"""simple docstring"""



import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__    				=					logging.get_logger(__name__)

__magic_name__    				=					[
    (
        "protein1",
        "MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA",
    ),
    ("protein2", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"),
    ("protein3", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"),
    ("protein4", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"),
]

__magic_name__    				=					{
    "esm1b_t33_650M_UR50S": esm_module.pretrained.esm1b_t33_650M_UR50S,
    "esm1v_t33_650M_UR90S_1": esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    "esm1v_t33_650M_UR90S_2": esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    "esm1v_t33_650M_UR90S_3": esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    "esm1v_t33_650M_UR90S_4": esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    "esm1v_t33_650M_UR90S_5": esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    "esm2_t48_15B_UR50D": esm_module.pretrained.esm2_t48_15B_UR50D,
    "esm2_t36_3B_UR50D": esm_module.pretrained.esm2_t36_3B_UR50D,
    "esm2_t33_650M_UR50D": esm_module.pretrained.esm2_t33_650M_UR50D,
    "esm2_t30_150M_UR50D": esm_module.pretrained.esm2_t30_150M_UR50D,
    "esm2_t12_35M_UR50D": esm_module.pretrained.esm2_t12_35M_UR50D,
    "esm2_t6_8M_UR50D": esm_module.pretrained.esm2_t6_8M_UR50D,
    "esmfold_v1": esmfold_va,
}

__magic_name__    				=					list("ARNDCQEGHILKMFPSTWYV")

__magic_name__    				=					restypes + ["X"]
__magic_name__    				=					restypes_with_x + ["<pad>", "<mask>", "<cls>", "<sep>", "<eos>"]





def   _lowerCamelCase ( )							->     Optional[Any]:


   '''simple docstring'''


   with TemporaryDirectory() as tempdir:
      a__     				=		'\n'.join(UpperCAmelCase__					)
      a__     				=		Path(UpperCAmelCase__					) / 'vocab.txt'
      vocab_file.write_text(UpperCAmelCase__					)
      a__     				=		EsmTokenizer(vocab_file=str(UpperCAmelCase__					)					)
   a__     				=		0  # Overlaps with 'A' but that seems to be what they want
   return hf_tokenizer





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__					)							->     int:


   '''simple docstring'''


   a__     				=		our_module.load_state_dict(original_module.state_dict()					)
   if status.missing_keys:
      raise ValueError(f'''Missing keys: {status.missing_keys}'''					)
   if status.unexpected_keys:
      raise ValueError(f'''Unexpected keys: {status.unexpected_keys}'''					)





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)							->     int:


   '''simple docstring'''


   if model.startswith('esmfold'					):
      a__     				=		MODEL_MAPPING[model]()
   else:
      a__				,       a__     				=		MODEL_MAPPING[model]()
   esm.eval()  # disable dropout

   if model.startswith('esmfold'					):
      a__     				=		esm.esm.embed_dim
      a__     				=		esm.esm.num_layers
      a__     				=		esm.esm.attention_heads
      a__     				=		4 * embed_dim
      a__     				=		esm.esm.token_dropout
      a__     				=		False  # This code path does not exist in ESM-2
      a__     				=		'rotary'
      a__     				=		True
      a__     				=		EsmFoldConfig()
      for key, val in esm.cfg.items():
         if hasattr(UpperCAmelCase__,UpperCAmelCase__					) and key != "trunk":
            setattr(UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)
      for key, val in esm.cfg.trunk.items():
         if hasattr(esmfold_config.trunk,UpperCAmelCase__					) and key != "structure_module":
            setattr(esmfold_config.trunk,UpperCAmelCase__,UpperCAmelCase__					)
      for key, val in esm.cfg.trunk.structure_module.items():
         if hasattr(esmfold_config.trunk.structure_module,UpperCAmelCase__					):
            setattr(esmfold_config.trunk.structure_module,UpperCAmelCase__,UpperCAmelCase__					)
   elif hasattr(UpperCAmelCase__,'args'					):
      # Indicates an ESM-1b or ESM-1v model
      a__     				=		esm.args.embed_dim
      a__     				=		esm.args.layers
      a__     				=		esm.args.attention_heads
      a__     				=		esm.args.ffn_embed_dim
      a__     				=		esm.args.token_dropout
      a__     				=		True if esm.emb_layer_norm_before else False
      a__     				=		'absolute'
      a__     				=		False
      a__     				=		None
   else:
      # Indicates an ESM-2 model
      a__     				=		esm.embed_dim
      a__     				=		esm.num_layers
      a__     				=		esm.attention_heads
      a__     				=		4 * embed_dim  # This is hardcoded in ESM-2
      a__     				=		esm.token_dropout
      a__     				=		False  # This code path does not exist in ESM-2
      a__     				=		'rotary'
      a__     				=		False
      a__     				=		None

