






"""simple docstring"""





import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[int]		,    _lowerCamelCase	:		Union[str, Any]		,    _lowerCamelCase	:		Union[str, Any]			)				->    List[Any]:
  lowerCamelCase_   					=						TaConfig.from_pretrained(_lowerCamelCase			)
  lowerCamelCase_   					=						FlaxTaForConditionalGeneration(config=_lowerCamelCase			)
  lowerCamelCase_   					=						checkpoints.load_tax_checkpoint(_lowerCamelCase			)

  lowerCamelCase_   					=						'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

  # Encoder
  for layer_index in range(config.num_layers			):
    lowerCamelCase_   					=						F'''layers_{str(_lowerCamelCase			)}'''

    # Self-Attention
    lowerCamelCase_   					=						tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
    lowerCamelCase_   					=						tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
    lowerCamelCase_   					=						tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
    lowerCamelCase_   					=						tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

    # Layer Normalization
    lowerCamelCase_   					=						tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

    if split_mlp_wi:
      lowerCamelCase_   					=						tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
      lowerCamelCase_   					=						tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
    else:
      lowerCamelCase_   					=						tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

    lowerCamelCase_   					=						tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

    # Layer Normalization
    lowerCamelCase_   					=						tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

    # Assigning
    lowerCamelCase_   					=						tax_attention_key
    lowerCamelCase_   					=						tax_attention_out
    lowerCamelCase_   					=						tax_attention_query
    lowerCamelCase_   					=						tax_attention_value

    lowerCamelCase_   					=						tax_attention_layer_norm

    if split_mlp_wi:
      lowerCamelCase_   					=						tax_mlp_wi_a
      lowerCamelCase_   					=						tax_mlp_wi_a
    else:
      lowerCamelCase_   					=						tax_mlp_wi

    lowerCamelCase_   					=						tax_mlp_wo
    lowerCamelCase_   					=						tax_mlp_layer_norm

  # Only for layer 0:
  lowerCamelCase_   					=						tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
  lowerCamelCase_   					=						tax_encoder_rel_embedding

  # Assigning
  lowerCamelCase_   					=						tax_model['target']['encoder']['encoder_norm']['scale']
  lowerCamelCase_   					=						tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_decoder_layers			):
    lowerCamelCase_   					=						F'''layers_{str(_lowerCamelCase			)}'''

    # Self-Attention
    lowerCamelCase_   					=						tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
    lowerCamelCase_   					=						tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
    lowerCamelCase_   					=						tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
    lowerCamelCase_   					=						tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

    # Layer Normalization
    lowerCamelCase_   					=						tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
        'scale'
    ]

    # Encoder-Decoder-Attention
    lowerCamelCase_   					=						tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['key'][
        'kernel'
    ]
    lowerCamelCase_   					=						tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['out'][
        'kernel'
    ]
    lowerCamelCase_   					=						tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['query'][
        'kernel'
    ]
    lowerCamelCase_   					=						tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['value'][
        'kernel'
    ]

    # Layer Normalization
    lowerCamelCase_   					=						tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

    # MLP
    if split_mlp_wi:
      lowerCamelCase_   					=						tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
      lowerCamelCase_   					=						tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
    else:
      lowerCamelCase_   					=						tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

    lowerCamelCase_   					=						tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

    # Layer Normalization
    lowerCamelCase_   					=						tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

    # Assigning
    lowerCamelCase_   					=						tax_attention_key
    lowerCamelCase_   					=						tax_attention_out
    lowerCamelCase_   					=						tax_attention_query
    lowerCamelCase_   					=						tax_attention_value

    lowerCamelCase_   					=						tax_pre_attention_layer_norm

    lowerCamelCase_   					=						tax_enc_dec_attention_key
    lowerCamelCase_   					=						tax_enc_dec_attention_out
    lowerCamelCase_   					=						tax_enc_dec_attention_query
    lowerCamelCase_   					=						tax_enc_dec_attention_value

    lowerCamelCase_   					=						tax_cross_layer_norm

    if split_mlp_wi:
      lowerCamelCase_   					=						tax_mlp_wi_a
      lowerCamelCase_   					=						tax_mlp_wi_a
    else:
      lowerCamelCase_   					=						tax_mlp_wi

    lowerCamelCase_   					=						tax_mlp_wo

    lowerCamelCase_   					=						txa_mlp_layer_norm

  # Decoder Normalization
  lowerCamelCase_   					=						tax_model['target']['decoder']['decoder_norm']['scale']
  lowerCamelCase_   					=						txa_decoder_norm

  # Only for layer 0:
  lowerCamelCase_   					=						tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
  lowerCamelCase_   					=						tax_decoder_rel_embedding

  # Token Embeddings
  lowerCamelCase_   					=						tax_model['target']['token_embedder']['embedding']
  lowerCamelCase_   					=						txa_token_embeddings

  # LM Head (only in v1.1 checkpoints)
  if "logits_dense" in tax_model["target"]["decoder"]:
    lowerCamelCase_   					=						tax_model['target']['decoder']['logits_dense']['kernel']

  flax_model.save_pretrained(_lowerCamelCase			)
  print('T5X Model was sucessfully converted!'			)


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE :    Any    =						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the TX5 checkpoint.'''
  )
  parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of T5 model.''')
  parser.add_argument(
      '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
  )
  _SCREAMING_SNAKE_CASE :    Any    =						parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)












"""simple docstring"""





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






class 					a						( enum.Enum		):
    SCREAMING_SNAKE_CASE						:					str						=			"""timm"""
    SCREAMING_SNAKE_CASE						:					List[Any]						=			"""transformers"""







