import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     Optional[int]	):
 UpperCamelCase_					:						Any =					AutoConfig.from_pretrained(lowerCamelCase	)
 UpperCamelCase_					:						Union[str, Any] =					FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCamelCase	)
 UpperCamelCase_					:						Optional[Any] =					checkpoints.load_tax_checkpoint(lowerCamelCase	)

 UpperCamelCase_					:						Optional[Any] =					'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

 if config.model_type == "t5":
  UpperCamelCase_					:						List[str] =					'SelfAttention'
 if config.model_type == "longt5" and config.encoder_attention_type == "local":
  UpperCamelCase_					:						Tuple =					'LocalSelfAttention'
 elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
  UpperCamelCase_					:						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	):
  UpperCamelCase_					:						Any =					F"layers_{str(lowerCamelCase	)}"

  # Self-Attention
  UpperCamelCase_					:						str =					tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
  UpperCamelCase_					:						str =					tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
  UpperCamelCase_					:						List[str] =					tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
  UpperCamelCase_					:						str =					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_					:						List[str] =					tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

  # Layer Normalization
  UpperCamelCase_					:						Optional[Any] =					tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

  if split_mlp_wi:
   UpperCamelCase_					:						Union[str, Any] =					tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
   UpperCamelCase_					:						Dict =					tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
  else:
   UpperCamelCase_					:						Dict =					tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

  UpperCamelCase_					:						List[str] =					tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

  # Layer Normalization
  UpperCamelCase_					:						List[str] =					tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

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

  UpperCamelCase_					:						Tuple =					tax_attention_layer_norm

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

  if split_mlp_wi:
   UpperCamelCase_					:						Optional[Any] =					tax_mlp_wi_a
   UpperCamelCase_					:						Any =					tax_mlp_wi_a
  else:
   UpperCamelCase_					:						List[Any] =					tax_mlp_wi

  UpperCamelCase_					:						str =					tax_mlp_wo
  UpperCamelCase_					:						List[Any] =					tax_mlp_layer_norm

  UpperCamelCase_					:						List[Any] =					flax_model_encoder_layer_block

 # Only for layer 0:
 UpperCamelCase_					:						Optional[Any] =					tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
 UpperCamelCase_					:						int =					tax_encoder_rel_embedding

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

 # Assigning
 UpperCamelCase_					:						Optional[int] =					tax_model['target']['encoder']['encoder_norm']['scale']
 UpperCamelCase_					:						Union[str, Any] =					tax_encoder_norm

 # Decoder
 for layer_index in range(config.num_layers	):
  UpperCamelCase_					:						str =					F"layers_{str(lowerCamelCase	)}"

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

  # Layer Normalization
  UpperCamelCase_					:						Optional[Any] =					tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
      'scale'
  ]

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

  # Layer Normalization
  UpperCamelCase_					:						Optional[int] =					tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

  # MLP
  if split_mlp_wi:
   UpperCamelCase_					:						Any =					tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
   UpperCamelCase_					:						List[str] =					tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
  else:
   UpperCamelCase_					:						Union[str, Any] =					tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

  UpperCamelCase_					:						List[str] =					tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

  # Layer Normalization
  UpperCamelCase_					:						Optional[Any] =					tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

  # Assigning
  UpperCamelCase_					:						Union[str, Any] =					flax_model.params['decoder']['block'][str(lowerCamelCase	)]['layer']
  UpperCamelCase_					:						Dict =					tax_attention_key
  UpperCamelCase_					:						Tuple =					tax_attention_out
  UpperCamelCase_					:						Any =					tax_attention_query
  UpperCamelCase_					:						List[str] =					tax_attention_value

  UpperCamelCase_					:						Union[str, Any] =					tax_pre_attention_layer_norm

  UpperCamelCase_					:						Optional[int] =					tax_enc_dec_attention_key
  UpperCamelCase_					:						Union[str, Any] =					tax_enc_dec_attention_out
  UpperCamelCase_					:						str =					tax_enc_dec_attention_query
  UpperCamelCase_					:						Tuple =					tax_enc_dec_attention_value

  UpperCamelCase_					:						Tuple =					tax_cross_layer_norm

  if split_mlp_wi:
   UpperCamelCase_					:						Optional[Any] =					tax_mlp_wi_a
   UpperCamelCase_					:						Any =					tax_mlp_wi_a
  else:
   UpperCamelCase_					:						Tuple =					tax_mlp_wi

