






'''simple docstring'''
import os
import unittest

from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english






@require_tokenizers
class        __SCREAMING_SNAKE_CASE (     lowerCamelCase		,			unittest.TestCase			):
 snake_case_												=							MobileBertTokenizer
 snake_case_												=							MobileBertTokenizerFast
 snake_case_												=							True
 snake_case_												=							True
 snake_case_												=							filter_non_english
 snake_case_												=							"""google/mobilebert-uncased"""




 def        __magic_name__		( self		:							Dict )		->  List[Any]:
       super().setUp()

       SCREAMING_SNAKE_CASE__       :			str													=[
           '''[UNK]''',
           '''[CLS]''',
           '''[SEP]''',
           '''[PAD]''',
           '''[MASK]''',
           '''want''',
           '''##want''',
           '''##ed''',
           '''wa''',
           '''un''',
           '''runn''',
           '''##ing''',
           ''',''',
           '''low''',
           '''lowest''',
       ]
       SCREAMING_SNAKE_CASE__       :			List[Any]													=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] ) )

       SCREAMING_SNAKE_CASE__       :			Tuple													=[
           (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2])  # else the 'google/' prefix is stripped
           for tokenizer_def in self.tokenizers_list
       ]




 def        __magic_name__		( self		:							str   , __lowercase		:							str )		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__       :			Tuple													='''UNwant\u00E9d,running'''
       SCREAMING_SNAKE_CASE__       :			List[str]													='''unwanted, running'''
       return input_text, output_text




 def        __magic_name__		( self		:							Tuple )		->  str:
       SCREAMING_SNAKE_CASE__       :			Dict													=self.tokenizer_class(self.vocab_file )

       SCREAMING_SNAKE_CASE__       :			Dict													=tokenizer.tokenize('''UNwant\u00E9d,running''' )
       self.assertListEqual(__lowercase   , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase )   , [9, 6, 7, 12, 10, 11] )




 def        __magic_name__		( self		:							Optional[int] )		->  List[Any]:
       if not self.test_rust_tokenizer:
             return

       SCREAMING_SNAKE_CASE__       :			Dict													=self.get_tokenizer()
       SCREAMING_SNAKE_CASE__       :			List[str]													=self.get_rust_tokenizer()

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													='''UNwant\u00E9d,running'''

       SCREAMING_SNAKE_CASE__       :			List[str]													=tokenizer.tokenize(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=rust_tokenizer.tokenize(__lowercase )
       self.assertListEqual(__lowercase   , __lowercase )

       SCREAMING_SNAKE_CASE__       :			Any													=tokenizer.encode(__lowercase   , add_special_tokens=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Any													=rust_tokenizer.encode(__lowercase   , add_special_tokens=__lowercase )
       self.assertListEqual(__lowercase   , __lowercase )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.get_rust_tokenizer()
       SCREAMING_SNAKE_CASE__       :			Dict													=tokenizer.encode(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=rust_tokenizer.encode(__lowercase )
       self.assertListEqual(__lowercase   , __lowercase )

       # With lower casing
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.get_tokenizer(do_lower_case=__lowercase )
       SCREAMING_SNAKE_CASE__       :			List[str]													=self.get_rust_tokenizer(do_lower_case=__lowercase )

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''UNwant\u00E9d,running'''

       SCREAMING_SNAKE_CASE__       :			List[str]													=tokenizer.tokenize(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=rust_tokenizer.tokenize(__lowercase )
       self.assertListEqual(__lowercase   , __lowercase )

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tokenizer.encode(__lowercase   , add_special_tokens=__lowercase )
       SCREAMING_SNAKE_CASE__       :			int													=rust_tokenizer.encode(__lowercase   , add_special_tokens=__lowercase )
       self.assertListEqual(__lowercase   , __lowercase )