   if is_folding_model:
      a__     				=		esm.esm.alphabet
   a__     				=		tuple(alphabet.all_toks					)
   a__     				=		alphabet.mask_idx
   a__     				=		alphabet.padding_idx

   if is_folding_model:
      a__     				=		esm.esm
   else:
      a__     				=		esm

   a__     				=		EsmConfig(
       vocab_size=original_esm_model.embed_tokens.num_embeddings,mask_token_id=UpperCAmelCase__,hidden_size=UpperCAmelCase__,num_hidden_layers=UpperCAmelCase__,num_attention_heads=UpperCAmelCase__,intermediate_size=UpperCAmelCase__,max_position_embeddings=10_26,layer_norm_eps=1e-5,attention_probs_dropout_prob=0.0,hidden_dropout_prob=0.0,pad_token_id=UpperCAmelCase__,emb_layer_norm_before=UpperCAmelCase__,token_dropout=UpperCAmelCase__,position_embedding_type=UpperCAmelCase__,is_folding_model=UpperCAmelCase__,esmfold_config=UpperCAmelCase__,vocab_list=UpperCAmelCase__,)
   if classification_head:
      a__     				=		esm.classification_heads['mnli'].out_proj.weight.shape[0]
   print('Our ESM config:',UpperCAmelCase__					)

   if model.startswith('esmfold'					):
      a__     				=		EsmForProteinFolding
   elif classification_head:
      a__     				=		EsmForSequenceClassification
   else:
      a__     				=		EsmForMaskedLM
   a__     				=		model_class(UpperCAmelCase__					)
   model.eval()

   # Now let's copy all the weights.
   # Embeddings
   a__     				=		original_esm_model.embed_tokens.weight
   if position_embedding_type == "absolute":
      a__     				=		original_esm_model.embed_positions.weight

   if config.emb_layer_norm_before:
      a__     				=		original_esm_model.emb_layer_norm_before.weight
      a__     				=		original_esm_model.emb_layer_norm_before.bias

   a__     				=		original_esm_model.emb_layer_norm_after.weight
   a__     				=		original_esm_model.emb_layer_norm_after.bias

   for i in range(config.num_hidden_layers					):
      # Encoder: start of layer
      a__     				=		model.esm.encoder.layer[i]
      # esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
      a__     				=		original_esm_model.layers[i]

      # self attention
      a__     				=		layer.attention.self
      assert (
          esm_layer.self_attn.k_proj.weight.data.shape
          == esm_layer.self_attn.q_proj.weight.data.shape
          == esm_layer.self_attn.v_proj.weight.data.shape
          == torch.Size((config.hidden_size, config.hidden_size)					)
      )

      a__     				=		esm_layer.self_attn.q_proj.weight
      a__     				=		esm_layer.self_attn.q_proj.bias
      a__     				=		esm_layer.self_attn.k_proj.weight
      a__     				=		esm_layer.self_attn.k_proj.bias
      a__     				=		esm_layer.self_attn.v_proj.weight
      a__     				=		esm_layer.self_attn.v_proj.bias

      if getattr(esm_layer.self_attn,'rot_emb',UpperCAmelCase__					) is not None:
         # Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
         # During the training of ESM-2 the model was converted to float16 precision, which also converts
         # the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
         # If we recompute inv_freq without this loss of precision then we will get subtly different rotary
         # embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
         # we make sure the new model copies the data from the old inv_freq.
         a__     				=		esm_layer.self_attn.rot_emb.inv_freq

      # LayerNorm changes for pre-activation
      a__     				=		esm_layer.self_attn_layer_norm.weight
      a__     				=		esm_layer.self_attn_layer_norm.bias
      a__     				=		esm_layer.final_layer_norm.weight
      a__     				=		esm_layer.final_layer_norm.bias

