code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(lowerCAmelCase__ ) , '''Tatoeba... | 303 |
def a__ ( snake_case = 1_000_000 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = 1
__SCREAMING_SNAKE_CASE : Optional[Any] = 1
__SCREAMING_SNAKE_CASE : Optional[int] = {1: 1}
for inputa in range(2 , snake_case ):
__SCREAMING_SNAKE... | 303 | 1 |
def a__ ( ):
"""simple docstring"""
for n in range(1 , 1_000_000 ):
yield n * (n + 1) // 2
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = 1
__SCREAMING_SNAKE_CASE : str = 2
while i * i <= n:
__SCRE... | 303 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBer... | 303 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", """DeiTOnnxConfi... | 303 |
import math
import os
import sys
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = ''''''
try:
with open(snake_case , '''rb''' ) as binary_file:
__SCREAMING_SNAKE_CASE : int = binary_file.read()
for dat in data:
... | 303 | 1 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''' , ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''' , ['''filename.csv''', '''filename with blanks.csv'''] )
@pytest.... | 303 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer,... | 303 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
"""configuration_layoutlmv3""": [
"""LAYOUTLMV3_PRETRAINED_CO... | 303 |
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/%Y %H:%M:%S""", level=logging.INFO
)
lowerca... | 303 | 1 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp... | 303 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
lowercase_ = 0b1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# b... | 303 | 1 |
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
lowercase_ = False
class __UpperCamelCase ( unittest.TestCase ):
... | 303 |
from heapq import heappop, heappush
import numpy as np
def a__ ( snake_case , snake_case , snake_case , snake_case , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : int = grid.shape
__SCREAMING_SNAKE_CASE : Tuple = ... | 303 | 1 |
def a__ ( snake_case ):
"""simple docstring"""
if len(snake_case ) <= 1:
return lst
__SCREAMING_SNAKE_CASE : List[Any] = 1
while i < len(snake_case ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : List[str... | 303 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_available():
raise Op... | 303 | 1 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things... | 303 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator... | 303 | 1 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
lowercase_ = get_tests_dir("""fixtures/test_sentencepiece_bp... | 303 |
import sys
from collections import defaultdict
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = []
def UpperCAmelCas... | 303 | 1 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def a__ ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = {
'''en''': '''Machine learning is great, isn\'t it?''',
'... | 303 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
lowercase_ = numpy.array([0, 0])
lowercase_ = numpy.array([0.5, 0.866_0254])
lowercase_ = numpy.array([1, 0])
lowercase_ ... | 303 | 1 |
from math import factorial
def a__ ( snake_case = 20 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
__SCREAMING_SNAKE_CASE : Optional[Any] = n // 2
return int(factoria... | 303 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerat... | 303 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
... | 303 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
lowercase_ = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safilesystem import SaFil... | 303 | 1 |
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
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""facebook/... | 303 |
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, SingleSentenceClassificati... | 303 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""xlm-roberta-base""": """https://huggingf... | 303 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}
class __UpperCa... | 303 | 1 |
import math
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(snake_case )
def a__ ( snake_case = 1 / 12_345 ):
"""simple docstring"""
... | 303 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
lowercase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class __UpperCamelCase ( lowerCAmelC... | 303 | 1 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path... | 303 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def a__ ( snake_case , snake_case=False ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = OmegaConf.load(snake_case )
if display:
print(yaml.dump(Om... | 303 | 1 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowercase_ = {
"""cola"... | 303 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
"""studio-ousia/luke-large... | 303 | 1 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ra... | 303 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 303 | 1 |
lowercase_ = {"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
lowercase_ = ["""a""", """b""", """c""", """d""", """e"""]
def a__ ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_... | 303 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __UpperCamelCase ( ... | 303 | 1 |
from scipy.stats import spearmanr
import datasets
lowercase_ = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imp... | 303 |
import os
import sys
lowercase_ = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
... | 303 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
"""configuration_clap""": [
"""CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ClapAudioConfig""",
"""ClapConfig""",
"""ClapTextConfig"... | 303 |
from __future__ import annotations
import numpy as np
def a__ ( snake_case ):
"""simple docstring"""
return np.maximum(0 , snake_case )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 303 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config... | 303 |
def a__ ( snake_case = 1_000_000 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = 1
__SCREAMING_SNAKE_CASE : Optional[Any] = 1
__SCREAMING_SNAKE_CASE : Optional[int] = {1: 1}
for inputa in range(2 , snake_case ):
__SCREAMING_SNAKE... | 303 | 1 |
from __future__ import annotations
def a__ ( snake_case ):
"""simple docstring"""
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(snake_case ) ):
