

import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def a_		(       UpperCamelCase_   :							int   ,					UpperCamelCase_   :							List[Any]   ,					UpperCamelCase_   :							Optional[int]  )							->   Any:







 """simple docstring"""

 lowerCamelCase      						=    TaConfig.from_pretrained(UpperCamelCase_  )
 lowerCamelCase      						=    FlaxTaForConditionalGeneration(config=UpperCamelCase_  )
 lowerCamelCase      						=    checkpoints.load_tax_checkpoint(UpperCamelCase_  )

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

 # Encoder
 for layer_index in range(config.num_layers  ):
  lowerCamelCase      						=    f'''layers_{str(UpperCamelCase_  )}'''

  # 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(UpperCamelCase_  )}'''

  # 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(UpperCamelCase_  )
 print('T5X Model was sucessfully converted!'  )


if __name__ == "__main__":
   _lowerCAmelCase :	str     				=    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.'
   )
   _lowerCAmelCase :	Tuple     				=    parser.parse_args()
   convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)







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,
)


_lowerCAmelCase :	List[Any]     				=    {
    'configuration_switch_transformers': [
        'SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwitchTransformersConfig',
        'SwitchTransformersOnnxConfig',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCAmelCase :	Tuple     				=    [
         'SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST',
         'SwitchTransformersEncoderModel',
         'SwitchTransformersForConditionalGeneration',
         'SwitchTransformersModel',
         'SwitchTransformersPreTrainedModel',
         'SwitchTransformersTop1Router',
         'SwitchTransformersSparseMLP',
     ]


if TYPE_CHECKING:
     from .configuration_switch_transformers import (
         SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SwitchTransformersConfig,
         SwitchTransformersOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_switch_transformers import (
              SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwitchTransformersEncoderModel,
              SwitchTransformersForConditionalGeneration,
              SwitchTransformersModel,
              SwitchTransformersPreTrainedModel,
              SwitchTransformersSparseMLP,
              SwitchTransformersTopaRouter,
          )


else:
     import sys

     _lowerCAmelCase :	List[Any]     				=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




