





'''simple docstring'''




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[str]:
 __UpperCAmelCase					=   AutoConfig.from_pretrained(_lowerCAmelCase    )
 __UpperCAmelCase					=   FlaxAutoModelForSeqaSeqLM.from_config(config=_lowerCAmelCase    )
 __UpperCAmelCase					=   checkpoints.load_tax_checkpoint(_lowerCAmelCase    )

 __UpperCAmelCase					=   'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

 if config.model_type == "t5":
  __UpperCAmelCase					=   'SelfAttention'
 if config.model_type == "longt5" and config.encoder_attention_type == "local":
  __UpperCAmelCase					=   'LocalSelfAttention'
 elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
  __UpperCAmelCase					=   'TransientGlobalSelfAttention'
 else:
  raise ValueError(
      'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
      ' attribute with a value from [\'local\', \'transient-global].'    )

 # Encoder
 for layer_index in range(config.num_layers    ):
  __UpperCAmelCase					=   F'layers_{str(_lowerCAmelCase    )}'

  # Self-Attention
  __UpperCAmelCase					=   tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
  __UpperCAmelCase					=   tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
  __UpperCAmelCase					=   tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
  __UpperCAmelCase					=   tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

  # Global input layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
   __UpperCAmelCase					=   tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

  # Layer Normalization
  __UpperCAmelCase					=   tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

  if split_mlp_wi:
   __UpperCAmelCase					=   tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
   __UpperCAmelCase					=   tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
  else:
   __UpperCAmelCase					=   tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

  __UpperCAmelCase					=   tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

  # Layer Normalization
  __UpperCAmelCase					=   tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

  # Assigning
  __UpperCAmelCase					=   flax_model.params['encoder']['block'][str(_lowerCAmelCase    )]['layer']
  __UpperCAmelCase					=   tax_attention_key
  __UpperCAmelCase					=   tax_attention_out
  __UpperCAmelCase					=   tax_attention_query
  __UpperCAmelCase					=   tax_attention_value

  __UpperCAmelCase					=   tax_attention_layer_norm

  # Global input layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
   __UpperCAmelCase					=   tax_global_layer_norm

  if split_mlp_wi:
   __UpperCAmelCase					=   tax_mlp_wi_a
   __UpperCAmelCase					=   tax_mlp_wi_a
  else:
   __UpperCAmelCase					=   tax_mlp_wi

  __UpperCAmelCase					=   tax_mlp_wo
  __UpperCAmelCase					=   tax_mlp_layer_norm

  __UpperCAmelCase					=   flax_model_encoder_layer_block

 # Only for layer 0:
 __UpperCAmelCase					=   tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
 __UpperCAmelCase					=   tax_encoder_rel_embedding

 # Side/global relative position_bias + layer norm
 if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
  __UpperCAmelCase					=   tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
  __UpperCAmelCase					=   tax_encoder_global_rel_embedding

 # Assigning
 __UpperCAmelCase					=   tax_model['target']['encoder']['encoder_norm']['scale']
 __UpperCAmelCase					=   tax_encoder_norm

 # Decoder
 for layer_index in range(config.num_layers    ):
  __UpperCAmelCase					=   F'layers_{str(_lowerCAmelCase    )}'

  # Self-Attention
  __UpperCAmelCase					=   tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
  __UpperCAmelCase					=   tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
  __UpperCAmelCase					=   tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
  __UpperCAmelCase					=   tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

  # Layer Normalization
  __UpperCAmelCase					=   tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
      'scale'
  ]

  # Encoder-Decoder-Attention
  __UpperCAmelCase					=   tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
  __UpperCAmelCase					=   tax_enc_dec_attention_module['key']['kernel']
  __UpperCAmelCase					=   tax_enc_dec_attention_module['out']['kernel']
  __UpperCAmelCase					=   tax_enc_dec_attention_module['query']['kernel']
  __UpperCAmelCase					=   tax_enc_dec_attention_module['value']['kernel']

  # Layer Normalization
  __UpperCAmelCase					=   tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

  # MLP
  if split_mlp_wi:
   __UpperCAmelCase					=   tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
   __UpperCAmelCase					=   tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
  else:
   __UpperCAmelCase					=   tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

  __UpperCAmelCase					=   tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

  # Layer Normalization
  __UpperCAmelCase					=   tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

  # Assigning
  __UpperCAmelCase					=   flax_model.params['decoder']['block'][str(_lowerCAmelCase    )]['layer']
  __UpperCAmelCase					=   tax_attention_key
  __UpperCAmelCase					=   tax_attention_out
  __UpperCAmelCase					=   tax_attention_query
  __UpperCAmelCase					=   tax_attention_value

