code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedul... | 551 | import argparse
from collections import defaultdict
import yaml
_lowercase: List[Any] = '''docs/source/en/_toctree.yml'''
def _lowerCamelCase ( snake_case ):
_lowerCAmelCase = defaultdict(snake_case )
_lowerCAmelCase = []
_lowerCAmelCase = ... | 192 | 0 |
def SCREAMING_SNAKE_CASE ( snake_case__ = 10 , snake_case__ = 22 ) -> int:
__UpperCAmelCase =range(1 , snake_case__ )
__UpperCAmelCase =range(1 , snake_case__ )
return sum(
1 for power in powers for base in bases if len(str(base**power ... | 142 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
UpperCamelCase_ = {
'<': operator.lt,
'<=': operator.le,
'==': operator.eq,
'!=': operator.ne,
'>=': operator.ge,
'>': operator.gt,
}
def SCREAMING_SNAKE_C... | 142 | 1 |
"""simple docstring"""
a = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
... | 7 |
from 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 .model... | 469 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[int] = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-ti... | 703 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
UpperCAmelCase_ : int = datasets.logging.get_logger(__name__)
UpperCAmelCase_ : Dict = '\\n@InProceedi... | 443 | 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_convbert import ConvBertTokenizer
_SCREAMING_SNAKE_CASE = logging.get_lo... | 163 |
"""simple docstring"""
from __future__ import annotations
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , ) -> tuple[str, float]:
"""simple docstring"""
if (stress, tangential_force, area).count(0 ) != 1:
... | 163 | 1 |
def a__ ( lowerCAmelCase__ ) -> bool:
UpperCAmelCase__ : str = [int(lowerCAmelCase__ ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(lowerCAmelCase__ ) == 4 and all(0 <= int(lowerCAmelCase__ ) <= 2_54 for octet in octets )
if __name__ == "... | 709 |
'''simple docstring'''
from __future__ import annotations
UpperCamelCase__ = 8.9_88e9 # units = N * m^s * C^-2
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> dict[str, float]:
UpperCAmelCase_... | 312 | 0 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
... | 369 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
"htt... | 369 | 1 |
import numpy as np
class _snake_case :
'''simple docstring'''
def __init__( self: Tuple ) -> Optional[int]:
UpperCAmelCase_ : List[str] = (0, 0)
UpperCAmelCase_ : int = None
UpperCAmelCase_ : Tuple =... | 322 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
UpperCamelCase_ = 50000
UpperCamelCase_ = 5000
UpperCamelCase_ ,UpperCamelCase_ = os.path.split(__file__)
UpperCamelCase_ = os.path.join(RESULTS_BAS... | 322 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class _UpperCamelCase ( UpperCAmelCase__ ):
'''simple docstring''... | 474 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmT... | 42 | 0 |
from torch import nn
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: Optional[Any] ):
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F'Unsupported ac... | 701 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from... | 601 | 0 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = {
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie... | 301 |
'''simple docstring'''
import warnings
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... | 301 | 1 |
'''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%... | 706 |
'''simple docstring'''
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_lowercase : Any ="Usage of script: script_name <size_of_canvas:int>"
_lowercase : Optional[Any] =[0] * 100 + [1] * 10
random.shuffle(cho... | 574 | 0 |
'''simple docstring'''
import math
import qiskit
def A_( A : int = 1 , A : int = 1 , A : int = 1):
if (
isinstance(A , A)
or isinstance(A , A)
or isinstance(A , A)
):
raise TypeError(... | 3 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeech... | 1 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase_ : str = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM... | 720 |
UpperCAmelCase_ : int = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
... | 367 | 0 |
'''simple docstring'''
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class lowercase_ (lowerCamelCa... | 41 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _A ( ):
"""simple docstring"""
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import di... | 41 | 1 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
SCREAMING_SNAKE_CASE = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
SCREAMING_SNAKE_CASE = [ord(letter) for letter in s... | 703 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
SCREAMING_SNAKE_CASE = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
