code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import json
  import os
  import re
  import unicodedata
  from typing import Dict, List, Optional, Tuple
  
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import logging
  
  
  _lowerCamelCase				:	Optional[Any]				    =       logging.get_logger(__name__)
  
  _lowerCamelCase				:	Optional[int]				    =       {
      'src_vocab_file': 'vocab-src.json',
      'tgt_vocab_file': 'vocab-tgt.json',
      'merges_file': 'merges.txt',
  }
  
  _lowerCamelCase				:	List[Any]				    =       {
      'src_vocab_file': {
          'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'
      },
      'tgt_vocab_file': {
          'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'
      },
      'merges_file': {'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'},
  }
  
  _lowerCamelCase				:	Union[str, Any]				    =       {'stas/tiny-wmt19-en-de': 1024}
  _lowerCamelCase				:	List[Any]				    =       {
      'stas/tiny-wmt19-en-de': {
          'langs': ['en', 'de'],
          'model_max_length': 1024,
          'special_tokens_map_file': None,
          'full_tokenizer_file': None,
      }
  }
  
  
  
  
  def    __a (     UpperCAmelCase    )      ->Any:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      set()
  	A							  =      word[0]
  	for char in word[1:]:
  		pairs.add((prev_char, char)    )
  		A							  =      char
  	return pairs
  
  
  
  
  def    __a (     UpperCAmelCase    )      ->str:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      text.replace("""，"""       ,			""","""    )
  	A							  =      re.sub(R"""。\s*"""       ,			""". """       ,			UpperCAmelCase    )
  	A							  =      text.replace("""、"""       ,			""","""    )
  	A							  =      text.replace("""”"""       ,			"""\""""    )
  	A							  =      text.replace("""“"""       ,			"""\""""    )
  	A							  =      text.replace("""∶"""       ,			""":"""    )
  	A							  =      text.replace("""："""       ,			""":"""    )
  	A							  =      text.replace("""？"""       ,			"""?"""    )
  	A							  =      text.replace("""《"""       ,			"""\""""    )
  	A							  =      text.replace("""》"""       ,			"""\""""    )
  	A							  =      text.replace("""）"""       ,			""")"""    )
  	A							  =      text.replace("""！"""       ,			"""!"""    )
  	A							  =      text.replace("""（"""       ,			"""("""    )
  	A							  =      text.replace("""；"""       ,			""";"""    )
  	A							  =      text.replace("""１"""       ,			"""1"""    )
  	A							  =      text.replace("""」"""       ,			"""\""""    )
  	A							  =      text.replace("""「"""       ,			"""\""""    )
  	A							  =      text.replace("""０"""       ,			"""0"""    )
  	A							  =      text.replace("""３"""       ,			"""3"""    )
  	A							  =      text.replace("""２"""       ,			"""2"""    )
  	A							  =      text.replace("""５"""       ,			"""5"""    )
  	A							  =      text.replace("""６"""       ,			"""6"""    )
  	A							  =      text.replace("""９"""       ,			"""9"""    )
  	A							  =      text.replace("""７"""       ,			"""7"""    )
  	A							  =      text.replace("""８"""       ,			"""8"""    )
  	A							  =      text.replace("""４"""       ,			"""4"""    )
  	A							  =      re.sub(R"""．\s*"""       ,			""". """       ,			UpperCAmelCase    )
  	A							  =      text.replace("""～"""       ,			"""~"""    )
  	A							  =      text.replace("""’"""       ,			"""'"""    )
  	A							  =      text.replace("""…"""       ,			"""..."""    )
  	A							  =      text.replace("""━"""       ,			"""-"""    )
  	A							  =      text.replace("""〈"""       ,			"""<"""    )
  	A							  =      text.replace("""〉"""       ,			""">"""    )
  	A							  =      text.replace("""【"""       ,			"""["""    )
  	A							  =      text.replace("""】"""       ,			"""]"""    )
  	A							  =      text.replace("""％"""       ,			"""%"""    )
  	return text
  