  UpperCamelCase_					:						List[str] =					tax_mlp_wo

  UpperCamelCase_					:						Any =					txa_mlp_layer_norm

  UpperCamelCase_					:						List[str] =					flax_model_decoder_layer_block

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

 # Only for layer 0:
 UpperCamelCase_					:						Union[str, Any] =					tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
 UpperCamelCase_					:						Optional[int] =					tax_decoder_rel_embedding

 # Token Embeddings
 UpperCamelCase_					:						str =					tax_model['target']['token_embedder']['embedding']
 UpperCamelCase_					:						Dict =					txa_token_embeddings

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

 flax_model.save_pretrained(lowerCamelCase	)
 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)




import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


a_    					=       False
try:
   a_    					=       _is_package_available('google.colab')
except ModuleNotFoundError:
   pass





@input.register
class 		_lowercase						:

 def __init__( self	:							Dict   ,      snake_case	:							str = None   ,      snake_case	:							list = []			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					0
  UpperCamelCase_					:						Optional[Any] =					choices
  UpperCamelCase_					:						Any =					prompt
  if sys.platform == "win32":
   UpperCamelCase_					:						Optional[Any] =					'*'
  else:
   UpperCamelCase_					:						str =					'➔ '

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]   ,      snake_case	:							List[str]   ,      snake_case	:							str = ""			)   ->						List[Any]:

  """simple docstring"""





  if sys.platform != "win32":
   writeColor(self.choices[index]   ,      3_2   ,      snake_case			)
  else:
   forceWrite(self.choices[index]   ,      snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							int			)   ->						List[Any]:

  """simple docstring"""





  if index == self.position:
   forceWrite(f" {self.arrow_char} "			)
   self.write_choice(snake_case			)
  else:
   forceWrite(f"    {self.choices[index]}"			)
  reset_cursor()

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							Direction   ,      snake_case	:							int = 1			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					self.position
  if direction == Direction.DOWN:
   if self.position + 1 >= len(self.choices			):
    return
   self.position += num_spaces
  else:
   if self.position - 1 < 0:
    return
   self.position -= num_spaces
  clear_line()
  self.print_choice(snake_case			)
  move_cursor(snake_case   ,      direction.name			)
  self.print_choice(self.position			)

 @input.mark(KEYMAP['up']			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						List[Any]:

  """simple docstring"""





  self.move_direction(Direction.UP			)

 @input.mark(KEYMAP['down']			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Optional[Any]:

  """simple docstring"""





  self.move_direction(Direction.DOWN			)

 @input.mark(KEYMAP['newline']			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Optional[int]:

  """simple docstring"""





  move_cursor(len(self.choices			) - self.position   ,      'DOWN'			)
  return self.position

 @input.mark(KEYMAP['interrupt']			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						Optional[Any]:

  """simple docstring"""





  move_cursor(len(self.choices			) - self.position   ,      'DOWN'			)
  raise KeyboardInterrupt

 @input.mark_multiple(*[KEYMAP[str(snake_case			)] for number in range(1_0			)]			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					int(chr(self.current_selection			)			)
  UpperCamelCase_					:						Optional[int] =					index - self.position
  if index == self.position:
   return
  if index < len(self.choices			):
   if self.position > index:
    self.move_direction(Direction.UP   ,      -movement			)
   elif self.position < index:
    self.move_direction(Direction.DOWN   ,      snake_case			)
   else:
    return
  else:
   return


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							int = 0			)   ->						Union[str, Any]:

  """simple docstring"""





  if self.prompt:
   linebreak()
   forceWrite(self.prompt   ,      '\n'			)
   if in_colab:
    forceWrite('Please input a choice index (starting from 0), and press enter'   ,      '\n'			)
   else:
    forceWrite('Please select a choice using the arrow or number keys, and selecting with enter'   ,      '\n'			)
  UpperCamelCase_					:						Optional[int] =					default_choice
  for i in range(len(self.choices			)			):
   self.print_choice(snake_case			)
   forceWrite('\n'			)
  move_cursor(len(self.choices			) - self.position   ,      'UP'			)
  with cursor.hide():
   while True:
    if in_colab:
     try:
      UpperCamelCase_					:						Tuple =					int(builtins.input()			)
     except ValueError:
      UpperCamelCase_					:						Tuple =					default_choice
    else:
     UpperCamelCase_					:						Optional[Any] =					self.handle_input()
    if choice is not None:
     reset_cursor()
     for _ in range(len(self.choices			) + 1			):
      move_cursor(1   ,      'UP'			)
      clear_line()
     self.write_choice(snake_case   ,      '\n'			)
     return choice