... | 186 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = []
create_all_state(1 , UpperCamelCase_ , UpperCamelCase_ , []... | 673 |
"""simple docstring"""
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)
def _lowerCAmelCase ... | 155 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 219 |
# Copyright 2022 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/licenses/LICENSE-2.0
#
# Unless requi... | 219 | 1 |
from functools import reduce
_a = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711... | 481 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import Sequence... | 198 | 0 |
from math import log
from scipy.constants import Boltzmann, physical_constants
a__ : Union[str, Any] = 3_0_0 # TEMPERATURE (unit = K)
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , ):
'''simple docstring'''
if donor_conc <... | 700 |
"""simple docstring"""
from math import asin, atan, cos, radians, sin, sqrt, tan
a__ : List[str] = 6_37_81_37.0
a__ : Tuple = 6_35_67_52.31_42_45
a__ : str = 6_3_7_8_1_3_7
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ,... | 553 | 0 |
"""simple docstring"""
def __snake_case ( SCREAMING_SNAKE_CASE: Optional[int] , SCREAMING_SNAKE_CASE: Optional[int] ):
"""simple docstring"""
while a != 0:
_lowerCAmelCase = b % a, a
return b
def __... | 580 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.tes... | 453 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor,... | 200 |
"""simple docstring"""
__a : List[Any] = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
__a : Union[str,... | 200 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attenti... | 525 |
'''simple docstring'''
def __A ( a_ : list[list[float]] ):
lowerCAmelCase : list[list[float]] = []
for data in source_data:
for i, el in enumerate(a_ ):
if len(a_ ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(a_ ... | 525 | 1 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__snake_case = (720, 1280) # Height, Width
__snake_case = (0.4, 0.6) # if height or width lower than this scale, drop it.
__snake_case ... | 701 |
"""simple docstring"""
import heapq
def _lowerCamelCase ( lowerCamelCase__ : dict ):
lowercase__ : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq ... | 128 | 0 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKI... | 116 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowercase : Tuple = {"""vocab_file""": ""... | 116 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict , SCREAMING_SNAKE_CASE_: List[str] ) -> str:
'''simple docstring'''
A__ = ""
for i in table:
res += inp[i - 1]
return res
def lowerCAmelCase__ ( SCREA... | 710 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ) -> ... | 626 | 0 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientForm... | 198 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
lowercase = 4
lowercase = 3
class UpperCamelCase_ ( snake_case_ ):
'''sim... | 198 | 1 |
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docs... | 714 |
"""simple docstring"""
from math import isqrt
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j... | 660 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_for... | 12 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_UpperCamelCase : int = logging.get_logger(__name__)
def __UpperCAmelCase ( A : Union[tf.Tensor, np.ndarray] ) -> List[int]:
if isinst... | 541 | 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 FeatureExtractionM... | 316 | import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class a__ ( A__ , unittest.TestCase ):
A = Bi... | 316 | 1 |
"""simple docstring"""
_lowerCAmelCase : int = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_lowerCAmelCase : Optional[int] = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_lowerCAmelCase : Optional[Any] = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wednesday",
4: ... | 289 |
"""simple docstring"""
import os
import sys
a_ = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
Au... | 76 | 0 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
snake_case__ : List[Any] = {
"""E""": 1_2.7_0,
"""T""": 9.0_6,
"""A""": 8.1_7,
"""O""": 7.5_1,
"""I""": 6.9_7,
"""N""": 6.7_5,
"""S""": 6.3_3,
"""H""": 6.0_9,
"""R""": 5.9_9,
"... | 655 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.tes... | 655 | 1 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
... | 101 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : List[str] = {
"configuration_roberta": ... | 602 | 0 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.u... | 1 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __magic_name__ :
def __init__( self : Optional[int] ) -> Optional[Any]:
UpperCAmelCase = ""
UpperCAmelCase = ""
UpperCAmelCase = []
