"""simple docstring"""







import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]="attention"      ):
     lowercase_	:				Optional[int]													=	params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
     lowercase_	:				Optional[Any]													=	params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
     lowercase_	:				int													=	params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
     lowercase_	:				Optional[Any]													=	params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
     return k, o, q, v



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optional[int]     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     List[str]     ,			__SCREAMING_SNAKE_CASE							:     int=False      ):





     if split_mlp_wi:
          lowercase_	:				int													=	params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
          lowercase_	:				Optional[Any]													=	params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
          lowercase_	:				Dict													=	(wi_a, wi_a)
     else:
          lowercase_	:				Optional[Any]													=	params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

     lowercase_	:				Dict													=	params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
     return wi, wo



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     List[str]     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]      ):
     return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     dict     ,			*, __SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     bool      ):
     lowercase_	:				str													=	traverse_util.flatten_dict(variables['target']      )
     lowercase_	:				Any													=	{'''/'''.join(UpperCAmelCase_      ): v for k, v in old.items()}

     # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
     lowercase_	:				Any													=	'''encoder/layers_0/mlp/wi_0/kernel''' in old
     print('Split MLP:'     ,			UpperCAmelCase_      )

     lowercase_	:				Optional[Any]													=	collections.OrderedDict()

     # Shared embeddings.
     lowercase_	:				Union[str, Any]													=	old['''token_embedder/embedding''']

     # Encoder.
     for i in range(UpperCAmelCase_      ):
          # Block i, layer 0 (Self Attention).
          lowercase_	:				Optional[Any]													=	tax_layer_norm_lookup(UpperCAmelCase_     ,			UpperCAmelCase_     ,			'encoder'     ,			'pre_attention_layer_norm'      )
          lowercase_	:				Optional[int]													=	tax_attention_lookup(UpperCAmelCase_     ,			UpperCAmelCase_     ,			'encoder'     ,			'attention'      )
          lowercase_	:				List[Any]													=	layer_norm
          lowercase_	:				str													=	k.T
          lowercase_	:				Dict													=	o.T
          lowercase_	:				int													=	q.T
          lowercase_	:				Optional[Any]													=	v.T

          # Block i, layer 1 (MLP).
          lowercase_	:				Tuple													=	tax_layer_norm_lookup(UpperCAmelCase_     ,			UpperCAmelCase_     ,			'encoder'     ,			'pre_mlp_layer_norm'      )
          lowercase_	:				List[Any]													=	tax_mlp_lookup(UpperCAmelCase_     ,			UpperCAmelCase_     ,			'encoder'     ,			UpperCAmelCase_      )
          lowercase_	:				Any													=	layer_norm
          if split_mlp_wi:
               lowercase_	:				Any													=	wi[0].T
               lowercase_	:				Tuple													=	wi[1].T
          else:
               lowercase_	:				List[str]													=	wi.T
          lowercase_	:				List[Any]													=	wo.T

     lowercase_	:				Union[str, Any]													=	old[
         '''encoder/relpos_bias/rel_embedding'''
     ].T
     lowercase_	:				Optional[Any]													=	old['''encoder/encoder_norm/scale''']

     if not is_encoder_only:
          # Decoder.
          for i in range(UpperCAmelCase_      ):
               # Block i, layer 0 (Self Attention).
               lowercase_	:				List[str]													=	tax_layer_norm_lookup(UpperCAmelCase_     ,			UpperCAmelCase_     ,			'decoder'     ,			'pre_self_attention_layer_norm'      )
               lowercase_	:				List[Any]													=	tax_attention_lookup(UpperCAmelCase_     ,			UpperCAmelCase_     ,			'decoder'     ,			'self_attention'      )
               lowercase_	:				List[Any]													=	layer_norm
               lowercase_	:				Tuple													=	k.T
               lowercase_	:				int													=	o.T
               lowercase_	:				Any													=	q.T
               lowercase_	:				Optional[int]													=	v.T

               # Block i, layer 1 (Cross Attention).
               lowercase_	:				str													=	tax_layer_norm_lookup(UpperCAmelCase_     ,			UpperCAmelCase_     ,			'decoder'     ,			'pre_cross_attention_layer_norm'      )
               lowercase_	:				Any													=	tax_attention_lookup(UpperCAmelCase_     ,			UpperCAmelCase_     ,			'decoder'     ,			'encoder_decoder_attention'      )
               lowercase_	:				str													=	layer_norm
               lowercase_	:				Optional[Any]													=	k.T
               lowercase_	:				Any													=	o.T
               lowercase_	:				Dict													=	q.T
               lowercase_	:				Optional[Any]													=	v.T

