
'''simple docstring'''
import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict			,     __SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       List[str]:





  """simple docstring"""

  return params[F"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict			,     __SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  Tuple			,     __SCREAMING_SNAKE_CASE						:  Union[str, Any]="attention" )				->       str:





  """simple docstring"""

  __A		:     List[Any]												=		np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :] )
  __A		:     Tuple												=		k_tmp.reshape(k_tmp.shape[0]			,     k_tmp.shape[1] * k_tmp.shape[2] )
  __A		:     Optional[Any]												=		np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :] )
  __A		:     Dict												=		o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]			,     o_tmp.shape[2] )
  __A		:     str												=		np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :] )
  __A		:     int												=		q_tmp.reshape(q_tmp.shape[0]			,     q_tmp.shape[1] * q_tmp.shape[2] )
  __A		:     Dict												=		np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :] )
  __A		:     Optional[Any]												=		v_tmp.reshape(v_tmp.shape[0]			,     v_tmp.shape[1] * v_tmp.shape[2] )
  return k, o, q, v







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[int]			,     __SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  Dict			,     __SCREAMING_SNAKE_CASE						:  List[Any]=False )				->       Dict:





  """simple docstring"""

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

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







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  Tuple			,     __SCREAMING_SNAKE_CASE						:  List[str] )				->       Union[str, Any]:





  """simple docstring"""

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







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[str]			,     *, __SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  Dict			,     __SCREAMING_SNAKE_CASE						:  Any = False )				->       List[Any]:





  """simple docstring"""

  __A		:     int												=		traverse_util.flatten_dict(variables["""target"""] )
  __A		:     str												=		{"/".join(lowercase_ ): v for k, v in old.items()}

  # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
  __A		:     Union[str, Any]												=		"encoder/encoder/mlp/wi_0/kernel" in old
  print("""Split MLP:"""			,     lowercase_ )

  __A		:     List[Any]												=		collections.OrderedDict()

  # Shared embeddings.
  __A		:     str												=		old["token_embedder/embedding"]

  # Encoder.
  for i in range(lowercase_ ):
    # Block i, layer 0 (Self Attention).
    __A		:     int												=		tax_layer_norm_lookup(lowercase_			,     lowercase_			,     """encoder"""			,     """pre_attention_layer_norm""" )
    __A		:     str												=		tax_attention_lookup(lowercase_			,     lowercase_			,     """encoder"""			,     """attention""" )
    __A		:     List[Any]												=		layer_norm
    __A		:     str												=		k.T
    __A		:     Optional[Any]												=		o.T
    __A		:     List[str]												=		q.T
    __A		:     int												=		v.T

    # Block i, layer 1 (MLP).
    __A		:     Dict												=		tax_layer_norm_lookup(lowercase_			,     lowercase_			,     """encoder"""			,     """pre_mlp_layer_norm""" )
    __A		:     int												=		tax_mlp_lookup(lowercase_			,     lowercase_			,     """encoder"""			,     lowercase_ )
    __A		:     int												=		layer_norm
    if split_mlp_wi:
      __A		:     Optional[int]												=		wi[0].T
      __A		:     Optional[Any]												=		wi[1].T
    else:
      __A		:     Any												=		wi.T
    __A		:     Optional[Any]												=		wo.T
    if scalable_attention:
      # convert the rel_embedding of each layer
      __A		:     Optional[int]												=		tax_relpos_bias_lookup(
          lowercase_			,     lowercase_			,     """encoder""" ).T

  __A		:     str												=		old["encoder/encoder_norm/scale"]

  if not scalable_attention:
    __A		:     Any												=		tax_relpos_bias_lookup(
        lowercase_			,     0			,     """encoder""" ).T
    __A		:     str												=		tax_relpos_bias_lookup(
        lowercase_			,     0			,     """decoder""" ).T