UpperCAmel... | 1 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ..... | 184 |
# Copyright 2023 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/licenses/LICENSE-2.0
#
# Unless r... | 266 | 0 |
import math
def A ( snake_case__ : list , snake_case__ : int = 0 , snake_case__ : int = 0 ) -> int:
'''simple docstring'''
__snake_case = end or len(snake_case_ )
for i in range(snake_case_ , snake_case_ ):
__sn... | 703 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
UpperCAmelCase__ : Any = logging.getLogger()
@unittest.skip... | 676 | 0 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytes... | 655 |
_snake_case = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def _A ( __magic_name__ ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = f'''a bytes-like object is re... | 655 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
),
}
... | 714 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ = {
'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Whis... | 638 | 0 |
import math
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [True] * n
lowercase = False
lowercase = False
lowercase = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
lowercase = i * 2
while index < n:
l... | 84 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from .... | 84 | 1 |
from __future__ import annotations
from collections.abc import Callable
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = 1_0_0 , ):
lowerCamelCase_ = x_start
lowerCamelCase_ = fnc(lowerCamelCase__ ... | 700 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
'''google/vit-base-patch16-224''': '''https://huggin... | 313 | 0 |
import numpy as np
def _lowerCamelCase ( lowerCamelCase_: np.ndarray ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def _lowerCamelCase ( lowerCamelCase_: np.ndarray ):
'''simple docstring'''
... | 256 |
from statistics import mean
import numpy as np
def _lowerCamelCase ( lowerCamelCase_: list , lowerCamelCase_: list , lowerCamelCase_: list , lowerCamelCase_: int ):
'''simple docstring'''
A : Tuple = 0
# Number of proc... | 256 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_lowerCAmelCase = True
except (ImportError, ModuleNotFoundError):
_lowerCAmelCase = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def _SCREAMING_SNAK... | 160 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gp... | 160 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCam... | 590 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , *_SCRE... | 590 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
_lowerCamelCase = logging.get_logger(__name__)
class __a ( _snake_case ):
def __init__( self : Dict , *lowercase__ : Any , **lowercas... | 572 |
'''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
_lowerCamelCase = logging.get_logger(_... | 572 | 1 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepa... | 377 |
class __lowercase :
def __init__( self : Optional[int] ) -> int:
"""simple docstring"""
UpperCAmelCase = {}
def _lowercase ( self : str ) -> None:
... | 377 | 1 |
def _a ( __SCREAMING_SNAKE_CASE : list[int] , __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
_lowerCAmelCase = len(__SCREAMING_SNAKE_CASE )
_lowerCAmelCase = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr va... | 585 |
import math
from numpy import inf
from scipy.integrate import quad
def _a ( __SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
if num <= 0:
raise ValueError('math domain error' )
return quad(__SCREAMING_SNAKE_CASE , 0 , __SCREAMING_SNAKE_CASE , ... | 585 | 1 |
import re
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]''' , str_ )]
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = split_input(str_ )
return "".jo... | 43 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case__ : Union[str, Any] = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'feature_extrac... | 408 | 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/licenses/LICENSE-2.0
#
... | 709 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from tr... | 384 | 0 |
"""simple docstring"""
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_a... | 19 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ =logging.get_logger(__name__)
UpperCamelCase__ ={
'kakaobrain/align-base': 'https://huggingface.co/k... | 249 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google/vivit-b-16x2-kine... | 708 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, Pa... | 453 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ... | 502 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if i... | 550 | 0 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowerCamelCase__ = (
"""4S 3H 2C 7S 5H""",
"""9D 8H 2C 6S 7H""",
"""2D 6D 9D TH 7D""",
