


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_      :		Optional[Any]							,			SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		str       ):






       """simple docstring"""





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


def      a	(							SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		Optional[Any]							,			SCREAMING_SNAKE_CASE_      :		Union[str, Any]="attention"       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]       )
       UpperCamelCase    :	List[Any]   	=		k_tmp.reshape(k_tmp.shape[0]							,			k_tmp.shape[1] * k_tmp.shape[2]       )
       UpperCamelCase    :	List[str]   	=		np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]       )
       UpperCamelCase    :	Union[str, Any]   	=		o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]							,			o_tmp.shape[2]       )
       UpperCamelCase    :	Dict   	=		np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]       )
       UpperCamelCase    :	Optional[int]   	=		q_tmp.reshape(q_tmp.shape[0]							,			q_tmp.shape[1] * q_tmp.shape[2]       )
       UpperCamelCase    :	Union[str, Any]   	=		np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]       )
       UpperCamelCase    :	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_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Any							,			SCREAMING_SNAKE_CASE_      :		Optional[int]=False       ):






       """simple docstring"""





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

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


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		Tuple							,			SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		Tuple       ):






       """simple docstring"""





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


def      a	(							SCREAMING_SNAKE_CASE_      :		dict							,			*, SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		bool							,			SCREAMING_SNAKE_CASE_      :		bool = False       ):






       """simple docstring"""





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

       UpperCamelCase    :	List[str]   	=		collections.OrderedDict()

       # Shared embeddings.
       UpperCamelCase    :	List[Any]   	=		old['''token_embedder/embedding''']

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

              # Block i, layer 1 (MLP).
              UpperCamelCase    :	str   	=		tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			'''encoder'''							,			'''pre_mlp_layer_norm'''       )
              UpperCamelCase	, UpperCamelCase    :	Dict   	=		tax_mlp_lookup(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			'''encoder'''							,			SCREAMING_SNAKE_CASE_       )
              UpperCamelCase    :	int   	=		layer_norm
              if split_mlp_wi:
                     UpperCamelCase    :	Dict   	=		wi[0].T
                     UpperCamelCase    :	List[str]   	=		wi[1].T
              else:
                     UpperCamelCase    :	Optional[int]   	=		wi.T
              UpperCamelCase    :	List[Any]   	=		wo.T
              if scalable_attention:
                     # convert the rel_embedding of each layer
                     UpperCamelCase    :	Optional[Any]   	=		tax_relpos_bias_lookup(
                         SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			'''encoder'''       ).T

       UpperCamelCase    :	List[Any]   	=		old['''encoder/encoder_norm/scale''']

       if not scalable_attention:
              UpperCamelCase    :	Any   	=		tax_relpos_bias_lookup(
                  SCREAMING_SNAKE_CASE_							,			0							,			'''encoder'''       ).T
              UpperCamelCase    :	Union[str, Any]   	=		tax_relpos_bias_lookup(
                  SCREAMING_SNAKE_CASE_							,			0							,			'''decoder'''       ).T

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

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

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

                     if scalable_attention:
                            # convert the rel_embedding of each layer
                            UpperCamelCase    :	Optional[Any]   	=		tax_relpos_bias_lookup(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			'''decoder'''       ).T

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

       return new


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






       """simple docstring"""





       UpperCamelCase    :	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:
              UpperCamelCase    :	Optional[Any]   	=		state_dict['''shared.weight''']

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

       return state_dict


def      a	(							SCREAMING_SNAKE_CASE_      :		List[str]							,			SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Optional[Any]							,			SCREAMING_SNAKE_CASE_      :		Optional[int]       ):






       """simple docstring"""





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


def      a	(							SCREAMING_SNAKE_CASE_      :		List[str]							,			SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		bool = False							,			SCREAMING_SNAKE_CASE_      :		bool = False							,			):






       """simple docstring"""





       UpperCamelCase    :	Optional[Any]   	=		MTaConfig.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:
              UpperCamelCase    :	List[Any]   	=		UMTaEncoderModel(SCREAMING_SNAKE_CASE_       )
       else:
              UpperCamelCase    :	str   	=		UMTaForConditionalGeneration(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_							,			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__":
  __UpperCAmelCase : Optional[int] 			=				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,
  )
  __UpperCAmelCase : Optional[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 pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class    UpperCAmelCase_	(							_a):
       '''simple docstring'''






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              with open(__SCREAMING_SNAKE_CASE ,					encoding='''utf-8'''						) as input_file:
                     UpperCamelCase    :	str   	=		re.compile(R'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'''						)
                     UpperCamelCase    :	Optional[int]   	=		input_file.read()
                     UpperCamelCase    :	Union[str, Any]   	=		regexp.search(__SCREAMING_SNAKE_CASE						)

              return match






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              with open(__SCREAMING_SNAKE_CASE ,					encoding='''utf-8'''						) as input_file:
                     UpperCamelCase    :	Optional[int]   	=		re.compile(R'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()''' ,					re.DOTALL						)
                     UpperCamelCase    :	Tuple   	=		input_file.read()
                     # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
                     UpperCamelCase    :	Dict   	=		regexp.finditer(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	int   	=		[match for match in matches if match is not None and match.group(1						) is not None]
              return matches[0] if matches else None






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		Path('''./datasets'''						)
              UpperCamelCase    :	Tuple   	=		list(dataset_paths.absolute().glob('''**/*.py'''						)						)

              for dataset in dataset_files:
                     if self._no_encoding_on_file_open(str(__SCREAMING_SNAKE_CASE						)						):
                            raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}"""						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		Path('''./datasets'''						)
              UpperCamelCase    :	Tuple   	=		list(dataset_paths.absolute().glob('''**/*.py'''						)						)

              for dataset in dataset_files:
                     if self._no_print_statements(str(__SCREAMING_SNAKE_CASE						)						):
                            raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead."""						)