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[Iterable[str]]		,    _lowerCamelCase	:		Optional[Iterable[int]]		,    _lowerCamelCase	:		Optional[Iterable[str]]			)				->    List[Any]:
  if stage_names is None:
    raise ValueError('Stage_names must be set for transformers backbones'			)

  if out_features is not None:
    if not isinstance(_lowerCamelCase		,    (list,)			):
      raise ValueError(F'''out_features must be a list {type(_lowerCamelCase			)}'''			)
    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(_lowerCamelCase		,    (list, tuple)			):
      raise ValueError(F'''out_indices must be a list or tuple, got {type(_lowerCamelCase			)}'''			)
    if any(idx >= len(_lowerCamelCase			) 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(_lowerCamelCase			) != len(_lowerCamelCase			):
      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 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[List[str]]		,    _lowerCamelCase	:		Optional[Union[List[int], Tuple[int]]]		,    _lowerCamelCase	:		List[str]		,    )				->    Union[str, Any]:
  if out_indices is None and out_features is None:
    lowerCamelCase_   					=						[len(_lowerCamelCase			) - 1]
    lowerCamelCase_   					=						[stage_names[-1]]
  elif out_indices is None and out_features is not None:
    lowerCamelCase_   					=						[stage_names.index(_lowerCamelCase			) for layer in out_features]
  elif out_features is None and out_indices is not None:
    lowerCamelCase_   					=						[stage_names[idx] for idx in out_indices]
  return out_features, out_indices







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[List[str]]		,    _lowerCamelCase	:		Optional[Union[List[int], Tuple[int]]]		,    _lowerCamelCase	:		List[str]		,    )				->    Tuple[List[str], List[int]]:
  verify_out_features_out_indices(out_features=_lowerCamelCase		,    out_indices=_lowerCamelCase		,    stage_names=_lowerCamelCase			)
  lowerCamelCase_					,     lowerCamelCase_   					=						_align_output_features_output_indices(
      out_features=_lowerCamelCase		,    out_indices=_lowerCamelCase		,    stage_names=_lowerCamelCase			)
  # Verify that the aligned out_features and out_indices are valid
  verify_out_features_out_indices(out_features=_lowerCamelCase		,    out_indices=_lowerCamelCase		,    stage_names=_lowerCamelCase			)
  return output_features, output_indices






class 					a						:
    SCREAMING_SNAKE_CASE						:					Optional[BackboneType]						=			None






    def        UpperCamelCase	(	self					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							List[str] )					->		None:
      if getattr(self	,						'_backbone'	,						__SCREAMING_SNAKE_CASE ) 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']
      lowerCamelCase_   					=						[stage['module'] for stage in self._backbone.feature_info.info]
      lowerCamelCase_   					=						[stage['num_chs'] for stage in self._backbone.feature_info.info]
      lowerCamelCase_   					=						self._backbone.feature_info.out_indices
      lowerCamelCase_   					=						self._backbone.feature_info.module_name()

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






    def        UpperCamelCase	(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							Any )					->		None:
      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'stage_names' )
      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'out_features'	,						__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'out_indices'	,						__SCREAMING_SNAKE_CASE )

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






    def        UpperCamelCase	(	self					:							Any	,						__SCREAMING_SNAKE_CASE					:							Dict )					->		None:
      lowerCamelCase_   					=						config

      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'use_timm_backbone'	,						__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

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






    @property
    def        UpperCamelCase	(	self					:							Dict )					->		Any:
      return self._out_features






    @out_features.setter
    def        UpperCamelCase	(	self					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							List[str] )					->		List[Any]:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    @property
    def        UpperCamelCase	(	self					:							List[Any] )					->		Dict:
      return self._out_indices






    @out_indices.setter
    def        UpperCamelCase	(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							Union[Tuple[int], List[int]] )					->		List[str]:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    @property
    def        UpperCamelCase	(	self					:							Tuple )					->		Tuple:
      # 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					:							Dict )					->		List[Any]:
      return [self.out_feature_channels[name] for name in self.out_features]






    def        UpperCamelCase	(	self					:							Optional[int]	,						*__SCREAMING_SNAKE_CASE					:							Any	,						**__SCREAMING_SNAKE_CASE					:							str )					->		Tuple:
      lowerCamelCase_   					=						dict(inspect.signature(self.forward ).parameters )
      lowerCamelCase_   					=						{k: v for k, v in kwargs.items() if k in signature}
      return self(*__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							Optional[bool] = None	,						__SCREAMING_SNAKE_CASE					:							Optional[bool] = None	,						__SCREAMING_SNAKE_CASE					:							Optional[bool] = None	,						)					->		int:
      raise NotImplementedError('This method should be implemented by the derived class.' )






    def        UpperCamelCase	(	self					:							Union[str, Any] )					->		List[str]:
      lowerCamelCase_   					=						super().to_dict()
      lowerCamelCase_   					=						output.pop('_out_features' )
      lowerCamelCase_   					=						output.pop('_out_indices' )
      return output






class 					a						:






    @property
    def        UpperCamelCase	(	self					:							int )					->		Optional[Any]:
      return self._out_features






    @out_features.setter
    def        UpperCamelCase	(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							List[str] )					->		List[str]:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    @property
    def        UpperCamelCase	(	self					:							Union[str, Any] )					->		Tuple:
      return self._out_indices






    @out_indices.setter
    def        UpperCamelCase	(	self					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							Union[Tuple[int], List[int]] )					->		str:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    def        UpperCamelCase	(	self					:							int )					->		str:
      lowerCamelCase_   					=						super().to_dict()
      lowerCamelCase_   					=						output.pop('_out_features' )
      lowerCamelCase_   					=						output.pop('_out_indices' )
      return output




