





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		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase="attention"     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase=False     ):
   '''simple docstring'''


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

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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,*, _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase = False     ):
   '''simple docstring'''


   A_     :				int			      =	traverse_util.flatten_dict(variables["""target"""]     )
   A_     :				Dict			      =	{"""/""".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_     :				Tuple			      =	"""encoder/encoder/mlp/wi_0/kernel""" in old
   print("""Split MLP:"""  ,UpperCAmelCase__     )

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

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

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

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

   A_     :				Dict			      =	old["""encoder/encoder_norm/scale"""]

   if not scalable_attention:
      A_     :				Dict			      =	tax_relpos_bias_lookup(
          UpperCAmelCase__  ,0  ,"""encoder"""     ).T
      A_     :				List[Any]			      =	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).
         A_     :				List[Any]			      =	tax_layer_norm_lookup(UpperCAmelCase__  ,UpperCAmelCase__  ,"""decoder"""  ,"""pre_self_attention_layer_norm"""     )
         A_     :				str			      =	tax_attention_lookup(UpperCAmelCase__  ,UpperCAmelCase__  ,"""decoder"""  ,"""self_attention"""     )
         A_     :				Tuple			      =	layer_norm
         A_     :				Optional[int]			      =	k.T
         A_     :				str			      =	o.T
         A_     :				List[Any]			      =	q.T
         A_     :				List[str]			      =	v.T

         # Block i, layer 1 (Cross Attention).
         A_     :				Tuple			      =	tax_layer_norm_lookup(UpperCAmelCase__  ,UpperCAmelCase__  ,"""decoder"""  ,"""pre_cross_attention_layer_norm"""     )
         A_     :				Union[str, Any]			      =	tax_attention_lookup(UpperCAmelCase__  ,UpperCAmelCase__  ,"""decoder"""  ,"""encoder_decoder_attention"""     )
         A_     :				Union[str, Any]			      =	layer_norm
         A_     :				Union[str, Any]			      =	k.T
         A_     :				str			      =	o.T
         A_     :				Dict			      =	q.T
         A_     :				Union[str, Any]			      =	v.T

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

         if scalable_attention:
            # convert the rel_embedding of each layer
            A_     :				Tuple			      =	tax_relpos_bias_lookup(UpperCAmelCase__  ,UpperCAmelCase__  ,"""decoder"""     ).T

      A_     :				List[Any]			      =	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_     :				Tuple			      =	old["""decoder/logits_dense/kernel"""].T

   return new



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''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_     :				Tuple			      =	state_dict["""shared.weight"""]

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

   return state_dict



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	checkpoints.load_tax_checkpoint(UpperCAmelCase__     )
   A_     :				Tuple			      =	convert_tax_to_pytorch(
       UpperCAmelCase__  ,num_layers=config.num_layers  ,is_encoder_only=UpperCAmelCase__  ,scalable_attention=UpperCAmelCase__     )
   A_     :				int			      =	make_state_dict(UpperCAmelCase__  ,UpperCAmelCase__     )
   model.load_state_dict(UpperCAmelCase__  ,strict=UpperCAmelCase__     )



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase = False  ,_lowerCAmelCase = False  ,):
   '''simple docstring'''


   A_     :				Any			      =	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:
      A_     :				Any			      =	UMTaEncoderModel(UpperCAmelCase__     )
   else:
      A_     :				Any			      =	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__":
      _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
      )
      parser.add_argument(
          """--scalable_attention""",
          action="""store_true""",
          help="""Whether the model uses scaled attention (umt5 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,
          args.scalable_attention,
      )










from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,):
   '''simple docstring'''


   A_      ,				A_     :				int			      =	coefficient_matrix.shape
   A_      ,				A_     :				Tuple			      =	constant_matrix.shape

   if rowsa != colsa:
      A_     :				int			      =	f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
      raise ValueError(_lowerCAmelCase     )

   if colsa != 1:
      A_     :				List[Any]			      =	f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
      raise ValueError(_lowerCAmelCase     )

   if rowsa != rowsa:
      A_     :				str			      =	(
          """Coefficient and constant matrices dimensions must be nxn and nx1 but """
          f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
      )
      raise ValueError(_lowerCAmelCase     )

   if len(_lowerCAmelCase     ) != rowsa:
      A_     :				Any			      =	(
          """Number of initial values must be equal to number of rows in coefficient """
          f"""matrix but received {len(_lowerCAmelCase     )} and {rowsa}"""
      )
      raise ValueError(_lowerCAmelCase     )

   if iterations <= 0:
      raise ValueError("""Iterations must be at least 1"""     )

   A_     :				NDArray[floataa]			      =	np.concatenate(
       (coefficient_matrix, constant_matrix)  ,axis=1     )

   A_      ,				A_     :				str			      =	table.shape

   strictly_diagonally_dominant(_lowerCAmelCase     )

   # Iterates the whole matrix for given number of times
   for _ in range(_lowerCAmelCase     ):
      A_     :				Union[str, Any]			      =	[]
      for row in range(_lowerCAmelCase     ):
         A_     :				str			      =	0
         for col in range(_lowerCAmelCase     ):
            if col == row:
               A_     :				Optional[Any]			      =	table[row][col]
            elif col == cols - 1:
               A_     :				List[str]			      =	table[row][col]
            else:
               temp += (-1) * table[row][col] * init_val[col]
         A_     :				str			      =	(temp + val) / denom
         new_val.append(_lowerCAmelCase     )
      A_     :				List[str]			      =	new_val

   return [float(_lowerCAmelCase     ) for i in new_val]



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_      ,				A_     :				str			      =	table.shape

   A_     :				Any			      =	True

   for i in range(0  ,_lowerCAmelCase     ):
      A_     :				Optional[Any]			      =	0
      for j in range(0  ,cols - 1     ):
         if i == j:
            continue
         else:
            total += table[i][j]

      if table[i][i] <= total:
         raise ValueError("""Coefficient matrix is not strictly diagonally dominant"""     )

   return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
      import doctest

      doctest.testmod()






