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





def 							snake_case				(		UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      __A													=      tf.image.resize(UpperCAmelCase   ,	(3_4_6, 3_4_6)						)
      __A													=      tf.image.crop_to_bounding_box(UpperCAmelCase   ,	(3_4_6 - 2_8_9) // 2   ,	(3_4_6 - 2_8_9) // 2   ,	2_8_9   ,	2_8_9						)
      return image





def 							snake_case				(		)->      List[Any]:







      """simple docstring"""
      __A													=      EfficientNetConfig.from_pretrained('google/efficientnet-b7'						)
      __A													=      2_8_9
      __A													=      6_4_0
      __A													=      {'0': 'LABEL_0', '1': 'LABEL_1'}
      __A													=      {'LABEL_0': 0, 'LABEL_1': 1}
      __A													=      []

      __A													=      BertConfig()
      __A													=      AlignConfig.from_text_vision_configs(
          text_config=UpperCAmelCase   ,	vision_config=UpperCAmelCase   ,	projection_dim=6_4_0						)
      return config





def 							snake_case				(		)->      Any:







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





def 							snake_case				(		)->      List[Any]:







      """simple docstring"""
      __A													=      EfficientNetImageProcessor(
          do_center_crop=UpperCAmelCase   ,	rescale_factor=1 / 127.5   ,	rescale_offset=UpperCAmelCase   ,	do_normalize=UpperCAmelCase   ,	include_top=UpperCAmelCase   ,	resample=Image.BILINEAR   ,	)
      __A													=      BertTokenizer.from_pretrained('bert-base-uncased'						)
      __A													=      6_4
      __A													=      AlignProcessor(image_processor=UpperCAmelCase   ,	tokenizer=UpperCAmelCase						)
      return processor





def 							snake_case				(		UpperCAmelCase						)->      Any:







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

      __A													=      []
      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:
            __A													=      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')						)

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

    # BERT text encoder
      __A													=      []
      __A													=      'tf_bert_model/bert'
      __A													=      'text_model'
      for i in range(1_2						):
            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:
                  __A													=      item[1]
      return key_mapping





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, Any]:







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

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

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

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





@torch.no_grad()
def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Any:







      """simple docstring"""
      __A													=      6_4
      __A													=      Tokenizer(UpperCAmelCase						)
      __A													=      align.Align('efficientnet-b7'   ,	'bert-base'   ,	6_4_0   ,	UpperCAmelCase   ,	tok.get_vocab_size()						)
      original_model.compile()
      original_model.load_weights(UpperCAmelCase						)

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

      # Load HuggingFace model
      __A													=      get_align_config()
      __A													=      AlignModel(UpperCAmelCase						).eval()
      __A													=      hf_model.state_dict()

      # Create src-to-dst parameter name mapping dictionary
      print('Converting parameters...'						)
      __A													=      rename_keys(UpperCAmelCase						)
      replace_params(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)

      # Initialize processor
      __A													=      get_processor()
      __A													=      processor(
          images=prepare_img()   ,	text='A picture of a cat'   ,	padding='max_length'   ,	max_length=6_4   ,	return_tensors='pt'						)

      # HF model inference
      hf_model.eval()
      with torch.no_grad():
            __A													=      hf_model(**UpperCAmelCase						)

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

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

      __A													=      original_model.image_encoder(UpperCAmelCase   ,	training=UpperCAmelCase						)
      __A													=      original_model.text_encoder(UpperCAmelCase   ,	training=UpperCAmelCase						)

      __A													=      tf.nn.la_normalize(UpperCAmelCase   ,	axis=-1						)
      __A													=      tf.nn.la_normalize(UpperCAmelCase   ,	axis=-1						)

      # Check whether original and HF model outputs match  -> np.allclose
      if not np.allclose(UpperCAmelCase   ,	UpperCAmelCase   ,	atol=1e-3						):
            raise ValueError('The predicted image features are not the same.'						)
      if not np.allclose(UpperCAmelCase   ,	UpperCAmelCase   ,	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(UpperCAmelCase						):
                  os.mkdir(UpperCAmelCase						)
            # Save converted model and image processor
            hf_model.save_pretrained(UpperCAmelCase						)
            processor.save_pretrained(UpperCAmelCase						)

