

"""simple docstring"""




import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_  :      Tuple							=      logging.get_logger(__name__)



def 						UpperCAmelCase__ (					A__ )   ->   Any:




   """simple docstring"""
   lowerCamelCase__						       =      tf.image.resize(A__						,     (346, 346) )
   lowerCamelCase__						       =      tf.image.crop_to_bounding_box(A__						,     (346 - 289) // 2						,     (346 - 289) // 2						,     289						,     289 )
   return image



def 						UpperCAmelCase__ (					)   ->   Optional[Any]:




   """simple docstring"""
   lowerCamelCase__						       =      EfficientNetConfig.from_pretrained("google/efficientnet-b7" )
   lowerCamelCase__						       =      289
   lowerCamelCase__						       =      640
   lowerCamelCase__						       =      {"0": "LABEL_0", "1": "LABEL_1"}
   lowerCamelCase__						       =      {"LABEL_0": 0, "LABEL_1": 1}
   lowerCamelCase__						       =      []

   lowerCamelCase__						       =      BertConfig()
   lowerCamelCase__						       =      AlignConfig.from_text_vision_configs(
       text_config=A__						,     vision_config=A__						,     projection_dim=640 )
   return config



def 						UpperCAmelCase__ (					)   ->   Dict:




   """simple docstring"""
   lowerCamelCase__						       =      "http://images.cocodataset.org/val2017/000000039769.jpg"
   lowerCamelCase__						       =      Image.open(requests.get(A__						,     stream=A__ ).raw )
   return im



def 						UpperCAmelCase__ (					)   ->   List[str]:




   """simple docstring"""
   lowerCamelCase__						       =      EfficientNetImageProcessor(
       do_center_crop=A__						,     rescale_factor=1 / 127.5						,     rescale_offset=A__						,     do_normalize=A__						,     include_top=A__						,     resample=Image.BILINEAR						,     )
   lowerCamelCase__						       =      BertTokenizer.from_pretrained("bert-base-uncased" )
   lowerCamelCase__						       =      64
   lowerCamelCase__						       =      AlignProcessor(image_processor=A__						,     tokenizer=A__ )
   return processor



def 						UpperCAmelCase__ (					A__ )   ->   int:




   """simple docstring"""
   # EfficientNet image encoder
   lowerCamelCase__						       =      [v.split("_" )[0].split("block" )[1] for v in original_param_names if v.startswith("block" )]
   lowerCamelCase__						       =      list(set(A__ ) )
   lowerCamelCase__						       =      sorted(A__ )
   lowerCamelCase__						       =      len(A__ )
   lowerCamelCase__						       =      {b: str(A__ ) for b, i in zip(A__						,     range(A__ ) )}

   lowerCamelCase__						       =      []
   rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight") )
   rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight") )
   rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias") )
   rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean") )
   rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var") )

   for b in block_names:
      lowerCamelCase__						       =      block_name_mapping[b]
      rename_keys.append((f'block{b}_expand_conv/kernel:0', f'encoder.blocks.{hf_b}.expansion.expand_conv.weight') )
      rename_keys.append((f'block{b}_expand_bn/gamma:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.weight') )
      rename_keys.append((f'block{b}_expand_bn/beta:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.bias') )
      rename_keys.append(
          (f'block{b}_expand_bn/moving_mean:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean') )
      rename_keys.append(
          (f'block{b}_expand_bn/moving_variance:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.running_var') )
      rename_keys.append(
          (f'block{b}_dwconv/depthwise_kernel:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight') )
      rename_keys.append((f'block{b}_bn/gamma:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight') )
      rename_keys.append((f'block{b}_bn/beta:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias') )
      rename_keys.append(
          (f'block{b}_bn/moving_mean:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean') )
      rename_keys.append(
          (f'block{b}_bn/moving_variance:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var') )

