code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def __lowercase ( snake_case ):
"""simple docstring"""
if num <= 0:
raise ValueError('''Input must be a positive integer''' )
__magic_name__ :List[Any] = [True] * (num + 1)
__magic_name__ :Tuple = 2
while p * p <= num:
if primes[p]:
... | 0 |
def _a ( a :list ) -> list:
if len(a ) < 2:
return collection
def circle_sort_util(a :list , a :int , a :int ) -> bool:
a = False
if low == high:
return swapped
a = low
a = high
while ... | 117 | 0 |
import math
def _A ( _lowercase , _lowercase ) -> float:
"""simple docstring"""
return math.pow(_lowercase , 2 ) - a
def _A ( _lowercase ) -> float:
"""simple docstring"""
return 2 * x
def _A ( _lowercase ... | 1 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
UpperCAmelCase__ = logging.getLogger(__name_... | 117 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {
"""configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}
try:
if not is_torch_available(... | 2 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class lowerc... | 117 | 0 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class SCREAMING_SNAKE_CASE__ ( tf.keras.layers.Layer):
def __init__( self , A_ , A_ , A_ , A_ , A_=1 , A_=False , **A_ )-> Any:
... | 3 |
from __future__ import annotations
class lowercase_ :
'''simple docstring'''
def __init__( self : List[str] , __UpperCAmelCase : int ) ->None:
"""simple docstring"""
a = order
# a_{0} ...... | 117 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__UpperCamelCase : Optional[int] ... | 4 |
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... | 117 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class UpperCAmelCase_ :
'''simple docstring'''
_lowercase : int
_lowercase ... | 5 |
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... | 117 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {'vocab_file': 'sentencepiece.mode... | 6 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
... | 117 | 0 |
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
a = ... | 7 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
fr... | 117 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
lowercase__ : List[str] = '''2020.9.26'''
lowercase__ : Any = '''xcodz-dot, cclaus, dhruvmanila'''
def _lowerCAmelCase ( __snake_case : float , _... | 8 |
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": ["LayoutXLMPr... | 117 | 0 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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
from ...te... | 9 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = [
["attention", "... | 117 | 0 |
import sys
from collections import defaultdict
class lowerCAmelCase_ :
def __init__( self : Optional[int] ):
_UpperCamelCase = []
def UpperCamelCase_ ( self : Any , _A : str ):
return self.node_position[verte... | 10 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
UpperCAmelCase__ = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _a ( a :... | 117 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
if not nums:
return 0
_a = nums[0]
_a = 0
for num in nums[1:]:
_a , _a = (
max_exclu... | 11 |
from ... import PretrainedConfig
UpperCAmelCase__ = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case... | 117 | 0 |
import argparse
import os
import re
lowerCamelCase__ : str = """src/diffusers"""
# Pattern that looks at the indentation in a line.
lowerCamelCase__ : str = re.compile(R"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCamelCase__ : Optional[Any]... | 12 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedC... | 117 | 0 |
'''simple docstring'''
from ....utils import logging
A__ : Tuple = logging.get_logger(__name__)
class UpperCAmelCase_ (_UpperCAmelCase ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_C... | 13 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _a ( a :Dict[str, torch.Tensor] ) -> Dict[str, torch.Tensor]:
a = []
a = []
a = []
for ... | 117 | 0 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
... | 14 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 117 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = ['''image_processor''', '''tokenizer''']
A__ = '''AutoImageProcessor'''
A__ ... | 15 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 117 | 0 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
... | 16 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowercase_ :
'''simple docstring'''
pass
| 117 | 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 __SCREAMING_SNAKE_CASE ( a__ : str ) -> List[s... | 17 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
... | 117 | 0 |
'''simple docstring'''
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_SCREAMING_SNAKE_CASE = namedtuple(
"_TestCommand... | 18 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase__ = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioG... | 117 | 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 = logging.get_logger(__name__)
_a = {
"""shi-labs/nat-mini-in... | 19 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
UpperCAmelCase__ = ... | 117 | 0 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSa... | 20 |
from typing import Any
import numpy as np
def _a ( a :np.ndarray ) -> bool:
return np.array_equal(a , matrix.conjugate().T )
def _a ( a :np.ndarray , a :np.ndarray ) -> Any:
a = v.conjugate().T
a = v_star.do... | 117 | 0 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __A ( tf.keras.layers.Layer ):
def __init__( self ... | 21 |
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,... | 117 | 0 |
'''simple docstring'''
import logging
import os
from .state import PartialState
class A ( logging.LoggerAdapter ):
@staticmethod
def __lowerCAmelCase ( lowerCAmelCase_ : Union[str, Any] ) -> Dict:
"""simp... | 22 |
def _a ( a :list ) -> list:
if len(a ) < 2:
return collection
def circle_sort_util(a :list , a :int , a :int ) -> bool:
a = False
if low == high:
return swapped
a = low
a = high
while ... | 117 | 0 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
snake_case__ : Tuple = logging.get_logger(__name__)
def _snake_case (__lowercase , __lowercase):
