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 |
|---|---|---|---|---|
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A_ (lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = (DDIMParallelScheduler,)
SCREAMING_SNAKE_CASE__ : Option... | 61 |
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A_ (lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = (DDIMParallelScheduler,)
SCREAMING_SNAKE_CASE__ : Option... | 61 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/co... | 61 |
"""simple docstring"""
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_te... | 61 | 1 |
"""simple docstring"""
import os
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : List[Any] = len(grid[0] )
UpperCAmelCase_ : Tuple = len(__lowerCamelCase )
UpperCAmelCase_ : List[Any] = 0
UpperCAmelCase_ : List[str] ... | 61 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 61 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
_a = logging.get_logger(__name__)
_a = {
'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
# S... | 61 |
"""simple docstring"""
from __future__ import annotations
import math
def __a ( __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : Any = u
for i in range(1, __lowerCamelCase ):
UpperCAmelCase_ : int = temp * (u - i)
return temp
def ... | 61 | 1 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Any = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for... | 61 |
"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transfo... | 61 | 1 |
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTest... | 61 |
"""simple docstring"""
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : List[str] = int(__lowerCamelCase )
if n_element < 1:
UpperCAmelCase_ : List[Any] = ValueError("a should be a positive number" )
raise my_error
UpperCAmelCase_ : List[An... | 61 | 1 |
"""simple docstring"""
def __a ( __lowerCamelCase ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 61 |
"""simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = 1 / sqrt(2 ) ):
UpperCAmelCase_ : int = tau * frequency / samplerate
UpperCAmelCase_ : List[... | 61 | 1 |
"""simple docstring"""
from __future__ import annotations
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
if days_between_payments <= 0:
raise ValueError("days_between_payments must be > 0" )
if daily_interest_rate < 0:
raise ValueError("daily_interest_rate mus... | 61 |
"""simple docstring"""
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae... | 61 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_a = logging.get_logger(__name__) # pylint: disable=invalid-name
class A_ (lowercase__ ):
... | 61 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_c... | 61 | 1 |
"""simple docstring"""
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
... | 61 |
"""simple docstring"""
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
fr... | 61 | 1 |
"""simple docstring"""
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import... | 61 |
"""simple docstring"""
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 .t... | 61 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def __a ( __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : Any = u
for i in range(1, __lowerCamelCase ):
UpperCAmelCase_ : int = temp * (u - i)
return temp
def ... | 61 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_a = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://h... | 61 | 1 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __a ( ):
UpperCAmelCase_ : List[Any] = {
"repo_name": ["test_repo1", "test_repo2", "test_repo3"],
"p... | 61 |
"""simple docstring"""
from __future__ import annotations
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
raise ValueError("Resistance... | 61 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ... | 61 |
"""simple docstring"""
import argparse
from collections import defaultdict
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : int = f"""{file}_{class_name}_{test_name}"""
done_test[_id] += 1
with ope... | 61 | 1 |
"""simple docstring"""
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CON... | 61 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class A_ :
'''simple docstring'''
pass
| 61 | 1 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class A_ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ):
"""simple docstring"""
U... | 61 |
"""simple docstring"""
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
return round(float(moles / volume ) * nfactor )
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
return round(float((moles * 0.0821 * temperature) / (volume) ) )
def ... | 61 | 1 |
"""simple docstring"""
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_te... | 61 |
"""simple docstring"""
import os
_a = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1_000}
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Union[str, Any] = 0
UpperCAmelCase_ : List[str] = 0
while index < len(__lowerCamelCase... | 61 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_c... | 61 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __a ( ):
