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 |
|---|---|---|---|---|
def _a ( SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
while second != 0:
__lowerCAmelCase = first & second
first ^= second
__lowerCAmelCase = c << 1
return firs... | 352 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, ... | 102 | 0 |
"""simple docstring"""
import os
import sys
import unittest
UpperCamelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
... | 353 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
UpperCamelCase__ = datasets.logging.get_logger(__name__)
UpperCamelCase__ = """\
@InProceedings{moosavi2019minimum,
... | 102 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"""fac... | 354 |
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
__lowerCAmelCase , __lowerCAmelCase = [], []
while len(SCREAMING_SNAKE_CASE_ ) > 1:
__lowerCAmelCase , __lowerCAmelCase = min(SCREAMING_SNAKE_CASE_ ), max(SCREAMING_SNAKE_... | 102 | 0 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"""facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json... | 355 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a__ ( unittest.TestCase ):
@prop... | 102 | 0 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
UpperCamelCase__ = TypeVar("""T""")
UpperCamelCase__ = Union[List[T], Tuple[T, ...]]
UpperCamelCase__ = Union[T, List[T], Dict[str, T]]
UpperCamelCase__ = Union[str, bytes, os.PathLike]
| 356 |
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, ... | 102 | 0 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedK... | 357 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cann... | 102 | 0 |
import argparse
import gc
import json
import os
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 Acceler... | 358 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,... | 102 | 0 |
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
__lowerCAmelCase = len(SCREAMING_SNAKE_CASE_ )
for i in range(1 , SCREAMING_SNAKE_CASE_ ):
__lowerCAmelCase = collection[i]
__lowerCAmelCase = 0
__lowerCAmelCa... | 359 |
import math
def _a ( SCREAMING_SNAKE_CASE_ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
r... | 102 | 0 |
from random import randint, random
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMIN... | 360 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin... | 102 | 0 |
"""simple docstring"""
import math
class a__ :
def __init__( self , _A=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
__lowerCAmelCase = n
__lowerCAmelCase = [
[math.inf for j in range(... | 361 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}
class a__ ... | 102 | 0 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
UpperCamelCase__ = log... | 362 |
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
class ... | 102 | 0 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ... | 363 |
from sklearn.metrics import mean_squared_error
import datasets
UpperCamelCase__ = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Pre... | 102 | 0 |
def _a ( SCREAMING_SNAKE_CASE_ : Any ):
__lowerCAmelCase = []
for data in source_data:
for i, el in enumerate(a_ ):
if len(a_ ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(a_ ) ... | 364 |
import enum
import shutil
import sys
UpperCamelCase__ , UpperCamelCase__ = shutil.get_terminal_size()
UpperCamelCase__ = {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}
class a__ ( enum.Enum ):
_a : Any = 0
_... | 102 | 0 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all ... | 365 |
from pathlib import Path
import fire
from tqdm import tqdm
def _a ( SCREAMING_SNAKE_CASE_ : Dict="ro" , SCREAMING_SNAKE_CASE_ : Union[str, Any]="en" , SCREAMING_SNAKE_CASE_ : Optional[Any]="wmt16" , SCREAMING_SNAKE_CASE_ : List[str]=None ... | 102 | 0 |
import torch
def _a ( ):
if torch.cuda.is_available():
__lowerCAmelCase = torch.cuda.device_count()
else:
__lowerCAmelCase = 0
print(F"""Successfully ran on {num_gpus} GPUs""" )
if __name__ == "__main__":
main()
| 366 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
UpperCamelCase__ = """\
"""
UpperCamelCase__ = """
Perplexity (PPL) is one of the most common metrics for ev... | 102 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingf... | 367 |
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 _a ( SCREAMING_SNAKE_CASE_ : Any ):
# encoder.embeddings are double copied in o... | 102 | 0 |
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 is_flax_availab... | 368 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common... | 102 | 0 |
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : str ):
return int((input_a, input_a).count(0 ) == 0 )
def _a ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 ... | 369 |
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,
require_t... | 102 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import TypedDict
class a__ ( __UpperCamelCase ):
_a : str
_a : int
def _a ( SCREAMING_SNAKE_CASE_ : str ):
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREA... | 370 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCamelCase__ = logging.getLogger(__name__)
class a__ :
def __init__( self ):
"""s... | 102 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b": "https://huggingfa... | 371 |
UpperCamelCase__ = """0.18.2"""
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_availab... | 102 | 0 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
UpperCamelCase__ = 50000
UpperCamelCase__ = 5000
UpperCamelCase__ , UpperCamelCase__ = os.path.split(__file__)
