'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase		:					int													=		logging.get_logger(__name__)

_lowercase		:					Tuple													=		{
    "facebook/regnet-y-040": "https://huggingface.co/facebook/regnet-y-040/blob/main/config.json",
}





class        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			'''regnet'''
			UpperCamelCase__							=			['''x''', '''y''']







			def __init__(      self							:    Dict							,     lowercase_							:    Any=3							,     lowercase_							:    int=32							,     lowercase_							:    Any=[128, 192, 512, 1088]							,     lowercase_							:    str=[2, 6, 12, 2]							,     lowercase_							:    Dict=64							,     lowercase_							:    List[Any]="y"							,     lowercase_							:    List[str]="relu"							,     **lowercase_							:    Tuple							,     ):
										super().__init__(**lowercase_       )
										if layer_type not in self.layer_types:
																	raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types       )}'''       )
										lowercase_   :     Tuple									=			num_channels
										lowercase_   :     Union[str, Any]									=			embedding_size
										lowercase_   :     int									=			hidden_sizes
										lowercase_   :     Any									=			depths
										lowercase_   :     List[Any]									=			groups_width
										lowercase_   :     Optional[Any]									=			layer_type
										lowercase_   :     Union[str, Any]									=			hidden_act
										# always downsample in the first stage
										lowercase_   :     Tuple									=			True









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__        =				{
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__        =				[
      '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''FlaubertForMultipleChoice''',
      '''FlaubertForQuestionAnswering''',
      '''FlaubertForQuestionAnsweringSimple''',
      '''FlaubertForSequenceClassification''',
      '''FlaubertForTokenClassification''',
      '''FlaubertModel''',
      '''FlaubertWithLMHeadModel''',
      '''FlaubertPreTrainedModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__        =				[
      '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFFlaubertForMultipleChoice''',
      '''TFFlaubertForQuestionAnsweringSimple''',
      '''TFFlaubertForSequenceClassification''',
      '''TFFlaubertForTokenClassification''',
      '''TFFlaubertModel''',
      '''TFFlaubertPreTrainedModel''',
      '''TFFlaubertWithLMHeadModel''',
  ]


if TYPE_CHECKING:
  from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
  from .tokenization_flaubert import FlaubertTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flaubert import (
        FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaubertForMultipleChoice,
        FlaubertForQuestionAnswering,
        FlaubertForQuestionAnsweringSimple,
        FlaubertForSequenceClassification,
        FlaubertForTokenClassification,
        FlaubertModel,
        FlaubertPreTrainedModel,
        FlaubertWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_flaubert import (
        TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFFlaubertForMultipleChoice,
        TFFlaubertForQuestionAnsweringSimple,
        TFFlaubertForSequenceClassification,
        TFFlaubertForTokenClassification,
        TFFlaubertModel,
        TFFlaubertPreTrainedModel,
        TFFlaubertWithLMHeadModel,
    )

else:
  import sys

  UpperCAmelCase__        =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

