from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
		import timm


if is_torch_available():
		from torch import Tensor




class  __magic_name__  (		UpperCAmelCase__      ,	UpperCAmelCase__    ):





			"""simple docstring"""




			lowerCAmelCase      :    int  			=   "pixel_values"
			lowerCAmelCase      :    Dict  			=   False
			lowerCAmelCase      :    Union[str, Any]  			=   TimmBackboneConfig



			def __init__(	self					:  List[str]		,  _lowercase					:  Dict		,  **_lowercase					:  str							):



				"""simple docstring"""



				requires_backends(self		,  '''timm'''							)
				super().__init__(lowerCamelCase__							)
				_UpperCamelCase:     Any							= config

				if config.backbone is None:
					raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''							)

				if config.backbone not in timm.list_models():
					raise ValueError(f"""backbone {config.backbone} is not supported by timm."""							)

				if hasattr(lowerCamelCase__		,  '''out_features'''							) and config.out_features is not None:
					raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''							)

				_UpperCamelCase:     Optional[Any]							= getattr(lowerCamelCase__		,  '''use_pretrained_backbone'''		,  lowerCamelCase__							)
				if pretrained is None:
					raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''							)

				# We just take the final layer by default. This matches the default for the transformers models.
				_UpperCamelCase:     int							= config.out_indices if getattr(lowerCamelCase__		,  '''out_indices'''		,  lowerCamelCase__							) is not None else (-1,)

				_UpperCamelCase:     List[Any]							= timm.create_model(
				    config.backbone		,  pretrained=lowerCamelCase__		,  features_only=config.features_only		,  in_chans=config.num_channels		,  out_indices=lowerCamelCase__		,  **lowerCamelCase__		,  )
				# These are used to control the output of the model when called. If output_hidden_states is True, then
				# return_layers is modified to include all layers.
				_UpperCamelCase:     List[str]							= self._backbone.return_layers
				_UpperCamelCase:     Optional[int]							= {layer["module"]: str(lowerCamelCase__							) for i, layer in enumerate(self._backbone.feature_info.info							)}
				super()._init_backbone(lowerCamelCase__							)



			@classmethod
			def   lowerCAmelCase						(	cls					:  List[str]		,  _lowercase					:  str		,  *_lowercase					:  Tuple		,  **_lowercase					:  Optional[Any]							):



				"""simple docstring"""



				requires_backends(cls		,  ['''vision''', '''timm''']							)
				from ...models.timm_backbone import TimmBackboneConfig

				_UpperCamelCase:     Any							= kwargs.pop('''config'''		,  TimmBackboneConfig()							)

				_UpperCamelCase:     Dict							= kwargs.pop('''use_timm_backbone'''		,  lowerCamelCase__							)
				if not use_timm:
					raise ValueError('''use_timm_backbone must be True for timm backbones'''							)

				_UpperCamelCase:     str							= kwargs.pop('''num_channels'''		,  config.num_channels							)
				_UpperCamelCase:     Dict							= kwargs.pop('''features_only'''		,  config.features_only							)
				_UpperCamelCase:     str							= kwargs.pop('''use_pretrained_backbone'''		,  config.use_pretrained_backbone							)
				_UpperCamelCase:     Optional[Any]							= kwargs.pop('''out_indices'''		,  config.out_indices							)
				_UpperCamelCase:     Dict							= TimmBackboneConfig(
				    backbone=lowerCamelCase__		,  num_channels=lowerCamelCase__		,  features_only=lowerCamelCase__		,  use_pretrained_backbone=lowerCamelCase__		,  out_indices=lowerCamelCase__		,  )
				return super()._from_config(lowerCamelCase__		,  **lowerCamelCase__							)



			def   lowerCAmelCase						(	self					:  Tuple		,  _lowercase					:  str							):



				"""simple docstring"""



				pass



			def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  Tuple		,  _lowercase					:  Union[str, Any]=None		,  _lowercase					:  List[Any]=None		,  _lowercase					:  Union[str, Any]=None		,  **_lowercase					:  Dict							):



				"""simple docstring"""



				_UpperCamelCase:     Any							= return_dict if return_dict is not None else self.config.use_return_dict
				_UpperCamelCase:     Optional[int]							= (
				    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
				)
				_UpperCamelCase:     Dict							= output_attentions if output_attentions is not None else self.config.output_attentions

				if output_attentions:
					raise ValueError('''Cannot output attentions for timm backbones at the moment'''							)

				if output_hidden_states:
					# We modify the return layers to include all the stages of the backbone
					_UpperCamelCase:     Optional[int]							= self._all_layers
					_UpperCamelCase:     List[str]							= self._backbone(lowerCamelCase__		,  **lowerCamelCase__							)
					_UpperCamelCase:     List[Any]							= self._return_layers
					_UpperCamelCase:     Tuple							= tuple(hidden_states[i] for i in self.out_indices							)
				else:
					_UpperCamelCase:     Any							= self._backbone(lowerCamelCase__		,  **lowerCamelCase__							)
					_UpperCamelCase:     Tuple							= None

				_UpperCamelCase:     Dict							= tuple(lowerCamelCase__							)
				_UpperCamelCase:     Optional[Any]							= tuple(lowerCamelCase__							) if hidden_states is not None else None

				if not return_dict:
					_UpperCamelCase:     Dict							= (feature_maps,)
					if output_hidden_states:
						_UpperCamelCase:     List[str]							= output + (hidden_states,)
					return output

				return BackboneOutput(feature_maps=lowerCamelCase__		,  hidden_states=lowerCamelCase__		,  attentions=lowerCamelCase__							)
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_			    =							{
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							['''BlenderbotTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''BlenderbotForCausalLM''',
      '''BlenderbotForConditionalGeneration''',
      '''BlenderbotModel''',
      '''BlenderbotPreTrainedModel''',
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''TFBlenderbotForConditionalGeneration''',
      '''TFBlenderbotModel''',
      '''TFBlenderbotPreTrainedModel''',
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''FlaxBlenderbotForConditionalGeneration''',
      '''FlaxBlenderbotModel''',
      '''FlaxBlenderbotPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_blenderbot import (
      BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BlenderbotConfig,
      BlenderbotOnnxConfig,
  )
  from .tokenization_blenderbot import BlenderbotTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_blenderbot import (
        BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BlenderbotForCausalLM,
        BlenderbotForConditionalGeneration,
        BlenderbotModel,
        BlenderbotPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_blenderbot import (
        TFBlenderbotForConditionalGeneration,
        TFBlenderbotModel,
        TFBlenderbotPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_blenderbot import (
        FlaxBlenderbotForConditionalGeneration,
        FlaxBlenderbotModel,
        FlaxBlenderbotPreTrainedModel,
    )

else:
  import sys

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