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

      # Block i, layer 1 (Cross Attention).
      __A		:     Dict												=		tax_layer_norm_lookup(lowercase_			,     lowercase_			,     """decoder"""			,     """pre_cross_attention_layer_norm""" )
      __A		:     int												=		tax_attention_lookup(lowercase_			,     lowercase_			,     """decoder"""			,     """encoder_decoder_attention""" )
      __A		:     List[str]												=		layer_norm
      __A		:     str												=		k.T
      __A		:     List[Any]												=		o.T
      __A		:     int												=		q.T
      __A		:     List[Any]												=		v.T

      # Block i, layer 2 (MLP).
      __A		:     Tuple												=		tax_layer_norm_lookup(lowercase_			,     lowercase_			,     """decoder"""			,     """pre_mlp_layer_norm""" )
      __A		:     Tuple												=		tax_mlp_lookup(lowercase_			,     lowercase_			,     """decoder"""			,     lowercase_ )
      __A		:     Optional[int]												=		layer_norm
      if split_mlp_wi:
        __A		:     List[Any]												=		wi[0].T
        __A		:     Optional[int]												=		wi[1].T
      else:
        __A		:     str												=		wi.T
      __A		:     Union[str, Any]												=		wo.T

      if scalable_attention:
        # convert the rel_embedding of each layer
        __A		:     Optional[int]												=		tax_relpos_bias_lookup(lowercase_			,     lowercase_			,     """decoder""" ).T

    __A		:     Tuple												=		old["decoder/decoder_norm/scale"]

    # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
    if "decoder/logits_dense/kernel" in old:
      __A		:     Any												=		old["decoder/logits_dense/kernel"].T

  return new







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  List[str] )				->       Tuple:





  """simple docstring"""

  __A		:     Dict												=		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		:     str												=		state_dict["shared.weight"]

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      __A		:     Optional[int]												=		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		:     int												=		state_dict["shared.weight"]

  return state_dict







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  str )				->       Dict:





  """simple docstring"""

  __A		:     int												=		checkpoints.load_tax_checkpoint(lowercase_ )
  __A		:     List[str]												=		convert_tax_to_pytorch(
      lowercase_			,     num_layers=config.num_layers			,     is_encoder_only=lowercase_			,     scalable_attention=lowercase_ )
  __A		:     Union[str, Any]												=		make_state_dict(lowercase_			,     lowercase_ )
  model.load_state_dict(lowercase_			,     strict=lowercase_ )







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict			,     __SCREAMING_SNAKE_CASE						:  Optional[int]			,     __SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] = False			,     __SCREAMING_SNAKE_CASE						:  Dict = False			,     )				->       Tuple:





  """simple docstring"""

  __A		:     int												=		MTaConfig.from_json_file(lowercase_ )
  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		:     Optional[int]												=		UMTaEncoderModel(lowercase_ )
  else:
    __A		:     Optional[int]												=		UMTaForConditionalGeneration(lowercase_ )

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

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

  # Verify that we can load the checkpoint.
  model.from_pretrained(lowercase_ )
  print("""Done""" )


if __name__ == "__main__":
     A__   :		Any 		=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
     )
     parser.add_argument(
         '--scalable_attention',
         action='store_true',
         help='Whether the model uses scaled attention (umt5 model)',
         default=False,
     )
     A__   :		List[str] 		=parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path,
         args.config_file,
         args.pytorch_dump_path,
         args.is_encoder_only,
         args.scalable_attention,
     )





'''simple docstring'''
import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       List[Any]:





  """simple docstring"""

  __A		:     Optional[int]												=		RemBertConfig.from_json_file(__SCREAMING_SNAKE_CASE )
  print("""Building PyTorch model from configuration: {}""".format(str(__SCREAMING_SNAKE_CASE ) ) )
  __A		:     str												=		RemBertModel(__SCREAMING_SNAKE_CASE )

  # Load weights from tf checkpoint
  load_tf_weights_in_rembert(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  # Save pytorch-model
  print("""Save PyTorch model to {}""".format(__SCREAMING_SNAKE_CASE ) )
  torch.save(model.state_dict()			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
     A__   :		Optional[Any] 		=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
     )
     parser.add_argument(
         '--rembert_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained RemBERT model. \n'
             'This specifies the model architecture.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     A__   :		Dict 		=parser.parse_args()
     convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