UpperCamelCase__ = os.path.j... | 350 |
import math
def _a ( SCREAMING_SNAKE_CASE_ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
r... | 102 | 0 |
from pathlib import Path
import fire
from tqdm import tqdm
def _a ( SCREAMING_SNAKE_CASE_ : Optional[Any]="ro" , SCREAMING_SNAKE_CASE_ : Any="en" , SCREAMING_SNAKE_CASE_ : List[Any]="wmt16" , SCREAMING_SNAKE_CASE_ : Optional... | 351 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_ut... | 102 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a__ ( __snake_case ):
_a : int = ["""image_processor""", """tokenizer"""]
_a : List[str] = """CLIPImageProcessor"""
_a : Any = ("""XL... | 352 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, ... | 102 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class a__ :
_a : List[Any] = None
_a : List[Any] = False
_a : List[Any] = False
_a : Any = Fa... | 353 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
UpperCamelCase__ = datasets.logging.get_logger(__name__)
UpperCamelCase__ = """\
@InProceedings{moosavi2019minimum,
... | 102 | 0 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
UpperCamelCase__ = 6378137.0
UpperCamelCase__ = 6356752.314245
UpperCamelCase__ = 6378137
def _a ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_S... | 354 |
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
__lowerCAmelCase , __lowerCAmelCase = [], []
while len(SCREAMING_SNAKE_CASE_ ) > 1:
__lowerCAmelCase , __lowerCAmelCase = min(SCREAMING_SNAKE_CASE_ ), max(SCREAMING_SNAKE_... | 102 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'''goo... | 355 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a__ ( unittest.TestCase ):
@prop... | 102 | 0 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a__ ( __UpperCamelCase ):
@staticmethod
@abstractmethod
def __SCREAMING_SNAKE_CASE( _A ):
"""simple docstring"""
raise NotImplementedError()
@abst... | 356 |
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, ... | 102 | 0 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
UpperCamelCase__ = """\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Al... | 357 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cann... | 102 | 0 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class a__ :
_a : float
_a : TreeNode | None = None
_a : TreeNode | None = None
def _a ( SCREAMING_SNAKE_CASE_ : Dict ):
# Validation
def... | 358 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,... | 102 | 0 |
from ..utils import DummyObject, requires_backends
class a__ ( metaclass=SCREAMING_SNAKE_CASE__ ):
_a : str = ['torch', 'scipy']
def __init__( self , *_A , **_A ):
"""simple docstring"""
requires_backends(self , ["torc... | 359 |
import math
def _a ( SCREAMING_SNAKE_CASE_ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
r... | 102 | 0 |
def _a ( SCREAMING_SNAKE_CASE_ : int = 3 , SCREAMING_SNAKE_CASE_ : int = 7 , SCREAMING_SNAKE_CASE_ : int = 1_00_00_00 ):
__lowerCAmelCase = 0
__lowerCAmelCase = 1
for current_denominator in range(1 , limit + 1 ):
... | 360 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin... | 102 | 0 |
"""simple docstring"""
import math
class a__ :
def __init__( self , _A=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
__lowerCAmelCase = n
__lowerCAmelCase = [
[math.inf for j in range(... | 361 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}
class a__ ... | 102 | 0 |
import math
def _a ( SCREAMING_SNAKE_CASE_ : Optional[Any] ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not pri... | 362 |
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
class ... | 102 | 0 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCamelCase__ = datasets.utils.logging.get_logger(__name__)
class a__ ( folder_based_builder.FolderBasedBuilderConfig ):
_a : b... | 363 |
from sklearn.metrics import mean_squared_error
import datasets
UpperCamelCase__ = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Pre... | 102 | 0 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
def _a ( SCREAMING_SNAKE_CASE_ : List[str]=None , SCRE... | 364 |
import enum
import shutil
import sys
UpperCamelCase__ , UpperCamelCase__ = shutil.get_terminal_size()
UpperCamelCase__ = {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}
class a__ ( enum.Enum ):
_a : Any = 0
_... | 102 | 0 |
"""simple docstring"""
from datetime import datetime as dt
import os
from github import Github
UpperCamelCase__ = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def _a ( ):
__lowerCAmel... | 365 |
from pathlib import Path
import fire
from tqdm import tqdm
def _a ( SCREAMING_SNAKE_CASE_ : Dict="ro" , SCREAMING_SNAKE_CASE_ : Union[str, Any]="en" , SCREAMING_SNAKE_CASE_ : Optional[Any]="wmt16" , SCREAMING_SNAKE_CASE_ : List[str]=None ... | 102 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase__ = {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}
t... | 366 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
UpperCamelCase__ = """\
"""
UpperCamelCase__ = """
Perplexity (PPL) is one of the most common metrics for ev... | 102 | 0 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def UpperCAmelCase_ ( ):
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirnam... | 367 |
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 _a ( SCREAMING_SNAKE_CASE_ : Any ):
# encoder.embeddings are double copied in o... | 102 | 0 |
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
return number | (1 << position)
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
return number & ~(1 << position)
