

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 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Optional[Any]						)		->				List[Any]:
 '''simple docstring'''


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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	Tuple="attention"						)		->				Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      SCREAMING_SNAKE_CASE		    =      np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]						)
 SCREAMING_SNAKE_CASE		    =      k_tmp.reshape(k_tmp.shape[0]		,				k_tmp.shape[1] * k_tmp.shape[2]						)
 SCREAMING_SNAKE_CASE		    =      np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]						)
 SCREAMING_SNAKE_CASE		    =      o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]		,				o_tmp.shape[2]						)
 SCREAMING_SNAKE_CASE		    =      np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]						)
 SCREAMING_SNAKE_CASE		    =      q_tmp.reshape(q_tmp.shape[0]		,				q_tmp.shape[1] * q_tmp.shape[2]						)
 SCREAMING_SNAKE_CASE		    =      np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]						)
 SCREAMING_SNAKE_CASE		    =      v_tmp.reshape(v_tmp.shape[0]		,				v_tmp.shape[1] * v_tmp.shape[2]						)
 return k, o, q, v


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Tuple=False						)		->				Dict:
 '''simple docstring'''


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

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Optional[Any]						)		->				List[Any]:
 '''simple docstring'''


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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	dict		,				*, _UpperCamelCase		:	int		,				_UpperCamelCase		:	bool		,				_UpperCamelCase		:	bool = False						)		->				Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      traverse_util.flatten_dict(variables['target']						)
 SCREAMING_SNAKE_CASE		    =      {'/'.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
 SCREAMING_SNAKE_CASE		    =      'encoder/encoder/mlp/wi_0/kernel' in old
 print('Split MLP:'		,				_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      collections.OrderedDict()

 # Shared embeddings.
 SCREAMING_SNAKE_CASE		    =      old['token_embedder/embedding']

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

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

 SCREAMING_SNAKE_CASE		    =      old['encoder/encoder_norm/scale']

 if not scalable_attention:
  SCREAMING_SNAKE_CASE		    =      tax_relpos_bias_lookup(
      _UpperCamelCase		,				0		,				'encoder'						).T
  SCREAMING_SNAKE_CASE		    =      tax_relpos_bias_lookup(
      _UpperCamelCase		,				0		,				'decoder'						).T

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

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

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

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

  SCREAMING_SNAKE_CASE		    =      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:
   SCREAMING_SNAKE_CASE		    =      old['decoder/logits_dense/kernel'].T

 return new


def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	bool						)		->				Optional[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      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:
  SCREAMING_SNAKE_CASE		    =      state_dict['shared.weight']

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

 return state_dict


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	List[str]		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Optional[int]						)		->				Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      checkpoints.load_tax_checkpoint(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      convert_tax_to_pytorch(
     _UpperCamelCase		,				num_layers=config.num_layers		,				is_encoder_only=_UpperCamelCase		,				scalable_attention=_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      make_state_dict(_UpperCamelCase		,				_UpperCamelCase						)
 model.load_state_dict(_UpperCamelCase		,				strict=_UpperCamelCase						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	bool = False		,				_UpperCamelCase		:	bool = False		,				)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      MTaConfig.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:
  SCREAMING_SNAKE_CASE		    =      UMTaEncoderModel(_UpperCamelCase						)
 else:
  SCREAMING_SNAKE_CASE		    =      UMTaForConditionalGeneration(_UpperCamelCase						)

 # Load weights from tf checkpoint
 load_tax_weights_in_ta(_UpperCamelCase		,				_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__":
      a_     :    str     		=   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_     :    int     		=   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,
      )




import re

from filelock import FileLock


try:
      import nltk

      a_     :    Optional[Any]     		=   True
except (ImportError, ModuleNotFoundError):
      a_     :    Union[str, Any]     		=   False

if NLTK_AVAILABLE:
      with FileLock(".lock") as lock:
            nltk.download("punkt", quiet=True)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 re.sub('<n>'		,				''		,				_UpperCamelCase						)  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(_UpperCamelCase						)						)

