
from typing import Any






def 						__magic_name__			(   __a      :	list						):



     '''simple docstring'''




     if not input_list:
          return []
     UpperCamelCase__				=    [input_list.count(__a						) for value in input_list]
     UpperCamelCase__				=    max(__a						)  # Gets the maximum count in the input list.
     # Gets values of modes
     return sorted({input_list[i] for i, value in enumerate(__a						) if value == y}						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()







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 						__magic_name__			(   __a      :	Dict	,		__a      :	Tuple	,		__a      :	int	,		__a      :	Dict="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 						__magic_name__			(   __a      :	Dict	,		__a      :	Any	,		__a      :	Dict	,		__a      :	Optional[Any]=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 						__magic_name__			(   __a      :	Tuple	,		__a      :	Tuple	,		__a      :	Optional[int]	,		__a      :	Dict						):



     '''simple docstring'''




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






def 						__magic_name__			(   __a      :	dict	,		*, __a      :	int	,		__a      :	bool						):



     '''simple docstring'''




     UpperCamelCase__				=    traverse_util.flatten_dict(variables["""target"""]						)
     UpperCamelCase__				=    {"""/""".join(__a						): 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:"""	,		__a						)

     UpperCamelCase__				=    collections.OrderedDict()

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

     # Encoder.
     for i in range(__a						):
          # Block i, layer 0 (Self Attention).
          UpperCamelCase__				=    tax_layer_norm_lookup(__a	,		__a	,		"""encoder"""	,		"""pre_attention_layer_norm"""						)
          UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    tax_attention_lookup(__a	,		__a	,		"""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(__a	,		__a	,		"""encoder"""	,		"""pre_mlp_layer_norm"""						)
          UpperCamelCase__		,	UpperCamelCase__				=    tax_mlp_lookup(__a	,		__a	,		"""encoder"""	,		__a						)
          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(__a						):
               # Block i, layer 0 (Self Attention).
               UpperCamelCase__				=    tax_layer_norm_lookup(__a	,		__a	,		"""decoder"""	,		"""pre_self_attention_layer_norm"""						)
               UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    tax_attention_lookup(__a	,		__a	,		"""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(__a	,		__a	,		"""decoder"""	,		"""pre_cross_attention_layer_norm"""						)
               UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    tax_attention_lookup(__a	,		__a	,		"""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(__a	,		__a	,		"""decoder"""	,		"""pre_mlp_layer_norm"""						)
               UpperCamelCase__		,	UpperCamelCase__				=    tax_mlp_lookup(__a	,		__a	,		"""decoder"""	,		__a						)
               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 						__magic_name__			(   __a      :	List[Any]	,		__a      :	bool						):



     '''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 						__magic_name__			(   __a      :	Optional[int]	,		__a      :	Optional[int]	,		__a      :	int	,		__a      :	Dict						):



     '''simple docstring'''




     UpperCamelCase__				=    checkpoints.load_tax_checkpoint(__a						)
     UpperCamelCase__				=    convert_tax_to_pytorch(__a	,		num_layers=config.num_layers	,		is_encoder_only=__a						)
     UpperCamelCase__				=    make_state_dict(__a	,		__a						)
     model.load_state_dict(__a	,		strict=__a						)






def 						__magic_name__			(   __a      :	Optional[Any]	,		__a      :	Optional[Any]	,		__a      :	Any	,		__a      :	bool = False						):



     '''simple docstring'''




     UpperCamelCase__				=    TaConfig.from_json_file(__a						)
     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(__a						)
     else:
          UpperCamelCase__				=    TaForConditionalGeneration(__a						)

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

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

     # Verify that we can load the checkpoint.
     model.from_pretrained(__a						)
     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
    )
    lowerCamelCase_						     =					parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
    )