def ... | 368 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common... | 102 | 0 |
def _a ( SCREAMING_SNAKE_CASE_ : int ):
assert isinstance(_snake_case , _snake_case ), F"""The input value of [n={number}] is not an integer"""
if number == 1:
return 2
elif number < 1:
__lowerCAmelCase = F"""The input value of [n={n... | 369 |
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,
require_t... | 102 | 0 |
"""simple docstring"""
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _a ( SCREAMING_SNAKE_CASE_ : Union[dict, l... | 370 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCamelCase__ = logging.getLogger(__name__)
class a__ :
def __init__( self ):
"""s... | 102 | 0 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class a__ ( _a ):
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
with open(snake_case_ , encoding="utf-8" ) as input_f... | 371 |
UpperCamelCase__ = """0.18.2"""
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_availab... | 102 | 0 |
def _a ( SCREAMING_SNAKE_CASE_ : int = 1_00_00_00 ):
__lowerCAmelCase = 1
__lowerCAmelCase = 1
__lowerCAmelCase = {1: 1}
for inputa in range(2 , __lowerCAmelCase ):
__lowerCAmelCase = 0
__lowerCAmel... | 350 |
import math
def _a ( SCREAMING_SNAKE_CASE_ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
r... | 102 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_... | 351 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_ut... | 102 | 0 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformer... | 352 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, ... | 102 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import Real... | 353 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
UpperCamelCase__ = datasets.logging.get_logger(__name__)
UpperCamelCase__ = """\
@InProceedings{moosavi2019minimum,
... | 102 | 0 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.sc... | 354 |
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
__lowerCAmelCase , __lowerCAmelCase = [], []
while len(SCREAMING_SNAKE_CASE_ ) > 1:
__lowerCAmelCase , __lowerCAmelCase = min(SCREAMING_SNAKE_CASE_ ), max(SCREAMING_SNAKE_... | 102 | 0 |
import socket
def _a ( ):
__lowerCAmelCase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__lowerCAmelCase = socket.gethostname()
__lowerCAmelCase = 1_23_12
sock.connect((host, port) )
sock.send(b"Hello server!" )
... | 355 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a__ ( unittest.TestCase ):
@prop... | 102 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
UpperCamelCase... | 356 |
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, ... | 102 | 0 |
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
class a__ ( __lowerCAmelCase ):
def __init__( self , _A=None , **_A ):
"""simple docstring"""
warnings... | 357 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cann... | 102 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCamelCase__ : List[Any] = {
"""CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
"""https://huggingfac... | 358 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,... | 102 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
UpperCamelCase__ = logging.get_lo... | 359 |
import math
def _a ( SCREAMING_SNAKE_CASE_ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
r... | 102 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,... | 360 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin... | 102 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta... | 361 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}
class a__ ... | 102 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase__ = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available():
... | 362 |
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
class ... | 102 | 0 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
UpperCamelCase__ = logging.get_logger(__name__)
class a__ ( _A ):
def __init__( self , *_A , **_A ):
"""simple docstring"""
... | 363 |
from sklearn.metrics import mean_squared_error
import datasets
UpperCamelCase__ = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Pre... | 102 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase__ = {
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
'GroupViTOnnxConfig',
... | 364 |
import enum
import shutil
import sys
UpperCamelCase__ , UpperCamelCase__ = shutil.get_terminal_size()
UpperCamelCase__ = {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}
class a__ ( enum.Enum ):
_a : Any = 0
_... | 102 | 0 |
"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .... | 365 |
from pathlib import Path
import fire
from tqdm import tqdm
def _a ( SCREAMING_SNAKE_CASE_ : Dict="ro" , SCREAMING_SNAKE_CASE_ : Union[str, Any]="en" , SCREAMING_SNAKE_CASE_ : Optional[Any]="wmt16" , SCREAMING_SNAKE_CASE_ : List[str]=None ... | 102 | 0 |
from __future__ import annotations
UpperCamelCase__ = list[tuple[int, int]]
UpperCamelCase__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0,... | 366 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
UpperCamelCase__ = """\
"""
UpperCamelCase__ = """
Perplexity (PPL) is one of the most common metrics for ev... | 102 | 0 |
import argparse
import os
import re
import packaging.version
UpperCamelCase__ = """examples/"""
UpperCamelCase__ = {
"""examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""": (re.compile(R"""^__v... | 367 |
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 _a ( SCREAMING_SNAKE_CASE_ : Any ):
