code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassi... | 101 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 227 | 0 |
"""simple docstring"""
def lowercase ( _snake_case : dict ) ->set:
"""simple docstring"""
__snake_case : int = set()
# edges = list of graph's edges
__snake_case : int = get_edges(_snake_case )
# While there are still elements in edges list, take... | 102 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: str = logging.get_logger(__name__)
_lowercase: List[str] = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
... | 227 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class __snake_case :
_a = 42
_a = None
_a = None
def UpperCamelCase( ):
lower... | 103 |
_lowercase: Dict = [
(1000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def a( A : str ) -> int:
"""simple docs... | 227 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
def __init__( self : ... | 104 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeni... | 227 | 0 |
"""simple docstring"""
# 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.or... | 105 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import Conf... | 227 | 0 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
__UpperCamelCase : List[Any] = {
... | 106 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ... | 227 | 0 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import Flax... | 107 |
from __future__ import annotations
_lowercase: Tuple = list[list[int]]
# assigning initial values to the grid
_lowercase: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5]... | 227 | 0 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : str =(IPNDMScheduler,)
a : int =(("num_inference... | 108 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase: Union[str, Any] = {
"configuration_bridgetower": [
"BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BridgeTowerConfig",
"BridgeT... | 227 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_... | 109 |
def a( A : list ) -> list:
"""simple docstring"""
if any(not isinstance(A , A ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(A ) ):
for i, (... | 227 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__A )
class snake_case_ ( __A ):
'''simple docstring'''
SCREAMING_SNAKE_CAS... | 8 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
def __init__(self , lowerCamelCase_ , lowerCamelCase_ ):
""... | 227 | 0 |
"""simple docstring"""
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('>=', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from tor... | 144 |
import random
def a( A : Optional[Any] , A : Optional[Any] , A : str ) -> List[Any]:
"""simple docstring"""
a = a[left_index]
a = left_index + 1
for j in range(left_index + 1 , A ):
if a... | 227 | 0 |
"""simple docstring"""
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def __init__( self : Tuple ... | 106 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.test... | 227 | 0 |
import operator as op
__lowerCAmelCase : List[str] = "scaler.pt"
__lowerCAmelCase : int = "pytorch_model"
__lowerCAmelCase : Optional[Any] = "random_states"
__lowerCAmelCase : Optional[Any] = "optimizer"
__lowerCAmelCase : Dict = "scheduler"
_... | 107 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm imp... | 227 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuratio... | 139 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 227 | 0 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet imp... | 103 |
import cmath
import math
def a( A : float , A : float , A : float , A : float ) -> complex:
"""simple docstring"""
a = math.radians(A )
a = math.radians(A )
# Convert voltage and c... | 227 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import requ... | 173 |
def a( A : int ) -> str:
"""simple docstring"""
if number > 0:
raise ValueError("input must be a negative integer" )
a = len(bin(A )[3:] )
a = bin(abs(A ) - (1 << binary_number_length) )[3:]
a ... | 227 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ : Union[str, Any] = SwinConfig(image_size=1_9_2)
if "base" in model_name... | 248 |
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 Image
from ..image_utils impor... | 227 | 0 |
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, SchedulerOutput
def snake_case__ ( SCREAMI... | 214 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
de... | 227 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( __snake_case : float , __snake_case : float , __snake_case : float ):
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('''One and only one argument mus... | 33 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = (DDPMScheduler,)
def UpperCamelCase_ (self , **lowerCamelCase_ ):
"""simple docstring"""
... | 227 | 0 |
from ... import PretrainedConfig
a_ = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class _lowercase ( snake_case_ ):
lowercase = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
lowercase = 'nezha'
def __init__... | 175 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def a( A : dict ) -> tuple:
"""simple doc... | 227 | 0 |
import os
from datetime import datetime as dt
from github import Github
lowerCAmelCase_ = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def __SCREAMING_SNAKE_CASE ():
... | 8 |
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
_lowercase: Union[str, Any] = get_t... | 227 | 0 |
"""simple docstring"""
from statistics import mean, stdev
def _snake_case ( lowerCamelCase__ : list , lowerCamelCase__ : int = 3 ) -> list:
