




from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFLEDForConditionalGeneration, TFLEDModel




@require_tf
class 						__lowercase		:




      """simple docstring"""





      _snake_case							     =						LEDConfig
      _snake_case							     =						{}
      _snake_case							     =						"""gelu"""

      def __init__(      self				,      A				,      A=1_3				,      A=7				,      A=True				,      A=False				,      A=9_9				,      A=3_2				,      A=2				,      A=4				,      A=3_7				,      A=0.1				,      A=0.1				,      A=2_0				,      A=2				,      A=1				,      A=0				,      A=4				,      )		->				int:
         snake_case      :				List[Any]				=       parent
         snake_case      :				Optional[Any]				=       batch_size
         snake_case      :				Any				=       seq_length
         snake_case      :				Any				=       is_training
         snake_case      :				Union[str, Any]				=       use_labels
         snake_case      :				Tuple				=       vocab_size
         snake_case      :				Any				=       hidden_size
         snake_case      :				str				=       num_hidden_layers
         snake_case      :				Union[str, Any]				=       num_attention_heads
         snake_case      :				Dict				=       intermediate_size

         snake_case      :				Any				=       hidden_dropout_prob
         snake_case      :				str				=       attention_probs_dropout_prob
         snake_case      :				Optional[int]				=       max_position_embeddings
         snake_case      :				Optional[Any]				=       eos_token_id
         snake_case      :				List[Any]				=       pad_token_id
         snake_case      :				List[str]				=       bos_token_id
         snake_case      :				Tuple				=       attention_window

         # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
         # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
         # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
         # because its local attention only attends to `self.attention_window` and one before and one after
         snake_case      :				Tuple				=       self.attention_window + 2

         # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
         # the `test_attention_outputs` and `test_hidden_states_output` tests
         snake_case      :				Dict				=       (
             self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
         )

      def 	UpperCAmelCase      (      self	)		->				Optional[int]:
         snake_case      :				Dict				=       ids_tensor([self.batch_size, self.seq_length - 1]				,      self.vocab_size	)
         snake_case      :				str				=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)				,      1	)
         snake_case      :				Dict				=       tf.concat([input_ids, eos_tensor]				,      axis=1	)

         snake_case      :				int				=       ids_tensor([self.batch_size, self.seq_length]				,      self.vocab_size	)

         snake_case      :				str				=       self.config_cls(
             vocab_size=self.vocab_size				,      d_model=self.hidden_size				,      encoder_layers=self.num_hidden_layers				,      decoder_layers=self.num_hidden_layers				,      encoder_attention_heads=self.num_attention_heads				,      decoder_attention_heads=self.num_attention_heads				,      encoder_ffn_dim=self.intermediate_size				,      decoder_ffn_dim=self.intermediate_size				,      dropout=self.hidden_dropout_prob				,      attention_dropout=self.attention_probs_dropout_prob				,      max_position_embeddings=self.max_position_embeddings				,      eos_token_ids=[2]				,      bos_token_id=self.bos_token_id				,      pad_token_id=self.pad_token_id				,      decoder_start_token_id=self.pad_token_id				,      attention_window=self.attention_window				,      **self.config_updates				,      )
         snake_case      :				Any				=       prepare_led_inputs_dict(A				,      A				,      A	)
         snake_case      :				List[Any]				=       tf.concat(
             [tf.zeros_like(A	)[:, :-1], tf.ones_like(A	)[:, -1:]]				,      axis=-1				,      )
         snake_case      :				Optional[int]				=       global_attention_mask
         return config, inputs_dict

      def 	UpperCAmelCase      (      self				,      A				,      A	)		->				List[Any]:
         snake_case      :				int				=       TFLEDModel(config=A	).get_decoder()
         snake_case      :				str				=       inputs_dict["""input_ids"""]

         snake_case      :				Tuple				=       input_ids[:1, :]
         snake_case      :				Union[str, Any]				=       inputs_dict["""attention_mask"""][:1, :]
         snake_case      :				Union[str, Any]				=       1

         # first forward pass
         snake_case      :				Tuple				=       model(A				,      attention_mask=A				,      use_cache=A	)

         snake_case	,					snake_case      :				Union[str, Any]				=       outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         snake_case      :				List[str]				=       ids_tensor((self.batch_size, 3)				,      config.vocab_size	)
         snake_case      :				Union[str, Any]				=       tf.cast(ids_tensor((self.batch_size, 3)				,      2	)				,      tf.inta	)

