






'''simple docstring'''



import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()


def 				lowercase_					(     __A			:							int       ,   __A			:							List[Any]       ,   __A			:							Union[str, Any]       ,   __A			:							Union[str, Any]="attention"      )							->   Union[str, Any]:



 """simple docstring"""




 lowercase       : str						=params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
 lowercase       : Optional[int]						=params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
 lowercase       : int						=params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
 lowercase       : List[str]						=params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
 return k, o, q, v


def 				lowercase_					(     __A			:							List[str]       ,   __A			:							Tuple       ,   __A			:							List[Any]       ,   __A			:							Union[str, Any]=False      )							->   List[str]:



 """simple docstring"""




 if split_mlp_wi:
  lowercase       : Optional[int]						=params[F'{prefix}/layers_{i}/mlp/wi_0/kernel']
  lowercase       : str						=params[F'{prefix}/layers_{i}/mlp/wi_1/kernel']
  lowercase       : str						=(wi_a, wi_a)
 else:
  lowercase       : Any						=params[F'{prefix}/layers_{i}/mlp/wi/kernel']

 lowercase       : str						=params[F'{prefix}/layers_{i}/mlp/wo/kernel']
 return wi, wo


def 				lowercase_					(     __A			:							Dict       ,   __A			:							Any       ,   __A			:							Tuple       ,   __A			:							str      )							->   Any:



 """simple docstring"""




 return params[F'{prefix}/layers_{i}/{layer_name}/scale']


def 				lowercase_					(     __A			:							dict       ,   *, __A			:							int       ,   __A			:							bool      )							->   List[str]:



 """simple docstring"""




 lowercase       : Dict						=traverse_util.flatten_dict(variables['''target''']      )
 lowercase       : int						={'''/'''.join(__A      ): v for k, v in old.items()}

 # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
 lowercase       : Dict						='''encoder/layers_0/mlp/wi_0/kernel''' in old
 print('''Split MLP:'''       ,   __A      )

 lowercase       : List[str]						=collections.OrderedDict()

 # Shared embeddings.
 lowercase       : List[Any]						=old['''token_embedder/embedding''']

 # Encoder.
 for i in range(__A      ):
  # Block i, layer 0 (Self Attention).
  lowercase       : Optional[int]						=tax_layer_norm_lookup(__A       ,   __A       ,   '''encoder'''       ,   '''pre_attention_layer_norm'''      )
  lowercase	,		lowercase	,		lowercase	,		lowercase       : Tuple						=tax_attention_lookup(__A       ,   __A       ,   '''encoder'''       ,   '''attention'''      )
  lowercase       : Dict						=layer_norm
  lowercase       : List[str]						=k.T
  lowercase       : List[Any]						=o.T
  lowercase       : Any						=q.T
  lowercase       : Optional[int]						=v.T

  # Block i, layer 1 (MLP).
  lowercase       : int						=tax_layer_norm_lookup(__A       ,   __A       ,   '''encoder'''       ,   '''pre_mlp_layer_norm'''      )
  lowercase	,		lowercase       : str						=tax_mlp_lookup(__A       ,   __A       ,   '''encoder'''       ,   __A      )
  lowercase       : List[str]						=layer_norm
  if split_mlp_wi:
   lowercase       : str						=wi[0].T
   lowercase       : str						=wi[1].T
  else:
   lowercase       : str						=wi.T
  lowercase       : Tuple						=wo.T

 lowercase       : List[str]						=old[
     '''encoder/relpos_bias/rel_embedding'''
 ].T
 lowercase       : List[str]						=old['''encoder/encoder_norm/scale''']

 if not is_encoder_only:
  # Decoder.
  for i in range(__A      ):
   # Block i, layer 0 (Self Attention).
   lowercase       : Dict						=tax_layer_norm_lookup(__A       ,   __A       ,   '''decoder'''       ,   '''pre_self_attention_layer_norm'''      )
   lowercase	,		lowercase	,		lowercase	,		lowercase       : str						=tax_attention_lookup(__A       ,   __A       ,   '''decoder'''       ,   '''self_attention'''      )
   lowercase       : int						=layer_norm
   lowercase       : Tuple						=k.T
   lowercase       : Union[str, Any]						=o.T
   lowercase       : Any						=q.T
   lowercase       : str						=v.T

   # Block i, layer 1 (Cross Attention).
   lowercase       : Any						=tax_layer_norm_lookup(__A       ,   __A       ,   '''decoder'''       ,   '''pre_cross_attention_layer_norm'''      )
   lowercase	,		lowercase	,		lowercase	,		lowercase       : Optional[int]						=tax_attention_lookup(__A       ,   __A       ,   '''decoder'''       ,   '''encoder_decoder_attention'''      )
   lowercase       : Tuple						=layer_norm
   lowercase       : Any						=k.T
   lowercase       : Tuple						=o.T
   lowercase       : int						=q.T
   lowercase       : str						=v.T

   # Block i, layer 2 (MLP).
   lowercase       : Union[str, Any]						=tax_layer_norm_lookup(__A       ,   __A       ,   '''decoder'''       ,   '''pre_mlp_layer_norm'''      )
   lowercase	,		lowercase       : str						=tax_mlp_lookup(__A       ,   __A       ,   '''decoder'''       ,   __A      )
   lowercase       : Dict						=layer_norm
   if split_mlp_wi:
    lowercase       : List[Any]						=wi[0].T
    lowercase       : Union[str, Any]						=wi[1].T
   else:
    lowercase       : Tuple						=wi.T
   lowercase       : Optional[int]						=wo.T

  lowercase       : Tuple						=old['''decoder/decoder_norm/scale''']
  lowercase       : str						=old[
      '''decoder/relpos_bias/rel_embedding'''
  ].T

  # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
  if "decoder/logits_dense/kernel" in old:
   lowercase       : Optional[Any]						=old['''decoder/logits_dense/kernel'''].T

 return new


def 				lowercase_					(     __A			:							Tuple       ,   __A			:							bool      )							->   List[str]:



 """simple docstring"""




 lowercase       : Optional[int]						=collections.OrderedDict([(k, torch.from_numpy(v.copy()      )) for (k, v) in converted_params.items()]      )

 # Add what is missing.
 if "encoder.embed_tokens.weight" not in state_dict:
  lowercase       : List[str]						=state_dict['''shared.weight''']

 if not is_encoder_only:
  if "decoder.embed_tokens.weight" not in state_dict:
   lowercase       : Optional[Any]						=state_dict['''shared.weight''']

  if "lm_head.weight" not in state_dict:  # For old 1.0 models.
   print('''Using shared word embeddings as lm_head.'''      )
   lowercase       : str						=state_dict['''shared.weight''']

 return state_dict


def 				lowercase_					(     __A			:							List[Any]       ,   __A			:							List[str]       ,   __A			:							Dict       ,   __A			:							Optional[Any]      )							->   List[str]:



 """simple docstring"""




 lowercase       : Optional[int]						=checkpoints.load_tax_checkpoint(__A      )
 lowercase       : Dict						=convert_tax_to_pytorch(__A       ,   num_layers=config.num_layers       ,   is_encoder_only=__A      )
 lowercase       : Dict						=make_state_dict(__A       ,   __A      )
 model.load_state_dict(__A       ,   strict=__A      )


def 				lowercase_					(     __A			:							Dict       ,   __A			:							Union[str, Any]       ,   __A			:							Any       ,   __A			:							bool = False      )							->   List[str]:



 """simple docstring"""




 lowercase       : Tuple						=TaConfig.from_json_file(__A      )
 print(F'Building PyTorch model from configuration: {config}'      )
 # Non-v1.1 checkpoints could also use T5Model, but this works for all.
 # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
 if is_encoder_only:
  lowercase       : Union[str, Any]						=TaEncoderModel(__A      )
 else:
  lowercase       : int						=TaForConditionalGeneration(__A      )

 # Load weights from tf checkpoint
 load_tax_weights_in_ta(__A       ,   __A       ,   __A       ,   __A      )

 # Save pytorch-model
 print(F'Save PyTorch model to {pytorch_dump_path}'      )
 model.save_pretrained(__A      )

 # Verify that we can load the checkpoint.
 model.from_pretrained(__A      )
 print('''Done'''      )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=						argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
      # Required parameters
      parser.add_argument(
          '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
      )
      parser.add_argument(
          '--config_file',
          default=None,
          type=str,
          required=True,
          help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
      )
      parser.add_argument(
          '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      parser.add_argument(
          '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
      )
      SCREAMING_SNAKE_CASE							=						parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
      )











'''simple docstring'''



import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
SCREAMING_SNAKE_CASE							=						{
    'E': 12.70,
    'T': 9.06,
    'A': 8.17,
    'O': 7.51,
    'I': 6.97,
    'N': 6.75,
    'S': 6.33,
    'H': 6.09,
    'R': 5.99,
    'D': 4.25,
    'L': 4.03,
    'C': 2.78,
    'U': 2.76,
    'M': 2.41,
    'W': 2.36,
    'F': 2.23,
    'G': 2.02,
    'Y': 1.97,
    'P': 1.93,
    'B': 1.29,
    'V': 0.98,
    'K': 0.77,
    'J': 0.15,
    'X': 0.15,
    'Q': 0.10,
    'Z': 0.07,
}
SCREAMING_SNAKE_CASE							=						'ETAOINSHRDLCUMWFGYPBVKJXQZ'
SCREAMING_SNAKE_CASE							=						'ABCDEFGHIJKLMNOPQRSTUVWXYZ'


def 				lowercase_					(     __A			:							str      )							->   dict[str, int]:



 """simple docstring"""




 lowercase       : List[str]						={letter: 0 for letter in string.ascii_uppercase}
 for letter in message.upper():
  if letter in LETTERS:
   letter_count[letter] += 1

 return letter_count


def 				lowercase_					(     __A			:							tuple      )							->   str:



 """simple docstring"""




 return x[0]


def 				lowercase_					(     __A			:							str      )							->   str:



 """simple docstring"""




 lowercase       : Optional[Any]						=get_letter_count(__A      )
 lowercase       : dict[int, list[str]]						={
     freq: [] for letter, freq in letter_to_freq.items()
 }
 for letter in LETTERS:
  freq_to_letter[letter_to_freq[letter]].append(__A      )

 lowercase       : dict[int, str]						={}

 for freq in freq_to_letter:
  freq_to_letter[freq].sort(key=ETAOIN.find       ,   reverse=__A      )
  lowercase       : Optional[int]						=''''''.join(freq_to_letter[freq]      )

 lowercase       : str						=list(freq_to_letter_str.items()      )
 freq_pairs.sort(key=__A       ,   reverse=__A      )

 lowercase       : list[str]						=[freq_pair[1] for freq_pair in freq_pairs]

 return "".join(__A      )


def 				lowercase_					(     __A			:							str      )							->   int:



 """simple docstring"""




 lowercase       : Optional[Any]						=get_frequency_order(__A      )
 lowercase       : Union[str, Any]						=0
 for common_letter in ETAOIN[:6]:
  if common_letter in freq_order[:6]:
   match_score += 1

 for uncommon_letter in ETAOIN[-6:]:
  if uncommon_letter in freq_order[-6:]:
   match_score += 1

 return match_score


if __name__ == "__main__":
      import doctest

      doctest.testmod()