      # self-attention output
      a__     				=		layer.attention.output
      assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
      a__     				=		esm_layer.self_attn.out_proj.weight
      a__     				=		esm_layer.self_attn.out_proj.bias

      # intermediate
      a__     				=		layer.intermediate
      assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
      a__     				=		esm_layer.fca.weight
      a__     				=		esm_layer.fca.bias

      # output
      a__     				=		layer.output
      assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
      a__     				=		esm_layer.fca.weight
      a__     				=		esm_layer.fca.bias
      # end of layer

   if is_folding_model:
      a__     				=		esm.esm_s_combine.data
      a__     				=		esm.afa_to_esm.data
      transfer_and_check_weights(esm.embedding,model.embedding					)
      transfer_and_check_weights(esm.esm_s_mlp,model.esm_s_mlp					)
      transfer_and_check_weights(esm.trunk,model.trunk					)
      transfer_and_check_weights(esm.distogram_head,model.distogram_head					)
      transfer_and_check_weights(esm.ptm_head,model.ptm_head					)
      transfer_and_check_weights(esm.lm_head,model.lm_head					)
      transfer_and_check_weights(esm.lddt_head,model.lddt_head					)

   elif classification_head:
      a__     				=		esm.esm.classification_heads['mnli'].dense.weight
      a__     				=		esm.classification_heads['mnli'].dense.bias
      a__     				=		esm.classification_heads['mnli'].out_proj.weight
      a__     				=		esm.classification_heads['mnli'].out_proj.bias
   else:
      # LM Head
      a__     				=		esm.lm_head.dense.weight
      a__     				=		esm.lm_head.dense.bias
      a__     				=		esm.lm_head.layer_norm.weight
      a__     				=		esm.lm_head.layer_norm.bias
      a__     				=		esm.lm_head.weight
      a__     				=		esm.lm_head.bias

   # Contact prediction head
   transfer_and_check_weights(esm.contact_head,model.esm.contact_head					)

   # Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
   if is_folding_model:
      # Folding models aren't trained on masked inputs and don't like mask tokens.
      a__     				=		SAMPLE_DATA[:2]
   else:
      a__     				=		SAMPLE_DATA

   if is_folding_model:
      a__     				=		get_esmfold_tokenizer()
      a__     				=		hf_tokenizer(
          [row[1] for row in sample_data],return_tensors='pt',padding=UpperCAmelCase__,add_special_tokens=UpperCAmelCase__					)
      a__				,       a__				,       a__				,       a__				,       a__     				=		esmfold_encode_sequences([row[1] for row in sample_data]					)
      a__     				=		torch.all(hf_tokens['input_ids'] == esmfold_aas					) and torch.all(
          hf_tokens['attention_mask'] == esmfold_mask					)
   else:
      # Let's check that we get the same results.
      a__     				=		alphabet.get_batch_converter()
      a__				,       a__				,       a__     				=		batch_converter(UpperCAmelCase__					)
      # Prepare tokenizer and make sure it matches
      with TemporaryDirectory() as tempdir:
         a__     				=		'\n'.join(alphabet.all_toks					)
         a__     				=		Path(UpperCAmelCase__					) / 'vocab.txt'
         vocab_file.write_text(UpperCAmelCase__					)
         a__     				=		EsmTokenizer(vocab_file=str(UpperCAmelCase__					)					)

      a__     				=		hf_tokenizer([row[1] for row in sample_data],return_tensors='pt',padding=UpperCAmelCase__					)
      a__     				=		torch.all(hf_tokens['input_ids'] == batch_tokens					)

   print('Do both models tokenizers output the same tokens?','🔥' if success else '💩'					)
   if not success:
      raise Exception('Tokenization does not match!'					)

   with torch.no_grad():
      if is_folding_model:
         # Let's test the model in parts
         # ESMFold always converts the ESM stem to float16, which requires float16 ops
         # that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
         # ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
         # original and the converted model on the GPU at the same time.
         a__     				=		esm.cuda().infer([row[1] for row in sample_data]					)
         a__     				=		model.cuda()(
             input_ids=hf_tokens['input_ids'].cuda(),attention_mask=hf_tokens['attention_mask'].cuda()					)
      else:
         a__     				=		model(**UpperCAmelCase__,output_hidden_states=UpperCAmelCase__					)
         a__     				=		our_output['logits']
         if classification_head:
            a__     				=		esm.model.classification_heads['mnli'](esm.extract_features(UpperCAmelCase__					)					)
         else:
            a__     				=		esm(hf_tokens['input_ids'],repr_layers=list(range(9_99					)					)					)
            a__     				=		their_output['logits']