         # append to next input_ids and
         snake_case      :				str				=       tf.concat([input_ids, next_tokens]				,      axis=-1	)
         snake_case      :				List[Any]				=       tf.concat([attention_mask, next_attn_mask]				,      axis=-1	)

         snake_case      :				int				=       model(A				,      attention_mask=A	)[0]
         snake_case      :				int				=       model(A				,      attention_mask=A				,      past_key_values=A	)[0]

         self.parent.assertEqual(next_tokens.shape[1]				,      output_from_past.shape[1]	)

         # select random slice
         snake_case      :				Optional[int]				=       int(ids_tensor((1,)				,      output_from_past.shape[-1]	)	)
         snake_case      :				List[Any]				=       output_from_no_past[:, -3:, random_slice_idx]
         snake_case      :				str				=       output_from_past[:, :, random_slice_idx]

         # test that outputs are equal for slice
         tf.debugging.assert_near(A				,      A				,      rtol=1e-3	)




def 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase						,lowercase						,lowercase=None						,lowercase=None						,lowercase=None						,lowercase=None						,)  ->   Tuple:




   if attention_mask is None:
      snake_case      :				Dict				=       tf.cast(tf.math.not_equal(lowercase						,config.pad_token_id     )						,tf.inta     )
   if decoder_attention_mask is None:
      snake_case      :				Tuple				=       tf.concat(
          [
              tf.ones(decoder_input_ids[:, :1].shape						,dtype=tf.inta     ),
              tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]						,config.pad_token_id     )						,tf.inta     ),
          ]						,axis=-1						,)
   if head_mask is None:
      snake_case      :				Dict				=       tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
   if decoder_head_mask is None:
      snake_case      :				Union[str, Any]				=       tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
   return {
       "input_ids": input_ids,
       "attention_mask": attention_mask,
       "decoder_input_ids": decoder_input_ids,
       "decoder_attention_mask": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
   }




@require_tf
class 						__lowercase		(_UpperCAmelCase     ,		_UpperCAmelCase     ,		unittest.TestCase							):




      """simple docstring"""





      _snake_case							     =						(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
      _snake_case							     =						(TFLEDForConditionalGeneration,) if is_tf_available() else ()
      _snake_case							     =						(
          {
              """conversational""": TFLEDForConditionalGeneration,
              """feature-extraction""": TFLEDModel,
              """summarization""": TFLEDForConditionalGeneration,
              """text2text-generation""": TFLEDForConditionalGeneration,
              """translation""": TFLEDForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      _snake_case							     =						True
      _snake_case							     =						False
      _snake_case							     =						False
      _snake_case							     =						False

      def 	UpperCAmelCase      (      self	)		->				Optional[Any]:
         snake_case      :				Tuple				=       TFLEDModelTester(self	)
         snake_case      :				List[Any]				=       ConfigTester(self				,      config_class=A	)

      def 	UpperCAmelCase      (      self	)		->				Any:
         self.config_tester.run_common_tests()

      def 	UpperCAmelCase      (      self	)		->				List[str]:
         snake_case      :				Optional[Any]				=       self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*A	)

      def 	UpperCAmelCase      (      self	)		->				Union[str, Any]:
         snake_case	,					snake_case      :				str				=       self.model_tester.prepare_config_and_inputs_for_common()
         snake_case      :				str				=       tf.zeros_like(inputs_dict["""attention_mask"""]	)
         snake_case      :				Any				=       2
         snake_case      :				Any				=       tf.where(
             tf.range(self.model_tester.seq_length	)[None, :] < num_global_attn_indices				,      1				,      inputs_dict["""global_attention_mask"""]				,      )

         snake_case      :				Union[str, Any]				=       True
         snake_case      :				Tuple				=       self.model_tester.seq_length
         snake_case      :				Dict				=       self.model_tester.encoder_seq_length

         def check_decoder_attentions_output(A	):
            snake_case      :				Optional[int]				=       outputs.decoder_attentions
            self.assertEqual(len(A	)				,      self.model_tester.num_hidden_layers	)
            self.assertListEqual(
                list(decoder_attentions[0].shape[-3:]	)				,      [self.model_tester.num_attention_heads, seq_length, seq_length]				,      )

         def check_encoder_attentions_output(A	):
            snake_case      :				List[str]				=       [t.numpy() for t in outputs.encoder_attentions]
            snake_case      :				Union[str, Any]				=       [t.numpy() for t in outputs.encoder_global_attentions]
            self.assertEqual(len(A	)				,      self.model_tester.num_hidden_layers	)
            self.assertEqual(len(A	)				,      self.model_tester.num_hidden_layers	)
            self.assertListEqual(
                list(attentions[0].shape[-3:]	)				,      [self.model_tester.num_attention_heads, seq_length, seq_length]				,      )
            self.assertListEqual(
                list(global_attentions[0].shape[-3:]	)				,      [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]				,      )