UpperCAmelCase_ : List[Any] = {
"repo_name": ["test_repo1", "test_repo2", "test_repo3"],
"p... | 61 | 1 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, s... | 61 |
"""simple docstring"""
from collections import namedtuple
_a = namedtuple('from_to', 'from_ to')
_a = {
'cubicmeter': from_to(1, 1),
'litre': from_to(0.001, 1_000),
'kilolitre': from_to(1, 1),
'gallon': from_to(0.0_0454, 264.172),
'cubicyard': from_to(0.7_6455, 1.... | 61 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=lowercase__ )
class A_ (lowercase__ ):
'''simple docstring'''
# `task` is not a ClassVar since we... | 61 |
"""simple docstring"""
from __future__ import annotations
def __a ( __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ , UpperCAmelCase_ : str = set(__lowerCamelCase ), [start]
while stack:
UpperCAmelCase_ : Any = stack.pop()
explored... | 61 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class A_ (metaclass=lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = ["""transformers""", """torch""", """note_seq"""]
def __init__( self , *lowercase_ , **lower... | 61 |
"""simple docstring"""
def __a ( __lowerCamelCase = 3, __lowerCamelCase = 7, __lowerCamelCase = 100_0000 ):
UpperCAmelCase_ : Dict = 0
UpperCAmelCase_ : List[Any] = 1
for current_denominator in range(1, limit + 1 ):
UpperCAmelCase_ : Dict ... | 61 | 1 |
"""simple docstring"""
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_a = False
try:
_a = ... | 61 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_a = 'src/diffusers'
# Matches is_xxx_available()
_a = re.compile(R'is\_([a-z_]*)_avai... | 61 | 1 |
"""simple docstring"""
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures... | 61 |
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A_ (lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = (DDIMParallelScheduler,)
SCREAMING_SNAKE_CASE__ : Option... | 61 | 1 |
"""simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttenti... | 61 |
"""simple docstring"""
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_te... | 61 | 1 |
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
_a = logging.get_logger(__name__)
de... | 61 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 61 | 1 |
"""simple docstring"""
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 .t... | 61 |
"""simple docstring"""
from __future__ import annotations
import math
def __a ( __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : Any = u
for i in range(1, __lowerCamelCase ):
UpperCAmelCase_ : int = temp * (u - i)
return temp
def ... | 61 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}
class A_ (lowercas... | 61 |
"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transfo... | 61 | 1 |
"""simple docstring"""
def __a ( __lowerCamelCase, __lowerCamelCase ):
# Check if the input is valid
if not len(__lowerCamelCase ) == len(__lowerCamelCase ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
... | 61 |
"""simple docstring"""
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : List[str] = int(__lowerCamelCase )
if n_element < 1:
UpperCAmelCase_ : List[Any] = ValueError("a should be a positive number" )
raise my_error
UpperCAmelCase_ : List[An... | 61 | 1 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
fro... | 61 |
"""simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = 1 / sqrt(2 ) ):
UpperCAmelCase_ : int = tau * frequency / samplerate
UpperCAmelCase_ : List[... | 61 | 1 |
"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transfo... | 61 |
"""simple docstring"""
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae... | 61 | 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',
}
def __a ( __lowerCamelCase ):
assert type(__lowerCame... | 61 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_c... | 61 | 1 |
"""simple docstring"""
import math
def __a ( __lowerCamelCase, __lowerCamelCase ):
if (
not isinstance(__lowerCamelCase, (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("power_factor must be a valid float value between -1 and 1." )
... | 61 |
"""simple docstring"""
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
fr... | 61 | 1 |
"""simple docstring"""
_a = 9.8_0665
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = g ):
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volume" )
if gravity <= 0:
... | 61 |
"""simple docstring"""
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 .t... | 61 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __a ( __lowerCamelCase ):
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = analyze_text(__lowerCamelCase )
UpperCAmelCase_ :... | 61 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_a = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://h... | 61 | 1 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_a = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation... | 61 |
"""simple docstring"""
from __future__ import annotations
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
raise ValueError("Resistance... | 61 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class A_ (lowercase__ ,unittest.TestCase ):
