





import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


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

snake_case								=							{
    """b0""": efficientnet.EfficientNetBa,
    """b1""": efficientnet.EfficientNetBa,
    """b2""": efficientnet.EfficientNetBa,
    """b3""": efficientnet.EfficientNetBa,
    """b4""": efficientnet.EfficientNetBa,
    """b5""": efficientnet.EfficientNetBa,
    """b6""": efficientnet.EfficientNetBa,
    """b7""": efficientnet.EfficientNetBa,
}

snake_case								=							{
    """b0""": {
        """hidden_dim""": 1_2_8_0,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 2_2_4,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 1_2_8_0,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 2_4_0,
        """dropout_rate""": 0.2,
        """dw_padding""": [1_6],
    },
    """b2""": {
        """hidden_dim""": 1_4_0_8,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 2_6_0,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 1_6],
    },
    """b3""": {
        """hidden_dim""": 1_5_3_6,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 3_0_0,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 1_8],
    },
    """b4""": {
        """hidden_dim""": 1_7_9_2,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 3_8_0,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 2_0_4_8,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 4_5_6,
        """dropout_rate""": 0.4,
        """dw_padding""": [1_3, 2_7],
    },
    """b6""": {
        """hidden_dim""": 2_3_0_4,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 5_2_8,
        """dropout_rate""": 0.5,
        """dw_padding""": [3_1],
    },
    """b7""": {
        """hidden_dim""": 2_5_6_0,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 6_0_0,
        """dropout_rate""": 0.5,
        """dw_padding""": [1_8],
    },
}

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:int  )   ->    Tuple:
  _lowercase						    =			EfficientNetConfig()
  _lowercase						    =			CONFIG_MAP[model_name]['hidden_dim']
  _lowercase						    =			CONFIG_MAP[model_name]['width_coef']
  _lowercase						    =			CONFIG_MAP[model_name]['depth_coef']
  _lowercase						    =			CONFIG_MAP[model_name]['image_size']
  _lowercase						    =			CONFIG_MAP[model_name]['dropout_rate']
  _lowercase						    =			CONFIG_MAP[model_name]['dw_padding']

  _lowercase						    =			'huggingface/label-files'
  _lowercase						    =			'imagenet-1k-id2label.json'
  _lowercase						    =			1000
  _lowercase						    =			json.load(open(hf_hub_download(snake_case__      ,				snake_case__      ,				repo_type='dataset'  )      ,				'r'  )  )
  _lowercase						    =			{int(snake_case__  ): v for k, v in idalabel.items()}

  _lowercase						    =			idalabel
  _lowercase						    =			{v: k for k, v in idalabel.items()}
  return config

def 				SCREAMING_SNAKE_CASE__							(	)   ->    Union[str, Any]:
  _lowercase						    =			'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase						    =			Image.open(requests.get(snake_case__      ,				stream=snake_case__  ).raw  )
  return im

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[Any]  )   ->    Tuple:
  _lowercase						    =			CONFIG_MAP[model_name]['image_size']
  _lowercase						    =			EfficientNetImageProcessor(
      size={'height': size, 'width': size}      ,				image_mean=[0.485, 0.456, 0.406]      ,				image_std=[0.47853944, 0.4732864, 0.47434163]      ,				do_center_crop=snake_case__      ,				)
  return preprocessor

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Union[str, Any]  )   ->    str:
  _lowercase						    =			[v.split('_'  )[0].split('block'  )[1] for v in original_param_names if v.startswith('block'  )]
  _lowercase						    =			sorted(set(snake_case__  )  )
  _lowercase						    =			len(snake_case__  )
  _lowercase						    =			{b: str(snake_case__  ) for b, i in zip(snake_case__      ,				range(snake_case__  )  )}

  _lowercase						    =			[]
  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:
    _lowercase						    =			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""")  )

  rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight')  )
  rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight')  )
  rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias')  )
  rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean')  )
  rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var')  )

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

  _lowercase						    =			'classifier.weight'
  _lowercase						    =			'classifier.bias'
  return key_mapping

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Tuple      ,				snake_case__	:Dict      ,				snake_case__	:List[Any]  )   ->    List[Any]:
  for key, value in tf_params.items():
    if "normalization" in key:
      continue

    _lowercase						    =			key_mapping[key]
    if "_conv" in key and "kernel" in key:
      _lowercase						    =			torch.from_numpy(snake_case__  ).permute(3      ,				2      ,				0      ,				1  )
    elif "depthwise_kernel" in key:
      _lowercase						    =			torch.from_numpy(snake_case__  ).permute(2      ,				3      ,				0      ,				1  )
    elif "kernel" in key:
      _lowercase						    =			torch.from_numpy(np.transpose(snake_case__  )  )
    else:
      _lowercase						    =			torch.from_numpy(snake_case__  )