lowerCamelCase_ : int =min(lowerCamelCase__ )
lowerCamelCase_ : Tuple =max(lowerCamelCas... | 144 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.p... | 227 | 0 |
"""simple docstring"""
from collections import deque
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : Dict = len(A_ )
lowerCAmelCase__ : Optional[int] = deque()
lowerCAmelCase__ : Optional[Any] = [False for _ in range(A_ )]
lowerCAmelCase__ : Union[str, Any] ... | 106 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 227 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtrac... | 107 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: str = logging.get_logger(__name__)
_lowercase: List[str] = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
... | 227 | 0 |
'''simple docstring'''
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
A_ = yaml.safe_load(
"\\nname: \"\"\nallow_empty: false\nallow_empty_text: true\nsubsect... | 139 |
_lowercase: Dict = [
(1000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def a( A : str ) -> int:
"""simple docs... | 227 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from... | 103 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeni... | 227 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import Autoencod... | 173 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import Conf... | 227 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_v... | 248 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ... | 227 | 0 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import Config... | 214 |
from __future__ import annotations
_lowercase: Tuple = list[list[int]]
# assigning initial values to the grid
_lowercase: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5]... | 227 | 0 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils... | 33 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase: Union[str, Any] = {
"configuration_bridgetower": [
"BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BridgeTowerConfig",
"BridgeT... | 227 | 0 |
import math
from collections.abc import Callable
def __lowercase ( lowerCamelCase : Callable[[float], float] , lowerCamelCase : float , lowerCamelCase : float ):
UpperCamelCase_ : Any = xa
UpperCamelCase_ : Any = xa
while True:
if x_n == x_na o... | 175 |
def a( A : list ) -> list:
"""simple docstring"""
if any(not isinstance(A , A ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(A ) ):
for i, (... | 227 | 0 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if digit_amount > 0:
return round(number - int(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
return number - int(SCREAMING_SNAKE_CASE__ )
if __name__ == "_... | 8 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
def __init__(self , lowerCamelCase_ , lowerCamelCase_ ):
""... | 227 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 144 |
import random
def a( A : Optional[Any] , A : Optional[Any] , A : str ) -> List[Any]:
"""simple docstring"""
a = a[left_index]
a = left_index + 1
for j in range(left_index + 1 , A ):
if a... | 227 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
__UpperCamelCase : Optional[Any] = {
"Salesforce/bli... | 106 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.test... | 227 | 0 |
from ....utils import logging
__lowerCAmelCase : Dict = logging.get_logger(__name__)
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
def __init__( self : str , __lowerCamelCase : Optional[Any] , __lowerCamelCase : ... | 107 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm imp... | 227 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A_ = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 139 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 227 | 0 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
f... | 103 |
import cmath
import math
def a( A : float , A : float , A : float , A : float ) -> complex:
"""simple docstring"""
a = math.radians(A )
a = math.radians(A )
# Convert voltage and c... | 227 | 0 |
"""simple docstring"""
class a :
def __init__( self : Tuple ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: List[str] ={}
def lowerCamelCase__ ( self : List[str] ) -> List[str]:
'''simple do... | 173 |
def a( A : int ) -> str:
"""simple docstring"""
if number > 0:
raise ValueError("input must be a negative integer" )
a = len(bin(A )[3:] )
a = bin(abs(A ) - (1 << binary_number_length) )[3:]
a ... | 227 | 0 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class A__(pl.LightningModule ):
"""simple docstring"""
def __init__( self , _lowercase ) -> Union[str, Any]:
super... | 248 |
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 Image
from ..image_utils impor... | 227 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
'''simple docstring'''
for param in module.parameters():
lowercase__ : Tuple = False
def snake_case__ ( ):
'... | 214 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
de... | 227 | 0 |
"""simple docstring"""
# Copyright 2021 The HuggingFace 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/lic... | 33 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = (DDPMScheduler,)
def UpperCamelCase_ (self , **lowerCamelCase_ ):
"""simple docstring"""
... | 227 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import C... | 175 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def a( A : dict ) -> tuple:
"""simple doc... | 227 | 0 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
def count_of_possible_combinations(SCREAMING_SNAKE_CASE__ ) -> int:
if target < 0:
return 0
if target == 0:
r... | 8 |
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
_lowercase: Union[str, Any] = get_t... | 227 | 0 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class lowercase__ :
def __init__( self : List[str] , ... | 144 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.p... | 227 | 0 |
"""simple docstring"""
__UpperCamelCase : Any = 9.8_0_6_6_5
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ = g ):
if fluid_density <= 0:
raise ValueError('''Impossible fluid density''' )
if volume < 0:
raise ValueError('''Impossible Object volume''' )
if gravi... | 106 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 227 | 0 |
def __magic_name__ ( A : list ):
'''simple docstring'''
a = len(A )
for i in range(1, A ):
a = collection[i]
a = 0
a = i - 1
while low <= high:
a = (low + high) // 2
if val < collection[mid]:
a ... | 107 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: str = logging.get_logger(__name__)
_lowercase: List[str] = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
... | 227 | 0 |
'''simple docstring'''
A_ = [
(10_00, "M"),
(9_00, "CM"),
(5_00, "D"),
(4_00, "CD"),
(1_00, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def A_ ( snake_case ... | 139 |
_lowercase: Dict = [
(1000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def a( A : str ) -> int:
"""simple docs... | 227 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Dict = logging.get_logger(__name__)
A__ : int = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}
class __snake_case ( ... | 103 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeni... | 227 | 0 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class a ( UpperCAmelCase__ ):
UpperCamelCase : Any = (KDPMaDiscreteScheduler,)
UpperCamelCase : List[str]... | 173 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import Conf... | 227 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
... | 248 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ... | 227 | 0 |
def snake_case__ ( SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
lowercase__ : int = 1
lowercase__ : str = 1
while repunit:
lowercase__ : Dict = (1... | 214 |
from __future__ import annotations
_lowercase: Tuple = list[list[int]]
# assigning initial values to the grid
_lowercase: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5]... | 227 | 0 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _UpperCAmelCase :
pass
| 33 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase: Union[str, Any] = {
"configuration_bridgetower": [
"BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BridgeTowerConfig",
"BridgeT... | 227 | 0 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( snake_case_ ):
lowercase = (DDPMScheduler,)
def SCREAMING_SNAKE_CASE__ ( self : List[str] , **snake_case : Optional[Any] ) -> str:... | 175 |
def a( A : list ) -> list:
"""simple docstring"""
if any(not isinstance(A , A ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(A ) ):
for i, (... | 227 | 0 |
from __future__ import annotations
lowerCAmelCase_ = list[list[int]]
# assigning initial values to the grid
lowerCAmelCase_ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5... | 8 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
def __init__(self , lowerCamelCase_ , lowerCamelCase_ ):
""... | 227 | 0 |
"""simple docstring"""
class lowercase__ : # Public class to implement a graph
def __init__( self : Dict , snake_case__ : Optional[int] , snake_case__ : Tuple , snake_case__ : List[Any] ):
lowerCamelCase_ : str =row
... | 144 |
import random
def a( A : Optional[Any] , A : Optional[Any] , A : str ) -> List[Any]:
"""simple docstring"""
a = a[left_index]
a = left_index + 1
for j in range(left_index + 1 , A ):
if a... | 227 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_a... | 106 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.test... | 227 | 0 |
from collections import defaultdict
from math import ceil, sqrt
def __magic_name__ ( A : int = 1000000, A : int = 10 ):
'''simple docstring'''
a = defaultdict(A )
for outer_width in range(3, (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
... | 107 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm imp... | 227 | 0 |
'''simple docstring'''
import math
import unittest
def A_ ( snake_case ):
assert isinstance(snake_case , snake_case ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
eli... | 139 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 227 | 0 |
def UpperCamelCase( __UpperCamelCase : list[list] ):
lowerCAmelCase_ : Any = current_set.copy()
for row_index, row in enumerate(__UpperCamelCase ):
lowerCAmelCase_ : Dict = row[0]
for column_index, column in enumerate(__UpperCamelCase ):
if magnitude == 0:
... | 103 |
import cmath
import math
def a( A : float , A : float , A : float , A : float ) -> complex:
"""simple docstring"""
a = math.radians(A )
a = math.radians(A )
# Convert voltage and c... | 227 | 0 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
... | 173 |
def a( A : int ) -> str:
"""simple docstring"""
if number > 0:
raise ValueError("input must be a negative integer" )
a = len(bin(A )[3:] )
a = bin(abs(A ) - (1 << binary_number_length) )[3:]
a ... | 227 | 0 |
from collections.abc import Generator
from math import sin
def _UpperCAmelCase ( a__):
'''simple docstring'''
if len(a__) != 3_2:
raise ValueError("""Input must be of length 32""")
a_ : str = B""""""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i... | 248 |