      rename_keys.append((f'block{b}_se_reduce/kernel:0', f'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight') )
      rename_keys.append((f'block{b}_se_reduce/bias:0', f'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias') )
      rename_keys.append((f'block{b}_se_expand/kernel:0', f'encoder.blocks.{hf_b}.squeeze_excite.expand.weight') )
      rename_keys.append((f'block{b}_se_expand/bias:0', f'encoder.blocks.{hf_b}.squeeze_excite.expand.bias') )
      rename_keys.append(
          (f'block{b}_project_conv/kernel:0', f'encoder.blocks.{hf_b}.projection.project_conv.weight') )
      rename_keys.append((f'block{b}_project_bn/gamma:0', f'encoder.blocks.{hf_b}.projection.project_bn.weight') )
      rename_keys.append((f'block{b}_project_bn/beta:0', f'encoder.blocks.{hf_b}.projection.project_bn.bias') )
      rename_keys.append(
          (f'block{b}_project_bn/moving_mean:0', f'encoder.blocks.{hf_b}.projection.project_bn.running_mean') )
      rename_keys.append(
          (f'block{b}_project_bn/moving_variance:0', f'encoder.blocks.{hf_b}.projection.project_bn.running_var') )

   lowerCamelCase__						       =      {}
   for item in rename_keys:
      if item[0] in original_param_names:
         lowerCamelCase__						       =      "vision_model." + item[1]

    # BERT text encoder
   lowerCamelCase__						       =      []
   lowerCamelCase__						       =      "tf_bert_model/bert"
   lowerCamelCase__						       =      "text_model"
   for i in range(12 ):
      rename_keys.append(
          (
              f'{old}/encoder/layer_._{i}/attention/self/query/kernel:0',
              f'{new}.encoder.layer.{i}.attention.self.query.weight',
          ) )
      rename_keys.append(
          (
              f'{old}/encoder/layer_._{i}/attention/self/query/bias:0',
              f'{new}.encoder.layer.{i}.attention.self.query.bias',
          ) )
      rename_keys.append(
          (
              f'{old}/encoder/layer_._{i}/attention/self/key/kernel:0',
              f'{new}.encoder.layer.{i}.attention.self.key.weight',
          ) )
      rename_keys.append(
          (
              f'{old}/encoder/layer_._{i}/attention/self/key/bias:0',
              f'{new}.encoder.layer.{i}.attention.self.key.bias',
          ) )
      rename_keys.append(
          (
              f'{old}/encoder/layer_._{i}/attention/self/value/kernel:0',
              f'{new}.encoder.layer.{i}.attention.self.value.weight',
          ) )
      rename_keys.append(
          (
              f'{old}/encoder/layer_._{i}/attention/self/value/bias:0',
              f'{new}.encoder.layer.{i}.attention.self.value.bias',
          ) )
      rename_keys.append(
          (
              f'{old}/encoder/layer_._{i}/attention/output/dense/kernel:0',
              f'{new}.encoder.layer.{i}.attention.output.dense.weight',
          ) )
      rename_keys.append(
          (
              f'{old}/encoder/layer_._{i}/attention/output/dense/bias:0',
              f'{new}.encoder.layer.{i}.attention.output.dense.bias',
          ) )
      rename_keys.append(
          (
              f'{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0',
              f'{new}.encoder.layer.{i}.attention.output.LayerNorm.weight',
          ) )
      rename_keys.append(
          (
              f'{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0',
              f'{new}.encoder.layer.{i}.attention.output.LayerNorm.bias',
          ) )
      rename_keys.append(
          (
              f'{old}/encoder/layer_._{i}/intermediate/dense/kernel:0',
              f'{new}.encoder.layer.{i}.intermediate.dense.weight',
          ) )
      rename_keys.append(
          (
              f'{old}/encoder/layer_._{i}/intermediate/dense/bias:0',
              f'{new}.encoder.layer.{i}.intermediate.dense.bias',
          ) )
      rename_keys.append(
          (f'{old}/encoder/layer_._{i}/output/dense/kernel:0', f'{new}.encoder.layer.{i}.output.dense.weight') )
      rename_keys.append(
          (f'{old}/encoder/layer_._{i}/output/dense/bias:0', f'{new}.encoder.layer.{i}.output.dense.bias') )
      rename_keys.append(
          (f'{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0', f'{new}.encoder.layer.{i}.output.LayerNorm.weight') )
      rename_keys.append(
          (f'{old}/encoder/layer_._{i}/output/LayerNorm/beta:0', f'{new}.encoder.layer.{i}.output.LayerNorm.bias') )