... | 61 |
"""simple docstring"""
import argparse
from collections import defaultdict
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : int = f"""{file}_{class_name}_{test_name}"""
done_test[_id] += 1
with ope... | 61 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'google/pix2struct-textcaps-base': (
'https://huggingface.co/google/pix2struct... | 61 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class A_ :
'''simple docstring'''
pass
| 61 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def __a ( __lowerCamelCase ):
return DownloadCommand(args.model, args.cache_dir, args.force, args.trust_remote_code )
class A_ (lowercase__ ):
'''simple docstring'''
@sta... | 61 |
"""simple docstring"""
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
return round(float(moles / volume ) * nfactor )
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
return round(float((moles * 0.0821 * temperature) / (volume) ) )
def ... | 61 | 1 |
"""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,
... | 61 |
"""simple docstring"""
import os
_a = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1_000}
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Union[str, Any] = 0
UpperCAmelCase_ : List[str] = 0
while index < len(__lowerCamelCase... | 61 | 1 |
"""simple docstring"""
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 .t... | 61 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __a ( ):
UpperCAmelCase_ : List[Any] = {
"repo_name": ["test_repo1", "test_repo2", "test_repo3"],
"p... | 61 | 1 |
"""simple docstring"""
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_loggi... | 61 |
"""simple docstring"""
from collections import namedtuple
_a = namedtuple('from_to', 'from_ to')
_a = {
'cubicmeter': from_to(1, 1),
'litre': from_to(0.001, 1_000),
'kilolitre': from_to(1, 1),
'gallon': from_to(0.0_0454, 264.172),
'cubicyard': from_to(0.7_6455, 1.... | 61 | 1 |
"""simple docstring"""
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_... | 61 |
"""simple docstring"""
from __future__ import annotations
def __a ( __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ , UpperCAmelCase_ : str = set(__lowerCamelCase ), [start]
while stack:
UpperCAmelCase_ : Any = stack.pop()
explored... | 61 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
... | 61 |
"""simple docstring"""
def __a ( __lowerCamelCase = 3, __lowerCamelCase = 7, __lowerCamelCase = 100_0000 ):
UpperCAmelCase_ : Dict = 0
UpperCAmelCase_ : List[Any] = 1
for current_denominator in range(1, limit + 1 ):
UpperCAmelCase_ : Dict ... | 61 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
_a = version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
def __a ( __lowerCam... | 61 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_a = 'src/diffusers'
# Matches is_xxx_available()
_a = re.compile(R'is\_([a-z_]*)_avai... | 61 | 1 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import ... | 61 |
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A_ (lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = (DDIMParallelScheduler,)
SCREAMING_SNAKE_CASE__ : Option... | 61 | 1 |
"""simple docstring"""
def __a ( __lowerCamelCase, __lowerCamelCase ):
return int(input_a == input_a == 0 )
def __a ( ):
print("Truth Table of NOR Gate:" )
print("| Input 1 | Input 2 | Output |" )
print(f"""| 0 | 0 | {nor_gate(0, 0 )} |""" )
p... | 61 |
"""simple docstring"""
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_te... | 61 | 1 |
"""simple docstring"""
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,... | 61 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 61 | 1 |
"""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()
... | 61 |
"""simple docstring"""
from __future__ import annotations
import math
def __a ( __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : Any = u
for i in range(1, __lowerCamelCase ):
UpperCAmelCase_ : int = temp * (u - i)
return temp
def ... | 61 | 1 |
"""simple docstring"""
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae... | 61 |
"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transfo... | 61 | 1 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
_a = TypeVar('KEY')
_a = TypeVar('VAL')
@dataclass(frozen=lowercase__ ,slots=lowercase__ )
class A_ (Generic[KEY, VAL] ):
... | 61 |
"""simple docstring"""
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : List[str] = int(__lowerCamelCase )
if n_element < 1:
UpperCAmelCase_ : List[Any] = ValueError("a should be a positive number" )
raise my_error
UpperCAmelCase_ : List[An... | 61 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_a = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://h... | 61 |
"""simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = 1 / sqrt(2 ) ):
UpperCAmelCase_ : int = tau * frequency / samplerate
UpperCAmelCase_ : List[... | 61 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'nielsr/canine-s': 2_048,
}
# Unicode defines 1,114,112 total “codepoints”
_a... | 61 |
"""simple docstring"""
