
requir... | 651 | 0 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_t... | 6 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_... | 651 | 0 |
"""simple docstring"""
a = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
... | 7 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_torch_... | 651 | 0 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
lowercase__ : Tuple = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE (a__ ):
def __init__( self , _UpperCAmelCase=None , *... | 8 |
from math import isqrt
def snake_case_ (__A : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(__A ) + 1 ) )
def snake_case_ (__A : int = 1_0**6 ) -> int:
__lowerCAmelCase : Tuple = 0
__lowerCAmelCase... | 651 | 0 |
# 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 ... | 9 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__UpperCAmelCase = logging.getLo... | 651 | 0 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _snake_case ( *__snake_case , __snake_case = None , __snake_case=True , __snake_case=2 ):
from .. import __version__
_UpperCamelCase = take_from
_UpperCame... | 10 |
import cva
import numpy as np
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase : float , lowerCAmelCase : int ) -> Tuple:
"""simple docstring"""
... | 651 | 0 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowerCAmelCase ():
"""simple docstring"""
assert xnor_gate(0 , 0) == 1
assert xnor_gate(0 , 1) == 0
assert xnor_gat... | 11 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class SCREAMING_SNAKE_CASE ( ... | 651 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multipl... | 12 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.con... | 651 | 0 |
'''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
from diffusers.... | 13 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class SCREAMING_SNAKE_CASE ( a_ ... | 651 | 0 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transfo... | 14 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""camembert-base""": """https://hu... | 651 | 0 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
A : Union[str, Any] = 0B1011001111101100100100000111101110110001100111... | 15 |
def snake_case_ (__A : list[int] , __A : list[int] ) -> None:
__lowerCAmelCase : Union[str, Any] = len(__A )
print("""The following activities are selected:""" )
# The first activity is always selected
__lowerCAmelCase : str = 0
print(__A ... | 651 | 0 |
from __future__ import annotations
import math
__A : Optional[int] = '2020.9.26'
__A : Any = 'xcodz-dot, cclaus, dhruvmanila'
def __a ( A__ : float , A__ : float , A__ : float , A__ : float , A__ : float ):
if not all(isinst... | 16 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__UpperCAmelCase = models.Sequential()
# Step 1 -... | 651 | 0 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_mu... | 17 |
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
__UpperCAmelCase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"""text-classificat... | 651 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct": ["MCTCTFeatureE... | 18 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import... | 651 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, ... | 19 |
#
# 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-distribut... | 651 | 0 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_... | 20 |
import math
def snake_case_ (__A : int = 1_0_0 ) -> int:
__lowerCAmelCase : List[str] = sum(i * i for i in range(1 , n + 1 ) )
__lowerCAmelCase : int = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares... | 651 | 0 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDIT... | 21 |
from __future__ import annotations
import requests
def snake_case_ (__A : str ) -> dict:
__lowerCAmelCase : Tuple = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(__A ).json()
def snake_case_ ... | 651 | 0 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
_a = abs(UpperCamelCase )
_a = 0
while n > 0:
res += n % 10
n //= 10
return res
def sn... | 22 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy,... | 651 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_se... | 23 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate ... | 651 | 0 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
__snake_case = 1
__snake_case = 1
while repunit:
__snake_case ... | 24 |
from __future__ import annotations
def snake_case_ (__A : list[int] , __A : list[int] , __A : list[int] , __A : list[list[str]] , __A : int , ) -> None:
__lowerCAmelCase : Any = len(__A )
# If row is equal to the size of the board it means the... | 651 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
),
... | 25 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassifi... | 651 | 0 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__UpperCamelCase = 200
# Number of elements selected in every generation of evolution. The selection takes
# place fro... | 26 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCAmelCase = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__UpperCAmelC... | 651 | 0 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__A : Union[str, Any] = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
... | 27 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
requir... | 651 | 0 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformer... | 28 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_... | 651 | 0 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowercase ( lowerCAmelCase__ ):
def wrapper(*lowerCAmelCase__ ,**lowerCAmelCase__ ):
lowerCamelCase_ = timeit... | 29 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_torch_... | 651 | 0 |
def lowerCamelCase__ ( _lowercase ):
'''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 = int(input('Enter n... | 30 |
from math import isqrt
def snake_case_ (__A : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(__A ) + 1 ) )
def snake_case_ (__A : int = 1_0**6 ) -> int:
__lowerCAmelCase : Tuple = 0
__lowerCAmelCase... | 651 | 0 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Opt... | 31 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__UpperCAmelCase = logging.getLo... | 651 | 0 |
print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))")) | 32 |
import cva
import numpy as np
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase : float , lowerCAmelCase : int ) -> Tuple:
"""simple docstring"""
... | 651 | 0 |
import collections
import importlib.util
import os
import re
from pathlib import Path
lowerCamelCase__ : str = """src/transformers"""
# Matches is_xxx_available()
lowerCamelCase__ : List[str] = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a... | 33 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class SCREAMING_SNAKE_CASE ( ... | 651 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHI... | 34 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.con... | 651 | 0 |
import argparse
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 Accelerator, Di... | 35 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class SCREAMING_SNAKE_CASE ( a_ ... | 651 | 0 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
__lowercase : Dict = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True,... | 36 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""camembert-base""": """https://hu... | 651 | 0 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 37 |
def snake_case_ (__A : list[int] , __A : list[int] ) -> None:
__lowerCAmelCase : Union[str, Any] = len(__A )
print("""The following activities are selected:""" )
# The first activity is always selected
__lowerCAmelCase : str = 0
print(__A ... | 651 | 0 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForS... | 38 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__UpperCAmelCase = models.Sequential()
# Step 1 -... | 651 | 0 |
import numpy as np
from PIL import Image
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = np.array(SCREAMING_SNAKE_CASE__ )
if arr.shape[0] != arr.shape[1]:
raise ValueError('''The i... | 39 |
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
__UpperCAmelCase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"""text-classificat... | 651 | 0 |
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