# encoder.embeddings are double copied in o... | 102 | 0 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCamelCase__ = '... | 368 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common... | 102 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | 369 |
import 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,
require_t... | 102 | 0 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
fr... | 370 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCamelCase__ = logging.getLogger(__name__)
class a__ :
def __init__( self ):
"""s... | 102 | 0 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : L... | 371 |
UpperCamelCase__ = """0.18.2"""
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_availab... | 102 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}
class a__ ( a_ ):
_a... | 350 |
import math
def _a ( SCREAMING_SNAKE_CASE_ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
r... | 102 | 0 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class a__ ( __SCREAMING_SNAKE_CASE ):
_a : List[str] = (KDPMaDiscreteScheduler,)
_a : Optional[int] = 1_0
... | 351 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_ut... | 102 | 0 |
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 BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name_... | 352 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, ... | 102 | 0 |
"""simple docstring"""
import sys
from pathlib import Path
UpperCamelCase__ = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
fr... | 353 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
UpperCamelCase__ = datasets.logging.get_logger(__name__)
UpperCamelCase__ = """\
@InProceedings{moosavi2019minimum,
... | 102 | 0 |
import sys
UpperCamelCase__ = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''668966489504452445... | 354 |
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
__lowerCAmelCase , __lowerCAmelCase = [], []
while len(SCREAMING_SNAKE_CASE_ ) > 1:
__lowerCAmelCase , __lowerCAmelCase = min(SCREAMING_SNAKE_CASE_ ), max(SCREAMING_SNAKE_... | 102 | 0 |
from collections.abc import Sequence
def _a ( SCREAMING_SNAKE_CASE_ : Sequence[float] , SCREAMING_SNAKE_CASE_ : bool = False ):
if not arr:
return 0
__lowerCAmelCase = 0 if allow_empty_subarrays else float("-inf" )
__lower... | 355 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a__ ( unittest.TestCase ):
@prop... | 102 | 0 |
def _a ( SCREAMING_SNAKE_CASE_ : Optional[Any] = 2_00_00_00 ):
__lowerCAmelCase = [0 for i in range(n + 1 )]
__lowerCAmelCase = 1
__lowerCAmelCase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i]... | 356 |
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, ... | 102 | 0 |
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 357 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cann... | 102 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ : int =... | 358 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,... | 102 | 0 |
import random
def _a ( SCREAMING_SNAKE_CASE_ : str ):
__lowerCAmelCase = num - 1
__lowerCAmelCase = 0
while s % 2 == 0:
__lowerCAmelCase = s // 2
t += 1
for _ in range(5 ):
__lowerCAmelCase ... | 359 |
import math
def _a ( SCREAMING_SNAKE_CASE_ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
r... | 102 | 0 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
UpperCamelCase__ = logging.get_logger(__name__)
class a__ ( UpperCAmelCase__ ):
def __init__( self , *_A , **_A ):
"""simple docstring"""
... | 360 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin... | 102 | 0 |
"""simple docstring"""
UpperCamelCase__ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yott... | 361 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}
class a__ ... | 102 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase__ = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""configuration_... | 362 |
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
class ... | 102 | 0 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import Flax... | 363 |
from sklearn.metrics import mean_squared_error
import datasets
UpperCamelCase__ = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Pre... | 102 | 0 |
from __future__ import annotations
from math import pow, sqrt
def _a ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float ):
if (resistance, reactance, impedance).count(0 ) != 1:
raise Valu... | 364 |
import enum
import shutil
import sys
UpperCamelCase__ , UpperCamelCase__ = shutil.get_terminal_size()
UpperCamelCase__ = {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}
class a__ ( enum.Enum ):
_a : Any = 0
_... | 102 | 0 |
"""simple docstring"""
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def _a ( SCREAMING_S... | 365 |
from pathlib import Path
import fire
from tqdm import tqdm
def _a ( SCREAMING_SNAKE_CASE_ : Dict="ro" , SCREAMING_SNAKE_CASE_ : Union[str, Any]="en" , SCREAMING_SNAKE_CASE_ : Optional[Any]="wmt16" , SCREAMING_SNAKE_CASE_ : List[str]=None ... | 102 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
Uppe... | 366 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
UpperCamelCase__ = """\
"""
UpperCamelCase__ = """
Perplexity (PPL) is one of the most common metrics for ev... | 102 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def UpperCAmelCase_ ( SCREAMING_SNAKE_CASE_ : Uni... | 367 |
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 _a ( SCREAMING_SNAKE_CASE_ : Any ):
# encoder.embeddings are double copied in o... | 102 | 0 |
def _a ( SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : str ):