... | 303 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBer... | 303 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowercase_ = logging.get_logger(__name__)
# TODO: upload to AWS
lowercase_ = {
"""yjernite/retribert-base-uncased""": (
"""https://huggingface.co/yjernite/retribert-base-uncased/resolve... | 303 |
import math
import os
import sys
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = ''''''
try:
with open(snake_case , '''rb''' ) as binary_file:
__SCREAMING_SNAKE_CASE : int = binary_file.read()
for dat in data:
... | 303 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
lowercase_ = logging.get_logger(__name__)
lowercas... | 303 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer,... | 303 | 1 |
lowercase_ = frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
lowercase_ = ... | 303 |
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/%Y %H:%M:%S""", level=logging.INFO
)
lowerca... | 303 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {
"""configuration_albert""": ["""ALBERT_... | 303 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
lowercase_ = 0b1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# b... | 303 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}
class __UpperCamelCase ( lowerCAmelCase__ ):
... | 303 |
from heapq import heappop, heappush
import numpy as np
def a__ ( snake_case , snake_case , snake_case , snake_case , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : int = grid.shape
__SCREAMING_SNAKE_CASE : Tuple = ... | 303 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""microsoft/focalnet-tiny""": """http... | 303 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_available():
raise Op... | 303 | 1 |
def a__ ( snake_case , snake_case , snake_case = 0 , snake_case = 0 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = right or len(snake_case ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[rig... | 303 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator... | 303 | 1 |
def a__ ( snake_case ):
"""simple docstring"""
if length <= 0 or not isinstance(snake_case , snake_case ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(snake_case )]
if __name__ == "__main__":
print(hexagonal_numbers... | 303 |
import sys
from collections import defaultdict
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = []
def UpperCAmelCas... | 303 | 1 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
requ... | 303 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
lowercase_ = numpy.array([0, 0])
lowercase_ = numpy.array([0.5, 0.866_0254])
lowercase_ = numpy.array([1, 0])
lowercase_ ... | 303 | 1 |
from ..utils import DummyObject, requires_backends
class __UpperCamelCase ( metaclass=lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self : List[Any] , *_A : ... | 303 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerat... | 303 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase_ = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""configuration_data2v... | 303 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
lowercase_ = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safilesystem import SaFil... | 303 | 1 |
def a__ ( snake_case , snake_case , snake_case ):
"""simple docstring"""
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(snake_case , n - 1 , snake_case ) * a) % mod
else:
__SCREAMING_SNAKE_CASE : List[str] = binary... | 303 |
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, SingleSentenceClassificati... | 303 | 1 |
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = [1]
for i in range(2 , snake_case ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
__SCREAMING_SNAKE_CASE ... | 303 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}
class __UpperCa... | 303 | 1 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from ut... | 303 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
lowercase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class __UpperCamelCase ( lowerCAmelC... | 303 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""shi-labs/nat-mini-in1k-224""": """h... | 303 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def a__ ( snake_case , snake_case=False ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = OmegaConf.load(snake_case )
if display:
print(yaml.dump(Om... | 303 | 1 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowercase_ = logging.get_logger(__name__)
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
def __init__( self : str , *_A ... | 303 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
"""studio-ousia/luke-large... | 303 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 303 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 303 | 1 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def a__ ( snake_case ):
"""simple docstring"""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wi... | 303 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __UpperCamelCase ( ... | 303 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase_ = {
"""configuration_pix2struct""": [
"""PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Pix2StructConfig""",
"""Pix2Str... | 303 |
import os
import sys
lowercase_ = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
... | 303 | 1 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.uti... | 303 |
from __future__ import annotations
import numpy as np
def a__ ( snake_case ):
"""simple docstring"""
return np.maximum(0 , snake_case )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 303 | 1 |
import warnings
from functools import wraps
from typing import Callable
def a__ ( snake_case ):
"""simple docstring"""
@wraps(snake_case )
def _inner_fn(*snake_case , **snake_case ):
warnings.warn(
(F'''\'{fn.__name__}\' is experimental and might be subject to breakin... | 303 |
def a__ ( snake_case = 1_000_000 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = 1
__SCREAMING_SNAKE_CASE : Optional[Any] = 1
__SCREAMING_SNAKE_CASE : Optional[int] = {1: 1}
for inputa in range(2 , snake_case ):
__SCREAMING_SNAKE... | 303 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.w... | 303 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBer... | 303 | 1 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
lowercase_ = input("""Enter image url: """).strip()
print(f'''Downloading image from {url} ...''')