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 Image
from ..image_utils impor... | 227 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokeniza... | 214 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
de... | 227 | 0 |
"""simple docstring"""
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsA... | 33 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = (DDPMScheduler,)
def UpperCamelCase_ (self , **lowerCamelCase_ ):
"""simple docstring"""
... | 227 | 0 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.... | 175 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def a( A : dict ) -> tuple:
"""simple doc... | 227 | 0 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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_configuration_common import ... | 8 |
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
_lowercase: Union[str, Any] = get_t... | 227 | 0 |
"""simple docstring"""
import json
import sys
def _snake_case ( lowerCamelCase__ : Dict , lowerCamelCase__ : Optional[Any] ) -> Any:
with open(lowerCamelCase__ , encoding="utf-8" ) as f:
lowerCamelCase_ : Optional[Any] =json.loa... | 144 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.p... | 227 | 0 |
"""simple docstring"""
from bisect import bisect
from itertools import accumulate
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ , A_ ):
lowerCAmelCase__ : Union[str, Any] = sorted(zip(A_ , A_ ) , key=lambda A_ : x[0] / x[1] , reverse=A_ )
low... | 106 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 227 | 0 |
__lowerCAmelCase : Dict = "Input must be a string of 8 numbers plus letter"
__lowerCAmelCase : List[str] = "TRWAGMYFPDXBNJZSQVHLCKE"
def __magic_name__ ( A : str ):
'''simple docstring'''
if not isinstance(A, A ):
a = F"""Expected string... | 107 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: str = logging.get_logger(__name__)
_lowercase: List[str] = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
... | 227 | 0 |
'''simple docstring'''
def A_ ( snake_case , snake_case , snake_case ):
SCREAMING_SNAKE_CASE:Tuple = len(snake_case )
SCREAMING_SNAKE_CASE:Tuple = [[0] * n for i in range(snake_case )]
for i in range(snake_case ):
SCREAMING_SNA... | 139 |
_lowercase: Dict = [
(1000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def a( A : str ) -> int:
"""simple docs... | 227 | 0 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEI... | 103 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeni... | 227 | 0 |
"""simple docstring"""
from itertools import product
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: Tuple =sides_number
SCREAMING_SNAKE_CASE_: List[str] =max_face_number * dice_number
SCREAMING_SNAKE_CASE_: Dict =[0] *... | 173 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import Conf... | 227 | 0 |
__snake_case : List[Any] = "Alexander Joslin"
import operator as op
from .stack import Stack
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ : Optional[Any] = {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}
a_ : Dict ... | 248 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ... | 227 | 0 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class SCREAMING_SNAKE_CASE__ :
def __init__( self , a):
lowercase__ : Tuple = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 wil... | 214 |
from __future__ import annotations
_lowercase: Tuple = list[list[int]]
# assigning initial values to the grid
_lowercase: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5]... | 227 | 0 |
"""simple docstring"""
import math
def lowercase ( __snake_case : int ):
lowercase_ : List[str] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__snake_case )
def lowercase ( __snake_case : floa... | 33 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase: Union[str, Any] = {
"configuration_bridgetower": [
"BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BridgeTowerConfig",
"BridgeT... | 227 | 0 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
a_ = logging.getLogger(__name__)
@da... | 175 |
def a( A : list ) -> list:
"""simple docstring"""
if any(not isinstance(A , A ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(A ) ):
for i, (... | 227 | 0 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''') )
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = credit_card_number
snake_case_ = 0
... | 8 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
def __init__(self , lowerCamelCase_ , lowerCamelCase_ ):
""... | 227 | 0 |
"""simple docstring"""
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
A__ : Any = logging.get_logger(__name__)
A__ : Dict = {name: getattr(transformers,... | 144 |
import random
def a( A : Optional[Any] , A : Optional[Any] , A : str ) -> List[Any]:
"""simple docstring"""
a = a[left_index]
a = left_index + 1
for j in range(left_index + 1 , A ):
if a... | 227 | 0 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( A_ , A_ ):
lowerCAmelCase__ : Tuple = len(A_ )
lowerCAmelCase__ : List[Any] = len(A_ )
lowerCAmelCase__ : Dict = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
lowerCAmelCase__ : Dict = True
... | 106 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.test... | 227 | 0 |
import numpy as np
def __magic_name__ ( A : np.array ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 107 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm imp... | 227 | 0 |
'''simple docstring'''
from math import pow, sqrt
def A_ ( *snake_case ):
