





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  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_="attention"   ):
       _lowerCamelCase				:  List[str]						=		params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
       _lowerCamelCase				:  List[Any]						=		params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
       _lowerCamelCase				:  Dict						=		params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
       _lowerCamelCase				:  Any						=		params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
       return k, o, q, v




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_=False   ):
       if split_mlp_wi:
              _lowerCamelCase				:  Union[str, Any]						=		params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
              _lowerCamelCase				:  Optional[Any]						=		params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
              _lowerCamelCase				:  List[Any]						=		(wi_a, wi_a)
       else:
              _lowerCamelCase				:  Union[str, Any]						=		params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

       _lowerCamelCase				:  Optional[Any]						=		params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
       return wi, wo




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_   ):
       return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]




def  __UpperCAmelCase(   lowercase_			,     *, lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  Any						=		traverse_util.flatten_dict(variables['''target''']   )
       _lowerCamelCase				:  Tuple						=		{'''/'''.join(lowercase_   ): v for k, v in old.items()}

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

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

       # Shared embeddings.
       _lowerCamelCase				:  List[str]						=		old['''token_embedder/embedding''']

       # Encoder.
       for i in range(lowercase_   ):
              # Block i, layer 0 (Self Attention).
              _lowerCamelCase				:  List[Any]						=		tax_layer_norm_lookup(lowercase_			,     lowercase_			,     '''encoder'''			,     '''pre_attention_layer_norm'''   )
              _lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase				:  List[str]						=		tax_attention_lookup(lowercase_			,     lowercase_			,     '''encoder'''			,     '''attention'''   )
              _lowerCamelCase				:  List[str]						=		layer_norm
              _lowerCamelCase				:  Any						=		k.T
              _lowerCamelCase				:  int						=		o.T
              _lowerCamelCase				:  int						=		q.T
              _lowerCamelCase				:  int						=		v.T

              # Block i, layer 1 (MLP).
              _lowerCamelCase				:  List[str]						=		tax_layer_norm_lookup(lowercase_			,     lowercase_			,     '''encoder'''			,     '''pre_mlp_layer_norm'''   )
              _lowerCamelCase,	_lowerCamelCase				:  List[Any]						=		tax_mlp_lookup(lowercase_			,     lowercase_			,     '''encoder'''			,     lowercase_   )
              _lowerCamelCase				:  Any						=		layer_norm
              if split_mlp_wi:
                     _lowerCamelCase				:  Union[str, Any]						=		wi[0].T
                     _lowerCamelCase				:  List[Any]						=		wi[1].T
              else:
                     _lowerCamelCase				:  Optional[Any]						=		wi.T
              _lowerCamelCase				:  Any						=		wo.T

       _lowerCamelCase				:  List[str]						=		old[
           '''encoder/relpos_bias/rel_embedding'''
       ].T
       _lowerCamelCase				:  str						=		old['''encoder/encoder_norm/scale''']

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

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

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

              _lowerCamelCase				:  List[str]						=		old['''decoder/decoder_norm/scale''']
              _lowerCamelCase				:  str						=		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:
                     _lowerCamelCase				:  List[Any]						=		old['''decoder/logits_dense/kernel'''].T

       return new




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  Tuple						=		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:
              _lowerCamelCase				:  Optional[int]						=		state_dict['''shared.weight''']

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     _lowerCamelCase				:  Optional[int]						=		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.'''   )
                     _lowerCamelCase				:  Optional[Any]						=		state_dict['''shared.weight''']

       return state_dict




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  List[Any]						=		checkpoints.load_tax_checkpoint(lowercase_   )
       _lowerCamelCase				:  Optional[Any]						=		convert_tax_to_pytorch(lowercase_			,     num_layers=config.num_layers			,     is_encoder_only=lowercase_   )
       _lowerCamelCase				:  Tuple						=		make_state_dict(lowercase_			,     lowercase_   )
       model.load_state_dict(lowercase_			,     strict=lowercase_   )




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_ = False   ):
       _lowerCamelCase				:  Any						=		TaConfig.from_json_file(lowercase_   )
       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:
              _lowerCamelCase				:  str						=		TaEncoderModel(lowercase_   )
       else:
              _lowerCamelCase				:  Optional[Any]						=		TaForConditionalGeneration(lowercase_   )

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

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

       # Verify that we can load the checkpoint.
       model.from_pretrained(lowercase_   )
       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
       )








import copy
import random

from transformers import CLIPTokenizer







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				*a__   ,				**a__):



             """simple docstring"""





             super().__init__(*a__   ,				**a__)
             _lowerCamelCase				:  Optional[Any]						=		{}


      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				**a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		super().add_tokens(a__   ,				*a__   ,				**a__)
             if num_added_tokens == 0:
                    raise ValueError(
                        F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
                        ''' `placeholder_token` that is not already in the tokenizer.''')


      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				a__=1   ,				**a__):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		[]
             if num_vec_per_token == 1:
                    self.try_adding_tokens(a__   ,				*a__   ,				**a__)
                    output.append(a__)
             else:
                    _lowerCamelCase				:  Dict						=		[]
                    for i in range(a__):
                           _lowerCamelCase				:  Optional[Any]						=		placeholder_token + F"""_{i}"""
                           self.try_adding_tokens(a__   ,				*a__   ,				**a__)
                           output.append(a__)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
             for token in self.token_map:
                    if token in placeholder_token:
                           raise ValueError(
                               F"""The tokenizer already has placeholder token {token} that can get confused with"""
                               F""" {placeholder_token}keep placeholder tokens independent""")
             _lowerCamelCase				:  int						=		output


      def 					__snake_case      (		self   ,				a__   ,				a__=False   ,				a__=1.0):



             """simple docstring"""





             if isinstance(a__   ,				a__):
                    _lowerCamelCase				:  Tuple						=		[]
                    for i in range(len(a__)):
                           output.append(self.replace_placeholder_tokens_in_text(text[i]   ,				vector_shuffle=a__))
                    return output
             for placeholder_token in self.token_map:
                    if placeholder_token in text:
                           _lowerCamelCase				:  Union[str, Any]						=		self.token_map[placeholder_token]
                           _lowerCamelCase				:  Optional[Any]						=		tokens[: 1 + int(len(a__) * prop_tokens_to_load)]
                           if vector_shuffle:
                                  _lowerCamelCase				:  int						=		copy.copy(a__)
                                  random.shuffle(a__)
                           _lowerCamelCase				:  List[str]						=		text.replace(a__   ,				''' '''.join(a__))
             return text


      def __call__(		self   ,				a__   ,				*a__   ,				a__=False   ,				a__=1.0   ,				**a__):



             """simple docstring"""





             return super().__call__(
                 self.replace_placeholder_tokens_in_text(
                     a__   ,				vector_shuffle=a__   ,				prop_tokens_to_load=a__)   ,				*a__   ,				**a__   ,				)



      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				a__=False   ,				a__=1.0   ,				**a__):



             """simple docstring"""





             return super().encode(
                 self.replace_placeholder_tokens_in_text(
                     a__   ,				vector_shuffle=a__   ,				prop_tokens_to_load=a__)   ,				*a__   ,				**a__   ,				)

