code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from __future__ import annotations
def _lowercase ( lowerCamelCase__ ) -> bool:
"""simple docstring"""
return len(set(a__ ) ) == len(a__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
... | 715 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_a : Dict = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG... | 10 | 0 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ = True , lowerCamelCase__ = math.inf , lowerCamelCase__ = -math.inf , lowerCamelCase__ = math.in... | 716 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Any = {
"kssteven/ibe... | 10 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_a : List[str] = {
'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
'tokenization_c... | 717 | '''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _lowercase ( ) -> Dict:
"""simple docstring"""
__UpperCAmelCase : str = HfArgumentParser(lowerCamelCase__ )
__Upper... | 10 | 0 |
'''simple docstring'''
import qiskit
def _lowercase ( lowerCamelCase__ = 2 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
__UpperCAmelCase : Any = qubits
# Using Aer's simulator
__UpperCAmelCase : Any ... | 718 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
... | 10 | 0 |
'''simple docstring'''
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _lowercase ( *lowerCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
if not isinstance(__a , __a ... | 719 | '''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import t... | 10 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Union[str, Any] = ... | 720 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumer... | 10 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> list[int]:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = 0
__UpperCAmelCase : str = ... | 721 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
"""simple docstring"""
return number | (1 << position)
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
... | 10 | 0 |
'''simple docstring'''
from collections import defaultdict
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
__UpperCAmelCase : Tuple = first_str.lower().strip()
__UpperCAmelCase ... | 700 | '''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_a : str = datasets.load_iris()
_a : List[Any] = np.array(data["data"])
_a : Optional[Any] = np.array(data["ta... | 10 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_a : Optional[int] = logging.get_logger(__name__)
_a : Union[str, Any] = {
... | 701 | '''simple docstring'''
class __A :
def __init__( self , UpperCamelCase_ ):
__UpperCAmelCase : Any = set_counts
__UpperCAmelCase : int = max(UpperCamelCase_ )
__UpperCAmelCase : List[... | 10 | 0 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ = 10 ) -> str:
"""simple docstring"""
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or n < 0:
raise ValueError("Invalid input" )
__UpperCAmelCase : ... | 702 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : Dict = (boundary[1] - boundary[0]) / steps
__UpperCAmelCase : Tuple = boundary[0... | 10 | 0 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> Optional[int]:
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(__SCREAMING_SNAKE_CASE , int(... | 703 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a : str = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_A... | 10 | 0 |
'''simple docstring'''
import qiskit
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : str = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum C... | 704 | '''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : str = logging.get_logger(__name__)
_a : ... | 10 | 0 |
'''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def _lowercase ( lowerCamelCase__ ) -> List[str]:
"""simple docstring"""
def decorator(lowerCamelCase__ ):
__UpperCAmelCase : List[Any] ... | 705 | '''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
... | 10 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Tuple = logging.get_logger(__name__)
class __A (__magic_name__ ):
snake_case :Optional[int] = "encoder-decoder"
snake_case :List[Any] ... | 706 | '''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
impor... | 10 | 0 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def _lowercase ( lowerCamelCase__ ) -> bytes:
"""simple docstring"""
if len(lowerCamelCase__ ) != 32:
raise ValueError("Input must be of length 32" )
... | 707 | '''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : Optional[Any] = logging.get_logger(__name__)
... | 10 | 0 |
'''simple docstring'''
import math
def _lowercase ( ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = input("Enter message: " )
__UpperCAmelCase : Dict = int(input(f"""Enter key [2-{len(lowerCAmel... | 708 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : int = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main... | 10 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Tuple = logging.get_logger(__name__)
_a : List[str] = {
"google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
# See all... | 709 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ = 100 ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = (n * (n + 1) // 2) ** 2
__UpperCAmelCase : Any = n * (n + 1) * (2 * n + 1) // 6
retu... | 10 | 0 |
import math
from collections.abc import Iterator
from itertools import takewhile
def _lowercase ( lowerCamelCase__ ) -> str:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number %... | 710 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative" )