  
  
  
  def    __a (     UpperCAmelCase    )      ->Optional[int]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      []
  	for char in text:
  		A							  =      unicodedata.category(UpperCAmelCase    )
  		if cat.startswith("""C"""    ):
  			continue
  		output.append(UpperCAmelCase    )
  	return "".join(UpperCAmelCase    )
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    VOCAB_FILES_NAMES
  		__lowerCAmelCase 				=    PRETRAINED_VOCAB_FILES_MAP
  		__lowerCAmelCase 				=    PRETRAINED_INIT_CONFIGURATION
  		__lowerCAmelCase 				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  		__lowerCAmelCase 				=    ['''input_ids''', '''attention_mask''']
  
  
  
  
  
  
  
  		def __init__(self						:      str      ,      _lowerCAmelCase						:      List[str]=None      ,      _lowerCAmelCase						:      Tuple=None      ,      _lowerCAmelCase						:      Tuple=None      ,      _lowerCAmelCase						:      Dict=None      ,      _lowerCAmelCase						:      Any=False      ,      _lowerCAmelCase						:      Dict="<unk>"      ,      _lowerCAmelCase						:      int="<s>"      ,      _lowerCAmelCase						:      Optional[int]="</s>"      ,      _lowerCAmelCase						:      Optional[int]="<pad>"      ,      **_lowerCAmelCase						:      Dict      ,      ):
  			super().__init__(
  			    langs=_lowerCAmelCase      ,      src_vocab_file=_lowerCAmelCase      ,      tgt_vocab_file=_lowerCAmelCase      ,      merges_file=_lowerCAmelCase      ,      do_lower_case=_lowerCAmelCase      ,      unk_token=_lowerCAmelCase      ,      bos_token=_lowerCAmelCase      ,      sep_token=_lowerCAmelCase      ,      pad_token=_lowerCAmelCase      ,      **_lowerCAmelCase      ,      )
  
  			try:
  				import sacremoses
  			except ImportError:
  				raise ImportError(
  				    """You need to install sacremoses to use XLMTokenizer. """
  				    """See https://pypi.org/project/sacremoses/ for installation."""      )
  
  			A							  =      sacremoses
  
  			A							  =      src_vocab_file
  			A							  =      tgt_vocab_file
  			A							  =      merges_file
  			A							  =      do_lower_case
  
  			# cache of sm.MosesPunctNormalizer instance
  			A							  =      {}
  			# cache of sm.MosesTokenizer instance
  			A							  =      {}
  			A							  =      {}
  
  			if langs and len(_lowerCAmelCase      ) == 2:
  				A  ,    A							  =      langs
  			else:
  				raise ValueError(
  				    F"""arg `langs` needs to be a list of 2 langs, e.g. ['en', 'ru'], but got {langs}. """
  				    """Usually that means that tokenizer can't find a mapping for the given model path """
  				    """in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer."""      )
  