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae... | 61 | 1 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, 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 import... | 61 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_c... | 61 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
... | 61 |
"""simple docstring"""
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
fr... | 61 | 1 |
"""simple docstring"""
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class A_ (lowercase__ ):
''... | 61 |
"""simple docstring"""
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 .t... | 61 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConf... | 61 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_a = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://h... | 61 | 1 |
"""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 __a ( __lowerCamelCase, __lowerCamelCase, __lowerCam... | 61 |
"""simple docstring"""
from __future__ import annotations
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
raise ValueError("Resistance... | 61 | 1 |
"""simple docstring"""
_a = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'dataclasses': 'datacl... | 61 |
"""simple docstring"""
import argparse
from collections import defaultdict
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : int = f"""{file}_{class_name}_{test_name}"""
done_test[_id] += 1
with ope... | 61 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : int = [
"encoder.version",
"decoder.version",
"model.encoder.vers... | 61 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class A_ :
'''simple docstring'''
pass
| 61 | 1 |
"""simple docstring"""
# 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.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # ... | 61 |
"""simple docstring"""
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
return round(float(moles / volume ) * nfactor )
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
return round(float((moles * 0.0821 * temperature) / (volume) ) )
def ... | 61 | 1 |
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
... | 61 |
"""simple docstring"""
import os
_a = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1_000}
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Union[str, Any] = 0
UpperCAmelCase_ : List[str] = 0
while index < len(__lowerCamelCase... | 61 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
_a = '2020.9.26'
_a = 'xcodz-dot, cclaus, dhruvmanila'
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
if not all(isinstance(__lowerCamelCa... | 61 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __a ( ):
UpperCAmelCase_ : List[Any] = {
"repo_name": ["test_repo1", "test_repo2", "test_repo3"],
"p... | 61 | 1 |
"""simple docstring"""
import os
from collections.abc import Iterator
def __a ( __lowerCamelCase = "." ):
for dir_path, dir_names, filenames in os.walk(__lowerCamelCase ):
UpperCAmelCase_ : Tuple = [d for d in dir_names if d != "scripts" and d[0] not in "._"]
for fi... | 61 |
"""simple docstring"""
from collections import namedtuple
_a = namedtuple('from_to', 'from_ to')
_a = {
'cubicmeter': from_to(1, 1),
'litre': from_to(0.001, 1_000),
'kilolitre': from_to(1, 1),
'gallon': from_to(0.0_0454, 264.172),
'cubicyard': from_to(0.7_6455, 1.... | 61 | 1 |
"""simple docstring"""
import math
from numpy import inf
from scipy.integrate import quad
def __a ( __lowerCamelCase ):
if num <= 0:
raise ValueError("math domain error" )
return quad(__lowerCamelCase, 0, __lowerCamelCase, args=(__lowerCamelCase) )[0]
def __a ( __lowe... | 61 |
"""simple docstring"""
from __future__ import annotations
def __a ( __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ , UpperCAmelCase_ : str = set(__lowerCamelCase ), [start]
while stack:
UpperCAmelCase_ : Any = stack.pop()
explored... | 61 | 1 |
"""simple docstring"""
class A_ :
'''simple docstring'''
def __init__( self ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = ""
UpperCAmelCase_ : Optional[int] = ""
UpperCAmelCase_ : Optional[Any] = []
def ... | 61 |
"""simple docstring"""
def __a ( __lowerCamelCase = 3, __lowerCamelCase = 7, __lowerCamelCase = 100_0000 ):
UpperCAmelCase_ : Dict = 0
UpperCAmelCase_ : List[Any] = 1
for current_denominator in range(1, limit + 1 ):
UpperCAmelCase_ : Dict ... | 61 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transfor... | 61 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_a = 'src/diffusers'
# Matches is_xxx_available()
_a = re.compile(R'is\_([a-z_]*)_avai... | 61 | 1 |
"""simple docstring"""
from collections import namedtuple
_a = namedtuple('from_to', 'from_ to')
_a = {
'cubicmeter': from_to(1, 1),
'litre': from_to(0.001, 1_000),
'kilolitre': from_to(1, 1),
'gallon': from_to(0.0_0454, 264.172),
'cubicyard': from_to(0.7_6455, 1.... | 61 |
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A_ (lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = (DDIMParallelScheduler,)
SCREAMING_SNAKE_CASE__ : Option... | 61 | 1 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMod... | 61 |
"""simple docstring"""