"""TC 8C 2S JH 6C""",
"""JH 8S TH AH QH""",
"""TS KS 5S 9S AC""",
"""KD 6S 9D ... | 713 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class SCREAMING_S... | 547 | 0 |
def __lowerCamelCase ( lowerCamelCase__ : int ):
'''simple docstring'''
lowerCamelCase = generate_pascal_triangle(lowerCamelCase__ )
for row_idx in range(lowerCamelCase__ ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
... | 457 |
# Lint as: python3
import itertools
import os
import re
UpperCAmelCase : Any = re.compile(r"([A-Z]+)([A-Z][a-z])")
UpperCAmelCase : Optional[Any] = re.compile(r"([a-z\d])([A-Z])")
UpperCAmelCase : Optional[Any] = re.compile(r"(?<!_)_(?!_)")
UpperCAmelCase : Any = re.compile... | 457 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowercase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 150 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE_ ( metaclass=_lowercase):
'''simple docstring'''
__magic_name__ : List[str] = ['''torch''']
def __init__( self , *lowerCamelCase__ , **... | 150 | 1 |
import math
def __A ( _A , _A = 0 , _A = 0 ):
"""simple docstring"""
__a = end or len(_A )
for i in range(_A , _A ):
__a = i
__a = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
__a ... | 197 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def __l... | 0 | 0 |
'''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def __lowerCAmelCase ( lowerCamelCase : str ):
'''simple docstring'''
def decorator(lowerCamelCase : List[Any] ):
__lowerCAmelCase = getattr(lowerCamelCase , ... | 719 |
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def __lowerCAmelCase ( lowerCamelCase : List[str] ):
'''simple docstring'''
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_co... | 39 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common impo... | 552 |
"""simple docstring"""
def UpperCAmelCase ( _lowercase : int = 1_0_0_0 ) -> int:
"""simple docstring"""
lowerCAmelCase_ , lowerCAmelCase_ = 1, 1
lowerCAmelCase_ = []
for i in range(1 , n + 1 ):
lowerCAmelCase_ = ... | 552 | 1 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase ) -> Tuple:
'''simple docstring'''
_lowerCamelCase : Tuple = len(_lowerCamelCase )
for i in range(length - 1 ):
_lowerCamelCase : str = i
for k in range(i + 1 ... | 386 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase = 10 , _lowerCamelCase = 22 ) -> int:
'''simple docstring'''
_lowerCamelCase : Tuple = range(1 , _lowerCamelCase )
_lowerCamelCase : Tuple = range(1 , _lowerCamelCase )... | 386 | 1 |
import csv
import tweepy
# Twitter API credentials
_SCREAMING_SNAKE_CASE = """"""
_SCREAMING_SNAKE_CASE = """"""
_SCREAMING_SNAKE_CASE = """"""
_SCREAMING_SNAKE_CASE = """"""
def lowercase( UpperCamelCase_ ) -> None:
'''simple docstring'''
# authorize twitter, initi... | 537 | import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewT... | 537 | 1 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import... | 707 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to t... | 152 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ) -> tuple[int, int]:
'''simple docstring'''
if b == 0:
return (1, 0)
((a__) , (a__)) : List[Any] = extended_euclid(lowerCA... | 642 |
"""simple docstring"""
import functools
def lowercase__ ( lowerCAmelCase__ : str , lowerCAmelCase__ : str ) -> int:
'''simple docstring'''
a__ : Any = len(lowerCAmelCase__ )
a__ : Optional[int] = len(lowerCAmelCase__ )
@functools.cache
d... | 642 | 1 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
im... | 718 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffus... | 411 | 0 |
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_dense_index,
)
import transformers
from transformer... | 568 |
from itertools import permutations
def lowerCAmelCase__ ( _a : tuple ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
snake_case_ : List[str] = [7, 11, 13, 17]
for i, te... | 568 | 1 |
'''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():
import... | 178 |
'''simple docstring'''
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_con... | 178 | 1 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
__magic_name__ : Tuple = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a c... | 102 |
'''simple docstring'''
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
a_ ... | 296 | 0 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Union[str, Any] = AutoCo... | 452 | import qiskit
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Dict = qiskit.Aer.get_backend('''aer_simulator''' )
__UpperCamelCase :Tuple = qiskit.QuantumCircuit(4 , 2 )
# encode in... | 452 | 1 |
'''simple docstring'''
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
A : List[Any] = """src/transformers""... | 349 |
'''simple docstring'''