  			with open(_lowerCAmelCase      ,      encoding="""utf-8"""      ) as src_vocab_handle:
  				A							  =      json.load(_lowerCAmelCase      )
  			with open(_lowerCAmelCase      ,      encoding="""utf-8"""      ) as tgt_vocab_handle:
  				A							  =      json.load(_lowerCAmelCase      )
  				A							  =      {v: k for k, v in tgt_vocab.items()}
  			with open(_lowerCAmelCase      ,      encoding="""utf-8"""      ) as merges_handle:
  				A							  =      merges_handle.read().split("""\n"""      )[:-1]
  			A							  =      [tuple(merge.split()[:2]      ) for merge in merges]
  			A							  =      dict(zip(_lowerCAmelCase      ,      range(len(_lowerCAmelCase      )      )      )      )
  			A							  =      {}
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ):
  			return self.get_src_vocab()
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      Any      ):
  			return self.src_vocab_size
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ,      _lowerCAmelCase						:      Union[str, Any]      ,      _lowerCAmelCase						:      Optional[Any]      ):
  			if lang not in self.cache_moses_punct_normalizer:
  				A							  =      self.sm.MosesPunctNormalizer(lang=_lowerCAmelCase      )
  				A							  =      punct_normalizer
  			return self.cache_moses_punct_normalizer[lang].normalize(_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Tuple      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      str      ):
  			if lang not in self.cache_moses_tokenizer:
  				A							  =      self.sm.MosesTokenizer(lang=_lowerCAmelCase      )
  				A							  =      moses_tokenizer
  			return self.cache_moses_tokenizer[lang].tokenize(
  			    _lowerCAmelCase      ,      aggressive_dash_splits=_lowerCAmelCase      ,      return_str=_lowerCAmelCase      ,      escape=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Any      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      Dict      ):
  			if lang not in self.cache_moses_detokenizer:
  				A							  =      self.sm.MosesDetokenizer(lang=_lowerCAmelCase      )
  				A							  =      moses_detokenizer
  			return self.cache_moses_detokenizer[lang].detokenize(_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ,      _lowerCAmelCase						:      Optional[int]      ,      _lowerCAmelCase						:      Tuple      ):
  			A							  =      replace_unicode_punct(_lowerCAmelCase      )
  			A							  =      self.moses_punct_norm(_lowerCAmelCase      ,      _lowerCAmelCase      )
  			A							  =      remove_non_printing_char(_lowerCAmelCase      )
  			return text
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      int      ):
  			return len(self.encoder      )
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      int      ):
  			return len(self.decoder      )
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[Any]      ):
  			return dict(self.encoder      ,      **self.added_tokens_encoder      )
  
  
  
  
  
  
  
  		def        A     (self						:      int      ):
  			return dict(self.decoder      ,      **self.added_tokens_decoder      )
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ,      _lowerCAmelCase						:      str      ):
  			A							  =      tuple(token[:-1]      ) + (token[-1] + """</w>""",)
  			if token in self.cache:
  				return self.cache[token]
  			A							  =      get_pairs(_lowerCAmelCase      )
  
  			if not pairs:
  				return token + "</w>"
  
  			while True:
  				A							  =      min(_lowerCAmelCase      ,      key=lambda _lowerCAmelCase      : self.bpe_ranks.get(_lowerCAmelCase      ,      float("""inf"""      )      )      )
  				if bigram not in self.bpe_ranks:
  					break
  				A  ,    A							  =      bigram
  				A							  =      []
  				A							  =      0
  				while i < len(_lowerCAmelCase      ):
  					try:
  						A							  =      word.index(_lowerCAmelCase      ,      _lowerCAmelCase      )
  					except ValueError:
  						new_word.extend(word[i:]      )
  						break
  					else:
  						new_word.extend(word[i:j]      )
  						A							  =      j
  
  					if word[i] == first and i < len(_lowerCAmelCase      ) - 1 and word[i + 1] == second:
  						new_word.append(first + second      )
  						i += 2
  					else:
  						new_word.append(word[i]      )
  						i += 1
  				A							  =      tuple(_lowerCAmelCase      )
  				A							  =      new_word
  				if len(_lowerCAmelCase      ) == 1:
  					break
  				else:
  					A							  =      get_pairs(_lowerCAmelCase      )
  			A							  =      """ """.join(_lowerCAmelCase      )
  			if word == "\n  </w>":
  				A							  =      """\n</w>"""
  			A							  =      word
  			return word
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      str="en"      ,      _lowerCAmelCase						:      List[str]=False      ):
  			A							  =      self.src_lang
  
  			if self.do_lower_case:
  				A							  =      text.lower()
  
  			if bypass_tokenizer:
  				A							  =      text.split()
  			else:
  				A							  =      self.moses_pipeline(_lowerCAmelCase      ,      lang=_lowerCAmelCase      )
  				A							  =      self.moses_tokenize(_lowerCAmelCase      ,      lang=_lowerCAmelCase      )
  
  			A							  =      []
  			for token in text:
  				if token:
  					split_tokens.extend(list(self.bpe(_lowerCAmelCase      ).split(""" """      )      )      )
  
  			return split_tokens
  
  
  
  
  
