






"""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        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__="attention" ):





						'''simple docstring'''






						lowerCAmelCase   :Any						=			params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
						lowerCAmelCase   :int						=			params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
						lowerCAmelCase   :Any						=			params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
						lowerCAmelCase   :List[str]						=			params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
						return k, o, q, v


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__=False ):





						'''simple docstring'''






						if split_mlp_wi:
												lowerCAmelCase   :str						=			params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
												lowerCAmelCase   :Optional[Any]						=			params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
												lowerCAmelCase   :List[str]						=			(wi_a, wi_a)
						else:
												lowerCAmelCase   :Union[str, Any]						=			params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

						lowerCAmelCase   :Optional[int]						=			params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
						return wi, wo


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]


def        UpperCAmelCase      (					a__     ,      *, a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[Any]						=			traverse_util.flatten_dict(variables['target'] )
						lowerCAmelCase   :List[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
						lowerCAmelCase   :Dict						=			'encoder/layers_0/mlp/wi_0/kernel' in old
						print('Split MLP:'     ,      UpperCAmelCase__ )

						lowerCAmelCase   :Dict						=			collections.OrderedDict()

						# Shared embeddings.
						lowerCAmelCase   :Dict						=			old['token_embedder/embedding']

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

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

						lowerCAmelCase   :Optional[Any]						=			old[
						    'encoder/relpos_bias/rel_embedding'
						].T
						lowerCAmelCase   :int						=			old['encoder/encoder_norm/scale']

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

																		# Block i, layer 1 (Cross Attention).
																		lowerCAmelCase   :Any						=			tax_layer_norm_lookup(UpperCAmelCase__     ,      UpperCAmelCase__     ,      'decoder'     ,      'pre_cross_attention_layer_norm' )
																		lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :List[Any]						=			tax_attention_lookup(UpperCAmelCase__     ,      UpperCAmelCase__     ,      'decoder'     ,      'encoder_decoder_attention' )
																		lowerCAmelCase   :str						=			layer_norm
																		lowerCAmelCase   :Union[str, Any]						=			k.T
																		lowerCAmelCase   :int						=			o.T
																		lowerCAmelCase   :List[Any]						=			q.T
																		lowerCAmelCase   :Optional[int]						=			v.T

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

												lowerCAmelCase   :Optional[int]						=			old['decoder/decoder_norm/scale']
												lowerCAmelCase   :int						=			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:
																		lowerCAmelCase   :Optional[int]						=			old['decoder/logits_dense/kernel'].T

						return new


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :str						=			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:
												lowerCAmelCase   :List[str]						=			state_dict['shared.weight']

						if not is_encoder_only:
												if "decoder.embed_tokens.weight" not in state_dict:
																		lowerCAmelCase   :Union[str, Any]						=			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.' )
																		lowerCAmelCase   :Optional[Any]						=			state_dict['shared.weight']

						return state_dict


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :List[Any]						=			checkpoints.load_tax_checkpoint(UpperCAmelCase__ )
						lowerCAmelCase   :Optional[int]						=			convert_tax_to_pytorch(UpperCAmelCase__     ,      num_layers=config.num_layers     ,      is_encoder_only=UpperCAmelCase__ )
						lowerCAmelCase   :Dict						=			make_state_dict(UpperCAmelCase__     ,      UpperCAmelCase__ )
						model.load_state_dict(UpperCAmelCase__     ,      strict=UpperCAmelCase__ )


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__ = False ):





						'''simple docstring'''






						lowerCAmelCase   :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:
												lowerCAmelCase   :Union[str, Any]						=			TaEncoderModel(UpperCAmelCase__ )
						else:
												lowerCAmelCase   :Optional[Any]						=			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
		)


'''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    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__="attention"					)      ->      Dict:
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
       return k, o, q, v
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__=False					)      ->      Optional[Any]:
       if split_mlp_wi:
              A_						       =   params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
              A_						       =   params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
              A_						       =   (wi_a, wi_a)
       else:
              A_						       =   params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