    # Replace HF parameters with original TF model parameters
    assert hf_params[hf_key].shape == new_hf_value.shape
    hf_params[hf_key].copy_(snake_case__  )

@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Any      ,				snake_case__	:str      ,				snake_case__	:Optional[Any]      ,				snake_case__	:Tuple  )   ->    List[Any]:
  _lowercase						    =			model_classes[model_name](
      include_top=snake_case__      ,				weights='imagenet'      ,				input_tensor=snake_case__      ,				input_shape=snake_case__      ,				pooling=snake_case__      ,				classes=1000      ,				classifier_activation='softmax'      ,				)

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

  # Load HuggingFace model
  _lowercase						    =			get_efficientnet_config(snake_case__  )
  _lowercase						    =			EfficientNetForImageClassification(snake_case__  ).eval()
  _lowercase						    =			hf_model.state_dict()

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

  # Initialize preprocessor and preprocess input image
  _lowercase						    =			convert_image_processor(snake_case__  )
  _lowercase						    =			preprocessor(images=prepare_img()      ,				return_tensors='pt'  )

  # HF model inference
  hf_model.eval()
  with torch.no_grad():
    _lowercase						    =			hf_model(**snake_case__  )
  _lowercase						    =			outputs.logits.detach().numpy()

  # Original model inference
  _lowercase						    =			False
  _lowercase						    =			CONFIG_MAP[model_name]['image_size']
  _lowercase						    =			prepare_img().resize((image_size, image_size)      ,				resample=PIL.Image.NEAREST  )
  _lowercase						    =			image.img_to_array(snake_case__  )
  _lowercase						    =			np.expand_dims(snake_case__      ,				axis=0  )
  _lowercase						    =			original_model.predict(snake_case__  )

  # Check whether original and HF model outputs match  -> np.allclose
  assert np.allclose(snake_case__      ,				snake_case__      ,				atol=1E-3  ), "The predicted logits are not the same."
  print('Model outputs match!'  )

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

  if push_to_hub:
    # Push model and image processor to hub
    print(F"""Pushing converted {model_name} to the hub..."""  )
    _lowercase						    =			F"""efficientnet-{model_name}"""
    preprocessor.push_to_hub(snake_case__  )
    hf_model.push_to_hub(snake_case__  )


if __name__ == "__main__":
      snake_case								=							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""b0""",
          type=str,
          help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
      )
      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""")

      snake_case								=							parser.parse_args()
      convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





'''simple docstring'''


import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

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


if is_torch_available():
 import torch


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


lowerCAmelCase_      :  List[str]				     =  logging.get_logger(__name__)
lowerCAmelCase_      :  List[str]				     =  {'vocab_file': 'spiece.model'}

lowerCAmelCase_      :  Union[str, Any]				     =  {
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

lowerCAmelCase_      :  str				     =  {
    'AI-Sweden/gpt-sw3-126m': 2048,
    'AI-Sweden/gpt-sw3-350m': 2048,
    'AI-Sweden/gpt-sw3-1.6b': 2048,
    'AI-Sweden/gpt-sw3-6.7b': 2048,
    'AI-Sweden/gpt-sw3-20b': 2048,
}







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       List[Any]   							=					VOCAB_FILES_NAMES
      __magic_name__  :       Dict   							=					PRETRAINED_VOCAB_FILES_MAP
      __magic_name__  :       Optional[int]   							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __magic_name__  :       List[str]   							=					['''input_ids''', '''attention_mask''']






      def __init__( self   :  Union[str, Any]						,						lowercase__   :  Optional[Any]						,						lowercase__   :  Optional[Any]=False						,						lowercase__   :  Any=False						,						lowercase__   :  Optional[Any]=False						,						lowercase__   :  Dict=None						,						lowercase__   :  Any=None						,						lowercase__   :  List[str]=None						,						lowercase__   :  int=None						,						lowercase__   :  Optional[Dict[str, Any]] = None						,						**lowercase__   :  List[str]						,						):


           '''simple docstring'''

           a_     :							Any     						=  {} if sp_model_kwargs is None else sp_model_kwargs

           a_     :							Optional[Any]     						=  kwargs.get("""name_or_path"""       )
           if name_or_path is None:
                logger.warning(
                    """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
                    """ you are testing the model, this can safely be ignored"""       )
                a_     :							List[Any]     						=  """None"""

           # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
           a_     :							Union[str, Any]     						=  """<|endoftext|>""" if eos_token is None else eos_token
           a_     :							Dict     						=  """<unk>""" if unk_token is None else unk_token
           if "gpt-sw3-7b" in name_or_path:
                a_     :							Tuple     						=  unk_token if pad_token is None else pad_token
                a_     :							Any     						=  eos_token if bos_token is None else bos_token
           else:
                a_     :							Optional[int]     						=  """<pad>""" if pad_token is None else pad_token
                a_     :							str     						=  """<s>""" if bos_token is None else bos_token

           super().__init__(
               do_lower_case=lowercase__						,						remove_space=lowercase__						,						keep_accents=lowercase__						,						bos_token=lowercase__						,						eos_token=lowercase__						,						unk_token=lowercase__						,						pad_token=lowercase__						,						sp_model_kwargs=self.sp_model_kwargs						,						**lowercase__						,						)

           a_     :							Optional[int]     						=  do_lower_case
           a_     :							Any     						=  remove_space
           a_     :							List[str]     						=  keep_accents
           a_     :							int     						=  vocab_file

           a_     :							Any     						=  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(lowercase__       )

           # Used for whitespace normalization in input texts
           # fmt : off
           a_     :							Dict     						=  {""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
           # fmt : on

           # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
           a_     :							Tuple     						=  re.compile(
               F"[{''.join(map(lowercase__						,						list(range(0						,						9       )       ) + list(range(11						,						32       )       ) + list(range(127						,						160       )       ) + [160, 173, 8203]       )       )}]"       )






      def __getstate__( self   :  str       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.__dict__.copy()
           a_     :							Tuple     						=  None
           return state






      def __setstate__( self   :  Tuple						,						lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  d

           # for backward compatibility
           if not hasattr(self						,						"""sp_model_kwargs"""       ):
                a_     :							Union[str, Any]     						=  {}

           a_     :							int     						=  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(self.vocab_file       )






      @property
      # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           return len(self.sp_model       )






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.non_printing_characters_re.sub(""""""						,						lowercase__       )

           # Normalize whitespaces
           a_     :							List[Any]     						=  """""".join([char if char not in self.whitespaces else """ """ for char in text]       )

           # NFC Unicode normalization
           a_     :							str     						=  unicodedata.normalize("""NFC"""						,						lowercase__       )
           return text






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  str						,						**lowercase__   :  Dict       ):


           '''simple docstring'''

           a_     :							Dict     						=  self.preprocess_text(lowercase__       )
           return self.sp_model.encode(lowercase__						,						out_type=lowercase__       )






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  str       ):


           '''simple docstring'''

           return self.sp_model.PieceToId(lowercase__       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  int       ):


           '''simple docstring'''

           return self.sp_model.IdToPiece(lowercase__       )






      @staticmethod
      def 							lowercase_						( lowercase__   :  str       ):


           '''simple docstring'''

           return out_string






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  List[str]       ):


           '''simple docstring'''

           a_     :							Dict     						=  []
           a_     :							int     						=  """"""
           a_     :							Union[str, Any]     						=  False
           for token in tokens:
                # make sure that special tokens are not decoded using sentencepiece model
                if token in self.all_special_tokens:
                     # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                     if not prev_is_special:
                          out_string += " "

                     out_string += self.sp_model.decode(lowercase__       ) + token
                     a_     :							int     						=  True
                     a_     :							Optional[int]     						=  []
                else:
                     current_sub_tokens.append(lowercase__       )
                     a_     :							List[str]     						=  False
           out_string += self.sp_model.decode(lowercase__       )

           return out_string






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  {self.convert_ids_to_tokens(lowercase__       ): i for i in range(self.vocab_size       )}
           vocab.update(self.added_tokens_encoder       )
           return vocab






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  str						,						lowercase__   :  Optional[str] = None       ):


           '''simple docstring'''

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

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

           return (out_vocab_file,)






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  Union[str, List[str]]						,						lowercase__   :  Union[str, bool] = False       ):


           '''simple docstring'''

           if isinstance(lowercase__						,						lowercase__       ):
                a_     :							List[str]     						=  self.preprocess_text(lowercase__       )
                a_     :							str     						=  self.sp_model.encode(lowercase__       )
           else:
                a_     :							Optional[Any]     						=  [self.preprocess_text(lowercase__       ) for t in text]
                a_     :							Any     						=  self.sp_model.encode(lowercase__       )

           if return_tensors is True or return_tensors == "pt":
                a_     :							Tuple     						=  torch.tensor(lowercase__       )

           return token_ids






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  Union[int, List[int]]       ):


           '''simple docstring'''

           return self.sp_model.decode(lowercase__       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  "Conversation"       ):


           '''simple docstring'''

           a_     :							Dict     						=  [F"User: {text}" if is_user else F"Bot: {text}" for is_user, text in conversation.iter_texts()]
           a_     :							Any     						=  (
               F"{self.eos_token}{self.bos_token}" + F"{self.bos_token}".join(lowercase__       ) + F"{self.bos_token}Bot:"
           )
           return self.encode(text=lowercase__       )
