





"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Optional[Any]      =       TaConfig.from_pretrained(_lowercase  )
						_UpperCamelCase     :   Dict      =       FlaxTaForConditionalGeneration(config=_lowercase  )
						_UpperCamelCase     :   List[str]      =       checkpoints.load_tax_checkpoint(_lowercase  )

						_UpperCamelCase     :   List[str]      =       '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

						# Encoder
						for layer_index in range(config.num_layers  ):
												_UpperCamelCase     :   Dict      =       F"""layers_{str(_lowercase  )}"""

												# Self-Attention
												_UpperCamelCase     :   Tuple      =       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
												_UpperCamelCase     :   Any      =       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
												_UpperCamelCase     :   List[str]      =       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
												_UpperCamelCase     :   Tuple      =       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

												# Layer Normalization
												_UpperCamelCase     :   Optional[int]      =       tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

												if split_mlp_wi:
																		_UpperCamelCase     :   List[str]      =       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																		_UpperCamelCase     :   int      =       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
												else:
																		_UpperCamelCase     :   Optional[Any]      =       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

												_UpperCamelCase     :   Optional[int]      =       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

												# Layer Normalization
												_UpperCamelCase     :   Optional[int]      =       tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

												# Assigning
												_UpperCamelCase     :   Dict      =       tax_attention_key
												_UpperCamelCase     :   Dict      =       tax_attention_out
												_UpperCamelCase     :   List[str]      =       tax_attention_query
												_UpperCamelCase     :   Dict      =       tax_attention_value

												_UpperCamelCase     :   Dict      =       tax_attention_layer_norm

												if split_mlp_wi:
																		_UpperCamelCase     :   str      =       tax_mlp_wi_a
																		_UpperCamelCase     :   Union[str, Any]      =       tax_mlp_wi_a
												else:
																		_UpperCamelCase     :   int      =       tax_mlp_wi

												_UpperCamelCase     :   str      =       tax_mlp_wo
												_UpperCamelCase     :   int      =       tax_mlp_layer_norm

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

						# Assigning
						_UpperCamelCase     :   int      =       tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
						_UpperCamelCase     :   Tuple      =       tax_encoder_norm

						# Decoder
						for layer_index in range(config.num_decoder_layers  ):
												_UpperCamelCase     :   int      =       F"""layers_{str(_lowercase  )}"""

												# Self-Attention
												_UpperCamelCase     :   Optional[int]      =       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
												_UpperCamelCase     :   List[Any]      =       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
												_UpperCamelCase     :   Optional[Any]      =       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
												_UpperCamelCase     :   List[Any]      =       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

												# Layer Normalization
												_UpperCamelCase     :   int      =       tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
												    '''scale'''
												]

												# Encoder-Decoder-Attention
												_UpperCamelCase     :   Optional[int]      =       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
												    '''kernel'''
												]
												_UpperCamelCase     :   List[Any]      =       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
												    '''kernel'''
												]
												_UpperCamelCase     :   List[Any]      =       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
												    '''kernel'''
												]
												_UpperCamelCase     :   Tuple      =       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
												    '''kernel'''
												]

												# Layer Normalization
												_UpperCamelCase     :   int      =       tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

												# MLP
												if split_mlp_wi:
																		_UpperCamelCase     :   str      =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																		_UpperCamelCase     :   Union[str, Any]      =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
												else:
																		_UpperCamelCase     :   Optional[int]      =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

												_UpperCamelCase     :   Dict      =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

												# Layer Normalization
												_UpperCamelCase     :   Dict      =       tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

												# Assigning
												_UpperCamelCase     :   Any      =       tax_attention_key
												_UpperCamelCase     :   str      =       tax_attention_out
												_UpperCamelCase     :   int      =       tax_attention_query
												_UpperCamelCase     :   Tuple      =       tax_attention_value

												_UpperCamelCase     :   Optional[int]      =       tax_pre_attention_layer_norm

												_UpperCamelCase     :   List[Any]      =       tax_enc_dec_attention_key
												_UpperCamelCase     :   Optional[Any]      =       tax_enc_dec_attention_out
												_UpperCamelCase     :   Tuple      =       tax_enc_dec_attention_query
												_UpperCamelCase     :   Tuple      =       tax_enc_dec_attention_value

												_UpperCamelCase     :   str      =       tax_cross_layer_norm

												if split_mlp_wi:
																		_UpperCamelCase     :   Optional[Any]      =       tax_mlp_wi_a
																		_UpperCamelCase     :   Tuple      =       tax_mlp_wi_a
												else:
																		_UpperCamelCase     :   Optional[int]      =       tax_mlp_wi

												_UpperCamelCase     :   str      =       tax_mlp_wo

												_UpperCamelCase     :   Dict      =       txa_mlp_layer_norm

						# Decoder Normalization
						_UpperCamelCase     :   int      =       tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
						_UpperCamelCase     :   Any      =       txa_decoder_norm

						# Only for layer 0:
						_UpperCamelCase     :   Optional[int]      =       tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
						_UpperCamelCase     :   Dict      =       tax_decoder_rel_embedding

						# Token Embeddings
						_UpperCamelCase     :   List[str]      =       tax_model['''target''']['''token_embedder''']['''embedding''']
						_UpperCamelCase     :   Tuple      =       txa_token_embeddings

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

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


if __name__ == "__main__":
						UpperCamelCase_	 =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."""
						)
						UpperCamelCase_	 =parser.parse_args()
						convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





'''simple docstring'''







from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
    import torch

lowerCAmelCase_      =   logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
    from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):







    def   lowerCAmelCase(  cls    :  Dict      ):







           '''simple docstring'''






           return cls







def   lowerCAmelCase(  a__    :  Union[Tuple, List]      , a__    :  Optional[Union[List["torch.Generator"], "torch.Generator"]] = None      , a__    :  Optional["torch.device"] = None      , a__    :  Optional["torch.dtype"] = None      , a__    :  Optional["torch.layout"] = None      , ):







       '''simple docstring'''






       lowerCamelCase__				   =      device
       lowerCamelCase__				   =      shape[0]

       lowerCamelCase__				   =      layout or torch.strided
       lowerCamelCase__				   =      device or torch.device("cpu"      )

       if generator is not None:
              lowerCamelCase__				   =      generator.device.type if not isinstance(a__      , a__      ) else generator[0].device.type
              if gen_device_type != device.type and gen_device_type == "cpu":
                     lowerCamelCase__				   =      "cpu"
                     if device != "mps":
                            logger.info(
                                f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
                                f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
                                f""" slighly speed up this function by passing a generator that was created on the {device} device."""      )
              elif gen_device_type != device.type and gen_device_type == "cuda":
                     raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""      )

       if isinstance(a__      , a__      ):
              lowerCamelCase__				   =      (1,) + shape[1:]
              lowerCamelCase__				   =      [
                  torch.randn(a__      , generator=generator[i]      , device=a__      , dtype=a__      , layout=a__      )
                  for i in range(a__      )
              ]
              lowerCamelCase__				   =      torch.cat(a__      , dim=0      ).to(a__      )
       else:
              lowerCamelCase__				   =      torch.randn(a__      , generator=a__      , device=a__      , dtype=a__      , layout=a__      ).to(a__      )

       return latents







def   lowerCAmelCase(  a__    :  Union[str, Any]      ):







       '''simple docstring'''






       if is_torch_version("<"      , "2.0.0"      ) or not hasattr(a__      , "_dynamo"      ):
              return False
       return isinstance(a__      , torch._dynamo.eval_frame.OptimizedModule      )