if not cash_flows:
rai... | 10 | 0 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(impo... | 711 | '''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_a : Union[str, Any] = HfApi()
_a : int = {}
# fmt: off
_a : Optional[int] = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347,... | 10 | 0 |
'''simple docstring'''
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausa... | 712 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : List[Any] = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt model... | 10 | 0 |
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 713 | '''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ) -> list[float]:
... | 10 | 0 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
"""simple docstring"""
if height >= 1:
move_tower(height - 1 , UpperCAmelCase__ ... | 714 | '''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _lowercase ( lowerCamelCase__ ) -> int:
"""simple docstring"""
__UpperCAmelCase : Any = prime_factors(lowerCamelCase__ ... | 10 | 0 |
'''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_... | 715 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_a : Dict = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG... | 10 | 0 |
'''simple docstring'''
import math
import tensorflow as tf
from packaging import version
def _lowercase ( lowerCamelCase__ ) -> str:
"""simple docstring"""
__UpperCAmelCase : Dict = tf.convert_to_tensor(lowerCamelCase__ )
__Up... | 716 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Any = {
"kssteven/ibe... | 10 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class __A :
snake_case :str = field(
... | 717 | '''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _lowercase ( ) -> Dict:
"""simple docstring"""
__UpperCAmelCase : str = HfArgumentParser(lowerCamelCase__ )
__Upper... | 10 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __A (UpperCamelC... | 718 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
... | 10 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_a : Any = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
... | 719 | '''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import t... | 10 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
_a : Optional[Any] = logging.get_logger(__name__)
class __A (__magic_name__ ):
def __init__( self , ... | 720 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumer... | 10 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
_a : str = R"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control... | 721 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
"""simple docstring"""
return number | (1 << position)
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
... | 10 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Dict = {
"vocab_file": "vocab.json... | 700 | '''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_a : str = datasets.load_iris()
_a : List[Any] = np.array(data["data"])
_a : Optional[Any] = np.array(data["ta... | 10 | 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,
... | 701 | '''simple docstring'''
class __A :
def __init__( self , UpperCamelCase_ ):
__UpperCAmelCase : Any = set_counts
__UpperCAmelCase : int = max(UpperCamelCase_ )
__UpperCAmelCase : List[... | 10 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
... | 702 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : Dict = (boundary[1] - boundary[0]) / steps
__UpperCAmelCase : Tuple = boundary[0... | 10 | 0 |
'''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def _lowercase ( lowerCamelCase__ ) -> Optional[int]:
"""simple docstring"""
def decorator(lowerCamelCase__ ):
__UpperCAmelCase : Dict ... | 703 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a : str = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_A... | 10 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : List[Any] = logging.get_logger(__name__)
_a : Optional[int] = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-p... | 704 | '''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : str = logging.get_logger(__name__)
_a : ... | 10 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
_a : Tuple = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,... | 705 | '''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
... | 10 | 0 |
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Dict:
"""simple docstring"""
__UpperCAmelCase ... | 706 | '''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
impor... | 10 | 0 |
'''simple docstring'''
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
... | 707 | '''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : Optional[Any] = logging.get_logger(__name__)
... | 10 | 0 |
'''simple docstring'''
import os
import sys
import transformers
_a : List[str] = "3"
print("Python version:", sys.version)
print("transformers version:", transformers.__version__)
try:
import torch
print("Torch version:", torch.__version__)
print("Cuda available:", torc... | 708 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : int = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main... | 10 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, 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,
... | 709 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ = 100 ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = (n * (n + 1) // 2) ** 2
__UpperCAmelCase : Any = n * (n + 1) * (2 * n + 1) // 6
retu... | 10 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a : Union[str, Any] = {
