code: 
  
  """simple docstring"""
  
  
  
  
  
  
  
  import collections
  
  from .utils import ExplicitEnum, is_torch_available, logging
  
  
  if is_torch_available():
         import torch
  
  
  lowerCamelCase_     :     Any    		=						logging.get_logger(__name__)
  
  
  
  
  
  
  class    __A       :
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self				,					__A				,					__A=21				,					__A=[]				,					__A=None		)      ->  Optional[int]:
       a					=model
       a					=trace_batch_nums
       a					=abort_after_batch_num
  
       # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
       a					=collections.deque([]				,					__SCREAMING_SNAKE_CASE		)
       a					=[]
       a					=0
       a					=0
       a					=False
       a					='''                 '''
  
       self.analyse_model()
  
       self.register_forward_hook()
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A=None		)      ->  str:
  
       if frame is not None:
           self.expand_frame(__SCREAMING_SNAKE_CASE		)
       self.frames.append('''\n'''.join(self.frame		)		)
       a					=[]  # start a new frame
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Any:
       self.frame.append(__SCREAMING_SNAKE_CASE		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
       print('''\n'''.join(self.frames		)		)
       a					=[]
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
       a					=[]
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
       print(f'''\nDetected inf/nan during batch_number={self.batch_number}'''		)
       print(f'''Last {len(self.frames		)} forward frames:'''		)
       print(f'''{"abs min":8} {"abs max":8} metadata'''		)
       print('''\n'''.join(self.frames		)		)
       print('''\n\n'''		)
       a					=[]
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
       a					={m: name for name, m in self.model.named_modules()}
       # self.longest_module_name = max(len(v) for v in self.module_names.values())
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A		)      ->  str:
  
       if torch.is_tensor(__SCREAMING_SNAKE_CASE		):
           self.expand_frame(get_abs_min_max(__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE		)		)
           if detect_overflow(__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE		):
               a					=True
       elif var is None:
           self.expand_frame(f'''{"None":>17} {ctx}'''		)
       else:
           self.expand_frame(f'''{"not a tensor":>17} {ctx}'''		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       self.expand_frame(f'''\n\n{self.prefix} *** Starting batch number={self.batch_number} ***'''		)
       self.expand_frame(f'''{"abs min":8} {"abs max":8} metadata'''		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
       self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A		)      ->  Tuple:
       self.expand_frame(f'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''		)
  
       # params
       for name, p in module.named_parameters(recurse=__SCREAMING_SNAKE_CASE		):
           self.analyse_variable(__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE		)
  
       # inputs
       if isinstance(__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE		):
           for i, x in enumerate(__SCREAMING_SNAKE_CASE		):
               self.analyse_variable(__SCREAMING_SNAKE_CASE				,					f'''input[{i}]'''		)
       else:
           self.analyse_variable(__SCREAMING_SNAKE_CASE				,					'''input'''		)
  
       # outputs
       if isinstance(__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE		):
           for i, x in enumerate(__SCREAMING_SNAKE_CASE		):
               # possibly a tuple of tuples
               if isinstance(__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE		):
                   for j, y in enumerate(__SCREAMING_SNAKE_CASE		):
                       self.analyse_variable(__SCREAMING_SNAKE_CASE				,					f'''output[{i}][{j}]'''		)
               else:
                   self.analyse_variable(__SCREAMING_SNAKE_CASE				,					f'''output[{i}]'''		)
       else:
           self.analyse_variable(__SCREAMING_SNAKE_CASE				,					'''output'''		)
  
       self.save_frame()
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
       self.model.apply(self._register_forward_hook		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  str:
       module.register_forward_hook(self.forward_hook		)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A		)      ->  str:
       a					=False
  
       a					=True if self.batch_number in self.trace_batch_nums else False
       if trace_mode:
           self.reset_saved_frames()
  
       if self.total_calls == 0:
           self.batch_start_frame()
       self.total_calls += 1
  
       # count batch numbers - the very first forward hook of the batch will be called when the
       # batch completes - i.e. it gets called very last - we know this batch has finished
       if module == self.model:
           self.batch_number += 1
           a					=True
  
       self.create_frame(__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE		)
  
       # if last_frame_of_batch:
       #     self.batch_end_frame()
  
       if trace_mode:
           self.trace_frames()
  
       if last_frame_of_batch:
           self.batch_start_frame()
  
       if self.detected_overflow and not trace_mode:
           self.dump_saved_frames()
  
           # now we can abort, as it's pointless to continue running
           raise ValueError(
               '''DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '''
               '''Please scroll up above this traceback to see the activation values prior to this event.'''		)
  
       # abort after certain batch if requested to do so
       if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
           raise ValueError(
               f'''DebugUnderflowOverflow: aborting after {self.batch_number} batches due to'''
               f''' `abort_after_batch_num={self.abort_after_batch_num}` arg'''		)
  
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=var.abs()
      return f'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=False
      if torch.isnan(lowercase	).any().item():
          a					=True
          print(f'''{ctx} has nans'''	)
      if torch.isinf(lowercase	).any().item():
          a					=True
          print(f'''{ctx} has infs'''	)
  
      # if needed to monitor large elements can enable the following
      if 0:  # and detected:
          a					=var[torch.ge(var.abs() ,					1_00	)]
          if naaa.numel() > 0:
              print(f'''{ctx}:  n100={naaa.numel()}'''	)
          a					=var[torch.ge(var.abs() ,					10_00	)]
          if naaaa.numel() > 0:
              print(f'''{ctx}: n1000={naaaa.numel()}'''	)
          a					=var[torch.ge(var.abs() ,					1_00_00	)]
          if naaaaa.numel() > 0:
              print(f'''{ctx}: n10000={naaaaa.numel()}'''	)
  
      if 0:
          print(f'''min={var.min():9.2e} max={var.max():9.2e}'''	)
  
      if 0:
          print(f'''min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})'''	)
  
      return detected
  
  
  
  
  
  
  class    __A       ( lowerCAmelCase_    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"""underflow_overflow"""
   __lowerCAmelCase				      =						"""tpu_metrics_debug"""
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)