UpperCamelCa... | 23 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
UpperCAmelCase__ = logging.getLogger(__name_... | 117 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase_ : List[str] = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'... | 24 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class lowerc... | 117 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Op... | 25 |
from __future__ import annotations
class lowercase_ :
'''simple docstring'''
def __init__( self : List[str] , __UpperCAmelCase : int ) ->None:
"""simple docstring"""
a = order
# a_{0} ...... | 117 | 0 |
'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_... | 26 |
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... | 117 | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils impo... | 27 |
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... | 117 | 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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_l... | 28 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
... | 117 | 0 |
"""simple docstring"""
import argparse
A_ = """docs/source/_static/js/custom.js"""
def lowercase ( lowerCAmelCase__ ):
with open(lowerCAmelCase__ ,encoding='''utf-8''' ,newline='''\n''' ) as f:
lowerCamelCase_ = f.readlines()
lowerCamelCase_ = 0
# First... | 29 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
fr... | 117 | 0 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class __a:
"""simple docstring"""
pass | 30 |
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": ["LayoutXLMPr... | 117 | 0 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import B... | 31 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = [
["attention", "... | 117 | 0 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __UpperCamelCase ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
__A : int = [("""size""", ctypes.c... | 32 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
UpperCAmelCase__ = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _a ( a :... | 117 | 0 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
def count_of_possible_combinations(__lowerCAmelCase ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(targe... | 33 |
from ... import PretrainedConfig
UpperCAmelCase__ = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case... | 117 | 0 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __snake_case ( ):
"""simple docstring"""
UpperCamelCase = ArgumentParser(
... | 34 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedC... | 117 | 0 |
def a ( A__ ) -> float:
'''simple docstring'''
if edge <= 0 or not isinstance(A__ , A__ ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((2_5 + 1_0 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def a ( A__ ) ->... | 35 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _a ( a :Dict[str, torch.Tensor] ) -> Dict[str, torch.Tensor]:
a = []
a = []
a = []
for ... | 117 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowercase : Dict = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnn... | 36 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 117 | 0 |
UpperCamelCase : int = [
"""DownloadConfig""",
"""DownloadManager""",
"""DownloadMode""",
"""StreamingDownloadManager""",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDow... | 37 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 117 | 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_ : int = logging.get_logger(__name__)
A_ : Dict = {
"google/bit-50": "https:/... | 38 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowercase_ :
'''simple docstring'''
pass
| 117 | 0 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __SCREAMING_SNAKE_CASE ():
snake_case_ = {
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
'''path'... | 39 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
... | 117 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCAmelCase_ ... | 40 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase__ = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioG... | 117 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = (PNDMScheduler,)
SCREAMING... | 41 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
UpperCAmelCase__ = ... | 117 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase ( UpperCAmelCas... | 42 |
from typing import Any
import numpy as np
def _a ( a :np.ndarray ) -> bool:
return np.array_equal(a , matrix.conjugate().T )
def _a ( a :np.ndarray , a :np.ndarray ) -> Any:
a = v.conjugate().T
a = v_star.do... | 117 | 0 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def _a... | 43 |
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,... | 117 | 0 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase_ : Dict = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : str = os.path.dirname(os.path.realpa... | 44 |
def _a ( a :list ) -> list:
if len(a ) < 2:
return collection
def circle_sort_util(a :list , a :int , a :int ) -> bool:
a = False
if low == high:
return swapped
a = low
a = high
while ... | 117 | 0 |
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
UpperCamelCase = {
"facebook/maskformer-swin-base-ade": (
"https://... | 45 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
UpperCAmelCase__ = logging.getLogger(__name_... | 117 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
_lowerCAmelCase : int = ['''small''', '''medium''', '''large''']
_lowerCAmelCase : str = '''lm_head.decoder.weight'''
_lowerCAmelCase : List[Any] = '''l... | 46 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class lowerc... | 117 | 0 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPrior... | 47 |
from __future__ import annotations
class lowercase_ :
'''simple docstring'''
def __init__( self : List[str] , __UpperCAmelCase : int ) ->None:
"""simple docstring"""
a = order
# a_{0} ...... | 117 | 0 |
'''simple docstring'''
from math import sqrt
def A ( UpperCamelCase_ : int ) -> int:
'''simple docstring'''
lowerCAmelCase__ = 0
for i in range(1 , int(sqrt(UpperCamelCase_ ) + 1 ) ):
if n % i == 0 and i != sqrt(UpperCamelCase_ ):
... | 48 |
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... | 117 | 0 |