  __UpperCAmelCase					=   tax_pre_attention_layer_norm

  __UpperCAmelCase					=   tax_enc_dec_attention_key
  __UpperCAmelCase					=   tax_enc_dec_attention_out
  __UpperCAmelCase					=   tax_enc_dec_attention_query
  __UpperCAmelCase					=   tax_enc_dec_attention_value

  __UpperCAmelCase					=   tax_cross_layer_norm

  if split_mlp_wi:
   __UpperCAmelCase					=   tax_mlp_wi_a
   __UpperCAmelCase					=   tax_mlp_wi_a
  else:
   __UpperCAmelCase					=   tax_mlp_wi

  __UpperCAmelCase					=   tax_mlp_wo

  __UpperCAmelCase					=   txa_mlp_layer_norm

  __UpperCAmelCase					=   flax_model_decoder_layer_block

 # Decoder Normalization
 __UpperCAmelCase					=   tax_model['target']['decoder']['decoder_norm']['scale']
 __UpperCAmelCase					=   txa_decoder_norm

 # Only for layer 0:
 __UpperCAmelCase					=   tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
 __UpperCAmelCase					=   tax_decoder_rel_embedding

 # Token Embeddings
 __UpperCAmelCase					=   tax_model['target']['token_embedder']['embedding']
 __UpperCAmelCase					=   txa_token_embeddings

 # LM Head (only in v1.1 and LongT5 checkpoints)
 if "logits_dense" in tax_model["target"]["decoder"]:
  __UpperCAmelCase					=   tax_model['target']['decoder']['logits_dense']['kernel']

 flax_model.save_pretrained(_lowerCAmelCase    )
 print('T5X Model was sucessfully converted!'    )


if __name__ == "__main__":
    _A:					str							=				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
    )
    parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
    parser.add_argument(
        """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
    )
    _A:					int							=				parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)












'''simple docstring'''




import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class UpperCAmelCase							(		UpperCAmelCase_					,			unittest.TestCase							):
    _A :			List[str]						   =       MgpstrTokenizer
    _A :			Any						   =       False
    _A :			List[Any]						   =       {}
    _A :			int						   =       False



    def 					__lowerCamelCase						(  self					):
     super().setUp()

     # fmt: off
     __UpperCAmelCase					=   ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
     # fmt: on
     __UpperCAmelCase					=   dict(zip(__A     ,			range(len(__A					)					)					)					)

     __UpperCAmelCase					=   os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']					)
     with open(self.vocab_file     ,			'w'     ,			encoding='utf-8'					) as fp:
      fp.write(json.dumps(__A					) + '\n'					)



    def 					__lowerCamelCase						(  self     ,			**__A					):
     return MgpstrTokenizer.from_pretrained(self.tmpdirname     ,			**__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   'tester'
     __UpperCAmelCase					=   'tester'
     return input_text, output_text



    @unittest.skip('MGP-STR always lower cases letters.'					)
    def 					__lowerCamelCase						(  self					):
     pass



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_tokenizers(do_lower_case=__A					)
     for tokenizer in tokenizers:
      with self.subTest(f'{tokenizer.__class__.__name__}'					):
       __UpperCAmelCase					=   '[SPECIAL_TOKEN]'

       tokenizer.add_special_tokens({'cls_token': special_token}					)
       __UpperCAmelCase					=   tokenizer.encode([special_token]     ,			add_special_tokens=__A					)
       self.assertEqual(len(__A					)     ,			1					)

       __UpperCAmelCase					=   tokenizer.decode(__A     ,			skip_special_tokens=__A					)
       self.assertTrue(special_token not in decoded					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_tokenizers()
     for tokenizer in tokenizers:
      with self.subTest(f'{tokenizer.__class__.__name__}'					):
       __UpperCAmelCase					,    __UpperCAmelCase					=   self.get_input_output_texts(__A					)

       __UpperCAmelCase					=   tokenizer.tokenize(__A					)
       __UpperCAmelCase					=   tokenizer.convert_tokens_to_ids(__A					)
       __UpperCAmelCase					=   tokenizer.encode(__A     ,			add_special_tokens=__A					)
       self.assertListEqual(__A     ,			__A					)

       __UpperCAmelCase					=   tokenizer.convert_ids_to_tokens(__A					)
       self.assertNotEqual(len(__A					)     ,			0					)
       __UpperCAmelCase					=   tokenizer.decode(__A					)
       self.assertIsInstance(__A     ,			__A					)

       self.assertEqual(text_a.replace(' '     ,			''					)     ,			__A					)



    @unittest.skip('MGP-STR tokenizer only handles one sequence.'					)
    def 					__lowerCamelCase						(  self					):
     pass




    @unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer'					)
    def 					__lowerCamelCase						(  self					):
     pass