      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__":
 a__		: str  							=							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")

 a__		: List[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 os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

lowerCAmelCase_:       Optional[Any]			     =       logging.get_logger(__name__)

lowerCAmelCase_:       Optional[int]			     =       {"vocab_file": "tokenizer.model"}

lowerCAmelCase_:       Optional[int]			     =       {
    "vocab_file": {
        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
    },
    "tokenizer_file": {
        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
    },
}
lowerCAmelCase_:       Optional[int]			     =       {
    "hf-internal-testing/llama-tokenizer": 2_0_4_8,
}
lowerCAmelCase_:       str			     =       "▁"

lowerCAmelCase_     ,				lowerCAmelCase_:       str			     =       "[INST]", "[/INST]"
lowerCAmelCase_     ,				lowerCAmelCase_:       Dict			     =       "<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
lowerCAmelCase_:       List[Any]			     =       "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don't know the answer to a question, please don't share false information."






class  a__     ( _a						):
   snake_case_				=   VOCAB_FILES_NAMES
   snake_case_				=   PRETRAINED_VOCAB_FILES_MAP
   snake_case_				=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   snake_case_				=   ["input_ids", "attention_mask"]


   def __init__(  self,					_UpperCAmelCase,					_UpperCAmelCase="<unk>",					_UpperCAmelCase="<s>",					_UpperCAmelCase="</s>",					_UpperCAmelCase=None,					_UpperCAmelCase = None,					_UpperCAmelCase=True,					_UpperCAmelCase=False,					_UpperCAmelCase=False,					_UpperCAmelCase=True,					**_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				{} if sp_model_kwargs is None else sp_model_kwargs
        lowercase__								=				AddedToken(_UpperCAmelCase,					lstrip=_UpperCAmelCase,					rstrip=_UpperCAmelCase	) if isinstance(_UpperCAmelCase,					_UpperCAmelCase	) else bos_token
        lowercase__								=				AddedToken(_UpperCAmelCase,					lstrip=_UpperCAmelCase,					rstrip=_UpperCAmelCase	) if isinstance(_UpperCAmelCase,					_UpperCAmelCase	) else eos_token
        lowercase__								=				AddedToken(_UpperCAmelCase,					lstrip=_UpperCAmelCase,					rstrip=_UpperCAmelCase	) if isinstance(_UpperCAmelCase,					_UpperCAmelCase	) else unk_token
        lowercase__								=				AddedToken(_UpperCAmelCase,					lstrip=_UpperCAmelCase,					rstrip=_UpperCAmelCase	) if isinstance(_UpperCAmelCase,					_UpperCAmelCase	) else pad_token
        super().__init__(
            bos_token=_UpperCAmelCase,					eos_token=_UpperCAmelCase,					unk_token=_UpperCAmelCase,					pad_token=_UpperCAmelCase,					add_bos_token=_UpperCAmelCase,					add_eos_token=_UpperCAmelCase,					sp_model_kwargs=self.sp_model_kwargs,					clean_up_tokenization_spaces=_UpperCAmelCase,					legacy=_UpperCAmelCase,					**_UpperCAmelCase,					)
        if legacy:
             logger.warning_once(
                 F'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
                 " read the related pull request available at https://github.com/huggingface/transformers/pull/24565"	)
        lowercase__								=				legacy
        lowercase__								=				vocab_file
        lowercase__								=				add_bos_token
        lowercase__								=				add_eos_token
        lowercase__								=				spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(_UpperCAmelCase	)


   def __getstate__(  self	):
        '''simple docstring'''



        lowercase__								=				self.__dict__.copy()
        lowercase__								=				None
        lowercase__								=				self.sp_model.serialized_model_proto()
        return state


   def __setstate__(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				d
        lowercase__								=				spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)


   @property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        return self.sp_model.get_piece_size()


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				{self.convert_ids_to_tokens(_UpperCAmelCase	): i for i in range(self.vocab_size	)}
        vocab.update(self.added_tokens_encoder	)
        return vocab


   def 						snake_case__						(  self,					_UpperCAmelCase,					**_UpperCAmelCase	):
        '''simple docstring'''



        if not self.legacy:
             lowercase__								=				SPIECE_UNDERLINE + text.replace(_UpperCAmelCase,					" "	)
        return super().tokenize(_UpperCAmelCase,					**_UpperCAmelCase	)