"""simple docstring"""
from __future__ import annotations
class _UpperCAmelCase :
def __init__( self : List[str] , _lowercase : Optional[Any]=None ):
__UpperCAmelCase = data
__UpperCAmelCase = None
def __repr__( self : Op... | 49 |
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... | 117 | 0 |
'''simple docstring'''
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_... | 50 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
... | 117 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : Tuple ):
UpperCAmelCase = {}
def __snake_case ( self... | 51 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
fr... | 117 | 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 __lower... | 52 |
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": ["LayoutXLMPr... | 117 | 0 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case : List[str] = logging.get_logger(__name... | 53 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = [
["attention", "... | 117 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .mod... | 54 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
UpperCAmelCase__ = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _a ( a :... | 117 | 0 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def UpperCAmelCase ( a_ ) -> List[Any]:
"""simple docstring"""
if (
(cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
or (cp >= 0x... | 55 |
from ... import PretrainedConfig
UpperCAmelCase__ = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case... | 117 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : Optional[Any] = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class _l... | 56 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedC... | 117 | 0 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...... | 57 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _a ( a :Dict[str, torch.Tensor] ) -> Dict[str, torch.Tensor]:
a = []
a = []
a = []
for ... | 117 | 0 |
"""simple docstring"""
import argparse
import os
import re
__lowerCAmelCase : Any = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__lowerCAmelCase : Any = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"ke... | 58 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 117 | 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 b... | 59 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 117 | 0 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Ima... | 60 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowercase_ :
'''simple docstring'''
pass
| 117 | 0 |
def _A ( lowerCAmelCase_ : list ):
"""simple docstring"""
lowerCAmelCase__ = False
while is_sorted is False: # Until all the indices are traversed keep looping
lowerCAmelCase__ = True
for i in range(0 , len(lowerCAmelC... | 61 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
... | 117 | 0 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_conf... | 62 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase__ = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioG... | 117 | 0 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
i... | 63 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
UpperCAmelCase__ = ... | 117 | 0 |
import os
def A__ ( ):
SCREAMING_SNAKE_CASE__: List[str]= os.path.dirname(os.path.realpath(snake_case_ ) )
SCREAMING_SNAKE_CASE__: Optional[int]= os.path.join(snake_case_ , '''triangle.txt''' )
with open(snake_case_ ) as f:
SCREAMING_SNAKE_CASE__: str= f.readlines()
S... | 64 |
from typing import Any
import numpy as np
def _a ( a :np.ndarray ) -> bool:
return np.array_equal(a , matrix.conjugate().T )
def _a ( a :np.ndarray , a :np.ndarray ) -> Any:
a = v.conjugate().T
a = v_star.do... | 117 | 0 |
"""simple docstring"""
import itertools
import math
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
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 mult... | 65 |
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,... | 117 | 0 |
from PIL import Image
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Image:
def brightness(SCREAMING_SNAKE_CASE ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError('level m... | 66 |
def _a ( a :list ) -> list:
if len(a ) < 2:
return collection
def circle_sort_util(a :list , a :int , a :int ) -> bool:
a = False
if low == high:
return swapped
a = low
a = high
while ... | 117 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)... | 67 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
UpperCAmelCase__ = logging.getLogger(__name_... | 117 | 0 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class _A ( UpperCamelCase ... | 68 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class lowerc... | 117 | 0 |
'''simple docstring'''
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make... | 69 |
from __future__ import annotations
class lowercase_ :
'''simple docstring'''
def __init__( self : List[str] , __UpperCAmelCase : int ) ->None:
"""simple docstring"""
a = order
# a_{0} ...... | 117 | 0 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( lowercase : int ):
'''simple docstring'''
lowerCamelCase_ = 2
lowerCamelCase_ = []
while i * i <= n:
if n % i:
i += 1
... | 70 |
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... | 117 | 0 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECK... | 71 |
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... | 117 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.util... | 72 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
... | 117 | 0 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class _snake_case :
def __init__( self , a , a , a , a , a , a=0.2 , a=0.2) -> List[Any]:
SCREAMING_SNAKE_CASE = bp_numa
SCREAMING_SNAKE_CASE ... | 73 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
fr... | 117 | 0 |
from __future__ import annotations
from typing import Any
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : List[Any] , _A : int = 6 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Node | None =... | 74 |
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": ["LayoutXLMPr... | 117 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if not... | 75 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = [