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_te... | 61 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 61 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 61 | 1 |
"""simple docstring"""
def __a ( __lowerCamelCase = 3, __lowerCamelCase = 7, __lowerCamelCase = 100_0000 ):
UpperCAmelCase_ : Dict = 0
UpperCAmelCase_ : List[Any] = 1
for current_denominator in range(1, limit + 1 ):
UpperCAmelCase_ : Dict ... | 61 |
"""simple docstring"""
from __future__ import annotations
import math
def __a ( __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : Any = u
for i in range(1, __lowerCamelCase ):
UpperCAmelCase_ : int = temp * (u - i)
return temp
def ... | 61 | 1 |
"""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,
AutoModelForSequenceClassif... | 61 |
"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transfo... | 61 | 1 |
"""simple docstring"""
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 = {
'hustvl/... | 61 |
"""simple docstring"""
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : List[str] = int(__lowerCamelCase )
if n_element < 1:
UpperCAmelCase_ : List[Any] = ValueError("a should be a positive number" )
raise my_error
UpperCAmelCase_ : List[An... | 61 | 1 |
"""simple docstring"""
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
# Initialise PyTorch model
UpperCAme... | 61 |
"""simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = 1 / sqrt(2 ) ):
UpperCAmelCase_ : int = tau * frequency / samplerate
UpperCAmelCase_ : List[... | 61 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 61 |
"""simple docstring"""
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae... | 61 | 1 |
"""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 import TokenizerTeste... | 61 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_c... | 61 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import ... | 61 |
"""simple docstring"""
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
fr... | 61 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependency... | 61 |
"""simple docstring"""
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 .t... | 61 | 1 |
"""simple docstring"""
from itertools import permutations
def __a ( __lowerCamelCase ):
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
UpperCAmelCase_ : List[str] = [7, 11, 13, ... | 61 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_a = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://h... | 61 | 1 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultiste... | 61 |
"""simple docstring"""
from __future__ import annotations
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
raise ValueError("Resistance... | 61 | 1 |
"""simple docstring"""
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
fr... | 61 |
"""simple docstring"""
import argparse
from collections import defaultdict
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : int = f"""{file}_{class_name}_{test_name}"""
done_test[_id] += 1
with ope... | 61 | 1 |
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
_a = HUGGINGFACE_HUB_CACHE
_a = 'config.json'
_a = 'diffusion_pytorch_model.bin'
_a = 'diffusion_flax_model.msgpack'
_a = 'model.on... | 61 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class A_ :
'''simple docstring'''
pass
| 61 | 1 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def __a ( __lowerCamelCase = "https://www.worldometers.info/coronavirus" ):
UpperCAmelCase_ : str = BeautifulSoup(requests.get(__lowerCamelCase ).text, "html.parser" )
UpperCAmelCase_ : List[str] ... | 61 |
"""simple docstring"""
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
return round(float(moles / volume ) * nfactor )
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
return round(float((moles * 0.0821 * temperature) / (volume) ) )
def ... | 61 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a = logging.get_logger(__name__)
_a = '▁'
_... | 61 |
"""simple docstring"""
import os
_a = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1_000}
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Union[str, Any] = 0
UpperCAmelCase_ : List[str] = 0
while index < len(__lowerCamelCase... | 61 | 1 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils ... | 61 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __a ( ):
UpperCAmelCase_ : List[Any] = {
"repo_name": ["test_repo1", "test_repo2", "test_repo3"],
"p... | 61 | 1 |
"""simple docstring"""
from typing import List
import numpy as np
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Dict = {key: len(__lowerCamelCase ) for key, value in gen_kwargs.items() if isinstance(__lowerCamelCase, __lowerCamelCase )}
if len(set(lists_lengths.values()... | 61 |
"""simple docstring"""
from collections import namedtuple
_a = namedtuple('from_to', 'from_ to')
_a = {
'cubicmeter': from_to(1, 1),
'litre': from_to(0.001, 1_000),
'kilolitre': from_to(1, 1),
'gallon': from_to(0.0_0454, 264.172),
'cubicyard': from_to(0.7_6455, 1.... | 61 | 1 |
"""simple docstring"""
def __a ( __lowerCamelCase, __lowerCamelCase ):
while b:
UpperCAmelCase_ , UpperCAmelCase_ : Dict = b, a % b