"configuration_blenderbot_small": [
"BLENDERBOT... | 710 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative" )
if not cash_flows:
rai... | 10 | 0 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
_a : Optional[int] = False
... | 711 | '''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_a : Union[str, Any] = HfApi()
_a : int = {}
# fmt: off
_a : Optional[int] = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347,... | 10 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : Union[str, Any] = {
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 712 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : List[Any] = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt model... | 10 | 0 |
'''simple docstring'''
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 ImageProcessingSavingTes... | 713 | '''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ) -> list[float]:
... | 10 | 0 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
"""simple docstring"""
while a != 0:
__UpperCAmelCase : Optional[Any] = b % a, a
return b
def _lowercase ( lowerCamelC... | 714 | '''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _lowercase ( lowerCamelCase__ ) -> int:
"""simple docstring"""
__UpperCAmelCase : Any = prime_factors(lowerCamelCase__ ... | 10 | 0 |
'''simple docstring'''
from string import ascii_uppercase
_a : List[str] = {str(ord(c) - 55): c for c in ascii_uppercase}
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> str:
"""simple docstring"""
if isinstance(lowerCamelCas... | 715 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_a : Dict = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG... | 10 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, float... | 716 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Any = {
"kssteven/ibe... | 10 | 0 |
'''simple docstring'''
import os
def _lowercase ( ) -> Optional[Any]:
"""simple docstring"""
__UpperCAmelCase : Tuple = os.path.join(os.path.dirname(lowerCamelCase__ ) , "num.txt" )
with open(lowerCamelCase__ ) as file_hand:
r... | 717 | '''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _lowercase ( ) -> Dict:
"""simple docstring"""
__UpperCAmelCase : str = HfArgumentParser(lowerCamelCase__ )
__Upper... | 10 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
... | 718 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
... | 10 | 0 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : Dict = (boundary[1] - boundary[0]) / steps
__UpperCAmelCase : Tuple = boundary[0... | 719 | '''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import t... | 10 | 0 |
'''simple docstring'''
class __A :
def __init__( self , UpperCamelCase_ ):
__UpperCAmelCase : Any = set_counts
__UpperCAmelCase : int = max(UpperCamelCase_ )
__UpperCAmelCase ... | 720 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumer... | 10 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class __A :
def __init__( self ):
__UpperCAmelCase : list[Any] = []
__UpperCAmelCase : int ... | 721 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
"""simple docstring"""
return number | (1 << position)
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
... | 10 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_a : List[str] = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_ctrl": ["CTRLTo... | 700 | '''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_a : str = datasets.load_iris()
_a : List[Any] = np.array(data["data"])
_a : Optional[Any] = np.array(data["ta... | 10 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
... | 701 | '''simple docstring'''
class __A :
def __init__( self , UpperCamelCase_ ):
__UpperCAmelCase : Any = set_counts
__UpperCAmelCase : int = max(UpperCamelCase_ )
__UpperCAmelCase : List[... | 10 | 0 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> None:
"""simple docstring"""
__UpperCAmelCase : str = len(lowerCamelCase__ )
print("The following activities are selected:" )
# ... | 702 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : Dict = (boundary[1] - boundary[0]) / steps
__UpperCAmelCase : Tuple = boundary[0... | 10 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : str = logging.get_logger(__name__)
_a : ... | 703 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a : str = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_A... | 10 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType... | 704 | '''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : str = logging.get_logger(__name__)
_a : ... | 10 | 0 |
'''simple docstring'''
from torch import nn
def _lowercase ( lowerCamelCase__ ) -> List[Any]:
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif ... | 705 | '''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
... | 10 | 0 |
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Dict:
"""simple docstring"""
__UpperCAmelCase : Any = [False] * len(lowerCamelCase__ )
__UpperCAmelCase : Tuple ... | 706 | '''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
impor... | 10 | 0 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
... | 707 | '''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : Optional[Any] = logging.get_logger(__name__)
... | 10 | 0 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> str:
"""simple docstring"""
return "\n".join(
f"""{number} * {i} = {number * i}""" for i in range(1 , number_of_terms + 1 ) )
