code: 
  
  """simple docstring"""
  
  
  
  
  
  
  
  import csv
  import dataclasses
  import json
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  from ...utils import is_tf_available, is_torch_available, logging
  
  
  lowerCamelCase_     :     List[str]    		=						logging.get_logger(__name__)
  
  
  
  
  
  
  @dataclass
  class    __A       :
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						42
   __lowerCAmelCase				      =						42
   __lowerCAmelCase				      =						None
   __lowerCAmelCase				      =						None
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
       return json.dumps(dataclasses.asdict(self		)				,					indent=2		) + "\n"
  
  
  
  
  
  
  @dataclass(frozen=UpperCAmelCase_    )
  class    __A       :
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						42
   __lowerCAmelCase				      =						None
   __lowerCAmelCase				      =						None
   __lowerCAmelCase				      =						None
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
       return json.dumps(dataclasses.asdict(self		)		) + "\n"
  
  
  
  
  
  
  class    __A       :
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  str:
       raise NotImplementedError()
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  str:
       raise NotImplementedError()
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Dict:
       raise NotImplementedError()
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Optional[int]:
       raise NotImplementedError()
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
       raise NotImplementedError()
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  int:
       if len(self.get_labels()		) > 1:
           a					=self.get_labels()[int(example.label		)]
       return example
  
  
  
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A=None		)      ->  int:
       with open(__A				,					'''r'''				,					encoding='''utf-8-sig'''		) as f:
           return list(csv.reader(__A				,					delimiter='''\t'''				,					quotechar=__A		)		)
  
  
  
  
  
  
  class    __A       ( UpperCAmelCase_    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self				,					__A=None				,					__A=None				,					__A="classification"				,					__A=False		)      ->  Optional[Any]:
       a					=[] if labels is None else labels
       a					=[] if examples is None else examples
       a					=mode
       a					=verbose
  
  
   def __len__(	self		)      ->  List[str]:
       return len(self.examples		)
  
  
   def __getitem__(	self				,					__A		)      ->  str:
       if isinstance(__A				,					__A		):
           return SingleSentenceClassificationProcessor(labels=self.labels				,					examples=self.examples[idx]		)
       return self.examples[idx]
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A=""				,					__A=0				,					__A=1				,					__A=None				,					__A=False				,					**__A		)      ->  Any:
       a					=cls(**__A		)
       processor.add_examples_from_csv(
           __A				,					split_name=__A				,					column_label=__A				,					column_text=__A				,					column_id=__A				,					skip_first_row=__A				,					overwrite_labels=__A				,					overwrite_examples=__A				,					)
       return processor
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A=None				,					**__A		)      ->  Union[str, Any]:
       a					=cls(**__A		)
       processor.add_examples(__A				,					labels=__A		)
       return processor
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A=""				,					__A=0				,					__A=1				,					__A=None				,					__A=False				,					__A=False				,					__A=False				,					)      ->  int:
       a					=self._read_tsv(__A		)
       if skip_first_row:
           a					=lines[1:]
       a					=[]
       a					=[]
       a					=[]
       for i, line in enumerate(__A		):
           texts.append(line[column_text]		)
           labels.append(line[column_label]		)
           if column_id is not None:
               ids.append(line[column_id]		)
           else:
               a					=f'''{split_name}-{i}''' if split_name else str(__A		)
               ids.append(__A		)
  
       return self.add_examples(
           __A				,					__A				,					__A				,					overwrite_labels=__A				,					overwrite_examples=__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A=None				,					__A=None				,					__A=False				,					__A=False		)      ->  Any:
       if labels is not None and len(__A		) != len(__A		):
           raise ValueError(
               f'''Text and labels have mismatched lengths {len(__A		)} and {len(__A		)}'''		)
       if ids is not None and len(__A		) != len(__A		):
           raise ValueError(f'''Text and ids have mismatched lengths {len(__A		)} and {len(__A		)}'''		)
       if ids is None:
           a					=[None] * len(__A		)
       if labels is None:
           a					=[None] * len(__A		)
       a					=[]
       a					=set()
       for text_or_text_and_label, label, guid in zip(__A				,					__A				,					__A		):
           if isinstance(__A				,					(tuple, list)		) and label is None:
               a	,   a					=text_or_text_and_label
           else:
               a					=text_or_text_and_label
           added_labels.add(__A		)
           examples.append(InputExample(guid=__A				,					text_a=__A				,					text_b=__A				,					label=__A		)		)
  
