





"""simple docstring"""
from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
   import tensorflow as tf

   from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor
class lowerCamelCase__	:



   def __init__(							self   , snake_case   , snake_case=3   , snake_case=3_2   , snake_case=3   , snake_case=1_0   , snake_case=[1_0, 2_0, 3_0, 4_0]   , snake_case=[1, 1, 2, 1]   , snake_case=True   , snake_case=True   , snake_case="relu"   , snake_case=3   , snake_case=None   , )			->     Dict:







      """simple docstring"""
      lowercase     :      Optional[Any]	=							parent
      lowercase     :      Optional[Any]	=							batch_size
      lowercase     :      int	=							image_size
      lowercase     :      Optional[Any]	=							num_channels
      lowercase     :      str	=							embeddings_size
      lowercase     :      List[Any]	=							hidden_sizes
      lowercase     :      List[Any]	=							depths
      lowercase     :      List[Any]	=							is_training
      lowercase     :      str	=							use_labels
      lowercase     :      Tuple	=							hidden_act
      lowercase     :      Optional[int]	=							num_labels
      lowercase     :      int	=							scope
      lowercase     :      Dict	=							len(__lowerCamelCase						)



   def  _UpperCAmelCase						(							self						)			->     Optional[Any]:







      """simple docstring"""
      lowercase     :      List[Any]	=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

      lowercase     :      Any	=							None
      if self.use_labels:
         lowercase     :      Dict	=							ids_tensor([self.batch_size]   , self.num_labels						)

      lowercase     :      int	=							self.get_config()
      return config, pixel_values, labels



   def  _UpperCAmelCase						(							self						)			->     Any:







      """simple docstring"""
      return RegNetConfig(
          num_channels=self.num_channels   , embeddings_size=self.embeddings_size   , hidden_sizes=self.hidden_sizes   , depths=self.depths   , hidden_act=self.hidden_act   , num_labels=self.num_labels   , )



   def  _UpperCAmelCase						(							self   , snake_case   , snake_case   , snake_case						)			->     List[Any]:







      """simple docstring"""
      lowercase     :      Union[str, Any]	=							TFRegNetModel(config=__lowerCamelCase						)
      lowercase     :      Union[str, Any]	=							model(__lowerCamelCase   , training=__lowerCamelCase						)
      # expected last hidden states: B, C, H // 32, W // 32
      self.parent.assertEqual(
          result.last_hidden_state.shape   , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)   , )



   def  _UpperCAmelCase						(							self   , snake_case   , snake_case   , snake_case						)			->     List[Any]:







      """simple docstring"""
      lowercase     :      int	=							self.num_labels
      lowercase     :      Optional[int]	=							TFRegNetForImageClassification(__lowerCamelCase						)
      lowercase     :      List[str]	=							model(__lowerCamelCase   , labels=__lowerCamelCase   , training=__lowerCamelCase						)
      self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)						)





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







      """simple docstring"""
      lowercase     :      Optional[Any]	=							self.prepare_config_and_inputs()
      lowercase	,       lowercase	,       lowercase     :      Tuple	=							config_and_inputs
      lowercase     :      Union[str, Any]	=							{"""pixel_values""": pixel_values}
      return config, inputs_dict




@require_tf
class lowerCamelCase__	(     __snake_case	,					__snake_case	,					unittest.TestCase					):
   __UpperCAmelCase										=       (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
   __UpperCAmelCase										=       (
       {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
       if is_tf_available()
       else {}
   )

   __UpperCAmelCase										=       False
   __UpperCAmelCase										=       False
   __UpperCAmelCase										=       False
   __UpperCAmelCase										=       False
   __UpperCAmelCase										=       False



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







      """simple docstring"""
      lowercase     :      int	=							TFRegNetModelTester(self						)
      lowercase     :      Optional[int]	=							ConfigTester(self   , config_class=__lowerCamelCase   , has_text_modality=__lowerCamelCase						)



   def  _UpperCAmelCase						(							self						)			->     Optional[int]:







      """simple docstring"""
      return



   @unittest.skip(reason="""RegNet does not use inputs_embeds"""						)
   def  _UpperCAmelCase						(							self						)			->     Dict:







      """simple docstring"""
      pass



   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""						)						) == 0   , reason="""TF does not support backprop for grouped convolutions on CPU."""   , )
   @slow
   def  _UpperCAmelCase						(							self						)			->     Optional[int]:







      """simple docstring"""
      super().test_keras_fit()



   @unittest.skip(reason="""RegNet does not support input and output embeddings"""						)
   def  _UpperCAmelCase						(							self						)			->     Optional[int]:







      """simple docstring"""
      pass



   def  _UpperCAmelCase						(							self						)			->     Any:







      """simple docstring"""
      lowercase	,       lowercase     :      Any	=							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         lowercase     :      Optional[Any]	=							model_class(__lowerCamelCase						)
         lowercase     :      str	=							inspect.signature(model.call						)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowercase     :      Dict	=							[*signature.parameters.keys()]

         lowercase     :      Any	=							["""pixel_values"""]
         self.assertListEqual(arg_names[:1]   , __lowerCamelCase						)



   def  _UpperCAmelCase						(							self						)			->     Dict:







      """simple docstring"""
      lowercase     :      str	=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCamelCase						)



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







      """simple docstring"""
      def check_hidden_states_output(snake_case   , snake_case   , snake_case						):
         lowercase     :      List[str]	=							model_class(__lowerCamelCase						)
         lowercase     :      Optional[int]	=							model(**self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase						)   , training=__lowerCamelCase						)

         lowercase     :      str	=							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         lowercase     :      Union[str, Any]	=							self.model_tester.num_stages
         self.assertEqual(len(__lowerCamelCase						)   , expected_num_stages + 1						)

         # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:]						)   , [self.model_tester.image_size // 2, self.model_tester.image_size // 2]   , )

      lowercase	,       lowercase     :      Dict	=							self.model_tester.prepare_config_and_inputs_for_common()
      lowercase     :      Optional[int]	=							["""basic""", """bottleneck"""]
      for model_class in self.all_model_classes:
         for layer_type in layers_type:
            lowercase     :      Any	=							layer_type
            lowercase     :      List[str]	=							True
            check_hidden_states_output(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase						)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            lowercase     :      List[str]	=							True

            check_hidden_states_output(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase						)



   def  _UpperCAmelCase						(							self						)			->     List[Any]:







      """simple docstring"""
      lowercase	,       lowercase     :      Dict	=							self.model_tester.prepare_config_and_inputs_for_common()

      def check_equivalence(snake_case   , snake_case   , snake_case   , snake_case={}						):
         lowercase     :      Tuple	=							model(__lowerCamelCase   , return_dict=__lowerCamelCase   , **__lowerCamelCase						)
         lowercase     :      Any	=							model(__lowerCamelCase   , return_dict=__lowerCamelCase   , **__lowerCamelCase						).to_tuple()

         def recursive_check(snake_case   , snake_case						):
            if isinstance(__lowerCamelCase   , (List, Tuple)						):
               for tuple_iterable_value, dict_iterable_value in zip(__lowerCamelCase   , __lowerCamelCase						):
                  recursive_check(__lowerCamelCase   , __lowerCamelCase						)
            elif tuple_object is None:
               return
            else:
               self.assertTrue(
                   all(tf.equal(__lowerCamelCase   , __lowerCamelCase						)						)   , msg=(
                       """Tuple and dict output are not equal. Difference:"""
                       f''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object						)						)}'''
                   )   , )

            recursive_check(__lowerCamelCase   , __lowerCamelCase						)

      for model_class in self.all_model_classes:
         lowercase     :      int	=							model_class(__lowerCamelCase						)