       SCREAMING_SNAKE_CASE__       :			List[Any]													=self.get_rust_tokenizer()
       SCREAMING_SNAKE_CASE__       :			List[str]													=tokenizer.encode(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=rust_tokenizer.encode(__lowercase )
       self.assertListEqual(__lowercase   , __lowercase )




 def        __magic_name__		( self		:							List[str] )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Tuple													=BasicTokenizer()

       self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz''' )   , ['''ah''', '''\u535A''', '''\u63A8''', '''zz'''] )




 def        __magic_name__		( self		:							List[Any] )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=BasicTokenizer(do_lower_case=__lowercase )

       self.assertListEqual(
           tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  ''' )   , ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
       self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' )   , ['''hello'''] )




 def        __magic_name__		( self		:							Union[str, Any] )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__       :			Any													=BasicTokenizer(do_lower_case=__lowercase   , strip_accents=__lowercase )

       self.assertListEqual(
           tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' )   , ['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
       self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' )   , ['''h\u00E9llo'''] )




 def        __magic_name__		( self		:							List[str] )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			Tuple													=BasicTokenizer(do_lower_case=__lowercase   , strip_accents=__lowercase )

       self.assertListEqual(
           tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' )   , ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
       self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' )   , ['''hello'''] )




 def        __magic_name__		( self		:							List[Any] )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__       :			Dict													=BasicTokenizer(do_lower_case=__lowercase )

       self.assertListEqual(
           tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' )   , ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
       self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' )   , ['''hello'''] )




 def        __magic_name__		( self		:							Optional[Any] )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__       :			Any													=BasicTokenizer(do_lower_case=__lowercase )

       self.assertListEqual(
           tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  ''' )   , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )




 def        __magic_name__		( self		:							List[Any] )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			int													=BasicTokenizer(do_lower_case=__lowercase   , strip_accents=__lowercase )

       self.assertListEqual(
           tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' )   , ['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )




 def        __magic_name__		( self		:							Union[str, Any] )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=BasicTokenizer(do_lower_case=__lowercase   , strip_accents=__lowercase )

       self.assertListEqual(
           tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' )   , ['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )




 def        __magic_name__		( self		:							Optional[Any] )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=BasicTokenizer(do_lower_case=__lowercase   , never_split=['''[UNK]'''] )

       self.assertListEqual(
           tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]''' )   , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]'''] )




 def        __magic_name__		( self		:							int )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

       SCREAMING_SNAKE_CASE__       :			int													={}
       for i, token in enumerate(__lowercase ):
             SCREAMING_SNAKE_CASE__       :			List[str]													=i
       SCREAMING_SNAKE_CASE__       :			List[Any]													=WordpieceTokenizer(vocab=__lowercase   , unk_token='''[UNK]''' )

       self.assertListEqual(tokenizer.tokenize('''''' )   , [] )

       self.assertListEqual(tokenizer.tokenize('''unwanted running''' )   , ['''un''', '''##want''', '''##ed''', '''runn''', '''##ing'''] )

       self.assertListEqual(tokenizer.tokenize('''unwantedX running''' )   , ['''[UNK]''', '''runn''', '''##ing'''] )




 def        __magic_name__		( self		:							List[Any] )		->  Dict:
       self.assertTrue(_is_whitespace(''' ''' ) )
       self.assertTrue(_is_whitespace('''\t''' ) )
       self.assertTrue(_is_whitespace('''\r''' ) )
       self.assertTrue(_is_whitespace('''\n''' ) )
       self.assertTrue(_is_whitespace('''\u00A0''' ) )

       self.assertFalse(_is_whitespace('''A''' ) )
       self.assertFalse(_is_whitespace('''-''' ) )




 def        __magic_name__		( self		:							List[str] )		->  Union[str, Any]:
       self.assertTrue(_is_control('''\u0005''' ) )

       self.assertFalse(_is_control('''A''' ) )
       self.assertFalse(_is_control(''' ''' ) )
       self.assertFalse(_is_control('''\t''' ) )
       self.assertFalse(_is_control('''\r''' ) )




 def        __magic_name__		( self		:							Union[str, Any] )		->  Any:
       self.assertTrue(_is_punctuation('''-''' ) )
       self.assertTrue(_is_punctuation('''$''' ) )
       self.assertTrue(_is_punctuation('''`''' ) )
       self.assertTrue(_is_punctuation('''.''' ) )

       self.assertFalse(_is_punctuation('''A''' ) )
       self.assertFalse(_is_punctuation(''' ''' ) )




 def        __magic_name__		( self		:							Tuple )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			Any													=self.get_tokenizer()
       SCREAMING_SNAKE_CASE__       :			int													=self.get_rust_tokenizer()

       # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
       self.assertListEqual([tokenizer.tokenize(__lowercase ) for t in ['''Test''', '''\xad''', '''test''']]   , [['''[UNK]'''], [], ['''[UNK]''']] )

       self.assertListEqual(
           [rust_tokenizer.tokenize(__lowercase ) for t in ['''Test''', '''\xad''', '''test''']]   , [['''[UNK]'''], [], ['''[UNK]''']] )




