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 |
|---|---|---|---|---|
def __UpperCamelCase ( _A , _A ):
def get_matched_characters(_A , _A ) -> str:
lowerCAmelCase_ = []
lowerCAmelCase_ = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
lowerCAmelCase_ = int(ma... | 278 |
def __UpperCamelCase ( _A ):
if not numbers:
return 0
if not isinstance(_A , (list, tuple) ) or not all(
isinstance(_A , _A ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' )
lowerCAmelCase_ = low... | 278 | 1 |
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
lowerCAmelCase_ = set()
return any(
node not in visited and depth_first_search(_A , _A , _A , _A )
for nod... | 278 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAG... | 278 | 1 |
import qiskit
def __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = qiskit.Aer.get_backend('''aer_simulator''' )
lowerCAmelCase_ = qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if bita == 1:
qc_ha.x(0 )
if bita == 1:
... | 278 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = [
'''decoder.version''',
'''decoder.output_projection.weight''',
'''_floa... | 278 | 1 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils impo... | 278 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to... | 278 | 1 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
_A = False
_A = True
_A = False
if __name__ == "__main__":
_A = argparse.ArgumentParser()
parser.add_argument(
... | 278 |
import argparse
from collections import defaultdict
import yaml
_A = '''docs/source/en/_toctree.yml'''
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = defaultdict(_A )
for doc in model_doc:
counts[doc["local"]] += 1
lowerCAmelCase_ = [key for k... | 278 | 1 |
_A = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_A = [{'''type''': '''code''', '''content'''... | 278 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class A ( __UpperCAmelCase ):
__snake_case = (UnCLIPScheduler,)
def SCREAMING_SNAKE_CASE__ ( self, **UpperCamelCase__ ):
"""simple docstring"""
lo... | 278 | 1 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the ro... | 278 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.se... | 278 | 1 |
from __future__ import annotations
import pandas as pd
def __UpperCamelCase ( _A , _A , _A ):
lowerCAmelCase_ = [0] * no_of_processes
lowerCAmelCase_ = [0] * no_of_processes
# Copy the burst time into remaining_time[]
for i in range(_A ):
lowe... | 278 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''google/vit-base-patch16-224''': '''https://hu... | 278 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
... | 278 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __UpperCamelCase ( _A... | 278 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A ( __UpperCAmelCase ):
__snake_case = 42
... | 278 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
_A = '''scheduler_config.json'''
class A ( __UpperCAmelCase ):
__snake_case = 1
__snake_case = 2
... | 278 | 1 |
def __UpperCamelCase ( _A , _A ):
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
lowerCAmelCase_ = str(bin(_A ) )[2:] # remove the leading "0b"
lowerCAmelCase_ = str(bin(_A ) )[2:]
lowerCAmelCase_ =... | 278 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A ( unittest.TestCase ):
@property
def ... | 278 | 1 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes... | 278 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAva... | 278 | 1 |
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_attention_paths,
re... | 278 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __UpperCamelCase ( _A = 3 ):
if isinstance(_A , _A ):
raise TypeError('''number of qubits must be a integer.''' )
if number_of_qubits <= 0:
... | 278 | 1 |
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = [0] * len(_A )
lowerCAmelCase_ = []
lowerCAmelCase_ = []
lowerCAmelCase_ = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in rang... | 278 |
from functools import lru_cache
@lru_cache
def __UpperCamelCase ( _A ):
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
... | 278 | 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
_A = logging.get_logger(__name__)
_A = {
'''google/mobilenet_v1_1.0_224''': '''https://hu... | 278 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = 384
... | 278 | 1 |
from math import factorial
def __UpperCamelCase ( _A , _A ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError('''Please enter positive integers... | 278 |
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 __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = args.log_outputs
lowerCAmelCase_ = ... | 278 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_A = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AltCLIPConfig''',
'''AltCLIPTextConfig''',
... | 278 |
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 = 1_114_112
# Below: Constan... | 278 | 1 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
... | 278 |
def __UpperCamelCase ( _A = 1000000 ):
lowerCAmelCase_ = 1
lowerCAmelCase_ = 1
lowerCAmelCase_ = {1: 1}
for inputa in range(2 , _A ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = inputa
while True:
... | 278 | 1 |
def __UpperCamelCase ( _A , _A , _A , _A ):
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex... | 278 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
f... | 278 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
'''configuration_bigbird_pegasus''': [
'''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BigBirdPegasusConfig''',
