






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     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     List[str]:
  return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE="attention"	)       ->     str:
  lowercase__			    =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]	)
  lowercase__			    =						k_tmp.reshape(k_tmp.shape[0]	,		k_tmp.shape[1] * k_tmp.shape[2]	)
  lowercase__			    =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]	)
  lowercase__			    =						o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]	,		o_tmp.shape[2]	)
  lowercase__			    =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]	)
  lowercase__			    =						q_tmp.reshape(q_tmp.shape[0]	,		q_tmp.shape[1] * q_tmp.shape[2]	)
  lowercase__			    =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]	)
  lowercase__			    =						v_tmp.reshape(v_tmp.shape[0]	,		v_tmp.shape[1] * v_tmp.shape[2]	)
  return k, o, q, v




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=False	)       ->     Dict:

  if split_mlp_wi:
    lowercase__			    =						params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
    lowercase__			    =						params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
    lowercase__			    =						(wi_a, wi_a)
  else:
    lowercase__			    =						params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

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




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Union[str, Any]:
  return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		*, _SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = False	)       ->     List[Any]:
  lowercase__			    =						traverse_util.flatten_dict(variables['target']	)
  lowercase__			    =						{"/".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
  lowercase__			    =						"encoder/encoder/mlp/wi_0/kernel" in old
  print('Split MLP:'	,		lowercase_	)

  lowercase__			    =						collections.OrderedDict()

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

  # Encoder.
  for i in range(lowercase_	):
    # Block i, layer 0 (Self Attention).
    lowercase__			    =						tax_layer_norm_lookup(lowercase_	,		lowercase_	,		'encoder'	,		'pre_attention_layer_norm'	)
    lowercase__			    =						tax_attention_lookup(lowercase_	,		lowercase_	,		'encoder'	,		'attention'	)
    lowercase__			    =						layer_norm
    lowercase__			    =						k.T
    lowercase__			    =						o.T
    lowercase__			    =						q.T
    lowercase__			    =						v.T

    # Block i, layer 1 (MLP).
    lowercase__			    =						tax_layer_norm_lookup(lowercase_	,		lowercase_	,		'encoder'	,		'pre_mlp_layer_norm'	)
    lowercase__			    =						tax_mlp_lookup(lowercase_	,		lowercase_	,		'encoder'	,		lowercase_	)
    lowercase__			    =						layer_norm
    if split_mlp_wi:
      lowercase__			    =						wi[0].T
      lowercase__			    =						wi[1].T
    else:
      lowercase__			    =						wi.T
    lowercase__			    =						wo.T
    if scalable_attention:
      # convert the rel_embedding of each layer
      lowercase__			    =						tax_relpos_bias_lookup(
          lowercase_	,		lowercase_	,		'encoder'	).T

  lowercase__			    =						old["encoder/encoder_norm/scale"]

  if not scalable_attention:
    lowercase__			    =						tax_relpos_bias_lookup(
        lowercase_	,		0	,		'encoder'	).T
    lowercase__			    =						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).
      lowercase__			    =						tax_layer_norm_lookup(lowercase_	,		lowercase_	,		'decoder'	,		'pre_self_attention_layer_norm'	)
      lowercase__			    =						tax_attention_lookup(lowercase_	,		lowercase_	,		'decoder'	,		'self_attention'	)
      lowercase__			    =						layer_norm
      lowercase__			    =						k.T
      lowercase__			    =						o.T
      lowercase__			    =						q.T
      lowercase__			    =						v.T

      # Block i, layer 1 (Cross Attention).
      lowercase__			    =						tax_layer_norm_lookup(lowercase_	,		lowercase_	,		'decoder'	,		'pre_cross_attention_layer_norm'	)
      lowercase__			    =						tax_attention_lookup(lowercase_	,		lowercase_	,		'decoder'	,		'encoder_decoder_attention'	)
      lowercase__			    =						layer_norm
      lowercase__			    =						k.T
      lowercase__			    =						o.T
      lowercase__			    =						q.T
      lowercase__			    =						v.T

      # Block i, layer 2 (MLP).
      lowercase__			    =						tax_layer_norm_lookup(lowercase_	,		lowercase_	,		'decoder'	,		'pre_mlp_layer_norm'	)
      lowercase__			    =						tax_mlp_lookup(lowercase_	,		lowercase_	,		'decoder'	,		lowercase_	)
      lowercase__			    =						layer_norm
      if split_mlp_wi:
        lowercase__			    =						wi[0].T
        lowercase__			    =						wi[1].T
      else:
        lowercase__			    =						wi.T
      lowercase__			    =						wo.T

      if scalable_attention:
        # convert the rel_embedding of each layer
        lowercase__			    =						tax_relpos_bias_lookup(lowercase_	,		lowercase_	,		'decoder'	).T

    lowercase__			    =						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:
      lowercase__			    =						old["decoder/logits_dense/kernel"].T

  return new




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Tuple:
  lowercase__			    =						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__			    =						state_dict["shared.weight"]

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      lowercase__			    =						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__			    =						state_dict["shared.weight"]

  return state_dict




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Dict:
  lowercase__			    =						checkpoints.load_tax_checkpoint(lowercase_	)
  lowercase__			    =						convert_tax_to_pytorch(
      lowercase_	,		num_layers=config.num_layers	,		is_encoder_only=lowercase_	,		scalable_attention=lowercase_	)
  lowercase__			    =						make_state_dict(lowercase_	,		lowercase_	)
  model.load_state_dict(lowercase_	,		strict=lowercase_	)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = False	,		)       ->     Tuple:
  lowercase__			    =						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:
    lowercase__			    =						UMTaEncoderModel(lowercase_	)
  else:
    lowercase__			    =						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__":
      lowercase_					     =	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,
      )
      lowercase_					     =	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,
      )









def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     bool:
  if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	):
    raise ValueError('Input series is not valid, valid series - [2, 4, 6]'	)
  if len(_SCREAMING_SNAKE_CASE	) == 0:
    raise ValueError('Input list must be a non empty list'	)
  if len(_SCREAMING_SNAKE_CASE	) == 1:
    return True
  lowercase__			    =						series[1] - series[0]
  for index in range(len(_SCREAMING_SNAKE_CASE	) - 1	):
    if series[index + 1] - series[index] != common_diff:
      return False
  return True




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     float:
  if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	):
    raise ValueError('Input series is not valid, valid series - [2, 4, 6]'	)
  if len(_SCREAMING_SNAKE_CASE	) == 0:
    raise ValueError('Input list must be a non empty list'	)
  lowercase__			    =						0
  for val in series:
    answer += val
  return answer / len(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      import doctest

      doctest.testmod()