      if is_folding_model:
         a__     				=		torch.max(torch.abs(our_output['positions'] - their_output['positions']					)					).item()
         a__     				=		torch.allclose(our_output['positions'],their_output['positions'],atol=1e-5					)
      else:
         a__     				=		torch.max(torch.abs(our_output - their_output					)					).item()
         a__     				=		torch.allclose(UpperCAmelCase__,UpperCAmelCase__,atol=1e-5					)

      print(f'''max_absolute_diff = {max_absolute_diff}'''					)  # ~ 1e-5
      print('Do both models output the same tensors?','🔥' if success else '💩'					)

      if not success:
         raise Exception('Something went wRoNg'					)

      if not is_folding_model:
         # Let's check contact prediction too
         a__     				=		model.predict_contacts(hf_tokens['input_ids'],hf_tokens['attention_mask']					)
         a__     				=		esm.predict_contacts(hf_tokens['input_ids']					)
         a__     				=		torch.max(torch.abs(our_output - their_output					)					).item()
         a__     				=		torch.allclose(UpperCAmelCase__,UpperCAmelCase__,atol=1e-5					)

         print('Contact prediction testing:'					)
         print(f'''max_absolute_diff = {max_absolute_diff}'''					)  # ~ 1e-5
         print('Do both models output the same tensors?','🔥' if success else '💩'					)

         if not success:
            raise Exception('Something went wRoNg'					)

      pathlib.Path(UpperCAmelCase__					).mkdir(parents=UpperCAmelCase__,exist_ok=UpperCAmelCase__					)
      print(f'''Saving model to {pytorch_dump_folder_path}'''					)
      model.save_pretrained(UpperCAmelCase__					)

      del esm  # Free up some memory before continuing

   print(f'''Saving tokenizer to {pytorch_dump_folder_path}'''					)
   hf_tokenizer.save_pretrained(UpperCAmelCase__					)

   if push_to_repo:
      model.push_to_hub(repo_id=UpperCAmelCase__,use_auth_token=UpperCAmelCase__					)
      hf_tokenizer.push_to_hub(repo_id=UpperCAmelCase__,use_auth_token=UpperCAmelCase__					)


if __name__ == "__main__":
     __magic_name__    				=					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--pytorch_dump_folder_path", type=str, required=True, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--classification_head", action="store_true", help="Whether to convert a final classification head."
     )
     parser.add_argument("--model", default=None, type=str, required=True, help="Name of model to convert.")
     parser.add_argument("--push_to_repo", type=str, help="Repo to upload to (including username!).")
     parser.add_argument("--auth_token", type=str, help="HuggingFace auth token.")
     __magic_name__    				=					parser.parse_args()
     convert_esm_checkpoint_to_pytorch(
         args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
     )



"""simple docstring"""



import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation

__magic_name__    				=					logging.get_logger(__name__)

__magic_name__    				=					{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

__magic_name__    				=					{
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
}

__magic_name__    				=					{
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}





@lru_cache()
def   _lowerCamelCase ( )							->     int:


   '''simple docstring'''


   a__     				=		(
       list(range(ord('!'					),ord('~'					) + 1					)					) + list(range(ord('¡'					),ord('¬'					) + 1					)					) + list(range(ord('®'					),ord('ÿ'					) + 1					)					)
   )
   a__     				=		bs[:]
   a__     				=		0
   for b in range(2**8					):
      if b not in bs:
         bs.append(UpperCAmelCase__					)
         cs.append(2**8 + n					)
         n += 1
   a__     				=		[chr(UpperCAmelCase__					) for n in cs]
   return dict(zip(UpperCAmelCase__,UpperCAmelCase__					)					)





def   _lowerCamelCase ( UpperCAmelCase__					)							->     List[str]:


   '''simple docstring'''


   a__     				=		set()
   a__     				=		word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)					)
      a__     				=		char
   return pairs




class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 a_       :	Union[str, Any]  			=VOCAB_FILES_NAMES
 a_       :	Optional[Any]  			=PRETRAINED_VOCAB_FILES_MAP
 a_       :	Dict  			=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 a_       :	str  			=["input_ids", "attention_mask"]


 def __init__(   self		:      Optional[int]					,   _snake_case		:      List[str]					,   _snake_case		:      Any					,   _snake_case		:      Union[str, Any]="replace"					,   _snake_case		:      Tuple="<|endoftext|>"					,   _snake_case		:      Optional[int]="<|endoftext|>"					,   _snake_case		:      Any="<|endoftext|>"					,   _snake_case		:      Dict=None					,   _snake_case		:      List[str]=False					,   _snake_case		:      Optional[int]=False					,   **_snake_case		:      Any					,   )				->			Optional[int]:




    '''simple docstring'''






    a__     				=		AddedToken(_snake_case					,   lstrip=_snake_case					,   rstrip=_snake_case				) if isinstance(_snake_case					,   _snake_case				) else bos_token
    a__     				=		AddedToken(_snake_case					,   lstrip=_snake_case					,   rstrip=_snake_case				) if isinstance(_snake_case					,   _snake_case				) else eos_token
    a__     				=		AddedToken(_snake_case					,   lstrip=_snake_case					,   rstrip=_snake_case				) if isinstance(_snake_case					,   _snake_case				) else unk_token
    a__     				=		AddedToken(_snake_case					,   lstrip=_snake_case					,   rstrip=_snake_case				) if isinstance(_snake_case					,   _snake_case				) else pad_token
    super().__init__(
        errors=_snake_case					,   unk_token=_snake_case					,   bos_token=_snake_case					,   eos_token=_snake_case					,   pad_token=_snake_case					,   add_prefix_space=_snake_case					,   add_bos_token=_snake_case					,   **_snake_case					,   )
    a__     				=		add_bos_token

    with open(_snake_case					,   encoding='utf-8'				) as vocab_handle:
       a__     				=		json.load(_snake_case				)
    a__     				=		{v: k for k, v in self.encoder.items()}
    a__     				=		errors  # how to handle errors in decoding
    a__     				=		bytes_to_unicode()
    a__     				=		{v: k for k, v in self.byte_encoder.items()}
    with open(_snake_case					,   encoding='utf-8'				) as merges_handle:
       a__     				=		merges_handle.read().split('\n'				)[1:-1]
    a__     				=		[tuple(merge.split()				) for merge in bpe_merges]
    a__     				=		dict(zip(_snake_case					,   range(len(_snake_case				)				)				)				)
    a__     				=		{}
    a__     				=		add_prefix_space

    # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
    a__     				=		re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'				)


 @property
 def        _lowerCAmelCase				(   self		:      Tuple				)				->			Optional[int]:




    '''simple docstring'''






    return len(self.encoder				)


 def        _lowerCAmelCase				(   self		:      Tuple				)				->			Any:




    '''simple docstring'''






    return dict(self.encoder					,   **self.added_tokens_encoder				)


 def        _lowerCAmelCase				(   self		:      str					,   _snake_case		:      Union[str, Any]				)				->			str:




    '''simple docstring'''






    if token in self.cache:
       return self.cache[token]
    a__     				=		tuple(_snake_case				)
    a__     				=		get_pairs(_snake_case				)

    if not pairs:
       return token

    while True:
       a__     				=		min(_snake_case					,   key=lambda _snake_case				: self.bpe_ranks.get(_snake_case					,   float('inf'				)				)				)
       if bigram not in self.bpe_ranks:
          break
       a__				,       a__     				=		bigram
       a__     				=		[]
       a__     				=		0
       while i < len(_snake_case				):
          try:
             a__     				=		word.index(_snake_case					,   _snake_case				)
          except ValueError:
             new_word.extend(word[i:]				)
             break
          else:
             new_word.extend(word[i:j]				)
             a__     				=		j

          if word[i] == first and i < len(_snake_case				) - 1 and word[i + 1] == second:
             new_word.append(first + second				)
             i += 2
          else:
             new_word.append(word[i]				)
             i += 1
       a__     				=		tuple(_snake_case				)
       a__     				=		new_word
       if len(_snake_case				) == 1:
          break
       else:
          a__     				=		get_pairs(_snake_case				)
    a__     				=		' '.join(_snake_case				)
    a__     				=		word
    return word


 def        _lowerCAmelCase				(   self		:      List[Any]					,   _snake_case		:      Dict					,   _snake_case		:      List[Any]=None				)				->			List[Any]:




    '''simple docstring'''






    if self.add_bos_token:
       a__     				=		[self.bos_token_id]
    else:
       a__     				=		[]

    a__     				=		bos_token_ids + token_ids_a

    if token_ids_a is None:
       return output

    return output + bos_token_ids + token_ids_a


 def        _lowerCAmelCase				(   self		:      Any					,   _snake_case		:      List[int]					,   _snake_case		:      Optional[List[int]] = None					,   _snake_case		:      bool = False				)				->			List[int]:




    '''simple docstring'''






    if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=_snake_case					,   token_ids_a=_snake_case					,   already_has_special_tokens=_snake_case				)

    if not self.add_bos_token:
       return super().get_special_tokens_mask(
           token_ids_a=_snake_case					,   token_ids_a=_snake_case					,   already_has_special_tokens=_snake_case				)

    if token_ids_a is None:
       return [1] + ([0] * len(_snake_case				))
    return [1] + ([0] * len(_snake_case				)) + [1] + ([0] * len(_snake_case				))


 def        _lowerCAmelCase				(   self		:      Dict					,   _snake_case		:      Optional[Any]				)				->			Tuple:




    '''simple docstring'''






    a__     				=		[]
    for token in re.findall(self.pat					,   _snake_case				):
       a__     				=		''.join(
           self.byte_encoder[b] for b in token.encode('utf-8'				)				)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
       bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_snake_case				).split(' '				)				)
    return bpe_tokens


 def        _lowerCAmelCase				(   self		:      Optional[Any]					,   _snake_case		:      Tuple				)				->			int:




    '''simple docstring'''






    return self.encoder.get(_snake_case					,   self.encoder.get(self.unk_token				)				)


 def        _lowerCAmelCase				(   self		:      List[Any]					,   _snake_case		:      int				)				->			List[str]:




    '''simple docstring'''






    return self.decoder.get(_snake_case				)


 def        _lowerCAmelCase				(   self		:      Tuple					,   _snake_case		:      Any				)				->			Optional[Any]:




    '''simple docstring'''






    a__     				=		''.join(_snake_case				)
    a__     				=		bytearray([self.byte_decoder[c] for c in text]				).decode('utf-8'					,   errors=self.errors				)
    return text


 def        _lowerCAmelCase				(   self		:      Optional[int]					,   _snake_case		:      str					,   _snake_case		:      Optional[str] = None				)				->			Tuple[str]:




    '''simple docstring'''






    if not os.path.isdir(_snake_case				):
       logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
       return
    a__     				=		os.path.join(
        _snake_case					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)
    a__     				=		os.path.join(
        _snake_case					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']				)

    with open(_snake_case					,   'w'					,   encoding='utf-8'				) as f:
       f.write(json.dumps(self.encoder					,   indent=2					,   sort_keys=_snake_case					,   ensure_ascii=_snake_case				) + '\n'				)

    a__     				=		0
    with open(_snake_case					,   'w'					,   encoding='utf-8'				) as writer:
       writer.write('#version: 0.2\n'				)
       for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,   key=lambda _snake_case				: kv[1]				):
          if index != token_index:
             logger.warning(
                 F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                 ' Please check that the tokenizer is not corrupted!'				)
             a__     				=		token_index
          writer.write(' '.join(_snake_case				) + '\n'				)
          index += 1

    return vocab_file, merge_file


 def        _lowerCAmelCase				(   self		:      int					,   _snake_case		:      Union[str, Any]					,   _snake_case		:      List[Any]=False					,   **_snake_case		:      Optional[int]				)				->			Union[str, Any]:




    '''simple docstring'''






    a__     				=		kwargs.pop('add_prefix_space'					,   self.add_prefix_space				)
    if is_split_into_words or add_prefix_space:
       a__     				=		' ' + text
    return (text, kwargs)



 def        _lowerCAmelCase				(   self		:      Dict					,   _snake_case		:      "Conversation"				)				->			List[int]:




    '''simple docstring'''






    a__     				=		[]
    for is_user, text in conversation.iter_texts():
       input_ids.extend(self.encode(_snake_case					,   add_special_tokens=_snake_case				) + [self.eos_token_id]				)
    if len(_snake_case				) > self.model_max_length:
       a__     				=		input_ids[-self.model_max_length :]
    return input_ids