'''BigBirdPegasusOnnxConfig''',
... | 278 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
... | 278 | 1 |
from importlib import import_module
from .logging import get_logger
_A = get_logger(__name__)
class A :
def __init__( self, UpperCamelCase__, UpperCamelCase__=None ):
"""simple docstring"""
lowerCAmelCase_ = attrs or []
if module is not None:
... | 278 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
_A = logging.get_logger(__name__)
class A ( __UpperCAmelCase ):
def __init__( self, *UpperCamelCase__, **UpperCamelCase__ ):
"""simple docstring"""
warnings.warn... | 278 | 1 |
def __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = int(_A )
# Initialize Result
lowerCAmelCase_ = []
# Traverse through all denomination
for denomination in reversed(_A ):
# Find denominations
while int(_A ) >= int(_A ):
... | 278 |
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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_A = l... | 278 | 1 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import C... | 278 |
def __UpperCamelCase ( _A ):
if not numbers:
return 0
if not isinstance(_A , (list, tuple) ) or not all(
isinstance(_A , _A ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' )
lowerCAmelCase_ = low... | 278 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
_A = logging.get_logger(__name__)
_A = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', ''... | 278 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAG... | 278 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class A ( __UpperCAmelCase ):
@staticmethod
@abstractmethod
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
"""simple docstring"""
raise NotImplementedError()
@abstractmethod... | 278 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = [
'''decoder.version''',
'''decoder.output_projection.weight''',
'''_floa... | 278 | 1 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
_A = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
_A = [file for file in filepaths if file != file.lower()]
if upper_... | 278 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to... | 278 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to... | 278 |
import argparse
from collections import defaultdict
import yaml
_A = '''docs/source/en/_toctree.yml'''
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = defaultdict(_A )
for doc in model_doc:
counts[doc["local"]] += 1
lowerCAmelCase_ = [key for k... | 278 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import... | 278 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class A ( __UpperCAmelCase ):
__snake_case = (UnCLIPScheduler,)
def SCREAMING_SNAKE_CASE__ ( self, **UpperCamelCase__ ):
"""simple docstring"""
lo... | 278 | 1 |
import os
import unicodedata
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 SPIECE_UNDERLINE, logging
_A = logging.get_logger(__name__)
_A = {'''vocab... | 278 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.se... | 278 | 1 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('''Googling.....''')
_A = '''https://www.google.com/search?q=''' + ''' '''.join(sys.argv[1:])
_A = requests.get(url, headers={'''UserAgent... | 278 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''google/vit-base-patch16-224''': '''https://hu... | 278 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_visi... | 278 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __UpperCamelCase ( _A... | 278 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
class A ( __UpperCAmelCase ):
__snake_case = 'open-l... | 278 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
_A = '''scheduler_config.json'''
class A ( __UpperCAmelCase ):
__snake_case = 1
__snake_case = 2
... | 278 | 1 |
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def __UpperCamelCase ( _A , _A , _A ):
lowerCAmelCase_ = OmegaConf.load(_A )
lowerCAmelCase_ = torch.load(_A , map_location='''cpu''' )['''model''... | 278 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A ( unittest.TestCase ):
@property
def ... | 278 | 1 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
clas... | 278 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAva... | 278 | 1 |
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = [int(_A ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(_A ) == 4 and all(0 <= int(_A ) <= 254 for octet in octets )
if __name__ == "__main__":
_A = input().strip()
_A = '''valid''' if is... | 278 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __UpperCamelCase ( _A = 3 ):
if isinstance(_A , _A ):
raise TypeError('''number of qubits must be a integer.''' )
if number_of_qubits <= 0:
... | 278 | 1 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def __UpperCamelCase ( _A , _A=() , _A=None , _A="no" , _A="29500" ):
lowerCAmelCase_ = False
lowerC... | 278 |
from functools import lru_cache
@lru_cache
def __UpperCamelCase ( _A ):
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
... | 278 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFe... | 278 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = 384
... | 278 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
'''configuration_clap''': [
'''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ClapAudioConfig''',
'''ClapConfig''',
'''ClapTextConfig''',
],
'... | 278 |
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 __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = args.log_outputs
lowerCAmelCase_ = ... | 278 | 1 |
import numpy as np
from transformers import Pipeline