 @slow
 def        __magic_name__		( self		:							List[Any] )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=self.tokenizer_class.from_pretrained('''google/mobilebert-uncased''' )

       SCREAMING_SNAKE_CASE__       :			str													=tokenizer.encode('''sequence builders'''   , add_special_tokens=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tokenizer.encode('''multi-sequence build'''   , add_special_tokens=__lowercase )

       SCREAMING_SNAKE_CASE__       :			Dict													=tokenizer.build_inputs_with_special_tokens(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Tuple													=tokenizer.build_inputs_with_special_tokens(__lowercase   , __lowercase )

       assert encoded_sentence == [1_01] + text + [1_02]
       assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]




 def        __magic_name__		( self		:							Any )		->  List[str]:
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
                   SCREAMING_SNAKE_CASE__       :			List[str]													=self.rust_tokenizer_class.from_pretrained(__lowercase   , **__lowercase )

                   SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=F"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
                   SCREAMING_SNAKE_CASE__       :			List[str]													=tokenizer_r.encode_plus(
                       __lowercase   , return_attention_mask=__lowercase   , return_token_type_ids=__lowercase   , return_offsets_mapping=__lowercase   , add_special_tokens=__lowercase   , )

                   SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tokenizer_r.do_lower_case if hasattr(__lowercase   , '''do_lower_case''' ) else False
                   SCREAMING_SNAKE_CASE__       :			int													=(
                       [
                           ((0, 0), tokenizer_r.cls_token),
                           ((0, 1), '''A'''),
                           ((1, 2), ''','''),
                           ((3, 5), '''na'''),
                           ((5, 6), '''##ï'''),
                           ((6, 8), '''##ve'''),
                           ((9, 15), tokenizer_r.mask_token),
                           ((16, 21), '''Allen'''),
                           ((21, 23), '''##NL'''),
                           ((23, 24), '''##P'''),
                           ((25, 33), '''sentence'''),
                           ((33, 34), '''.'''),
                           ((0, 0), tokenizer_r.sep_token),
                       ]
                       if not do_lower_case
                       else [
                           ((0, 0), tokenizer_r.cls_token),
                           ((0, 1), '''a'''),
                           ((1, 2), ''','''),
                           ((3, 8), '''naive'''),
                           ((9, 15), tokenizer_r.mask_token),
                           ((16, 21), '''allen'''),
                           ((21, 23), '''##nl'''),
                           ((23, 24), '''##p'''),
                           ((25, 33), '''sentence'''),
                           ((33, 34), '''.'''),
                           ((0, 0), tokenizer_r.sep_token),
                       ]
                   )

                   self.assertEqual(
                       [e[1] for e in expected_results]   , tokenizer_r.convert_ids_to_tokens(tokens['''input_ids'''] ) )
                   self.assertEqual([e[0] for e in expected_results]   , tokens['''offset_mapping'''] )




 def        __magic_name__		( self		:							Optional[int] )		->  str:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=['''的''', '''人''', '''有''']
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=''''''.join(__lowercase )
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
                   SCREAMING_SNAKE_CASE__       :			List[str]													=True
                   SCREAMING_SNAKE_CASE__       :			int													=self.tokenizer_class.from_pretrained(__lowercase   , **__lowercase )
                   SCREAMING_SNAKE_CASE__       :			Dict													=self.rust_tokenizer_class.from_pretrained(__lowercase   , **__lowercase )

                   SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tokenizer_p.encode(__lowercase   , add_special_tokens=__lowercase )
                   SCREAMING_SNAKE_CASE__       :			Optional[int]													=tokenizer_r.encode(__lowercase   , add_special_tokens=__lowercase )

                   SCREAMING_SNAKE_CASE__       :			Optional[int]													=tokenizer_r.convert_ids_to_tokens(__lowercase )
                   SCREAMING_SNAKE_CASE__       :			Dict													=tokenizer_p.convert_ids_to_tokens(__lowercase )

                   # it is expected that each Chinese character is not preceded by "##"
                   self.assertListEqual(__lowercase   , __lowercase )
                   self.assertListEqual(__lowercase   , __lowercase )