SCREAMING_SNAKE_CASE:Union[str, Any] = len(snake_case ) > 0 and all(value > 0.0 for value in values )
return result
def A_ ( snake_case , snake_c... | 139 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 227 | 0 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def UpperCamelCase( __UpperCamelCase : Union[str, Any] ... | 103 |
import cmath
import math
def a( A : float , A : float , A : float , A : float ) -> complex:
"""simple docstring"""
a = math.radians(A )
a = math.radians(A )
# Convert voltage and c... | 227 | 0 |
"""simple docstring"""
import os
from distutils.util import strtobool
def __magic_name__ ( lowercase , lowercase ):
for e in env_keys:
SCREAMING_SNAKE_CASE_: List[str] =int(os.environ.get(lowercase , -1 ) )
if val >= 0:
... | 173 |
def a( A : int ) -> str:
"""simple docstring"""
if number > 0:
raise ValueError("input must be a negative integer" )
a = len(bin(A )[3:] )
a = bin(abs(A ) - (1 << binary_number_length) )[3:]
a ... | 227 | 0 |
from math import ceil
def _UpperCAmelCase ( a__ , a__):
'''simple docstring'''
a_ : Union[str, Any] = list(range(0 , a__))
a_ : Union[str, Any] = [item for sublist in list(device_map.values()) for item in sublist]
# Duplicate check
a_ : str ... | 248 |
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 Image
from ..image_utils impor... | 227 | 0 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def snak... | 214 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
de... | 227 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vis... | 33 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = (DDPMScheduler,)
def UpperCamelCase_ (self , **lowerCamelCase_ ):
"""simple docstring"""
... | 227 | 0 |
import datasets
from .evaluate import evaluate
a_ = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={2016}\n}\n"
a_ ... | 175 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def a( A : dict ) -> tuple:
"""simple doc... | 227 | 0 |
from ..utils import DummyObject, requires_backends
class snake_case_ ( metaclass=__A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = ["onnx"]
def __init__( self : Tuple , *_UpperCamelCase : Dict ... | 8 |
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
_lowercase: Union[str, Any] = get_t... | 227 | 0 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _snake_case ( lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCa... | 144 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.p... | 227 | 0 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__UpperCamelCase : Dict = "\\n@misc{chen2021evaluating,\... | 106 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 227 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ["""image_processor""", """tokenizer"""]
... | 107 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: str = logging.get_logger(__name__)
_lowercase: List[str] = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
... | 227 | 0 |
'''simple docstring'''
def A_ ( ):
SCREAMING_SNAKE_CASE:Tuple = 0
for i in range(1 , 1001 ):
total += i**i
return str(snake_case )[-10:]
if __name__ == "__main__":
print(solution())
| 139 |
_lowercase: Dict = [
(1000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def a( A : str ) -> int:
"""simple docs... | 227 | 0 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils ... | 103 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeni... | 227 | 0 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common imp... | 173 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import Conf... | 227 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__snake_case : str = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kernel", "weight"),
... | 248 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ... | 227 | 0 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Tuple ... | 214 |
from __future__ import annotations
_lowercase: Tuple = list[list[int]]
# assigning initial values to the grid
_lowercase: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5]... | 227 | 0 |
"""simple docstring"""
def lowercase ( __snake_case : int ):
if number > 0:
raise ValueError('''input must be a negative integer''' )
lowercase_ : List[str] = len(bin(__snake_case )[3:] )
lowercase_ : Union[str, Any] = bin(abs(__s... | 33 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase: Union[str, Any] = {
"configuration_bridgetower": [
"BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BridgeTowerConfig",
"BridgeT... | 227 | 0 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.sc... | 175 |
def a( A : list ) -> list:
"""simple docstring"""
if any(not isinstance(A , A ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(A ) ):
for i, (... | 227 | 0 |
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
lowerCAmelCase_ = logging.get_logger(__name__)
class snake_case_ ... | 8 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
def __init__(self , lowerCamelCase_ , lowerCamelCase_ ):
""... | 227 | 0 |
"""simple docstring"""
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenizatio... | 144 |
import random
def a( A : Optional[Any] , A : Optional[Any] , A : str ) -> List[Any]:
"""simple docstring"""
a = a[left_index]
a = left_index + 1
for j in range(left_index + 1 , A ):
if a... | 227 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
__UpperCamelCase : Dict = {
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_ARCHI... | 106 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.test... | 227 | 0 |
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