






import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )				->			Dict:
     UpperCamelCase__    :     Optional[int]        =						TaConfig.from_pretrained(__a    )
     UpperCamelCase__    :     Dict        =						FlaxTaForConditionalGeneration(config=__a    )
     UpperCamelCase__    :     List[Any]        =						checkpoints.load_tax_checkpoint(__a    )

     UpperCamelCase__    :     List[str]        =						'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

     # Encoder
     for layer_index in range(config.num_layers    ):
          UpperCamelCase__    :     str        =						f'layers_{str(__a    )}'

          # Self-Attention
          UpperCamelCase__    :     Dict        =						tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
          UpperCamelCase__    :     int        =						tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
          UpperCamelCase__    :     int        =						tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
          UpperCamelCase__    :     Tuple        =						tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

          # Layer Normalization
          UpperCamelCase__    :     Optional[int]        =						tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

          if split_mlp_wi:
               UpperCamelCase__    :     Optional[int]        =						tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               UpperCamelCase__    :     Tuple        =						tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               UpperCamelCase__    :     int        =						tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          UpperCamelCase__    :     int        =						tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          UpperCamelCase__    :     Optional[Any]        =						tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          UpperCamelCase__    :     Dict        =						tax_attention_key
          UpperCamelCase__    :     str        =						tax_attention_out
          UpperCamelCase__    :     Any        =						tax_attention_query
          UpperCamelCase__    :     str        =						tax_attention_value

          UpperCamelCase__    :     List[Any]        =						tax_attention_layer_norm

          if split_mlp_wi:
               UpperCamelCase__    :     int        =						tax_mlp_wi_a
               UpperCamelCase__    :     Dict        =						tax_mlp_wi_a
          else:
               UpperCamelCase__    :     Tuple        =						tax_mlp_wi

          UpperCamelCase__    :     List[str]        =						tax_mlp_wo
          UpperCamelCase__    :     Union[str, Any]        =						tax_mlp_layer_norm

     # Only for layer 0:
     UpperCamelCase__    :     Optional[Any]        =						tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
     UpperCamelCase__    :     Union[str, Any]        =						tax_encoder_rel_embedding

     # Assigning
     UpperCamelCase__    :     List[str]        =						tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
     UpperCamelCase__    :     Union[str, Any]        =						tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers    ):
          UpperCamelCase__    :     Optional[int]        =						f'layers_{str(__a    )}'

          # Self-Attention
          UpperCamelCase__    :     Optional[Any]        =						tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
          UpperCamelCase__    :     str        =						tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
          UpperCamelCase__    :     Dict        =						tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
          UpperCamelCase__    :     int        =						tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

          # Layer Normalization
          UpperCamelCase__    :     Tuple        =						tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
              '''scale'''
          ]

          # Encoder-Decoder-Attention
          UpperCamelCase__    :     List[str]        =						tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
              '''kernel'''
          ]
          UpperCamelCase__    :     List[str]        =						tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
              '''kernel'''
          ]
          UpperCamelCase__    :     Optional[Any]        =						tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
              '''kernel'''
          ]
          UpperCamelCase__    :     List[Any]        =						tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
              '''kernel'''
          ]

          # Layer Normalization
          UpperCamelCase__    :     Any        =						tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

          # MLP
          if split_mlp_wi:
               UpperCamelCase__    :     List[Any]        =						tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               UpperCamelCase__    :     Any        =						tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               UpperCamelCase__    :     Union[str, Any]        =						tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          UpperCamelCase__    :     Dict        =						tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          UpperCamelCase__    :     Optional[int]        =						tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          UpperCamelCase__    :     Optional[int]        =						tax_attention_key
          UpperCamelCase__    :     Tuple        =						tax_attention_out
          UpperCamelCase__    :     Optional[int]        =						tax_attention_query
          UpperCamelCase__    :     Dict        =						tax_attention_value

          UpperCamelCase__    :     List[Any]        =						tax_pre_attention_layer_norm

          UpperCamelCase__    :     Optional[int]        =						tax_enc_dec_attention_key
          UpperCamelCase__    :     Optional[int]        =						tax_enc_dec_attention_out
          UpperCamelCase__    :     Optional[Any]        =						tax_enc_dec_attention_query
          UpperCamelCase__    :     List[Any]        =						tax_enc_dec_attention_value