  
  
  		def        A     (self						:      str      ,      _lowerCAmelCase						:      Tuple      ):
  			return self.encoder.get(_lowerCAmelCase      ,      self.encoder.get(self.unk_token      )      )
  
  
  
  
  
  
  
  		def        A     (self						:      List[Any]      ,      _lowerCAmelCase						:      List[Any]      ):
  			return self.decoder.get(_lowerCAmelCase      ,      self.unk_token      )
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[Any]      ,      _lowerCAmelCase						:      Tuple      ):
  			A							  =      [t.replace(""" """      ,      """"""      ).replace("""</w>"""      ,      """ """      ) for t in tokens]
  			A							  =      """""".join(_lowerCAmelCase      ).split()
  			# detokenize
  			A							  =      self.moses_detokenize(_lowerCAmelCase      ,      self.tgt_lang      )
  			return text
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[Any]      ,      _lowerCAmelCase						:      List[int]      ,      _lowerCAmelCase						:      Optional[List[int]] = None      ):
  			A							  =      [self.sep_token_id]
  
  			# no bos used in fairseq
  			if token_ids_a is None:
  				return token_ids_a + sep
  			return token_ids_a + sep + token_ids_a + sep
  
  
  
  
  
  
  
  		def        A     (self						:      List[str]      ,      _lowerCAmelCase						:      List[int]      ,      _lowerCAmelCase						:      Optional[List[int]] = None      ,      _lowerCAmelCase						:      bool = False      ):
  
  			if already_has_special_tokens:
  				return super().get_special_tokens_mask(
  				    token_ids_a=_lowerCAmelCase      ,      token_ids_a=_lowerCAmelCase      ,      already_has_special_tokens=_lowerCAmelCase      )
  			# no bos used in fairseq
  			if token_ids_a is not None:
  				return ([0] * len(_lowerCAmelCase      )) + [1] + ([0] * len(_lowerCAmelCase      )) + [1]
  			return ([0] * len(_lowerCAmelCase      )) + [1]
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ,      _lowerCAmelCase						:      List[int]      ,      _lowerCAmelCase						:      Optional[List[int]] = None      ):
  			A							  =      [self.sep_token_id]
  
  			# no bos used in fairseq
  			if token_ids_a is None:
  				return len(token_ids_a + sep      ) * [0]
  			return len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[Any]      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      Optional[str] = None      ):
  			if not os.path.isdir(_lowerCAmelCase      ):
  				logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
  				return
  
  			A							  =      os.path.join(
  			    _lowerCAmelCase      ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""src_vocab_file"""]      )
  			A							  =      os.path.join(
  			    _lowerCAmelCase      ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""tgt_vocab_file"""]      )
  			A							  =      os.path.join(
  			    _lowerCAmelCase      ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]      )
  
  			with open(_lowerCAmelCase      ,      """w"""      ,      encoding="""utf-8"""      ) as f:
  				f.write(json.dumps(self.encoder      ,      indent=2      ,      sort_keys=_lowerCAmelCase      ,      ensure_ascii=_lowerCAmelCase      ) + """\n"""      )
  
  			with open(_lowerCAmelCase      ,      """w"""      ,      encoding="""utf-8"""      ) as f:
  				A							  =      {v: k for k, v in self.decoder.items()}
  				f.write(json.dumps(_lowerCAmelCase      ,      indent=2      ,      sort_keys=_lowerCAmelCase      ,      ensure_ascii=_lowerCAmelCase      ) + """\n"""      )
  
  			A							  =      0
  			with open(_lowerCAmelCase      ,      """w"""      ,      encoding="""utf-8"""      ) as writer:
  				for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,      key=lambda _lowerCAmelCase      : kv[1]      ):
  					if index != token_index:
  						logger.warning(
  						    F"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
  						    """ Please check that the tokenizer is not corrupted!"""      )
  						A							  =      token_index
  					writer.write(""" """.join(_lowerCAmelCase      ) + """\n"""      )
  					index += 1
  
  			return src_vocab_file, tgt_vocab_file, merges_file
  
  
  
  
  