   rename_keys.append((f'{old}/embeddings/word_embeddings/weight:0', f'{new}.embeddings.word_embeddings.weight') )
   rename_keys.append(
       (f'{old}/embeddings/position_embeddings/embeddings:0', f'{new}.embeddings.position_embeddings.weight') )
   rename_keys.append(
       (f'{old}/embeddings/token_type_embeddings/embeddings:0', f'{new}.embeddings.token_type_embeddings.weight') )
   rename_keys.append((f'{old}/embeddings/LayerNorm/gamma:0', f'{new}.embeddings.LayerNorm.weight') )
   rename_keys.append((f'{old}/embeddings/LayerNorm/beta:0', f'{new}.embeddings.LayerNorm.bias') )

   rename_keys.append((f'{old}/pooler/dense/kernel:0', f'{new}.pooler.dense.weight') )
   rename_keys.append((f'{old}/pooler/dense/bias:0', f'{new}.pooler.dense.bias') )
   rename_keys.append(("dense/kernel:0", "text_projection.weight") )
   rename_keys.append(("dense/bias:0", "text_projection.bias") )
   rename_keys.append(("dense/bias:0", "text_projection.bias") )
   rename_keys.append(("temperature:0", "temperature") )

   for item in rename_keys:
      if item[0] in original_param_names:
         lowerCamelCase__						       =      item[1]
   return key_mapping



def 						UpperCAmelCase__ (					A__						,     A__						,     A__ )   ->   str:




   """simple docstring"""
   list(hf_params.keys() )

   for key, value in tf_params.items():
      if key not in key_mapping:
         continue

      lowerCamelCase__						       =      key_mapping[key]
      if "_conv" in key and "kernel" in key:
         lowerCamelCase__						       =      torch.from_numpy(A__ ).permute(3						,     2						,     0						,     1 )
      elif "embeddings" in key:
         lowerCamelCase__						       =      torch.from_numpy(A__ )
      elif "depthwise_kernel" in key:
         lowerCamelCase__						       =      torch.from_numpy(A__ ).permute(2						,     3						,     0						,     1 )
      elif "kernel" in key:
         lowerCamelCase__						       =      torch.from_numpy(np.transpose(A__ ) )
      elif "temperature" in key:
         lowerCamelCase__						       =      value
      elif "bn/gamma" or "bn/beta" in key:
         lowerCamelCase__						       =      torch.from_numpy(np.transpose(A__ ) ).squeeze()
      else:
         lowerCamelCase__						       =      torch.from_numpy(A__ )

      # Replace HF parameters with original TF model parameters
      hf_params[hf_key].copy_(A__ )



@torch.no_grad()
def 						UpperCAmelCase__ (					A__						,     A__						,     A__						,     A__ )   ->   str:




   """simple docstring"""
   lowerCamelCase__						       =      64
   lowerCamelCase__						       =      Tokenizer(A__ )
   lowerCamelCase__						       =      align.Align("efficientnet-b7"						,     "bert-base"						,     640						,     A__						,     tok.get_vocab_size() )
   original_model.compile()
   original_model.load_weights(A__ )

   lowerCamelCase__						       =      original_model.trainable_variables
   lowerCamelCase__						       =      original_model.non_trainable_variables
   lowerCamelCase__						       =      {param.name: param.numpy() for param in tf_params}
   for param in tf_non_train_params:
      lowerCamelCase__						       =      param.numpy()
   lowerCamelCase__						       =      list(tf_params.keys() )

   # Load HuggingFace model
   lowerCamelCase__						       =      get_align_config()
   lowerCamelCase__						       =      AlignModel(A__ ).eval()
   lowerCamelCase__						       =      hf_model.state_dict()

   # Create src-to-dst parameter name mapping dictionary
   print("Converting parameters..." )
   lowerCamelCase__						       =      rename_keys(A__ )
   replace_params(A__						,     A__						,     A__ )

   # Initialize processor
   lowerCamelCase__						       =      get_processor()
   lowerCamelCase__						       =      processor(
       images=prepare_img()						,     text="A picture of a cat"						,     padding="max_length"						,     max_length=64						,     return_tensors="pt" )