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        if not self.legacy:
             lowercase__								=				text.startswith(_UpperCAmelCase	)
             if is_first:
                  lowercase__								=				text[1:]

        lowercase__								=				self.sp_model.encode(_UpperCAmelCase,					out_type=_UpperCAmelCase	)

        if not self.legacy and not is_first and not text.startswith(" "	) and tokens[0].startswith(_UpperCAmelCase	):
             lowercase__								=				([tokens[0][1:]] if len(tokens[0]	) > 1 else []) + tokens[1:]
        return tokens


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        return self.sp_model.piece_to_id(_UpperCAmelCase	)


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				self.sp_model.IdToPiece(_UpperCAmelCase	)
        return token


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				[]
        lowercase__								=				""
        lowercase__								=				False
        for i, token in enumerate(_UpperCAmelCase	):
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                  if not prev_is_special and i != 0:
                       out_string += " "
                  out_string += self.sp_model.decode(_UpperCAmelCase	) + token
                  lowercase__								=				True
                  lowercase__								=				[]
             else:
                  current_sub_tokens.append(_UpperCAmelCase	)
                  lowercase__								=				False
        out_string += self.sp_model.decode(_UpperCAmelCase	)
        return out_string


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = None	):
        '''simple docstring'''



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

        if os.path.abspath(self.vocab_file	) != os.path.abspath(_UpperCAmelCase	) and os.path.isfile(self.vocab_file	):
             copyfile(self.vocab_file,					_UpperCAmelCase	)
        elif not os.path.isfile(self.vocab_file	):
             with open(_UpperCAmelCase,					"wb"	) as fi:
                  lowercase__								=				self.sp_model.serialized_model_proto()
                  fi.write(_UpperCAmelCase	)

        return (out_vocab_file,)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase=None	):
        '''simple docstring'''



        lowercase__								=				[self.bos_token_id] if self.add_bos_token else []
        lowercase__								=				[self.eos_token_id] if self.add_eos_token else []

        lowercase__								=				bos_token_id + token_ids_a + eos_token_id

        if token_ids_a is not None:
             lowercase__								=				output + bos_token_id + token_ids_a + eos_token_id

        return output


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = None,					_UpperCAmelCase = False	):
        '''simple docstring'''



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

        lowercase__								=				[1] if self.add_bos_token else []
        lowercase__								=				[1] if self.add_eos_token else []

        if token_ids_a is None:
             return bos_token_id + ([0] * len(_UpperCAmelCase	)) + eos_token_id
        return (
            bos_token_id
            + ([0] * len(_UpperCAmelCase	))
            + eos_token_id
            + bos_token_id
            + ([0] * len(_UpperCAmelCase	))
            + eos_token_id
        )


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = None	):
        '''simple docstring'''



        lowercase__								=				[self.bos_token_id] if self.add_bos_token else []
        lowercase__								=				[self.eos_token_id] if self.add_eos_token else []

        lowercase__								=				[0] * len(bos_token_id + token_ids_a + eos_token_id	)

        if token_ids_a is not None:
             output += [1] * len(bos_token_id + token_ids_a + eos_token_id	)

        return output


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				list(conversation.iter_texts()	)
        if not all([is_user for is_user, msg in dialogue[::2]]	) or not all(
            [not is_user for is_user, msg in dialogue[1::2]]	):
             raise ValueError(
                 "The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"	)

        lowercase__								=				[]
        if len(conversation.past_user_inputs	) > 0:
             if not conversation.past_user_inputs[0].startswith(_UpperCAmelCase	) or E_SYS not in conversation.past_user_inputs[0]:
                  lowercase__								=				(
                      B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                  )
        elif not dialogue[0][1].startswith(_UpperCAmelCase	) or E_SYS not in dialogue[0][1]:
             lowercase__								=				(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

        dialog_tokens += sum(
            [
                [self.bos_token_id]
                + self.encode(
                    F'''{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} ''',					add_special_tokens=_UpperCAmelCase	)
                + [self.eos_token_id]
                for prompt, answer in zip(dialogue[::2],					dialogue[1::2]	)
            ],					[],					)
        if not (dialogue[-1][0]):
             raise ValueError(F'''Last message must be from user, got {dialogue[-1]['role']}'''	)
        dialog_tokens += [self.bos_token_id] + self.encode(
            F'''{B_INST} {(dialogue[-1][1]).strip()} {E_INST}''',					add_special_tokens=_UpperCAmelCase	)
        return dialog_tokens