         lowercase     :      Any	=							self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase						)
         lowercase     :      Tuple	=							self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase						)
         check_equivalence(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase						)

         lowercase     :      str	=							self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase   , return_labels=__lowerCamelCase						)
         lowercase     :      Optional[int]	=							self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase   , return_labels=__lowerCamelCase						)
         check_equivalence(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase						)

         lowercase     :      Tuple	=							self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase						)
         lowercase     :      Optional[Any]	=							self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase						)
         check_equivalence(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , {"""output_hidden_states""": True}						)

         lowercase     :      Any	=							self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase   , return_labels=__lowerCamelCase						)
         lowercase     :      str	=							self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase   , return_labels=__lowerCamelCase						)
         check_equivalence(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , {"""output_hidden_states""": True}						)



   def  _UpperCAmelCase						(							self						)			->     Any:







      """simple docstring"""
      lowercase     :      int	=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase						)





   @slow
   def  _UpperCAmelCase						(							self						)			->     Union[str, Any]:







      """simple docstring"""
      for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowercase     :      List[Any]	=							TFRegNetModel.from_pretrained(__lowerCamelCase						)
         self.assertIsNotNone(__lowerCamelCase						)




def       __snake_case    (   )		->					List[Any]:
   lowercase     :      Dict	=							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
   return image







@require_tf
@require_vision
class lowerCamelCase__	(     unittest.TestCase					):



   @cached_property
   def  _UpperCAmelCase						(							self						)			->     Union[str, Any]:







      """simple docstring"""
      return (
          AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
          if is_vision_available()
          else None
      )





   @slow
   def  _UpperCAmelCase						(							self						)			->     int:







      """simple docstring"""
      lowercase     :      Optional[int]	=							TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

      lowercase     :      Tuple	=							self.default_image_processor
      lowercase     :      Tuple	=							prepare_img()
      lowercase     :      Tuple	=							image_processor(images=__lowerCamelCase   , return_tensors="""tf"""						)

      # forward pass
      lowercase     :      List[Any]	=							model(**__lowerCamelCase   , training=__lowerCamelCase						)

      # verify the logits
      lowercase     :      Dict	=							tf.TensorShape((1, 1_0_0_0)						)
      self.assertEqual(outputs.logits.shape   , __lowerCamelCase						)

      lowercase     :      List[str]	=							tf.constant([-0.41_80, -1.50_51, -3.48_36]						)

      tf.debugging.assert_near(outputs.logits[0, :3]   , __lowerCamelCase   , atol=1E-4						)





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

__A			:					Any						=			logging.get_logger(__name__)


__A			:					Any						=			{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

__A			:					Optional[Any]						=			{
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

__A			:					Union[str, Any]						=			{'facebook/blenderbot-3B': 1_2_8}





@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   (
        list(range(ord("!"       )			,  ord("~"       ) + 1       )       ) + list(range(ord("¡"       )			,  ord("¬"       ) + 1       )       ) + list(range(ord("®"       )			,  ord("ÿ"       ) + 1       )       )
    )
    SCREAMING_SNAKE_CASE										=   bs[:]
    SCREAMING_SNAKE_CASE										=   0
    for b in range(2**8       ):
        if b not in bs:
            bs.append(A__       )
            cs.append(2**8 + n       )
            n += 1
    SCREAMING_SNAKE_CASE										=   [chr(A__       ) for n in cs]
    return dict(zip(A__			,  A__       )       )





def 	__a     (      A__			:  List[Any]       ):
    SCREAMING_SNAKE_CASE										=   set()
    SCREAMING_SNAKE_CASE										=   word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)       )
        SCREAMING_SNAKE_CASE										=   char
    return pairs