  
  
  		def __getstate__(self						:      List[Any]      ):
  			A							  =      self.__dict__.copy()
  			A							  =      None
  			return state
  
  
  
  
  
  
  
  		def __setstate__(self						:      Optional[int]      ,      _lowerCAmelCase						:      Tuple      ):
  			A							  =      d
  
  			try:
  				import sacremoses
  			except ImportError:
  				raise ImportError(
  				    """You need to install sacremoses to use XLMTokenizer. """
  				    """See https://pypi.org/project/sacremoses/ for installation."""      )
  
  			A							  =      sacremoses
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->List[Any]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      TaConfig.from_pretrained(UpperCAmelCase    )
  	A							  =      FlaxTaForConditionalGeneration(config=UpperCAmelCase    )
  	A							  =      checkpoints.load_tax_checkpoint(UpperCAmelCase    )
  
  	A							  =      """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]
  
  	# Encoder
  	for layer_index in range(config.num_layers    ):
  		A							  =      f"""layers_{str(UpperCAmelCase    )}"""
  
  		# Self-Attention
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]
  
  		if split_mlp_wi:
  			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
  			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
  		else:
  			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
  		# Assigning
  		A							  =      tax_attention_key
  		A							  =      tax_attention_out
  		A							  =      tax_attention_query
  		A							  =      tax_attention_value
  
  		A							  =      tax_attention_layer_norm
  
  		if split_mlp_wi:
  			A							  =      tax_mlp_wi_a
  			A							  =      tax_mlp_wi_a
  		else:
  			A							  =      tax_mlp_wi
  
  		A							  =      tax_mlp_wo
  		A							  =      tax_mlp_layer_norm
  
  	# Only for layer 0:
  	A							  =      tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  	A							  =      tax_encoder_rel_embedding
  
  	# Assigning
  	A							  =      tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
  	A							  =      tax_encoder_norm
  
  	# Decoder
  	for layer_index in range(config.num_decoder_layers    ):
  		A							  =      f"""layers_{str(UpperCAmelCase    )}"""
  
  		# Self-Attention
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
  		    """scale"""
  		]
  
  		# Encoder-Decoder-Attention
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
  		    """kernel"""
  		]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
  		    """kernel"""
  		]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
  		    """kernel"""
  		]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
  		    """kernel"""
  		]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]
  
  		# MLP
  		if split_mlp_wi:
  			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
  			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
  		else:
  			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
  		# Assigning
  		A							  =      tax_attention_key
  		A							  =      tax_attention_out
  		A							  =      tax_attention_query
  		A							  =      tax_attention_value
  
  		A							  =      tax_pre_attention_layer_norm
  
  		A							  =      tax_enc_dec_attention_key
  		A							  =      tax_enc_dec_attention_out
  		A							  =      tax_enc_dec_attention_query
  		A							  =      tax_enc_dec_attention_value
  
  		A							  =      tax_cross_layer_norm
  
  		if split_mlp_wi:
  			A							  =      tax_mlp_wi_a
  			A							  =      tax_mlp_wi_a
  		else:
  			A							  =      tax_mlp_wi
  
  		A							  =      tax_mlp_wo
  
  		A							  =      txa_mlp_layer_norm
  
  	# Decoder Normalization
  	A							  =      tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
  	A							  =      txa_decoder_norm
  
  	# Only for layer 0:
  	A							  =      tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  	A							  =      tax_decoder_rel_embedding
  
  	# Token Embeddings
  	A							  =      tax_model["""target"""]["""token_embedder"""]["""embedding"""]
  	A							  =      txa_token_embeddings
  
  	# LM Head (only in v1.1 checkpoints)
  	if "logits_dense" in tax_model["target"]["decoder"]:
  		A							  =      tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]
  
  	flax_model.save_pretrained(UpperCAmelCase    )
  	print("""T5X Model was sucessfully converted!"""    )
  
  
  if __name__ == "__main__":
  				_lowerCamelCase				:	Tuple				    =       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				:	Union[str, Any]				    =       parser.parse_args()
  				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
  
  