






"""simple docstring"""



import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union

class 			a	(  enum.Enum      ):
    UpperCAmelCase_					:	Dict      	="timm"
    UpperCAmelCase_					:	Union[str, Any]      	="transformers"


def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[Iterable[str]]						,   __snake_case     :    Optional[Iterable[int]]						,   __snake_case     :    Optional[Iterable[str]]       ):





  '''simple docstring'''





  if stage_names is None:
    raise ValueError('Stage_names must be set for transformers backbones'       )

  if out_features is not None:
    if not isinstance(lowercase__						,   (list,)       ):
      raise ValueError(f'out_features must be a list {type(lowercase__       )}'       )
    if any(feat not in stage_names for feat in out_features       ):
      raise ValueError(f'out_features must be a subset of stage_names: {stage_names} got {out_features}'       )

  if out_indices is not None:
    if not isinstance(lowercase__						,   (list, tuple)       ):
      raise ValueError(f'out_indices must be a list or tuple, got {type(lowercase__       )}'       )
    if any(idx >= len(lowercase__       ) for idx in out_indices       ):
      raise ValueError('out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'       )

  if out_features is not None and out_indices is not None:
    if len(lowercase__       ) != len(lowercase__       ):
      raise ValueError('out_features and out_indices should have the same length if both are set'       )
    if out_features != [stage_names[idx] for idx in out_indices]:
      raise ValueError('out_features and out_indices should correspond to the same stages if both are set'       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[List[str]]						,   __snake_case     :    Optional[Union[List[int], Tuple[int]]]						,   __snake_case     :    List[str]						,   ):





  '''simple docstring'''





  if out_indices is None and out_features is None:
    lowercase				       =			[len(lowercase__       ) - 1]
    lowercase				       =			[stage_names[-1]]
  elif out_indices is None and out_features is not None:
    lowercase				       =			[stage_names.index(lowercase__       ) for layer in out_features]
  elif out_features is None and out_indices is not None:
    lowercase				       =			[stage_names[idx] for idx in out_indices]
  return out_features, out_indices
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[List[str]]						,   __snake_case     :    Optional[Union[List[int], Tuple[int]]]						,   __snake_case     :    List[str]						,   ):





  '''simple docstring'''





  verify_out_features_out_indices(out_features=lowercase__						,   out_indices=lowercase__						,   stage_names=lowercase__       )
  lowercase				       =			_align_output_features_output_indices(
      out_features=lowercase__						,   out_indices=lowercase__						,   stage_names=lowercase__       )
  # Verify that the aligned out_features and out_indices are valid
  verify_out_features_out_indices(out_features=lowercase__						,   out_indices=lowercase__						,   stage_names=lowercase__       )
  return output_features, output_indices

class 			a	:
    UpperCAmelCase_					:	Optional[BackboneType]      	=None







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      if getattr(self				,						'_backbone'				,						__A			) is None:
        raise ValueError('self._backbone must be set before calling _init_timm_backbone'			)

      # These will diagree with the defaults for the transformers models e.g. for resnet50
      # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
      # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
      lowercase				       =			[stage["""module"""] for stage in self._backbone.feature_info.info]
      lowercase				       =			[stage["""num_chs"""] for stage in self._backbone.feature_info.info]
      lowercase				       =			self._backbone.feature_info.out_indices
      lowercase				       =			self._backbone.feature_info.module_name()

      # We verify the out indices and out features are valid
      verify_out_features_out_indices(
          out_features=__A				,						out_indices=__A				,						stage_names=self.stage_names			)
      lowercase				       =			out_features, out_indices







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			getattr(__A				,						'stage_names'			)
      lowercase				       =			getattr(__A				,						'out_features'				,						__A			)
      lowercase				       =			getattr(__A				,						'out_indices'				,						__A			)

      lowercase				       =			stage_names
      lowercase				       =			get_aligned_output_features_output_indices(
          out_features=__A				,						out_indices=__A				,						stage_names=__A			)
      # Number of channels for each stage. This is set in the transformer backbone model init
      lowercase				       =			None







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			config

      lowercase				       =			getattr(__A				,						'use_timm_backbone'				,						__A			)
      lowercase				       =			BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

      if self.backbone_type == BackboneType.TIMM:
        self._init_timm_backbone(__A			)
      elif self.backbone_type == BackboneType.TRANSFORMERS:
        self._init_transformers_backbone(__A			)
      else:
        raise ValueError(F'backbone_type {self.backbone_type} not supported.'			)







    @property
    def      UpperCamelCase_ (     self			):
      return self._out_features







    @out_features.setter
    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			get_aligned_output_features_output_indices(
          out_features=__A				,						out_indices=__A				,						stage_names=self.stage_names			)







    @property
    def      UpperCamelCase_ (     self			):
      return self._out_indices







    @out_indices.setter
    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			get_aligned_output_features_output_indices(
          out_features=__A				,						out_indices=__A				,						stage_names=self.stage_names			)







    @property
    def      UpperCamelCase_ (     self			):
      # the current backbones will output the number of channels for each stage
      # even if that stage is not in the out_features list.
      return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names			)}







    @property
    def      UpperCamelCase_ (     self			):
      return [self.out_feature_channels[name] for name in self.out_features]







    def      UpperCamelCase_ (     self				,						*_lowerCamelCase				,						**_lowerCamelCase			):
      lowercase				       =			dict(inspect.signature(self.forward			).parameters			)
      lowercase				       =			{k: v for k, v in kwargs.items() if k in signature}
      return self(*__A				,						**__A			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase = None				,						_lowerCamelCase = None				,						_lowerCamelCase = None				,						):
      raise NotImplementedError('This method should be implemented by the derived class.'			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			super().to_dict()
      lowercase				       =			output.pop('_out_features'			)
      lowercase				       =			output.pop('_out_indices'			)
      return output


class 			a	:







    @property
    def      UpperCamelCase_ (     self			):
      return self._out_features







    @out_features.setter
    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			get_aligned_output_features_output_indices(
          out_features=__A				,						out_indices=__A				,						stage_names=self.stage_names			)







    @property
    def      UpperCamelCase_ (     self			):
      return self._out_indices







    @out_indices.setter
    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			get_aligned_output_features_output_indices(
          out_features=__A				,						out_indices=__A				,						stage_names=self.stage_names			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			super().to_dict()
      lowercase				       =			output.pop('_out_features'			)
      lowercase				       =			output.pop('_out_indices'			)
      return output




"""simple docstring"""






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,
)


__UpperCAmelCase 					=    {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    ['MarianTokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    [
				    'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MarianForCausalLM',
				    'MarianModel',
				    'MarianMTModel',
				    'MarianPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    ['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    ['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
				from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_marian import MarianTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_marian import (
								    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MarianForCausalLM,
								    MarianModel,
								    MarianMTModel,
								    MarianPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
				import sys

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