       # Update examples
       if overwrite_examples:
           a					=examples
       else:
           self.examples.extend(__A		)
  
       # Update labels
       if overwrite_labels:
           a					=list(__A		)
       else:
           a					=list(set(self.labels		).union(__A		)		)
  
       return self.examples
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A=None				,					__A=False				,					__A=0				,					__A=True				,					__A=None				,					)      ->  Any:
       if max_length is None:
           a					=tokenizer.max_len
  
       a					={label: i for i, label in enumerate(self.labels		)}
  
       a					=[]
       for ex_index, example in enumerate(self.examples		):
           if ex_index % 1_0000 == 0:
               logger.info(f'''Tokenizing example {ex_index}'''		)
  
           a					=tokenizer.encode(
               example.text_a				,					add_special_tokens=__A				,					max_length=min(__A				,					tokenizer.max_len		)				,					)
           all_input_ids.append(__A		)
  
       a					=max(len(__A		) for input_ids in all_input_ids		)
  
       a					=[]
       for ex_index, (input_ids, example) in enumerate(zip(__A				,					self.examples		)		):
           if ex_index % 1_0000 == 0:
               logger.info(f'''Writing example {ex_index}/{len(self.examples		)}'''		)
           # The mask has 1 for real tokens and 0 for padding tokens. Only real
           # tokens are attended to.
           a					=[1 if mask_padding_with_zero else 0] * len(__A		)
  
           # Zero-pad up to the sequence length.
           a					=batch_length - len(__A		)
           if pad_on_left:
               a					=([pad_token] * padding_length) + input_ids
               a					=([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
           else:
               a					=input_ids + ([pad_token] * padding_length)
               a					=attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
  
           if len(__A		) != batch_length:
               raise ValueError(f'''Error with input length {len(__A		)} vs {batch_length}'''		)
           if len(__A		) != batch_length:
               raise ValueError(f'''Error with input length {len(__A		)} vs {batch_length}'''		)
  
           if self.mode == "classification":
               a					=label_map[example.label]
           elif self.mode == "regression":
               a					=float(example.label		)
           else:
               raise ValueError(self.mode		)
  
           if ex_index < 5 and self.verbose:
               logger.info('''*** Example ***'''		)
               logger.info(f'''guid: {example.guid}'''		)
               logger.info(f'''input_ids: {" ".join([str(__A		) for x in input_ids]		)}'''		)
               logger.info(f'''attention_mask: {" ".join([str(__A		) for x in attention_mask]		)}'''		)
               logger.info(f'''label: {example.label} (id = {label})'''		)
  
           features.append(InputFeatures(input_ids=__A				,					attention_mask=__A				,					label=__A		)		)
  
       if return_tensors is None:
           return features
       elif return_tensors == "tf":
           if not is_tf_available():
               raise RuntimeError('''return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'''		)
           import tensorflow as tf
  
           def gen():
               for ex in features:
                   yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)
  
           a					=tf.data.Dataset.from_generator(
               __A				,					({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)				,					({'''input_ids''': tf.TensorShape([None]		), '''attention_mask''': tf.TensorShape([None]		)}, tf.TensorShape([]		))				,					)
           return dataset
       elif return_tensors == "pt":
           if not is_torch_available():
               raise RuntimeError('''return_tensors set to \'pt\' but PyTorch can\'t be imported'''		)
           import torch
           from torch.utils.data import TensorDataset
  
           a					=torch.tensor([f.input_ids for f in features]				,					dtype=torch.long		)
           a					=torch.tensor([f.attention_mask for f in features]				,					dtype=torch.long		)
           if self.mode == "classification":
               a					=torch.tensor([f.label for f in features]				,					dtype=torch.long		)
           elif self.mode == "regression":
               a					=torch.tensor([f.label for f in features]				,					dtype=torch.float		)
  
           a					=TensorDataset(__A				,					__A				,					__A		)
           return dataset
       else:
           raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''		)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=TaConfig.from_pretrained(lowercase	)
      a					=FlaxTaForConditionalGeneration(config=lowercase	)
      a					=checkpoints.load_tax_checkpoint(lowercase	)
  
      a					='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']
  
      # Encoder
      for layer_index in range(config.num_layers	):
          a					=f'''layers_{str(lowercase	)}'''
  
          # 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(lowercase	)}'''
  
          # 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(lowercase	)
      print('''T5X Model was sucessfully converted!'''	)
  
  
  if __name__ == "__main__":
         lowerCamelCase_     :     Dict    		=						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_     :     Optional[int]    		=						parser.parse_args()
         convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)