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = np.max(_A , axis=-1 , keepdims=_A )
lowerCAmelCase_ = np.exp(outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=_A )
class A ( ... | 278 |
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 = 1_114_112
# Below: Constan... | 278 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ... | 278 |
def __UpperCamelCase ( _A = 1000000 ):
lowerCAmelCase_ = 1
lowerCAmelCase_ = 1
lowerCAmelCase_ = {1: 1}
for inputa in range(2 , _A ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = inputa
while True:
... | 278 | 1 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_m... | 278 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
f... | 278 | 1 |
from __future__ import annotations
def __UpperCamelCase ( _A ):
if not nums:
raise ValueError('''List is empty''' )
return sum(_A ) / len(_A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 278 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
... | 278 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class A ( __UpperCAmelCase , __UpperCAmelCase ):
@register_to_config
def __init__( self, UpperCamelCa... | 278 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
_A = logging.get_logger(__name__)
class A ( __UpperCAmelCase ):
def __init__( self, *UpperCamelCase__, **UpperCamelCase__ ):
"""simple docstring"""
warnings.warn... | 278 | 1 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class A ( enum.Enum ):
__snake_case ... | 278 |
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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_A = l... | 278 | 1 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __UpperCamelCase ( _A ):
lowe... | 278 |
def __UpperCamelCase ( _A ):
if not numbers:
return 0
if not isinstance(_A , (list, tuple) ) or not all(
isinstance(_A , _A ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' )
lowerCAmelCase_ = low... | 278 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
f... | 278 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAG... | 278 | 1 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_A = logging.get_logger(__name__)
_A = '''▁''... | 278 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = [
'''decoder.version''',
'''decoder.output_projection.weight''',
'''_floa... | 278 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAG... | 278 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to... | 278 | 1 |
import math
def __UpperCamelCase ( _A ):
return math.sqrt(_A ) * math.sqrt(_A ) == num
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = n
while left <= right:
lowerCAmelCase_ = (left + right) // 2
... | 278 |
import argparse
from collections import defaultdict
import yaml
_A = '''docs/source/en/_toctree.yml'''
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = defaultdict(_A )
for doc in model_doc:
counts[doc["local"]] += 1
lowerCAmelCase_ = [key for k... | 278 | 1 |
from ...processing_utils import ProcessorMixin
class A ( __UpperCAmelCase ):
__snake_case = ['image_processor', 'feature_extractor']
__snake_case = 'TvltImageProcessor'
__snake_case = 'TvltFeatureExtractor'
def __init__( self, UpperCamelCase__, UpperCamelCase__... | 278 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class A ( __UpperCAmelCase ):
__snake_case = (UnCLIPScheduler,)
def SCREAMING_SNAKE_CASE__ ( self, **UpperCamelCase__ ):
"""simple docstring"""
lo... | 278 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.uti... | 278 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.se... | 278 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder impor... | 278 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''google/vit-base-patch16-224''': '''https://hu... | 278 | 1 |
def __UpperCamelCase ( _A , _A ):
while a != 0:
lowerCAmelCase_ , lowerCAmelCase_ = b % a, a
return b
def __UpperCamelCase ( _A , _A ):
if gcd(_A , _A ) != 1:
lowerCAmelCase_ = f"mod inverse of {a!r} and {m!r} does not ex... | 278 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __UpperCamelCase ( _A... | 278 | 1 |
_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''',
... | 278 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
_A = '''scheduler_config.json'''
class A ( __UpperCAmelCase ):
__snake_case = 1
__snake_case = 2
... | 278 | 1 |
import os
def __UpperCamelCase ( ):
lowerCAmelCase_ = os.path.dirname(os.path.realpath(_A ) )
lowerCAmelCase_ = os.path.join(_A , '''triangle.txt''' )
with open(_A ) as f:
lowerCAmelCase_ = f.readlines()
lowerCAmelCase_ = [... | 278 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A ( unittest.TestCase ):
@property
def ... | 278 | 1 |
from jiwer import compute_measures
import datasets
_A = '''\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech... | 278 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAva... | 278 | 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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_A = l... | 278 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __UpperCamelCase ( _A = 3 ):
if isinstance(_A , _A ):
raise TypeError('''number of qubits must be a integer.''' )
if number_of_qubits <= 0:
... | 278 | 1 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
... | 278 |
from functools import lru_cache
@lru_cache
def __UpperCamelCase ( _A ):
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
... | 278 | 1 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , num_examples=42 , dataset_name='''my_dataset''' ... | 278 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = 384