         for model_class in self.all_model_classes:
            snake_case      :				int				=       True
            snake_case      :				Optional[int]				=       False
            snake_case      :				List[Any]				=       False
            snake_case      :				int				=       model_class(A	)
            snake_case      :				List[str]				=       model(self._prepare_for_class(A				,      A	)	)
            snake_case      :				Tuple				=       len(A	)
            self.assertEqual(config.output_hidden_states				,      A	)
            check_encoder_attentions_output(A	)

            if self.is_encoder_decoder:
               snake_case      :				Tuple				=       model_class(A	)
               snake_case      :				Tuple				=       model(self._prepare_for_class(A				,      A	)	)
               self.assertEqual(config.output_hidden_states				,      A	)
               check_decoder_attentions_output(A	)

            # Check that output attentions can also be changed via the config
            del inputs_dict["output_attentions"]
            snake_case      :				str				=       True
            snake_case      :				List[str]				=       model_class(A	)
            snake_case      :				Any				=       model(self._prepare_for_class(A				,      A	)	)
            self.assertEqual(config.output_hidden_states				,      A	)
            check_encoder_attentions_output(A	)

            # Check attention is always last and order is fine
            snake_case      :				List[Any]				=       True
            snake_case      :				List[str]				=       True
            snake_case      :				Union[str, Any]				=       model_class(A	)
            snake_case      :				Dict				=       model(self._prepare_for_class(A				,      A	)	)

            self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)				,      len(A	)	)
            self.assertEqual(model.config.output_hidden_states				,      A	)
            check_encoder_attentions_output(A	)

      @unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing."""	)
      def 	UpperCAmelCase      (      self	)		->				Union[str, Any]:
         pass

      def 	UpperCAmelCase      (      self	)		->				List[str]:
         # TODO: Head-masking not yet implement
         pass




def 						SCREAMING_SNAKE_CASE__					(    lowercase     )  ->   Dict:
   return tf.constant(lowercase						,dtype=tf.intaa     )


lowerCamelCase       :       List[Any]        =    1e-4




@slow
@require_tf
class 						__lowercase		(unittest.TestCase							):




      """simple docstring"""





      def 	UpperCAmelCase      (      self	)		->				int:
         snake_case      :				int				=       TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""	).led

         # change to intended input here
         snake_case      :				Dict				=       _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
         snake_case      :				List[str]				=       _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
         snake_case      :				List[Any]				=       prepare_led_inputs_dict(model.config				,      A				,      A	)
         snake_case      :				Tuple				=       model(**A	)[0]
         snake_case      :				List[Any]				=       (1, 1_0_2_4, 7_6_8)
         self.assertEqual(output.shape				,      A	)
         # change to expected output here
         snake_case      :				Tuple				=       tf.convert_to_tensor(
             [[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]]				,      )
         tf.debugging.assert_near(output[:, :3, :3]				,      A				,      atol=1e-3	)

      def 	UpperCAmelCase      (      self	)		->				Optional[Any]:
         snake_case      :				List[str]				=       TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""	)

         # change to intended input here
         snake_case      :				str				=       _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
         snake_case      :				Dict				=       _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
         snake_case      :				Union[str, Any]				=       prepare_led_inputs_dict(model.config				,      A				,      A	)
         snake_case      :				Optional[Any]				=       model(**A	)[0]
         snake_case      :				int				=       (1, 1_0_2_4, model.config.vocab_size)
         self.assertEqual(output.shape				,      A	)
         # change to expected output here
         snake_case      :				int				=       tf.convert_to_tensor(
             [[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]]				,      )
         tf.debugging.assert_near(output[:, :3, :3]				,      A				,      atol=1e-3				,      rtol=1e-3	)












"""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


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__					=   {"vocab_file": "spiece.model"}

SCREAMING_SNAKE_CASE__					=   {
    "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",
    }
}

SCREAMING_SNAKE_CASE__					=   {
    "AI-Sweden/gpt-sw3-126m": 2_048,
    "AI-Sweden/gpt-sw3-350m": 2_048,
    "AI-Sweden/gpt-sw3-1.6b": 2_048,
    "AI-Sweden/gpt-sw3-6.7b": 2_048,
    "AI-Sweden/gpt-sw3-20b": 2_048,
}

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE					=      ['input_ids', 'attention_mask']





      def __init__(       self      ,						lowercase      ,						lowercase=False      ,						lowercase=False      ,						lowercase=False      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase = None      ,						**lowercase      ,						)		->     None:
            lowerCAmelCase  						=       {} if sp_model_kwargs is None else sp_model_kwargs

            lowerCAmelCase  						=       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""" )
                  lowerCAmelCase  						=       """None"""

            # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
            lowerCAmelCase  						=       """<|endoftext|>""" if eos_token is None else eos_token
            lowerCAmelCase  						=       """<unk>""" if unk_token is None else unk_token
            if "gpt-sw3-7b" in name_or_path:
                  lowerCAmelCase  						=       unk_token if pad_token is None else pad_token
                  lowerCAmelCase  						=       eos_token if bos_token is None else bos_token
            else:
                  lowerCAmelCase  						=       """<pad>""" if pad_token is None else pad_token
                  lowerCAmelCase  						=       """<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      ,						)

            lowerCAmelCase  						=       do_lower_case
            lowerCAmelCase  						=       remove_space
            lowerCAmelCase  						=       keep_accents
            lowerCAmelCase  						=       vocab_file

            lowerCAmelCase  						=       spm.SentencePieceProcessor(**self.sp_model_kwargs )
            self.sp_model.Load(lowercase )

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

            # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
            lowerCAmelCase  						=       re.compile(
                f'[{"".join(map(lowercase      ,						list(range(0      ,						9 ) ) + list(range(11      ,						32 ) ) + list(range(127      ,						160 ) ) + [160, 173, 8_203] ) )}]' )





      def __getstate__(       self )		->     Optional[int]:
            lowerCAmelCase  						=       self.__dict__.copy()
            lowerCAmelCase  						=       None
            return state





      def __setstate__(       self      ,						lowercase )		->     str:
            lowerCAmelCase  						=       d

            # for backward compatibility
            if not hasattr(self      ,						"""sp_model_kwargs""" ):
                  lowerCAmelCase  						=       {}

            lowerCAmelCase  						=       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     _snake_case			(       self )		->     int:
            return len(self.sp_model )





      def     _snake_case			(       self      ,						lowercase )		->     str:
            lowerCAmelCase  						=       self.non_printing_characters_re.sub(""""""      ,						lowercase )

            # Normalize whitespaces
            lowerCAmelCase  						=       """""".join([char if char not in self.whitespaces else """ """ for char in text] )

            # NFC Unicode normalization
            lowerCAmelCase  						=       unicodedata.normalize("""NFC"""      ,						lowercase )
            return text





      def     _snake_case			(       self      ,						lowercase      ,						**lowercase )		->     List[str]:
            lowerCAmelCase  						=       self.preprocess_text(lowercase )
            return self.sp_model.encode(lowercase      ,						out_type=lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     int:
            return self.sp_model.PieceToId(lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     str:
            return self.sp_model.IdToPiece(lowercase )





      @staticmethod
      def     _snake_case			(       lowercase )		->     str:
            return out_string





      def     _snake_case			(       self      ,						lowercase )		->     str:
            lowerCAmelCase  						=       []
            lowerCAmelCase  						=       """"""
            lowerCAmelCase  						=       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
                        lowerCAmelCase  						=       True
                        lowerCAmelCase  						=       []
                  else:
                        current_sub_tokens.append(lowercase )
                        lowerCAmelCase  						=       False
            out_string += self.sp_model.decode(lowercase )

            return out_string





      def     _snake_case			(       self )		->     Dict[str, int]:
            lowerCAmelCase  						=       {self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
            vocab.update(self.added_tokens_encoder )
            return vocab





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None )		->     Tuple[str]:
            if not os.path.isdir(lowercase ):
                  logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
                  return
            lowerCAmelCase  						=       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:
                        lowerCAmelCase  						=       self.sp_model.serialized_model_proto()
                        fi.write(lowercase )

            return (out_vocab_file,)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = False )		->     Union[List[int], List[List[int]], "torch.Tensor"]:

            if isinstance(lowercase      ,						lowercase ):
                  lowerCAmelCase  						=       self.preprocess_text(lowercase )
                  lowerCAmelCase  						=       self.sp_model.encode(lowercase )
            else:
                  lowerCAmelCase  						=       [self.preprocess_text(lowercase ) for t in text]
                  lowerCAmelCase  						=       self.sp_model.encode(lowercase )

            if return_tensors is True or return_tensors == "pt":
                  lowerCAmelCase  						=       torch.tensor(lowercase )

            return token_ids





      def     _snake_case			(       self      ,						lowercase )		->     str:
            return self.sp_model.decode(lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     List[int]:
            lowerCAmelCase  						=       [f'User: {text}' if is_user else f'Bot: {text}' for is_user, text in conversation.iter_texts()]
            lowerCAmelCase  						=       (
                f'{self.eos_token}{self.bos_token}' + f'{self.bos_token}'.join(lowercase ) + f'{self.bos_token}Bot:'
            )
            return self.encode(text=lowercase )