   # HF model inference
   hf_model.eval()
   with torch.no_grad():
      lowerCamelCase__						       =      hf_model(**A__ )

   lowerCamelCase__						       =      outputs.image_embeds.detach().numpy()
   lowerCamelCase__						       =      outputs.text_embeds.detach().numpy()

   # Original model inference
   lowerCamelCase__						       =      False
   lowerCamelCase__						       =      EfficientNetImageProcessor(
       do_center_crop=A__						,     do_rescale=A__						,     do_normalize=A__						,     include_top=A__						,     resample=Image.BILINEAR						,     )
   lowerCamelCase__						       =      tf_image_processor(images=prepare_img()						,     return_tensors="tf"						,     data_format="channels_last" )["pixel_values"]
   lowerCamelCase__						       =      tok(tf.constant(["A picture of a cat"] ) )

   lowerCamelCase__						       =      original_model.image_encoder(A__						,     training=A__ )
   lowerCamelCase__						       =      original_model.text_encoder(A__						,     training=A__ )

   lowerCamelCase__						       =      tf.nn.la_normalize(A__						,     axis=-1 )
   lowerCamelCase__						       =      tf.nn.la_normalize(A__						,     axis=-1 )

   # Check whether original and HF model outputs match  -> np.allclose
   if not np.allclose(A__						,     A__						,     atol=1E-3 ):
      raise ValueError("The predicted image features are not the same." )
   if not np.allclose(A__						,     A__						,     atol=1E-3 ):
      raise ValueError("The predicted text features are not the same." )
   print("Model outputs match!" )

   if save_model:
      # Create folder to save model
      if not os.path.isdir(A__ ):
         os.mkdir(A__ )
      # Save converted model and image processor
      hf_model.save_pretrained(A__ )
      processor.save_pretrained(A__ )

   if push_to_hub:
      # Push model and image processor to hub
      print("Pushing converted ALIGN to the hub..." )
      processor.push_to_hub("align-base" )
      hf_model.push_to_hub("align-base" )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_  :      Optional[int]							=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--checkpoint_path''',
         default='''./weights/model-weights''',
         type=str,
         help='''Path to the pretrained TF ALIGN checkpoint.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''',
         default='''hf_model''',
         type=str,
         help='''Path to the output PyTorch model directory.''',
     )
     parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
     parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')

     SCREAMING_SNAKE_CASE_  :      Union[str, Any]							=      parser.parse_args()
     convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)







"""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

SCREAMING_SNAKE_CASE_  :      Optional[int]							=      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_  :      Optional[int]							=      {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

SCREAMING_SNAKE_CASE_  :      Dict							=      {
    '''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''',
    },
}

SCREAMING_SNAKE_CASE_  :      Optional[Any]							=      {
    '''gpt2''': 1024,
    '''gpt2-medium''': 1024,
    '''gpt2-large''': 1024,
    '''gpt2-xl''': 1024,
    '''distilgpt2''': 1024,
}



@lru_cache()
def 						UpperCAmelCase__ (					)   ->   Optional[int]:




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



def 						UpperCAmelCase__ (					A__ )   ->   int:




   """simple docstring"""
   lowerCamelCase__						       =      set()
   lowerCamelCase__						       =      word[0]
   for char in word[1:]:
      pairs.add((prev_char, char) )
      lowerCamelCase__						       =      char
   return pairs


class 		_A							(      __a       ):
      __a             =						VOCAB_FILES_NAMES
      __a             =						PRETRAINED_VOCAB_FILES_MAP
      __a             =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __a             =						['input_ids', 'attention_mask']





