






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




class 				_lowerCAmelCase(       enum.Enum					):





       """simple docstring"""







       a				:		Union[str, Any]									='''timm'''
       a				:		int									='''transformers'''







def   snake_case					(UpperCAmelCase__	,    UpperCAmelCase__	,    UpperCAmelCase__  )       ->				Dict:







  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   snake_case					(UpperCAmelCase__	,    UpperCAmelCase__	,    UpperCAmelCase__	,    )       ->				Any:







  if out_indices is None and out_features is None:
    UpperCamelCase_:					List[Any]          =				[len(_lowercase  ) - 1]
    UpperCamelCase_:					Dict          =				[stage_names[-1]]
  elif out_indices is None and out_features is not None:
    UpperCamelCase_:					Tuple          =				[stage_names.index(_lowercase  ) for layer in out_features]
  elif out_features is None and out_indices is not None:
    UpperCamelCase_:					Union[str, Any]          =				[stage_names[idx] for idx in out_indices]
  return out_features, out_indices

def   snake_case					(UpperCAmelCase__	,    UpperCAmelCase__	,    UpperCAmelCase__	,    )       ->				int:
  verify_out_features_out_indices(out_features=_lowercase	,    out_indices=_lowercase	,    stage_names=_lowercase  )
  UpperCamelCase_      ,UpperCamelCase_:					Any          =				_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 				_lowerCAmelCase:





       """simple docstring"""







       a				:		str									=None





       def 			_a    (					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']
         UpperCamelCase_:					List[str]          =				[stage['module'] for stage in self._backbone.feature_info.info]
         UpperCamelCase_:					List[str]          =				[stage['num_chs'] for stage in self._backbone.feature_info.info]
         UpperCamelCase_:					Optional[int]          =				self._backbone.feature_info.out_indices
         UpperCamelCase_:					str          =				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							)
         UpperCamelCase_      ,UpperCamelCase_:					Optional[int]          =				out_features, out_indices





       def 			_a    (					self  ,			_lowerCamelCase							):
         UpperCamelCase_:					Tuple          =				getattr(__A  ,			'stage_names'							)
         UpperCamelCase_:					List[str]          =				getattr(__A  ,			'out_features'  ,			__A							)
         UpperCamelCase_:					Dict          =				getattr(__A  ,			'out_indices'  ,			__A							)

         UpperCamelCase_:					Optional[int]          =				stage_names
         UpperCamelCase_      ,UpperCamelCase_:					Dict          =				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
         UpperCamelCase_:					Dict          =				None





       def 			_a    (					self  ,			_lowerCamelCase							):
         UpperCamelCase_:					Tuple          =				config

         UpperCamelCase_:					Any          =				getattr(__A  ,			'use_timm_backbone'  ,			__A							)
         UpperCamelCase_:					List[Any]          =				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 			_a    (					self							):
         return self._out_features





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





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





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





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





       def 			_a    (					self  ,			*_lowerCamelCase  ,			**_lowerCamelCase							):
         UpperCamelCase_:					Optional[int]          =				dict(inspect.signature(self.forward							).parameters							)
         UpperCamelCase_:					List[str]          =				{k: v for k, v in kwargs.items() if k in signature}
         return self(*__A  ,			**__A							)





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





       def 			_a    (					self							):
         UpperCamelCase_:					Any          =				super().to_dict()
         UpperCamelCase_:					int          =				output.pop('_out_features'							)
         UpperCamelCase_:					Tuple          =				output.pop('_out_indices'							)
         return output







class 				_lowerCAmelCase:





       """simple docstring"""







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





       @out_features.setter
       def 			_a    (					self  ,			_lowerCamelCase							):
         UpperCamelCase_      ,UpperCamelCase_:					List[str]          =				get_aligned_output_features_output_indices(
             out_features=__A  ,			out_indices=__A  ,			stage_names=self.stage_names							)





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





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





       def 			_a    (					self							):
         UpperCamelCase_:					Optional[Any]          =				super().to_dict()
         UpperCamelCase_:					int          =				output.pop('_out_features'							)
         UpperCamelCase_:					List[str]          =				output.pop('_out_indices'							)
         return output







import copy

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


__A          = logging.get_logger(__name__)

__A          = {}





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "fsmt_decoder"

							def __init__(     self:      List[Any]						,   __A:      Dict=0						,   __A:      str=0      )			-> Optional[int]:
												super().__init__()
												_A									=       vocab_size
												_A									=       bos_token_id





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "fsmt"
							A_       =    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

							def __init__(     self:      Optional[Any]						,   __A:      Optional[Any]=["en", "de"]						,   __A:      List[str]=4_20_24						,   __A:      Any=4_20_24						,   __A:      Optional[int]="relu"						,   __A:      List[str]=10_24						,   __A:      str=2_00						,   __A:      Tuple=10_24						,   __A:      List[str]=40_96						,   __A:      List[Any]=12						,   __A:      Union[str, Any]=16						,   __A:      List[str]=0.0						,   __A:      int=40_96						,   __A:      Union[str, Any]=12						,   __A:      List[str]=16						,   __A:      Union[str, Any]=0.0						,   __A:      int=0.0						,   __A:      str=0.1						,   __A:      int=0.0						,   __A:      Union[str, Any]=0.02						,   __A:      Tuple=2						,   __A:      int=True						,   __A:      Dict=True						,   __A:      int=False						,   __A:      Any=5						,   __A:      Tuple=1.0						,   __A:      Optional[int]=False						,   __A:      List[Any]=True						,   __A:      Tuple=1						,   __A:      List[Any]=0						,   __A:      Any=2						,   __A:      Optional[Any]=2						,   **__A:      Optional[Any]						,   )			-> Tuple:
												_A									=       langs
												_A									=       src_vocab_size
												_A									=       tgt_vocab_size
												_A									=       d_model  # encoder_embed_dim and decoder_embed_dim

												_A									=       encoder_ffn_dim
												_A									=       _A									=       encoder_layers
												_A									=       encoder_attention_heads
												_A									=       encoder_layerdrop
												_A									=       decoder_layerdrop
												_A									=       decoder_ffn_dim
												_A									=       decoder_layers
												_A									=       decoder_attention_heads
												_A									=       max_position_embeddings
												_A									=       init_std  # Normal(0, this parameter)
												_A									=       activation_function

												_A									=       DecoderConfig(vocab_size=__A						,   bos_token_id=__A      )
												if "decoder" in common_kwargs:
																	del common_kwargs["decoder"]

												_A									=       scale_embedding  # scale factor will be sqrt(d_model) if True

												# 3 Types of Dropout
												_A									=       attention_dropout
												_A									=       activation_dropout
												_A									=       dropout

												_A									=       use_cache
												super().__init__(
												    pad_token_id=__A						,   bos_token_id=__A						,   eos_token_id=__A						,   decoder_start_token_id=__A						,   is_encoder_decoder=__A						,   tie_word_embeddings=__A						,   forced_eos_token_id=__A						,   max_length=__A						,   num_beams=__A						,   length_penalty=__A						,   early_stopping=__A						,   **__A						,   )



							def 					__A       (     self:      Optional[int]      )			-> List[Any]:
												_A									=       copy.deepcopy(self.__dict__      )
												_A									=       self.decoder.to_dict()
												_A									=       self.__class__.model_type
												return output