... | 278 | 1 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( __UpperCAmelCase ):
__snake_case = (DDIMParallelScheduler,)
__snake_case = (('eta', 0.0), ('num_inference_steps', 50))
def SCREAMING_SNAKE_CASE__ ( ... | 278 |
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 __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = args.log_outputs
lowerCAmelCase_ = ... | 278 | 1 |
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __UpperCamelCase ( _A = 100 ):
lowerCAmelCase_ = 1
lowerCAmelCase_ = 2
for i in range(... | 278 |
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 = 1_114_112
# Below: Constan... | 278 | 1 |
from __future__ import annotations
_A = []
def __UpperCamelCase ( _A , _A , _A ):
for i in range(len(_A ) ):
if board[row][i] == 1:
return False
for i in range(len(_A ) ):
if board[i][column] == 1:
return False
... | 278 |
def __UpperCamelCase ( _A = 1000000 ):
lowerCAmelCase_ = 1
lowerCAmelCase_ = 1
lowerCAmelCase_ = {1: 1}
for inputa in range(2 , _A ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = inputa
while True:
... | 278 | 1 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax... | 278 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
f... | 278 | 1 |
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common i... | 278 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
... | 278 | 1 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM... | 278 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
_A = logging.get_logger(__name__)
class A ( __UpperCAmelCase ):
def __init__( self, *UpperCamelCase__, **UpperCamelCase__ ):
"""simple docstring"""
warnings.warn... | 278 | 1 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class A ( __UpperCAmelCase ):
__snake_case = CustomTokenizer
pass
| 278 |
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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_A = l... | 278 | 1 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_... | 278 |
def __UpperCamelCase ( _A ):
if not numbers:
return 0
if not isinstance(_A , (list, tuple) ) or not all(
isinstance(_A , _A ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' )
lowerCAmelCase_ = low... | 278 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
_A = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_A = {
''... | 278 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAG... | 278 | 1 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import Aut... | 278 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = [
'''decoder.version''',
'''decoder.output_projection.weight''',
'''_floa... | 278 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image,... | 278 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to... | 278 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/m... | 278 |
import argparse
from collections import defaultdict
import yaml
_A = '''docs/source/en/_toctree.yml'''
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = defaultdict(_A )
for doc in model_doc:
counts[doc["local"]] += 1
lowerCAmelCase_ = [key for k... | 278 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class A ( __UpperCAmelCase ):
__snake_case = 'Wav2Vec2FeatureExtractor... | 278 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class A ( __UpperCAmelCase ):
__snake_case = (UnCLIPScheduler,)
def SCREAMING_SNAKE_CASE__ ( self, **UpperCamelCase__ ):
"""simple docstring"""
lo... | 278 | 1 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_A = get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')
@requ... | 278 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.se... | 278 | 1 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class A ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
__snake_case = [('size', ctypes.c_int), ('visible', ... | 278 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''google/vit-base-patch16-224''': '''https://hu... | 278 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | 278 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __UpperCamelCase ( _A... | 278 | 1 |
import heapq
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*... | 278 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
_A = '''scheduler_config.json'''
class A ( __UpperCAmelCase ):
__snake_case = 1
__snake_case = 2
... | 278 | 1 |
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 transformers.utils import loggi... | 278 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A ( unittest.TestCase ):
@property
def ... | 278 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils imp... | 278 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAva... | 278 | 1 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __UpperCamelCase ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pyt... | 278 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __UpperCamelCase ( _A = 3 ):
if isinstance(_A , _A ):
raise TypeError('''number of qubits must be a integer.''' )
if number_of_qubits <= 0:
... | 278 | 1 |
from __future__ import annotations
_A = [True] * 1_000_001
_A = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
_A = False
i += 1
def __UpperCamelCase ( _A ):
return seive[n]
def __UpperCamelCase (... | 278 |
from functools import lru_cache
@lru_cache
def __UpperCamelCase ( _A ):
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
... | 278 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pi... | 278 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = 384