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__="replace"     ,    SCREAMING_SNAKE_CASE__="<|endoftext|>"     ,    SCREAMING_SNAKE_CASE__="<|endoftext|>"     ,    SCREAMING_SNAKE_CASE__="<|endoftext|>"     ,    SCREAMING_SNAKE_CASE__=None     ,    SCREAMING_SNAKE_CASE__=False     ,    SCREAMING_SNAKE_CASE__=False     ,    **SCREAMING_SNAKE_CASE__     ,    ) ->       List[str]:
         lowerCamelCase__						       =      AddedToken(SCREAMING_SNAKE_CASE__     ,    lstrip=SCREAMING_SNAKE_CASE__     ,    rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) else bos_token
         lowerCamelCase__						       =      AddedToken(SCREAMING_SNAKE_CASE__     ,    lstrip=SCREAMING_SNAKE_CASE__     ,    rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) else eos_token
         lowerCamelCase__						       =      AddedToken(SCREAMING_SNAKE_CASE__     ,    lstrip=SCREAMING_SNAKE_CASE__     ,    rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) else unk_token
         lowerCamelCase__						       =      AddedToken(SCREAMING_SNAKE_CASE__     ,    lstrip=SCREAMING_SNAKE_CASE__     ,    rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) else pad_token
         super().__init__(
             errors=SCREAMING_SNAKE_CASE__     ,    unk_token=SCREAMING_SNAKE_CASE__     ,    bos_token=SCREAMING_SNAKE_CASE__     ,    eos_token=SCREAMING_SNAKE_CASE__     ,    pad_token=SCREAMING_SNAKE_CASE__     ,    add_prefix_space=SCREAMING_SNAKE_CASE__     ,    add_bos_token=SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__     ,    )
         lowerCamelCase__						       =      add_bos_token

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

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         lowerCamelCase__						       =      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		) ->       Optional[int]:
         return len(self.encoder		)





      def        _lowerCamelCase	(	self		) ->       str:
         return dict(self.encoder     ,    **self.added_tokens_encoder		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       Any:
         if token in self.cache:
            return self.cache[token]
         lowerCamelCase__						       =      tuple(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      get_pairs(SCREAMING_SNAKE_CASE__		)

         if not pairs:
            return token

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

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





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__=None		) ->       Optional[int]:
         if self.add_bos_token:
            lowerCamelCase__						       =      [self.bos_token_id]
         else:
            lowerCamelCase__						       =      []

         lowerCamelCase__						       =      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     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None     ,    SCREAMING_SNAKE_CASE__ = False		) ->       List[int]:
         if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=SCREAMING_SNAKE_CASE__     ,    token_ids_a=SCREAMING_SNAKE_CASE__     ,    already_has_special_tokens=SCREAMING_SNAKE_CASE__		)

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

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





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       Tuple:
         lowerCamelCase__						       =      []
         for token in re.findall(self.pat     ,    SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						       =      "".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(SCREAMING_SNAKE_CASE__		).split(" "		)		)
         return bpe_tokens





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       Tuple:
         return self.encoder.get(SCREAMING_SNAKE_CASE__     ,    self.encoder.get(self.unk_token		)		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       Any:
         return self.decoder.get(SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       str:
         lowerCamelCase__						       =      "".join(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      bytearray([self.byte_decoder[c] for c in text]		).decode("utf-8"     ,    errors=self.errors		)
         return text





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None		) ->       Tuple[str]:
         if not os.path.isdir(SCREAMING_SNAKE_CASE__		):
            logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
            return
         lowerCamelCase__						       =      os.path.join(
             SCREAMING_SNAKE_CASE__     ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
         lowerCamelCase__						       =      os.path.join(
             SCREAMING_SNAKE_CASE__     ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

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

         lowerCamelCase__						       =      0
         with open(SCREAMING_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 SCREAMING_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!"		)
                  lowerCamelCase__						       =      token_index
               writer.write(" ".join(SCREAMING_SNAKE_CASE__		) + "\n"		)
               index += 1

         return vocab_file, merge_file





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__=False     ,    **SCREAMING_SNAKE_CASE__		) ->       int:
         lowerCamelCase__						       =      kwargs.pop("add_prefix_space"     ,    self.add_prefix_space		)
         if is_split_into_words or add_prefix_space:
            lowerCamelCase__						       =      " " + text
         return (text, kwargs)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       List[int]:
         lowerCamelCase__						       =      []
         for is_user, text in conversation.iter_texts():
            input_ids.extend(self.encode(SCREAMING_SNAKE_CASE__     ,    add_special_tokens=SCREAMING_SNAKE_CASE__		) + [self.eos_token_id]		)
         if len(SCREAMING_SNAKE_CASE__		) > self.model_max_length:
            lowerCamelCase__						       =      input_ids[-self.model_max_length :]
         return input_ids