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization ... | 349 | 1 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
_snake_case : List[Any] = ['image_processor', 'tokenizer']
_snake_... | 228 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
s... | 228 | 1 |
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_a... | 430 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class UpperCamelCase__ :
a__ : torch.Tensor # [batch_size x 3]
a__ : torch.Tensor # [batch_size x 3]
a__ : torch.Tensor # [batch_size x 3]
a__ : torch.Tensor # [batch_size x 3]... | 344 | 0 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_... | 484 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : int = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
raise Op... | 484 | 1 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase = 0 ) -> list:
"""simple docstring"""
snake_case_ : Dict = length or len(_UpperCamelCase )
snake_case_ : Any = False
for i in range(length - 1 ):
if l... | 60 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,... | 508 | 0 |
"""simple docstring"""
def lowerCAmelCase (__UpperCamelCase : str , __UpperCamelCase : int ):
"""simple docstring"""
__UpperCamelCase =[[] for _ in range(__UpperCamelCase )]
__UpperCamelCase =key - 1
if key <= 0:
raise Valu... | 296 | """simple docstring"""
class _lowercase :
"""simple docstring"""
def __init__( self : Tuple , UpperCamelCase__ : Any ) -> int:
'''simple docstring'''
__UpperCamelCase =arr.split(''',''' )
... | 296 | 1 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowercase_ ( __snake_case ):
_lowerCamelCase = [... | 670 | import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
def __init__( self , *lowercase_ , **lowercase_ ):... | 670 | 1 |
from PIL import Image
def SCREAMING_SNAKE_CASE( UpperCamelCase ,UpperCamelCase ) -> int:
def brightness(UpperCamelCase ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -255.0 <= level <= 255.0:
raise ValueError('level must be between -255.0 (black) and 255.0 ... | 720 |
'''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ... | 471 | 0 |
'''simple docstring'''
import argparse
_lowercase = """docs/source/_static/js/custom.js"""
def A (__lowerCamelCase :List[Any] ):
with open(__lowerCamelCase , encoding="""utf-8""" , newline="""\n""" ) as f:
_lowerCAmelCase = f.readlines()
... | 5 | '''simple docstring'''
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 ... | 390 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling... | 709 | from __future__ import annotations
from collections.abc import Callable
def a__ ( a , a , a , a = 1_0_0 , ) -> float:
A_ : Any = x_start
A_ : int = fnc(a )
A_ : int = 0.0
for _ ... | 236 | 0 |
import os
# Precomputes a list of the 100 first triangular numbers
_UpperCamelCase : int =[int(0.5 * n * (n + 1)) for n in range(1, 101)]
def a__ () -> Tuple:
_A : Union[str, Any] = os.path.dirname(os.path.realpath(__A ) )
_A : Optional[int] = os.pat... | 206 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
__lowerCAmelCase = {
# 1536-... | 466 | 0 |
"""simple docstring"""
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_... | 615 |
"""simple docstring"""
import string
from math import logaa
def _lowercase ( __snake_case ,__snake_case ) -> int:
__lowerCAmelCase : int = document.translate(
str.maketrans("" ,"" ,string.punctuation ) ).replace("\n" ,"" )
... | 615 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = 'timm_backbone'
def __init__( self :... | 75 |
import math
def UpperCAmelCase ( UpperCAmelCase )-> int:
'''simple docstring'''
if not isinstance(UpperCAmelCase ,UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ = f'''Input value of [number={number}] must be an integer'''
raise TypeError(UpperCA... | 393 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Trajecto... | 65 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip insta... | 65 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ : List[str] = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""":... | 233 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowercase : Union[str, Any] = {"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig""... | 210 | 0 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Optional[Any] = {
"""huggingface/autoformer-tourism-monthly""": """https://huggingface.co/h... | 703 | from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import Prio... | 584 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, tor... | 201 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def a ( a ) ->List[Any]:
'''simpl... | 201 | 1 |
# 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 import ... | 714 | def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case ):