lowercase_ = BeautifulSoup(requests.get(url).content, """html.parser""... | 303 |
import math
import os
import sys
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = ''''''
try:
with open(snake_case , '''rb''' ) as binary_file:
__SCREAMING_SNAKE_CASE : int = binary_file.read()
for dat in data:
... | 303 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}
class __UpperCa... | 303 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer,... | 303 | 1 |
from typing import Any
def a__ ( snake_case ):
"""simple docstring"""
if not input_list:
return []
__SCREAMING_SNAKE_CASE : int = [input_list.count(snake_case ) for value in input_list]
__SCREAMING_SNAKE_CASE : Any = max(snake_case ) # Gets the maximum count in the... | 303 |
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/%Y %H:%M:%S""", level=logging.INFO
)
lowerca... | 303 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configur... | 303 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
lowercase_ = 0b1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# b... | 303 | 1 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def a__ ( snake_case , snake_case , snake_case ):
"""simple docstring"""
# Initialise PyTorc... | 303 |
from heapq import heappop, heappush
import numpy as np
def a__ ( snake_case , snake_case , snake_case , snake_case , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : int = grid.shape
__SCREAMING_SNAKE_CASE : Tuple = ... | 303 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase_ = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormerConfig""",
"""SwiftFor... | 303 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_available():
raise Op... | 303 | 1 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerat... | 303 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator... | 303 | 1 |
import operator
def a__ ( snake_case , snake_case = False , snake_case = None ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = operator.lt if reverse else operator.gt
__SCREAMING_SNAKE_CASE : Optional[int] = solution or []
if not arr:
return sol... | 303 |
import sys
from collections import defaultdict
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = []
def UpperCAmelCas... | 303 | 1 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import Backbo... | 303 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
lowercase_ = numpy.array([0, 0])
lowercase_ = numpy.array([0.5, 0.866_0254])
lowercase_ = numpy.array([1, 0])
lowercase_ ... | 303 | 1 |
from math import isqrt
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case , snake_case ... | 303 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerat... | 303 | 1 |
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, ids_tensor, random_attention_... | 303 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
lowercase_ = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safilesystem import SaFil... | 303 | 1 |
import argparse
import os
# New Code #
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 Acce... | 303 |
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, SingleSentenceClassificati... | 303 | 1 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def a__ ( snake_case , snake_case=1 ):
"""simple docstring"""
if n_shave_prefix_segments >= 0:
return ".".join(path.split('''.''' )[n_shave_prefix_segments:] )
els... | 303 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}
class __UpperCa... | 303 | 1 |
def a__ ( ):
"""simple docstring"""
return [list(range(1_000 - i , -1_000 - i , -1 ) ) for i in range(1_000 )]
lowercase_ = generate_large_matrix()
lowercase_ = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[... | 303 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
lowercase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class __UpperCamelCase ( lowerCAmelC... | 303 | 1 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer,... | 303 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def a__ ( snake_case , snake_case=False ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = OmegaConf.load(snake_case )
if display:
print(yaml.dump(Om... | 303 | 1 |
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_v... | 303 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
"""studio-ousia/luke-large... | 303 | 1 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
... | 303 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 303 | 1 |
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = len(snake_case )
for _ in range(snake_case ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE :... | 303 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __UpperCamelCase ( ... | 303 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_albert import Alb... | 303 |
import os
import sys
lowercase_ = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
... | 303 | 1 |
import argparse
import os
import re
lowercase_ = """src/transformers"""
# Pattern that looks at the indentation in a line.
lowercase_ = re.compile(R"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
lowercase_ = re.compile(R"""^\s*\"([^\"... | 303 |
from __future__ import annotations
import numpy as np
def a__ ( snake_case ):
"""simple docstring"""
return np.maximum(0 , snake_case )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 303 | 1 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join ... | 303 |
def a__ ( snake_case = 1_000_000 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = 1
__SCREAMING_SNAKE_CASE : Optional[Any] = 1
__SCREAMING_SNAKE_CASE : Optional[int] = {1: 1}
for inputa in range(2 , snake_case ):
__SCREAMING_SNAKE... | 303 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
lower... | 303 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBer... | 303 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""huggingface/time-series-transformer-tourism-monthly""": (
"""https://huggingface.co/h... | 303 |
import math
import os
import sys
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = ''''''
try:
with open(snake_case , '''rb''' ) as binary_file:
__SCREAMING_SNAKE_CASE : int = binary_file.read()
for dat in data:
... | 303 | 1 |
import sys
from collections import defaultdict
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = []
def UpperCAmelCas... | 303 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer,... | 303 | 1 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..mod... | 303 |
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/%Y %H:%M:%S""", level=logging.INFO
)
lowerca... | 303 | 1 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 303 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
lowercase_ = 0b1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# b... | 303 | 1 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, B... | 303 |
from heapq import heappop, heappush
import numpy as np
def a__ ( snake_case , snake_case , snake_case , snake_case , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : int = grid.shape
__SCREAMING_SNAKE_CASE : Tuple = ... | 303 | 1 |
# 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 required by applicab... | 303 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_available():
raise Op... | 303 | 1 |