if __name... | 708 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : int = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main... | 10 | 0 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
__UpperCAmelCase : Dict ... | 709 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ = 100 ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = (n * (n + 1) // 2) ** 2
__UpperCAmelCase : Any = n * (n + 1) * (2 * n + 1) // 6
retu... | 10 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : int = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
),
# See... | 710 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative" )
if not cash_flows:
rai... | 10 | 0 |
'''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class __A (__magic_name__ ):
... | 711 | '''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_a : Union[str, Any] = HfApi()
_a : int = {}
# fmt: off
_a : Optional[int] = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347,... | 10 | 0 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transform... | 712 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : List[Any] = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt model... | 10 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class __A (__magic_name__ ):
'''simple docstring'''
def __init__( self ):
# test for the above condition
self.test()
... | 713 | '''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ) -> list[float]:
... | 10 | 0 |
'''simple docstring'''
import logging
import os
from .state import PartialState
class __A (logging.LoggerAdapter ):
@staticmethod
def _snake_case ( UpperCamelCase_ ):
__UpperCAmelCase : Any = PartialState()
... | 714 | '''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _lowercase ( lowerCamelCase__ ) -> int:
"""simple docstring"""
__UpperCAmelCase : Any = prime_factors(lowerCamelCase__ ... | 10 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowercase ( lowerCamelCase__ ) -> list[int]:
"""simple docstring"""
if len(lowerCamelCase__ ) == 0:
return array
__UpperCAmelCase : str = min(lowerCam... | 715 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_a : Dict = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG... | 10 | 0 |
'''simple docstring'''
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_a : int = abspath(join(dirname(dirnam... | 716 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Any = {
"kssteven/ibe... | 10 | 0 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ ) -> bool:
"""simple docstring"""
return str(lowerCamelCase__ ) == str(lowerCamelCase__ )[::-1]
def _lowercase ( lowerCamelCase__ ) -> int:
"""simple docstring"""
... | 717 | '''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _lowercase ( ) -> Dict:
"""simple docstring"""
__UpperCAmelCase : str = HfArgumentParser(lowerCamelCase__ )
__Upper... | 10 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
_a : List[Any] = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
],... | 718 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
... | 10 | 0 |
'''simple docstring'''
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
c... | 719 | '''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import t... | 10 | 0 |
'''simple docstring'''
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
_a : int = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n ... | 720 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumer... | 10 | 0 |
'''simple docstring'''
from __future__ import annotations
_a : int = []
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> bool:
"""simple docstring"""
for i in range(len(lowerCamelCase__ ) ... | 721 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
"""simple docstring"""
return number | (1 << position)
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
... | 10 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import Tokenize... | 700 | '''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_a : str = datasets.load_iris()
_a : List[Any] = np.array(data["data"])
_a : Optional[Any] = np.array(data["ta... | 10 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowercase ( lowerCamelCase__ ) -> list[int]:
"""simple docstring"""
return [ord(lowerCamelCase__ ) - 96 for elem in plain]
def _lowercase ( lowerCamelCase__ ) -> str:
... | 701 | '''simple docstring'''
class __A :
def __init__( self , UpperCamelCase_ ):
__UpperCAmelCase : Any = set_counts
__UpperCAmelCase : int = max(UpperCamelCase_ )
__UpperCAmelCase : List[... | 10 | 0 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
_a : Union[str, Any] = datasets.logging.get_logger(__name__)
_a : Tuple = "\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust M... | 702 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : Dict = (boundary[1] - boundary[0]) / steps
__UpperCAmelCase : Tuple = boundary[0... | 10 | 0 |
'''simple docstring'''
import os
def _lowercase ( lowerCamelCase__ = "input.txt" ) -> int:
"""simple docstring"""
with open(os.path.join(os.path.dirname(lowerCamelCase__ ) , lowerCamelCase__ ) ) as input_file:
__UpperCA... | 703 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a : str = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_A... | 10 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : Optional[Any] = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
... | 704 | '''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : str = logging.get_logger(__name__)
_a : ... | 10 | 0 |
'''simple docstring'''
from collections.abc import Callable
class __A :
def __init__( self , UpperCamelCase_ = None ):