               # Block i, layer 2 (MLP).
               lowercase_	:				Optional[int]													=	tax_layer_norm_lookup(UpperCAmelCase_     ,			UpperCAmelCase_     ,			'decoder'     ,			'pre_mlp_layer_norm'      )
               lowercase_	:				List[Any]													=	tax_mlp_lookup(UpperCAmelCase_     ,			UpperCAmelCase_     ,			'decoder'     ,			UpperCAmelCase_      )
               lowercase_	:				Optional[int]													=	layer_norm
               if split_mlp_wi:
                    lowercase_	:				int													=	wi[0].T
                    lowercase_	:				Tuple													=	wi[1].T
               else:
                    lowercase_	:				str													=	wi.T
               lowercase_	:				Dict													=	wo.T

          lowercase_	:				Any													=	old['''decoder/decoder_norm/scale''']
          lowercase_	:				Optional[Any]													=	old[
              '''decoder/relpos_bias/rel_embedding'''
          ].T

          # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
          if "decoder/logits_dense/kernel" in old:
               lowercase_	:				Union[str, Any]													=	old['''decoder/logits_dense/kernel'''].T

     return new



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any     ,			__SCREAMING_SNAKE_CASE							:     bool      ):
     lowercase_	:				List[Any]													=	collections.OrderedDict([(k, torch.from_numpy(v.copy()      )) for (k, v) in converted_params.items()]      )

     # Add what is missing.
     if "encoder.embed_tokens.weight" not in state_dict:
          lowercase_	:				Optional[Any]													=	state_dict['''shared.weight''']

     if not is_encoder_only:
          if "decoder.embed_tokens.weight" not in state_dict:
               lowercase_	:				Tuple													=	state_dict['''shared.weight''']

          if "lm_head.weight" not in state_dict:  # For old 1.0 models.
               print('Using shared word embeddings as lm_head.'      )
               lowercase_	:				Optional[Any]													=	state_dict['''shared.weight''']

     return state_dict



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]      ):
     lowercase_	:				Tuple													=	checkpoints.load_tax_checkpoint(UpperCAmelCase_      )
     lowercase_	:				Optional[int]													=	convert_tax_to_pytorch(UpperCAmelCase_     ,			num_layers=config.num_layers     ,			is_encoder_only=UpperCAmelCase_      )
     lowercase_	:				Tuple													=	make_state_dict(UpperCAmelCase_     ,			UpperCAmelCase_      )
     model.load_state_dict(UpperCAmelCase_     ,			strict=UpperCAmelCase_      )



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     bool = False      ):
     lowercase_	:				List[Any]													=	TaConfig.from_json_file(UpperCAmelCase_      )
     print(F'''Building PyTorch model from configuration: {config}'''      )
     # Non-v1.1 checkpoints could also use T5Model, but this works for all.
     # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
     if is_encoder_only:
          lowercase_	:				Any													=	TaEncoderModel(UpperCAmelCase_      )
     else:
          lowercase_	:				List[str]													=	TaForConditionalGeneration(UpperCAmelCase_      )

     # Load weights from tf checkpoint
     load_tax_weights_in_ta(UpperCAmelCase_     ,			UpperCAmelCase_     ,			UpperCAmelCase_     ,			UpperCAmelCase_      )

     # Save pytorch-model
     print(F'''Save PyTorch model to {pytorch_dump_path}'''      )
     model.save_pretrained(UpperCAmelCase_      )

     # Verify that we can load the checkpoint.
     model.from_pretrained(UpperCAmelCase_      )
     print('Done'      )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE    	=argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
       # Required parameters
       parser.add_argument(
           "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
       )
       parser.add_argument(
           "--config_file",
           default=None,
           type=str,
           required=True,
           help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
       )
       parser.add_argument(
           "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       parser.add_argument(
           "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
       )
       __SCREAMING_SNAKE_CASE    	=parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
       )





snake_case				:    str       				=     '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

snake_case				:    List[Any]       				=     [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
snake_case				:    int       				=     {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}