def a__ ( snake_case ):
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
__SCREAMING_SNAKE_CASE : Tuple = sorted(string.lower() )
return len(snake_case ) == len(set(snake_case ) ... | 303 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator... | 303 | 1 |
import fire
from utils import calculate_rouge, save_json
def a__ ( snake_case , snake_case , snake_case=None , **snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = [x.strip() for x in open(snake_case ).readlines()]
__SCREAMING_SN... | 303 |
import sys
from collections import defaultdict
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = []
def UpperCAmelCas... | 303 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
lowercase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class __UpperCamelCase ( lowerCAmelC... | 303 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
lowercase_ = numpy.array([0, 0])
lowercase_ = numpy.array([0.5, 0.866_0254])
lowercase_ = numpy.array([1, 0])
lowercase_ ... | 303 | 1 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_min... | 303 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerat... | 303 | 1 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import APIRoute
... | 303 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
lowercase_ = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safilesystem import SaFil... | 303 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class __Upp... | 303 |
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, SingleSentenceClassificati... | 303 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase_ = {"""tokenization_bertweet""": ["""BertweetTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
lowercase_ = _LazyModule(__name__, globals(... | 303 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}
class __UpperCa... | 303 | 1 |
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from tokenizers import p... | 303 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
lowercase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class __UpperCamelCase ( lowerCAmelC... | 303 | 1 |
def a__ ( snake_case ):
"""simple docstring"""
if not head:
return True
# split the list to two parts
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : int = head.next, head
while fast and fast.next:
__SCREAMING_SNAKE_CASE : Union[str, Any] = fast.next.next
__... | 303 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def a__ ( snake_case , snake_case=False ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = OmegaConf.load(snake_case )
if display:
print(yaml.dump(Om... | 303 | 1 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = ... | 303 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
"""studio-ousia/luke-large... | 303 | 1 |
from heapq import heappop, heappush
import numpy as np
def a__ ( snake_case , snake_case , snake_case , snake_case , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : int = grid.shape
__SCREAMING_SNAKE_CASE : Tuple = ... | 303 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 303 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_av... | 303 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __UpperCamelCase ( ... | 303 | 1 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
lowercase_ = logging.getLogger(__name__)
class __UpperCamelCase :
"""simple docstring"""
def __... | 303 |
import os
import sys
lowercase_ = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
... | 303 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""kssteven/ibert-roberta-base""": """https... | 303 |
from __future__ import annotations
import numpy as np
def a__ ( snake_case ):
"""simple docstring"""
return np.maximum(0 , snake_case )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 303 | 1 |
import unittest
from transformers import BertGenerationConfig, 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 ModelTesterMi... | 303 |
def a__ ( snake_case = 1_000_000 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = 1
__SCREAMING_SNAKE_CASE : Optional[Any] = 1
__SCREAMING_SNAKE_CASE : Optional[int] = {1: 1}
for inputa in range(2 , snake_case ):
__SCREAMING_SNAKE... | 303 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""MIT/ast-finetuned-audioset-10-10-0.4593""": (
"""https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/co... | 303 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBer... | 303 | 1 |
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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosity... | 303 |
import math
import os
import sys
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = ''''''
try:
with open(snake_case , '''rb''' ) as binary_file:
__SCREAMING_SNAKE_CASE : int = binary_file.read()
for dat in data:
... | 303 | 1 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not... | 303 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer,... | 303 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
"""simple ... | 303 |
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/%Y %H:%M:%S""", level=logging.INFO
)
lowerca... | 303 | 1 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENER... | 303 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
lowercase_ = 0b1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# b... | 303 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@r... | 303 |
from heapq import heappop, heappush
import numpy as np
def a__ ( snake_case , snake_case , snake_case , snake_case , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : int = grid.shape
__SCREAMING_SNAKE_CASE : Tuple = ... | 303 | 1 |
from torch import nn
def a__ ( snake_case ):
"""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 activation function: {act_fn}''' )
| 303 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_available():
raise Op... | 303 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
"""configuration_owlvit""": [
"""OWLVI... | 303 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator... | 303 | 1 |
import math
import qiskit
def a__ ( snake_case = 1 , snake_case = 1 , snake_case = 1 ):
"""simple docstring"""
if (
isinstance(snake_case , snake_case )
or isinstance(snake_case , snake_case )
or isinstance(snake_case , snake_ca... | 303 |
import sys
from collections import defaultdict
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = []
def UpperCAmelCas... | 303 | 1 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
lowercase_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_copies # noqa: E402
# This is the reference... | 303 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
lowercase_ = numpy.array([0, 0])
lowercase_ = numpy.array([0.5, 0.866_0254])
lowercase_ = numpy.array([1, 0])
lowercase_ ... | 303 | 1 |
from __future__ import annotations
lowercase_ = [True] * 1_000_001
lowercase_ = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
lowercase_ = False
i += 1
def a__ ( snake_case ):
"""simple docstr... | 303 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerat... | 303 | 1 |
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