       A_						       =   params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
       return wi, wo
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Any:
       return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def    UpperCAmelCase__     (   UpperCAmelCase__,							*, UpperCAmelCase__,							UpperCAmelCase__					)      ->      Union[str, Any]:
       A_						       =   traverse_util.flatten_dict(variables["""target"""]					)
       A_						       =   {"""/""".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
       A_						       =   """encoder/layers_0/mlp/wi_0/kernel""" in old
       print("""Split MLP:""",							UpperCAmelCase__					)

       A_						       =   collections.OrderedDict()

       # Shared embeddings.
       A_						       =   old["""token_embedder/embedding"""]

       # Encoder.
       for i in range(UpperCAmelCase__					):
              # Block i, layer 0 (Self Attention).
              A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							"""pre_attention_layer_norm"""					)
              A_       ,      A_       ,      A_       ,      A_						       =   tax_attention_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							"""attention"""					)
              A_						       =   layer_norm
              A_						       =   k.T
              A_						       =   o.T
              A_						       =   q.T
              A_						       =   v.T

              # Block i, layer 1 (MLP).
              A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							"""pre_mlp_layer_norm"""					)
              A_       ,      A_						       =   tax_mlp_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							UpperCAmelCase__					)
              A_						       =   layer_norm
              if split_mlp_wi:
                     A_						       =   wi[0].T
                     A_						       =   wi[1].T
              else:
                     A_						       =   wi.T
              A_						       =   wo.T

       A_						       =   old[
           """encoder/relpos_bias/rel_embedding"""
       ].T
       A_						       =   old["""encoder/encoder_norm/scale"""]

       if not is_encoder_only:
              # Decoder.
              for i in range(UpperCAmelCase__					):
                     # Block i, layer 0 (Self Attention).
                     A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""pre_self_attention_layer_norm"""					)
                     A_       ,      A_       ,      A_       ,      A_						       =   tax_attention_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""self_attention"""					)
                     A_						       =   layer_norm
                     A_						       =   k.T
                     A_						       =   o.T
                     A_						       =   q.T
                     A_						       =   v.T

                     # Block i, layer 1 (Cross Attention).
                     A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""pre_cross_attention_layer_norm"""					)
                     A_       ,      A_       ,      A_       ,      A_						       =   tax_attention_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""encoder_decoder_attention"""					)
                     A_						       =   layer_norm
                     A_						       =   k.T
                     A_						       =   o.T
                     A_						       =   q.T
                     A_						       =   v.T

                     # Block i, layer 2 (MLP).
                     A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""pre_mlp_layer_norm"""					)
                     A_       ,      A_						       =   tax_mlp_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							UpperCAmelCase__					)
                     A_						       =   layer_norm
                     if split_mlp_wi:
                            A_						       =   wi[0].T
                            A_						       =   wi[1].T
                     else:
                            A_						       =   wi.T
                     A_						       =   wo.T

              A_						       =   old["""decoder/decoder_norm/scale"""]
              A_						       =   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:
                     A_						       =   old["""decoder/logits_dense/kernel"""].T

       return new
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      List[Any]:
       A_						       =   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:
              A_						       =   state_dict["""shared.weight"""]

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     A_						       =   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."""					)
                     A_						       =   state_dict["""shared.weight"""]

       return state_dict
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Union[str, Any]:
       A_						       =   checkpoints.load_tax_checkpoint(UpperCAmelCase__					)
       A_						       =   convert_tax_to_pytorch(UpperCAmelCase__,							num_layers=config.num_layers,							is_encoder_only=UpperCAmelCase__					)
       A_						       =   make_state_dict(UpperCAmelCase__,							UpperCAmelCase__					)
       model.load_state_dict(UpperCAmelCase__,							strict=UpperCAmelCase__					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__ = False					)      ->      List[Any]:
       A_						       =   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:
              A_						       =   TaEncoderModel(UpperCAmelCase__					)
       else:
              A_						       =   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__":
      __lowerCamelCase									=     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
      )
      __lowerCamelCase									=     parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
      )