          UpperCamelCase__    :     List[Any]        =						tax_cross_layer_norm

          if split_mlp_wi:
               UpperCamelCase__    :     Tuple        =						tax_mlp_wi_a
               UpperCamelCase__    :     Tuple        =						tax_mlp_wi_a
          else:
               UpperCamelCase__    :     Any        =						tax_mlp_wi

          UpperCamelCase__    :     int        =						tax_mlp_wo

          UpperCamelCase__    :     Dict        =						txa_mlp_layer_norm

     # Decoder Normalization
     UpperCamelCase__    :     int        =						tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
     UpperCamelCase__    :     Tuple        =						txa_decoder_norm

     # Only for layer 0:
     UpperCamelCase__    :     List[str]        =						tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
     UpperCamelCase__    :     Tuple        =						tax_decoder_rel_embedding

     # Token Embeddings
     UpperCamelCase__    :     List[str]        =						tax_model['''target''']['''token_embedder''']['''embedding''']
     UpperCamelCase__    :     Union[str, Any]        =						txa_token_embeddings

     # LM Head (only in v1.1 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          UpperCamelCase__    :     str        =						tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

     flax_model.save_pretrained(__a    )
     print("T5X Model was sucessfully converted!"    )


if __name__ == "__main__":
 lowerCamelCase			:							Union[str, Any]       =argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the TX5 checkpoint.'''
 )
 parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of T5 model.''')
 parser.add_argument(
     '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
 )
 lowerCamelCase			:							List[Any]       =parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class     __a						(					A__ ,     unittest.TestCase ):
 _lowerCAmelCase : str 				=				MPNetTokenizer
 _lowerCAmelCase : List[str] 				=				MPNetTokenizerFast
 _lowerCAmelCase : List[Any] 				=				True
 _lowerCAmelCase : Union[str, Any] 				=				True







 def       __lowercase      (	self      :     List[Any]		):


      '''simple docstring'''






      super().setUp()

      UpperCamelCase__    :     List[Any]        =						[
          "[UNK]",
          "[CLS]",
          "[SEP]",
          "[PAD]",
          "[MASK]",
          "want",
          "##want",
          "##ed",
          "wa",
          "un",
          "runn",
          "##ing",
          ",",
          "low",
          "lowest",
      ]
      UpperCamelCase__    :     int        =						os.path.join(self.tmpdirname							,     VOCAB_FILES_NAMES["vocab_file"]		)
      with open(self.vocab_file							,     "w"							,     encoding="utf-8"		) as vocab_writer:
           vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)







 def       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     List[str]		):


      '''simple docstring'''






      UpperCamelCase__    :     List[str]        =						"UNwant\u00E9d,running"
      UpperCamelCase__    :     List[str]        =						"unwanted, running"
      return input_text, output_text







 def       __lowercase      (	self      :     Any		):


      '''simple docstring'''






      UpperCamelCase__    :     str        =						self.tokenizer_class(self.vocab_file		)

      UpperCamelCase__    :     Any        =						tokenizer.tokenize("UNwant\u00E9d,running"		)
      self.assertListEqual(SCREAMING_SNAKE_CASE							,     ["un", "##want", "##ed", ",", "runn", "##ing"]		)
      self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE		)							,     [9, 6, 7, 12, 10, 11]		)







 @slow
 def       __lowercase      (	self      :     str		):


      '''simple docstring'''






      UpperCamelCase__    :     Union[str, Any]        =						self.tokenizer_class.from_pretrained("microsoft/mpnet-base"		)

      UpperCamelCase__    :     Optional[int]        =						tokenizer.encode("sequence builders"							,     add_special_tokens=SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     Optional[int]        =						tokenizer.encode("multi-sequence build"							,     add_special_tokens=SCREAMING_SNAKE_CASE		)

      UpperCamelCase__    :     Tuple        =						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     Optional[int]        =						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)

      assert encoded_sentence == [0] + text + [2]
      assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]