

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      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   List[str]     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]="attention"      ):



     '''simple docstring'''





     lowercase__   :       str				=	params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
     lowercase__   :       Dict				=	params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
     lowercase__   :       Any				=	params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
     lowercase__   :       Tuple				=	params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
     return k, o, q, v

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]=False      ):



     '''simple docstring'''





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

     lowercase__   :       Union[str, Any]				=	params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
     return wi, wo

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]      ):



     '''simple docstring'''





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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   dict     ,					*, SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   bool      ):



     '''simple docstring'''





     lowercase__   :       str				=	traverse_util.flatten_dict(variables['target']      )
     lowercase__   :       List[str]				=	{'/'.join(SCREAMING_SNAKE_CASE_      ): v for k, v in old.items()}

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

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

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

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

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

     lowercase__   :       Tuple				=	old[
         'encoder/relpos_bias/rel_embedding'
     ].T
     lowercase__   :       List[str]				=	old['encoder/encoder_norm/scale']

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

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

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

          lowercase__   :       str				=	old['decoder/decoder_norm/scale']
          lowercase__   :       List[str]				=	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__   :       Any				=	old['decoder/logits_dense/kernel'].T

     return new

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Dict     ,					SCREAMING_SNAKE_CASE_		:   bool      ):



     '''simple docstring'''





     lowercase__   :       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:
          lowercase__   :       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__   :       List[str]				=	state_dict['shared.weight']

     return state_dict

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[str]     ,					SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]      ):



     '''simple docstring'''





     lowercase__   :       int				=	checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Optional[Any]				=	convert_tax_to_pytorch(SCREAMING_SNAKE_CASE_     ,					num_layers=config.num_layers     ,					is_encoder_only=SCREAMING_SNAKE_CASE_      )
     lowercase__   :       List[str]				=	make_state_dict(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
     model.load_state_dict(SCREAMING_SNAKE_CASE_     ,					strict=SCREAMING_SNAKE_CASE_      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   bool = False      ):



     '''simple docstring'''





     lowercase__   :       Any				=	TaConfig.from_json_file(SCREAMING_SNAKE_CASE_      )
     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(SCREAMING_SNAKE_CASE_      )
     else:
          lowercase__   :       Dict				=	TaForConditionalGeneration(SCREAMING_SNAKE_CASE_      )

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

     # Save pytorch-model
     print(f"""Save PyTorch model to {pytorch_dump_path}"""      )
     model.save_pretrained(SCREAMING_SNAKE_CASE_      )

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


if __name__ == "__main__":
       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
       )
       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
       )




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   BertModel     ,					SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     lowercase__   :       Tuple				=	('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

     lowercase__   :       Dict				=	(
         ('layer.', 'layer_'),
         ('word_embeddings.weight', 'word_embeddings'),
         ('position_embeddings.weight', 'position_embeddings'),
         ('token_type_embeddings.weight', 'token_type_embeddings'),
         ('.', '/'),
         ('LayerNorm/weight', 'LayerNorm/gamma'),
         ('LayerNorm/bias', 'LayerNorm/beta'),
         ('weight', 'kernel'),
     )

     if not os.path.isdir(SCREAMING_SNAKE_CASE_      ):
          os.makedirs(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       str				=	model.state_dict()

     def to_tf_var_name(SCREAMING_SNAKE_CASE_		:   str      ):
          for patt, repl in iter(SCREAMING_SNAKE_CASE_      ):
               lowercase__   :       Optional[int]				=	name.replace(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
          return f"""bert/{name}"""

     def create_tf_var(SCREAMING_SNAKE_CASE_		:   np.ndarray     ,					SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   tf.Session      ):
          lowercase__   :       Optional[Any]				=	tf.dtypes.as_dtype(tensor.dtype      )
          lowercase__   :       List[str]				=	tf.get_variable(dtype=SCREAMING_SNAKE_CASE_     ,					shape=tensor.shape     ,					name=SCREAMING_SNAKE_CASE_     ,					initializer=tf.zeros_initializer()      )
          session.run(tf.variables_initializer([tf_var]      )      )
          session.run(SCREAMING_SNAKE_CASE_      )
          return tf_var

     tf.reset_default_graph()
     with tf.Session() as session:
          for var_name in state_dict:
               lowercase__   :       Tuple				=	to_tf_var_name(SCREAMING_SNAKE_CASE_      )
               lowercase__   :       str				=	state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose      ):
                    lowercase__   :       str				=	torch_tensor.T
               lowercase__   :       Any				=	create_tf_var(tensor=SCREAMING_SNAKE_CASE_     ,					name=SCREAMING_SNAKE_CASE_     ,					session=SCREAMING_SNAKE_CASE_      )
               tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
               lowercase__   :       Optional[int]				=	session.run(SCREAMING_SNAKE_CASE_      )
               print(f"""Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )}"""      )

          lowercase__   :       Tuple				=	tf.train.Saver(tf.trainable_variables()      )
          saver.save(SCREAMING_SNAKE_CASE_     ,					os.path.join(SCREAMING_SNAKE_CASE_     ,					model_name.replace('-'     ,					'_'      ) + '.ckpt'      )      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[Any]=None      ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	argparse.ArgumentParser()
     parser.add_argument('--model_name'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='model name e.g. bert-base-uncased'      )
     parser.add_argument(
         '--cache_dir'     ,					type=SCREAMING_SNAKE_CASE_     ,					default=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='Directory containing pytorch model'      )
     parser.add_argument('--pytorch_model_path'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='/path/to/<pytorch-model-name>.bin'      )
     parser.add_argument('--tf_cache_dir'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='Directory in which to save tensorflow model'      )
     lowercase__   :       List[str]				=	parser.parse_args(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       int				=	BertModel.from_pretrained(
         pretrained_model_name_or_path=args.model_name     ,					state_dict=torch.load(args.pytorch_model_path      )     ,					cache_dir=args.cache_dir     ,					)

     convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_     ,					ckpt_dir=args.tf_cache_dir     ,					model_name=args.model_name      )


if __name__ == "__main__":
       main()