return a
def __a ( __lowerCamelCase, __lowerCamelCase ):
return a if b == 0 else euclidean_gcd_recursive(__lowerCamelCas... | 61 |
"""simple docstring"""
from __future__ import annotations
def __a ( __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ , UpperCAmelCase_ : str = set(__lowerCamelCase ), [start]
while stack:
UpperCAmelCase_ : Any = stack.pop()
explored... | 61 | 1 |
"""simple docstring"""
_a = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100_000)]
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Optional[Any] = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_dig... | 61 |
"""simple docstring"""
def __a ( __lowerCamelCase = 3, __lowerCamelCase = 7, __lowerCamelCase = 100_0000 ):
UpperCAmelCase_ : Dict = 0
UpperCAmelCase_ : List[Any] = 1
for current_denominator in range(1, limit + 1 ):
UpperCAmelCase_ : Dict ... | 61 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, i... | 61 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_a = 'src/diffusers'
# Matches is_xxx_available()
_a = re.compile(R'is\_([a-z_]*)_avai... | 61 | 1 |
"""simple docstring"""
import numpy as np
def __a ( __lowerCamelCase ):
return 1 / (1 + np.exp(-vector ))
def __a ( __lowerCamelCase ):
return vector * sigmoid(__lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 61 |
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A_ (lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = (DDIMParallelScheduler,)
SCREAMING_SNAKE_CASE__ : Option... | 61 | 1 |
"""simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from acceler... | 61 |
"""simple docstring"""
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_te... | 61 | 1 |
"""simple docstring"""
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class A_ (lowercase__ ):
'''simple docstring'''
# to overwrite at feature e... | 61 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 61 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
class A_ :
'''simple docstring'''
def __init__( self , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : list[dict] = []
self.adlist.append(
{"value": ""... | 61 |
"""simple docstring"""
from __future__ import annotations
import math
def __a ( __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : Any = u
for i in range(1, __lowerCamelCase ):
UpperCAmelCase_ : int = temp * (u - i)
return temp
def ... | 61 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_a = {
'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
'configuration_data... | 61 |
"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transfo... | 61 | 1 |
"""simple docstring"""
from manim import *
class A_ (lowercase__ ):
'''simple docstring'''
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : str = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase_ : Optional[... | 61 |
"""simple docstring"""
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : List[str] = int(__lowerCamelCase )
if n_element < 1:
UpperCAmelCase_ : List[Any] = ValueError("a should be a positive number" )
raise my_error
UpperCAmelCase_ : List[An... | 61 | 1 |
"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def __a ( __lowerCamelCase ):
return (data["dat... | 61 |
"""simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = 1 / sqrt(2 ) ):
UpperCAmelCase_ : int = tau * frequency / samplerate
UpperCAmelCase_ : List[... | 61 | 1 |
"""simple docstring"""
from math import factorial
def __a ( __lowerCamelCase = 20 ):
UpperCAmelCase_ : List[Any] = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
UpperCAmelCase_ : int = n // 2
return int(fact... | 61 |
"""simple docstring"""
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae... | 61 | 1 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A_ (lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = ["""image_processor""", """tokenizer"""]
SCREAMING_SNAKE_CASE__ ... | 61 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_c... | 61 | 1 |
"""simple docstring"""
class A_ :
'''simple docstring'''
def __init__( self , lowercase_ ):
"""simple docstring"""
# we need a list not a string, so do something to change the type
UpperCAmelCase_ : Any = arr.split("," )
def UpperCamelCase__... | 61 |
"""simple docstring"""
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
fr... | 61 | 1 |
"""simple docstring"""
def __a ( __lowerCamelCase, __lowerCamelCase ):
return int((input_a, input_a).count(0 ) == 0 )
def __a ( ):
assert and_gate(0, 0 ) == 0
assert and_gate(0, 1 ) == 0
assert and_gate(1, 0 ) == 0
assert and_gate(1, 1 ) == 1
if __name__ == "__main_... | 61 |
"""simple docstring"""
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 .t... | 61 | 1 |
"""simple docstring"""
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Tuple = len(__lowerCamelCase )
for _ in range(__lowerCamelCase ):
for i in range(_ % 2, arr_size - 1, 2 ):
if arr[i + 1] < arr[i]:
UpperCAmelCase_ , UpperCAmelCase_ : ... | 61 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_a = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://h... | 61 | 1 |
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