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      VOCAB_FILES_NAMES
   lowerCamelCase__				=      PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				=      ["input_ids", "attention_mask"]





   def __init__(       self					:			Union[str, Any]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			Any="replace"     , __lowerCamelCase					:			Optional[Any]="<s>"     , __lowerCamelCase					:			Optional[Any]="</s>"     , __lowerCamelCase					:			Any="</s>"     , __lowerCamelCase					:			Union[str, Any]="<s>"     , __lowerCamelCase					:			List[str]="<unk>"     , __lowerCamelCase					:			Optional[Any]="<pad>"     , __lowerCamelCase					:			Dict="<mask>"     , __lowerCamelCase					:			Any=False     , **__lowerCamelCase					:			Optional[Any]     , ):
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else bos_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else eos_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else sep_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else cls_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else unk_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else mask_token

       super().__init__(
           errors=__lowerCamelCase     , bos_token=__lowerCamelCase     , eos_token=__lowerCamelCase     , unk_token=__lowerCamelCase     , sep_token=__lowerCamelCase     , cls_token=__lowerCamelCase     , pad_token=__lowerCamelCase     , mask_token=__lowerCamelCase     , add_prefix_space=__lowerCamelCase     , **__lowerCamelCase     , )

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

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





   @property
   # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
   def      _snake_case		(       self					:			str			):
       return len(self.encoder			)





   def      _snake_case		(       self					:			Union[str, Any]			):
       return dict(self.encoder     , **self.added_tokens_encoder			)





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			List[Any]			):
       if token in self.cache:
           return self.cache[token]
       SCREAMING_SNAKE_CASE										=   tuple(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   get_pairs(__lowerCamelCase			)

       if not pairs:
           return token

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

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





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   []
       for token in re.findall(self.pat     , __lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   "".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(__lowerCamelCase			).split(" "			)			)
       return bpe_tokens





   def      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			Dict			):
       return self.encoder.get(__lowerCamelCase     , self.encoder.get(self.unk_token			)			)





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			Optional[int]			):
       return self.decoder.get(__lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   "".join(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   bytearray([self.byte_decoder[c] for c in text]			).decode("utf-8"     , errors=self.errors			)
       return text





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Optional[str] = None			):
       if not os.path.isdir(__lowerCamelCase			):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
           return
       SCREAMING_SNAKE_CASE										=   os.path.join(
           __lowerCamelCase     , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)
       SCREAMING_SNAKE_CASE										=   os.path.join(
           __lowerCamelCase     , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]			)

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

       SCREAMING_SNAKE_CASE										=   0
       with open(__lowerCamelCase     , "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 __lowerCamelCase			: 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!"			)
                   SCREAMING_SNAKE_CASE										=   token_index
               writer.write(" ".join(__lowerCamelCase			) + "\n"			)
               index += 1

       return vocab_file, merge_file





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None     , __lowerCamelCase					:			bool = False			):
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__lowerCamelCase     , token_ids_a=__lowerCamelCase     , already_has_special_tokens=__lowerCamelCase			)

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





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None			):
       SCREAMING_SNAKE_CASE										=   [self.sep_token_id]
       SCREAMING_SNAKE_CASE										=   [self.cls_token_id]

       if token_ids_a is None:
           return len(cls + token_ids_a + sep			) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Any=False     , **__lowerCamelCase					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   kwargs.pop("add_prefix_space"     , self.add_prefix_space			)
       if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase			) > 0 and not text[0].isspace()):
           SCREAMING_SNAKE_CASE										=   " " + text
       return (text, kwargs)





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None			):
       return token_ids_a + [self.eos_token_id]





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			"Conversation"			):
       SCREAMING_SNAKE_CASE										=   []
       for is_user, text in conversation.iter_texts():
           if is_user:
               # We need to space prefix as it's being done within blenderbot
               inputs.append(" " + text			)
           else:
               # Generated responses should contain them already.
               inputs.append(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   "  ".join(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.encode(__lowerCamelCase			)
       if len(__lowerCamelCase			) > self.model_max_length:
           SCREAMING_SNAKE_CASE										=   input_ids[-self.model_max_length :]
           logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."			)
       return input_ids