["attention", "... | 117 | 0 |
"""simple docstring"""
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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/licen... | 76 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
UpperCAmelCase__ = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _a ( a :... | 117 | 0 |
"""simple docstring"""
import comet # From: unbabel-comet
import torch
import datasets
A = datasets.logging.get_logger(__name__)
A = """\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
title = {Unbabel'... | 77 |
from ... import PretrainedConfig
UpperCAmelCase__ = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case... | 117 | 0 |
'''simple docstring'''
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 accelera... | 78 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedC... | 117 | 0 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
... | 79 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _a ( a :Dict[str, torch.Tensor] ) -> Dict[str, torch.Tensor]:
a = []
a = []
a = []
for ... | 117 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy,... | 80 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 117 | 0 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
_snake_case : Optional[int] = logging.get_logger(__name__)
class a (_lowerCAmelCase ):
"""simple docstring"""
def __init__( self : Optional[Any] , ... | 81 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 117 | 0 |
"""simple docstring"""
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
lowerCamelCase = logging.get_logger(__name__)
def a__ ( lowerCAmelCase__=None , l... | 82 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowercase_ :
'''simple docstring'''
pass
| 117 | 0 |
"""simple docstring"""
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, requ... | 83 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
... | 117 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
# ... | 84 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase__ = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioG... | 117 | 0 |
from __future__ import annotations
def _a ( lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
if b == 0:
return (1, 0)
((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) : List[Any] = extended_euclid(lowercase__ , a... | 85 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
UpperCAmelCase__ = ... | 117 | 0 |
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 import RobertaToken... | 86 |
from typing import Any
import numpy as np
def _a ( a :np.ndarray ) -> bool:
return np.array_equal(a , matrix.conjugate().T )
def _a ( a :np.ndarray , a :np.ndarray ) -> Any:
a = v.conjugate().T
a = v_star.do... | 117 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCamelCase : Optional[Any] = {
"""configuration_pix2struct""": [
"""PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Pix2StructConfi... | 87 |
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,... | 117 | 0 |
"""simple docstring"""
def _snake_case ( __snake_case : int ):
"""simple docstring"""
_lowerCamelCase : Dict = int(__snake_case )
if n_element < 1:
_lowerCamelCase : List[Any] = ValueError("""a should be a positive number""" )
... | 88 |
def _a ( a :list ) -> list:
if len(a ) < 2:
return collection
def circle_sort_util(a :list , a :int , a :int ) -> bool:
a = False
if low == high:
return swapped
a = low
a = high
while ... | 117 | 0 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class _lowerCamelCase( _a, unittest... | 89 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
UpperCAmelCase__ = logging.getLogger(__name_... | 117 | 0 |
'''simple docstring'''
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(ch... | 90 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class lowerc... | 117 | 0 |
"""simple docstring"""
import os
def _snake_case ( snake_case__ : str = "input.txt" ):
with open(os.path.join(os.path.dirname(snake_case__ ) , snake_case__ ) ) as input_file:
A = [
[int(snake_case__ ) for element in line.split(',' )]
for line in input_file.readlines()
... | 91 |
from __future__ import annotations
class lowercase_ :
'''simple docstring'''
def __init__( self : List[str] , __UpperCAmelCase : int ) ->None:
"""simple docstring"""
a = order
# a_{0} ...... | 117 | 0 |
'''simple docstring'''
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
UpperCamelCase_ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = None
@experime... | 92 |
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... | 117 | 0 |
"""simple docstring"""
from __future__ import annotations
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[int]: # noqa: E741
"""simple docstring"""
while r - l > 1:
lowerCAmelCase__ :List[str] = ... | 93 |
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... | 117 | 0 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from tr... | 94 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
... | 117 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeli... | 95 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
fr... | 117 | 0 |
"""simple docstring"""
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __A :
def lowerCamelCase__ ( self : List[Any] , __snake_case : Tuple ) -> ... | 96 |
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": ["LayoutXLMPr... | 117 | 0 |
def a ( snake_case__: int ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for di... | 97 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = [
["attention", "... | 117 | 0 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_fl... | 98 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
UpperCAmelCase__ = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _a ( a :... | 117 | 0 |
import comet # From: unbabel-comet
import torch
import datasets
SCREAMING_SNAKE_CASE = datasets.logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = '\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie... | 99 |
from ... import PretrainedConfig
UpperCAmelCase__ = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case... | 117 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.