'''simple docstring'''

import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


__a:			Dict	       =       logging.get_logger(__name__)

__a:			int	       =       {"""vocab_file""": """vocab.txt"""}

__a:			Dict	       =       {
    """vocab_file""": {
        """facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
        """facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
    },
}

__a:			Dict	       =       {
    """facebook/esm2_t6_8M_UR50D""": 10_24,
    """facebook/esm2_t12_35M_UR50D""": 10_24,
}
def       __UpperCamelCase	(       UpperCAmelCase					):
 with open(UpperCAmelCase			,					'''r'''					) as f:
  lowercase__    :   Dict						= f.read().splitlines()
  return [l.strip() for l in lines]





class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE 		=   PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE 		=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE 		=   ["input_ids", "attention_mask"]

       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase="<unk>"     ,       __lowerCAmelCase="<cls>"     ,       __lowerCAmelCase="<pad>"     ,       __lowerCAmelCase="<mask>"     ,       __lowerCAmelCase="<eos>"     ,       **__lowerCAmelCase     ,       )  ->  Any:
        super().__init__(**__lowerCAmelCase			)
        lowercase__    :   Optional[int]						= load_vocab_file(__lowerCAmelCase			)
        lowercase__    :   List[Any]						= dict(enumerate(self.all_tokens			)			)
        lowercase__    :   Any						= {tok: ind for ind, tok in enumerate(self.all_tokens			)}
        lowercase__    :   int						= unk_token
        lowercase__    :   List[Any]						= cls_token
        lowercase__    :   Union[str, Any]						= pad_token
        lowercase__    :   Union[str, Any]						= mask_token
        lowercase__    :   Union[str, Any]						= eos_token
        lowercase__    :   List[Any]						= self.all_tokens
        self._create_trie(self.unique_no_split_tokens			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  str:
        return self._id_to_token.get(__lowerCAmelCase     ,       self.unk_token			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  int:
        return self._token_to_id.get(__lowerCAmelCase     ,       self._token_to_id.get(self.unk_token			)			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  List[str]:
        return text.split()

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase=False			)  ->  Tuple:
        return len(self._id_to_token			)

       def _lowerCAmelCase(						self			)  ->  int:
        return {token: i for i, token in enumerate(self.all_tokens			)}

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  int:
        return self._token_to_id.get(__lowerCAmelCase     ,       self._token_to_id.get(self.unk_token			)			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  str:
        return self._id_to_token.get(__lowerCAmelCase     ,       self.unk_token			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  List[int]:
        lowercase__    :   Optional[Any]						= [self.cls_token_id]
        lowercase__    :   List[Any]						= [self.eos_token_id]  # No sep token in ESM vocabulary
        if token_ids_a is None:
         if self.eos_token_id is None:
          return cls + token_ids_a
         else:
          return cls + token_ids_a + sep
        elif self.eos_token_id is None:
         raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!'''			)
        return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = False			)  ->  List[int]:
        if already_has_special_tokens:
         if token_ids_a is not None:
          raise ValueError(
              '''You should not supply a second sequence if the provided sequence of '''
              '''ids is already formatted with special tokens for the model.'''			)

         return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
        lowercase__    :   Union[str, Any]						= [1] + ([0] * len(__lowerCAmelCase			)) + [1]
        if token_ids_a is not None:
         mask += [0] * len(__lowerCAmelCase			) + [1]
        return mask

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  List[Any]:
        lowercase__    :   Tuple						= os.path.join(__lowerCAmelCase     ,       (filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt'''			)
        with open(__lowerCAmelCase     ,       '''w'''			) as f:
         f.write('''\n'''.join(self.all_tokens			)			)
        return (vocab_file,)

       @property
       def _lowerCAmelCase(						self			)  ->  int:
        return self.get_vocab_size(with_added_tokens=__lowerCAmelCase			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = False			)  ->  int:
        return super()._add_tokens(__lowerCAmelCase     ,       special_tokens=__lowerCAmelCase			)


'''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       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase="attention"					):
 lowercase__    :   Dict						= params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
 lowercase__    :   Dict						= params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
 lowercase__    :   int						= params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
 lowercase__    :   Any						= params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
 return k, o, q, v
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=False					):
 if split_mlp_wi:
  lowercase__    :   Tuple						= params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
  lowercase__    :   Tuple						= params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
  lowercase__    :   Dict						= (wi_a, wi_a)
 else:
  lowercase__    :   List[Any]						= params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

 lowercase__    :   int						= params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
 return wi, wo
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]
def       __UpperCamelCase	(       UpperCAmelCase		,					*, UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Union[str, Any]						= traverse_util.flatten_dict(variables['''target''']					)
 lowercase__    :   Any						= {'''/'''.join(UpperCAmelCase					): v for k, v in old.items()}

 # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
 lowercase__    :   List[str]						= '''encoder/layers_0/mlp/wi_0/kernel''' in old
 print('''Split MLP:'''		,					UpperCAmelCase					)

 lowercase__    :   Union[str, Any]						= collections.OrderedDict()

 # Shared embeddings.
 lowercase__    :   str						= old['''token_embedder/embedding''']

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

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

 lowercase__    :   Dict						= old[
     '''encoder/relpos_bias/rel_embedding'''
 ].T
 lowercase__    :   str						= old['''encoder/encoder_norm/scale''']

 if not is_encoder_only:
  # Decoder.
  for i in range(UpperCAmelCase					):
   # Block i, layer 0 (Self Attention).
   lowercase__    :   Optional[Any]						= tax_layer_norm_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''decoder'''		,					'''pre_self_attention_layer_norm'''					)
   lowercase__			,							lowercase__			,							lowercase__			,							lowercase__    :   Optional[Any]						= tax_attention_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''decoder'''		,					'''self_attention'''					)
   lowercase__    :   List[str]						= layer_norm
   lowercase__    :   List[Any]						= k.T
   lowercase__    :   List[Any]						= o.T
   lowercase__    :   List[str]						= q.T
   lowercase__    :   Optional[Any]						= v.T

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

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

  lowercase__    :   Any						= old['''decoder/decoder_norm/scale''']
  lowercase__    :   Optional[Any]						= 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__    :   Dict						= old['''decoder/logits_dense/kernel'''].T

 return new
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   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__    :   Optional[int]						= state_dict['''shared.weight''']

 if not is_encoder_only:
  if "decoder.embed_tokens.weight" not in state_dict:
   lowercase__    :   List[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__    :   Union[str, Any]						= state_dict['''shared.weight''']

 return state_dict
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   List[str]						= checkpoints.load_tax_checkpoint(UpperCAmelCase					)
 lowercase__    :   str						= convert_tax_to_pytorch(UpperCAmelCase		,					num_layers=config.num_layers		,					is_encoder_only=UpperCAmelCase					)
 lowercase__    :   List[Any]						= make_state_dict(UpperCAmelCase		,					UpperCAmelCase					)
 model.load_state_dict(UpperCAmelCase		,					strict=UpperCAmelCase					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = False					):
 lowercase__    :   Dict						= TaConfig.from_json_file(UpperCAmelCase					)
 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__    :   List[str]						= TaEncoderModel(UpperCAmelCase					)
 else:
  lowercase__    :   Tuple						= TaForConditionalGeneration(UpperCAmelCase					)

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

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

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


if __name__ == "__main__":
  __a:			Optional[Any]	       =       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
  )
  __a:			Tuple	       =       parser.parse_args()
  convert_tax_checkpoint_to_pytorch(
      args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
  )