# Stores actual heap items.
__UpperCAmelCase : list = []
# Stores indexes ... | 705 | '''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
... | 10 | 0 |
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative" )
if not cash_flows:
raise ValueError("Cash flows list c... | 706 | '''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
impor... | 10 | 0 |
'''simple docstring'''
import warnings
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
_a : Dict = logging.get_logger(__name__)
... | 707 | '''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : Optional[Any] = logging.get_logger(__name__)
... | 10 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_a : Dict = logging.get_logger(__name__)
_a : Union[str, Any] = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main... | 708 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : int = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main... | 10 | 0 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Generic, TypeVar
_a : int = TypeVar("_T")
class __A (Generic[_T] ):
def __init__( self , UpperCamelCase_ = None ):
__UpperCAmelCase : list... | 709 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ = 100 ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = (n * (n + 1) // 2) ** 2
__UpperCAmelCase : Any = n * (n + 1) * (2 * n + 1) // 6
retu... | 10 | 0 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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, random_attention_ma... | 710 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative" )
if not cash_flows:
rai... | 10 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class __A :
def __init__( self ):
__UpperCAmelCase : List[str] = {}
def _snak... | 711 | '''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_a : Union[str, Any] = HfApi()
_a : int = {}
# fmt: off
_a : Optional[int] = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347,... | 10 | 0 |
'''simple docstring'''
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_a ... | 712 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : List[Any] = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt model... | 10 | 0 |
'''simple docstring'''
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
impo... | 713 | '''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ) -> list[float]:
... | 10 | 0 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr im... | 714 | '''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _lowercase ( lowerCamelCase__ ) -> int:
"""simple docstring"""
__UpperCAmelCase : Any = prime_factors(lowerCamelCase__ ... | 10 | 0 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_a : Optional[Any] = logging.get_logger("transformers.models.speecht5")
def _lowercase ( lowerCamelCase__ ... | 715 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_a : Dict = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG... | 10 | 0 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCamelCase__ , int(b / 2 ... | 716 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Any = {
"kssteven/ibe... | 10 | 0 |
'''simple docstring'''
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
... | 717 | '''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _lowercase ( ) -> Dict:
"""simple docstring"""
__UpperCAmelCase : str = HfArgumentParser(lowerCamelCase__ )
__Upper... | 10 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax... | 718 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
... | 10 | 0 |
'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOu... | 719 | '''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import t... | 10 | 0 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ = 100 ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = (n * (n + 1) // 2) ** 2
__UpperCAmelCase : Any = n * (n + 1) * (2 * n + 1) // 6
... | 720 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumer... | 10 | 0 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __A (__magic_name__ , __magic_name__ ):
@re... | 721 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
"""simple docstring"""
return number | (1 << position)
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
... | 10 | 0 |
'''simple docstring'''
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
_... | 700 | '''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_a : str = datasets.load_iris()
_a : List[Any] = np.array(data["data"])
_a : Optional[Any] = np.array(data["ta... | 10 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : str = logging.get_logger(__name__)
_a : Optional[int] = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/reso... | 701 | '''simple docstring'''
class __A :
def __init__( self , UpperCamelCase_ ):
__UpperCAmelCase : Any = set_counts
__UpperCAmelCase : int = max(UpperCamelCase_ )
__UpperCAmelCase : List[... | 10 | 0 |
'''simple docstring'''
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __A (unittest.TestCase ):
def _snake_case ... | 702 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : Dict = (boundary[1] - boundary[0]) / steps
__UpperCAmelCase : Tuple = boundary[0... | 10 | 0 |
'''simple docstring'''
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"pipelines_utils",
"0.22.0",
"Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from di... | 703 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a : str = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_A... | 10 | 0 |
'''simple docstring'''
_a : Dict = [
"VerificationMode",
"Version",
"disable_progress_bar",
"enable_progress_bar",
"is_progress_bar_enabled",
"experimental",
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, ... | 704 | '''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : str = logging.get_logger(__name__)
_a : ... | 10 | 0 |
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