# Return True if there is node that has not iterated.
_UpperCamelCase = [False] * len(__snake_case )
_UpperCamelCase = []
queue.append(__snake_case )
_UpperCamelCase = ... | 71 | 0 |
def a_ ( lowerCAmelCase_ : Union[str, Any], lowerCAmelCase_ : List[str] ):
__lowerCAmelCase = ''
for i in table:
res += inp[i - 1]
return res
def a_ ( lowerCAmelCase_ : Union[str, Any] ):
return data[1:] + data[0]
def a_ ... | 53 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
"""configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
"""processing_git""": ["""GitProc... | 451 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_ut... | 720 |
'''simple docstring'''
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
lowercase ... | 159 | 0 |
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_av... | 405 | from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
lowercase = HfArgumentParser(InitializationArguments)
lowercase = parser.parse_args()
# Load codeparrot tokenizer trained for Python... | 240 | 0 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase_ ( unittest.TestCase ):
... | 567 |
def _a ( __lowercase , __lowercase = 0 ) -> list:
"""simple docstring"""
__UpperCamelCase = length or len(__lowercase )
__UpperCamelCase = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
... | 567 | 1 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ : Optional[int] =OmegaConf.load(lowerCamelCase )
if display:
... | 21 |
"""simple docstring"""
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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImag... | 388 | 0 |
"""simple docstring"""
def lowercase ( UpperCamelCase : int ):
"""simple docstring"""
if upper_limit < 0:
raise ValueError("Limit for the Catalan sequence must be ≥ 0" )
A__ : int =[0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
A__ : List... | 595 | """simple docstring"""
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils... | 595 | 1 |
"""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 ( ) -> Any:
"""simple docstring"""
A = ArgumentParser(
descrip... | 690 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase_ : Dict = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Graphormer... | 442 | 0 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
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... | 714 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGE... | 81 | 0 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _A :
def __init__( self : int , _A : Collection[float] | None = None ) -> None:
"""simple docst... | 217 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
a_ = logging.getLogger(__... | 25 | 0 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
Euler... | 28 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase( __a ):
'''simple docstring'''
lowercase__ = (IPNDMScheduler,)
lowercase__ ... | 28 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
"""configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}
try:
if not is_t... | 5 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large"... | 532 | 0 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
__SCREAMING_SNAKE_CASE = """\
@inproceedings{popovic-2015-chrf,
title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
author = \"Popovi{\'c}, Maja\",
booktitl... | 17 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__SCREAMING_SNAKE_CASE = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConfig""",
... | 17 | 1 |
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: float , UpperCamelCase__: list[float] ):
if discount_rate < 0:
raise ValueError("""Discount rate cannot be negative""" )
if not cash_flows:
raise ValueError("""Cash flows list cannot be empty""" )
SCREAMING_SNAKE_CA... | 6 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( lowerCamelCase_ :List[Any] , lo... | 334 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
... | 21 |
"""simple docstring"""
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
... | 21 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
_lowerCamelCase = logging... | 6 |
snake_case_ : List[Any] = "Alexander Joslin"
import operator as op
from .stack import Stack
def __a ( __UpperCAmelCase : str ) -> int:
"""simple docstring"""
lowerCamelCase_ : List[str] = {"*": op.mul, "/": op.truediv, "+": op.add, "-": ... | 488 | 0 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def __snake_case ( _UpperCAmelCase : float, _UpperCAmelCase : float, _UpperCAmelCase : int):
UpperCamelCase = x
UpperCamelCase = y
for step in range(_Up... | 350 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : Dict = logging.get_logger(__name__)
snake_case_ : Union[str, Any] = {
'facebook/encodec_24khz': '... | 350 | 1 |
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