... | 278 | 1 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscrete... | 278 |
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 __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = args.log_outputs
lowerCAmelCase_ = ... | 278 | 1 |
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 = 1_114_112
# Below: Constan... | 278 |
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 = 1_114_112
# Below: Constan... | 278 | 1 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class A ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
debug_launcher(test_script.main ... | 278 |
def __UpperCamelCase ( _A = 1000000 ):
lowerCAmelCase_ = 1
lowerCAmelCase_ = 1
lowerCAmelCase_ = {1: 1}
for inputa in range(2 , _A ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = inputa
while True:
... | 278 | 1 |
def __UpperCamelCase ( _A = 1000000 ):
lowerCAmelCase_ = 1
lowerCAmelCase_ = 1
lowerCAmelCase_ = {1: 1}
for inputa in range(2 , _A ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = inputa
while True:
... | 278 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
f... | 278 | 1 |
def __UpperCamelCase ( _A ):
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = number
while duplicate > 0:
lowerCAmelCase_ , lowerCAmelCase_ ... | 278 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
... | 278 | 1 |
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 and must be smaller than N_POPU... | 278 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
_A = logging.get_logger(__name__)
class A ( __UpperCAmelCase ):
def __init__( self, *UpperCamelCase__, **UpperCamelCase__ ):
"""simple docstring"""
warnings.warn... | 278 | 1 |
import numpy as np
def __UpperCamelCase ( _A ):
return 1 / (1 + np.exp(-vector ))
def __UpperCamelCase ( _A ):
return vector * sigmoid(_A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 278 |
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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_A = l... | 278 | 1 |
from __future__ import annotations
def __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = get_failure_array(_A )
# 2) Step through text searching for pattern
lowerCAmelCase_ , lowerCAmelCase_ = 0, 0 # index into text, pattern
while i < len(_A ):
... | 278 |
def __UpperCamelCase ( _A ):
if not numbers:
return 0
if not isinstance(_A , (list, tuple) ) or not all(
isinstance(_A , _A ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' )
lowerCAmelCase_ = low... | 278 | 1 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
_A = 4
_A = 3
class A ( __UpperCAmelCase ):
pass
def __UpperCamelCase ( _A ):... | 278 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAG... | 278 | 1 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
_A = logging.get_logger(__name__)
class A ( __UpperCAmelCase ):
def __init__( self, *UpperCamelCase__, **UpperCamelCase__ ):
"""simple docstring"""
warnings.warn... | 278 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = [
'''decoder.version''',
'''decoder.output_projection.weight''',
'''_floa... | 278 | 1 |
def __UpperCamelCase ( _A , _A ):
return abs(_A ) if a == 0 else greatest_common_divisor(b % a , _A )
def __UpperCamelCase ( _A , _A ):
while y: # --> when y=0 then loop will terminate and return x as final GCD.
lowerCAmelCase_ , lowerCAmelCase_ = ... | 278 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to... | 278 | 1 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLay... | 278 |
import argparse
from collections import defaultdict
import yaml
_A = '''docs/source/en/_toctree.yml'''
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = defaultdict(_A )
for doc in model_doc:
counts[doc["local"]] += 1
lowerCAmelCase_ = [key for k... | 278 | 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 appl... | 278 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class A ( __UpperCAmelCase ):
__snake_case = (UnCLIPScheduler,)
def SCREAMING_SNAKE_CASE__ ( self, **UpperCamelCase__ ):
"""simple docstring"""
lo... | 278 | 1 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
_A = logging.get_logger(__name__)
class A ( __UpperCAmelCase ):
def __init__( self, *UpperCamelCase__, **UpperCamelCase__ ):
"""simple docstring"""
... | 278 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.se... | 278 | 1 |
def __UpperCamelCase ( _A = 600851475143 ):
try:
lowerCAmelCase_ = int(_A )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than ... | 278 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''google/vit-base-patch16-224''': '''https://hu... | 278 | 1 |
def __UpperCamelCase ( _A ):
if not isinstance(_A , _A ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(_A ) == 0:
raise ValueError('''Input list must be a non empty list''' )
if len(_A ) == 1:
return True
... | 278 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __UpperCamelCase ( _A... | 278 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json''',
'''Br... | 278 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
_A = '''scheduler_config.json'''
class A ( __UpperCAmelCase ):
__snake_case = 1
__snake_case = 2
... | 278 | 1 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHead... | 278 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A ( unittest.TestCase ):
@property
def ... | 278 | 1 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_A = '''src/transformers'''
# This is to make sure the transformers modul... | 278 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAva... | 278 | 1 |
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