                   SCREAMING_SNAKE_CASE__       :			Tuple													=False
                   SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.rust_tokenizer_class.from_pretrained(__lowercase   , **__lowercase )
                   SCREAMING_SNAKE_CASE__       :			List[Any]													=self.tokenizer_class.from_pretrained(__lowercase   , **__lowercase )

                   SCREAMING_SNAKE_CASE__       :			List[str]													=tokenizer_r.encode(__lowercase   , add_special_tokens=__lowercase )
                   SCREAMING_SNAKE_CASE__       :			Optional[int]													=tokenizer_p.encode(__lowercase   , add_special_tokens=__lowercase )

                   SCREAMING_SNAKE_CASE__       :			int													=tokenizer_r.convert_ids_to_tokens(__lowercase )
                   SCREAMING_SNAKE_CASE__       :			str													=tokenizer_p.convert_ids_to_tokens(__lowercase )

                   # it is expected that only the first Chinese character is not preceded by "##".
                   SCREAMING_SNAKE_CASE__       :			Dict													=[
                       F"##{token}" if idx != 0 else token for idx, token in enumerate(__lowercase )
                   ]
                   self.assertListEqual(__lowercase   , __lowercase )
                   self.assertListEqual(__lowercase   , __lowercase )







'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Any													=AutoConfig.from_pretrained(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=checkpoints.load_tax_checkpoint(UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

      if config.model_type == "t5":
            SCREAMING_SNAKE_CASE__       :			str													='''SelfAttention'''
      if config.model_type == "longt5" and config.encoder_attention_type == "local":
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''LocalSelfAttention'''
      elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            SCREAMING_SNAKE_CASE__       :			Dict													='''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							):
            SCREAMING_SNAKE_CASE__       :			Dict													=f"layers_{str(UpperCamelCase__							)}"

            # Self-Attention
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[Any]													=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":
                  SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

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

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            SCREAMING_SNAKE_CASE__       :			Tuple													=flax_model.params['''encoder''']['''block'''][str(UpperCamelCase__							)]['''layer''']
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_attention_out
            SCREAMING_SNAKE_CASE__       :			int													=tax_attention_query
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_attention_value

            SCREAMING_SNAKE_CASE__       :			Dict													=tax_attention_layer_norm

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_global_layer_norm

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi_a
                  SCREAMING_SNAKE_CASE__       :			Any													=tax_mlp_wi_a
            else:
                  SCREAMING_SNAKE_CASE__       :			Tuple													=tax_mlp_wi

            SCREAMING_SNAKE_CASE__       :			int													=tax_mlp_wo
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_mlp_layer_norm

            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=flax_model_encoder_layer_block

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

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

      # Assigning
      SCREAMING_SNAKE_CASE__       :			List[str]													=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
      SCREAMING_SNAKE_CASE__       :			int													=tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_layers							):
            SCREAMING_SNAKE_CASE__       :			int													=f"layers_{str(UpperCamelCase__							)}"

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

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

            # Encoder-Decoder-Attention
            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_enc_dec_attention_module['''key''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			Any													=tax_enc_dec_attention_module['''out''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_enc_dec_attention_module['''query''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_enc_dec_attention_module['''value''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

            # MLP
            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                  SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                  SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            SCREAMING_SNAKE_CASE__       :			Tuple													=flax_model.params['''decoder''']['''block'''][str(UpperCamelCase__							)]['''layer''']
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_attention_out
            SCREAMING_SNAKE_CASE__       :			int													=tax_attention_query
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_attention_value

            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_pre_attention_layer_norm

            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_enc_dec_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_enc_dec_attention_out
            SCREAMING_SNAKE_CASE__       :			str													=tax_enc_dec_attention_query
            SCREAMING_SNAKE_CASE__       :			str													=tax_enc_dec_attention_value

            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_cross_layer_norm

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi_a
                  SCREAMING_SNAKE_CASE__       :			int													=tax_mlp_wi_a
            else:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi

            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_mlp_wo

            SCREAMING_SNAKE_CASE__       :			str													=txa_mlp_layer_norm

            SCREAMING_SNAKE_CASE__       :			List[Any]													=flax_model_decoder_layer_block

      # Decoder Normalization
      SCREAMING_SNAKE_CASE__       :			Any													=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=txa_decoder_norm

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

      # Token Embeddings
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''token_embedder''']['''embedding''']
      SCREAMING_SNAKE_CASE__       :			List[Any]													=txa_token_embeddings

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

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


if __name__ == "__main__":
  a_					=	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_					=	parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)