





import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase="attention"					):

       '''simple docstring'''





       _UpperCamelCase              =						params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
       _UpperCamelCase              =						params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
       _UpperCamelCase              =						params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
       _UpperCamelCase              =						params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
       return k, o, q, v


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase=False					):

       '''simple docstring'''





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

       _UpperCamelCase              =						params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
       return wi, wo


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):

       '''simple docstring'''





       return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       *, lowerCAmelCase    ,       lowerCAmelCase					):

       '''simple docstring'''





       _UpperCamelCase              =						traverse_util.flatten_dict(variables['''target''']					)
       _UpperCamelCase              =						{'''/'''.join(lowerCAmelCase					): v for k, v in old.items()}

       # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
       _UpperCamelCase              =						'''encoder/layers_0/mlp/wi_0/kernel''' in old
       print('''Split MLP:'''    ,       lowerCAmelCase					)

       _UpperCamelCase              =						collections.OrderedDict()

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

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

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

       _UpperCamelCase              =						old[
           '''encoder/relpos_bias/rel_embedding'''
       ].T
       _UpperCamelCase              =						old['''encoder/encoder_norm/scale''']

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

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

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

              _UpperCamelCase              =						old['''decoder/decoder_norm/scale''']
              _UpperCamelCase              =						old[
                  '''decoder/relpos_bias/rel_embedding'''
              ].T

              # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
              if "decoder/logits_dense/kernel" in old:
                     _UpperCamelCase              =						old['''decoder/logits_dense/kernel'''].T

       return new


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):

       '''simple docstring'''





       _UpperCamelCase              =						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              =						state_dict['''shared.weight''']

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

       return state_dict


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):

       '''simple docstring'''





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


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase = False					):

       '''simple docstring'''





       _UpperCamelCase              =						TaConfig.from_json_file(lowerCAmelCase					)
       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              =						TaEncoderModel(lowerCAmelCase					)
       else:
              _UpperCamelCase              =						TaForConditionalGeneration(lowerCAmelCase					)

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

       # Save pytorch-model
       print(f'''Save PyTorch model to {pytorch_dump_path}'''					)
       model.save_pretrained(lowerCAmelCase					)

       # Verify that we can load the checkpoint.
       model.from_pretrained(lowerCAmelCase					)
       print('''Done'''					)


if __name__ == "__main__":
       lowercase    : Union[str, 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
       )
       lowercase    : str    	=  parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
       )













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase = 0    ,       lowerCAmelCase = 0					):
       _UpperCamelCase              =						right or len(lowerCAmelCase					) - 1
       if left > right:
              return -1
       elif list_data[left] == key:
              return left
       elif list_data[right] == key:
              return right
       else:
              return search(lowerCAmelCase    ,       lowerCAmelCase    ,       left + 1    ,       right - 1					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