# Return True if there is node that has not iterated.
__lowerCAmelCase = [False] ... | 368 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common... | 102 | 0 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
__lowerCAmelCase = list(__UpperCamelCase )
__lowerCAmelCase = l... | 369 |
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,
require_t... | 102 | 0 |
"""simple docstring"""
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] ):
if not head:
return True
# split the list to two parts
__lowerCAmelCase , __lowerCAmelCase = head.next, head
while fast and fast.next:
__lowerCAmelCas... | 370 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCamelCase__ = logging.getLogger(__name__)
class a__ :
def __init__( self ):
"""s... | 102 | 0 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import Auto... | 371 |
UpperCamelCase__ = """0.18.2"""
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_availab... | 102 | 0 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
UpperCamelCase__ = logging.getLogger(__name__)
@dataclass
class a__ ( snake_case_ ):
_a : Optional[fl... | 350 |
import math
def _a ( SCREAMING_SNAKE_CASE_ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
r... | 102 | 0 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transforme... | 351 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_ut... | 102 | 0 |
import os
import sys
import unittest
UpperCamelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
... | 352 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, ... | 102 | 0 |
"""simple docstring"""
from math import pi, sqrt, tan
def _a ( SCREAMING_SNAKE_CASE_ : float ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def _a ( SCR... | 353 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
UpperCamelCase__ = datasets.logging.get_logger(__name__)
UpperCamelCase__ = """\
@InProceedings{moosavi2019minimum,
... | 102 | 0 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
__lowerCAmelCase = 0
if star... | 354 |
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
__lowerCAmelCase , __lowerCAmelCase = [], []
while len(SCREAMING_SNAKE_CASE_ ) > 1:
__lowerCAmelCase , __lowerCAmelCase = min(SCREAMING_SNAKE_CASE_ ), max(SCREAMING_SNAKE_... | 102 | 0 |
from statistics import mean
import numpy as np
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Any ):
__lowerCAmelCase = 0
# Number of proces... | 355 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a__ ( unittest.TestCase ):
@prop... | 102 | 0 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
UpperCamelCase__ = logging.getLogger()
@unittest.skip("""Temporarily disable the doc tests.""" ... | 356 |
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, ... | 102 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
f... | 357 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cann... | 102 | 0 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
UpperCamelCase__ : int = 50000
UpperCamelCase__ : Dict = 5000
UpperCamelCase__ : List[Any] = os.path.split(__file__)
UpperCamelCase__ : List[str] ... | 358 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,... | 102 | 0 |
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def _a ( SCREAMI... | 359 |
import math
def _a ( SCREAMING_SNAKE_CASE_ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
r... | 102 | 0 |
class a__ :
def __init__( self , _A , _A=None , _A=None ):
"""simple docstring"""
__lowerCAmelCase = data
__lowerCAmelCase = previous
__lowerCAmelCase = next_node
def __str_... | 360 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin... | 102 | 0 |
"""simple docstring"""
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nes... | 361 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}
class a__ ... | 102 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import ... | 362 |
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
class ... | 102 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pi... | 363 |
from sklearn.metrics import mean_squared_error
import datasets
UpperCamelCase__ = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Pre... | 102 | 0 |
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