
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

lowercase  : Union[str, Any]						   =							logging.get_logger(__name__)


lowercase  : Optional[int]						   =							{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

lowercase  : Union[str, 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"""
    },
}

lowercase  : Dict						   =							{"""facebook/blenderbot-3B""": 128}







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 						_snake_case(  )			->   List[str]:
       lowercase							:					Union[str, Any]	     =				(
           list(range(ord("""!"""    ) ,     ord("""~"""    ) + 1    )    ) + list(range(ord("""¡"""    ) ,     ord("""¬"""    ) + 1    )    ) + list(range(ord("""®"""    ) ,     ord("""ÿ"""    ) + 1    )    )
       )
       lowercase							:					Union[str, Any]	     =				bs[:]
       lowercase							:					List[Any]	     =				0
       for b in range(2**8    ):
              if b not in bs:
                     bs.append(SCREAMING_SNAKE_CASE__    )
                     cs.append(2**8 + n    )
                     n += 1
       lowercase							:					Optional[Any]	     =				[chr(SCREAMING_SNAKE_CASE__    ) for n in cs]
       return dict(zip(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   List[Any]:
       lowercase							:					str	     =				set()
       lowercase							:					Tuple	     =				word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)    )
              lowercase							:					Any	     =				char
       return pairs




class __snake_case		(				lowerCAmelCase					):
    _a		:						Optional[Any]=    VOCAB_FILES_NAMES
    _a		:						Dict=    PRETRAINED_VOCAB_FILES_MAP
    _a		:						Dict=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _a		:						List[str]=    ["input_ids", "attention_mask"]
    def __init__(  self		,snake_case		,snake_case		,snake_case="replace"		,snake_case="<s>"		,snake_case="</s>"		,snake_case="</s>"		,snake_case="<s>"		,snake_case="<unk>"		,snake_case="<pad>"		,snake_case="<mask>"		,snake_case=False		,**snake_case		,):



           '''simple docstring'''







           lowercase							:					Any	     =				AddedToken(snake_case		,lstrip=snake_case		,rstrip=snake_case				) if isinstance(snake_case		,snake_case				) else bos_token
           lowercase							:					List[Any]	     =				AddedToken(snake_case		,lstrip=snake_case		,rstrip=snake_case				) if isinstance(snake_case		,snake_case				) else eos_token
           lowercase							:					Optional[Any]	     =				AddedToken(snake_case		,lstrip=snake_case		,rstrip=snake_case				) if isinstance(snake_case		,snake_case				) else sep_token
           lowercase							:					Union[str, Any]	     =				AddedToken(snake_case		,lstrip=snake_case		,rstrip=snake_case				) if isinstance(snake_case		,snake_case				) else cls_token
           lowercase							:					Optional[int]	     =				AddedToken(snake_case		,lstrip=snake_case		,rstrip=snake_case				) if isinstance(snake_case		,snake_case				) else unk_token
           lowercase							:					Union[str, Any]	     =				AddedToken(snake_case		,lstrip=snake_case		,rstrip=snake_case				) if isinstance(snake_case		,snake_case				) else pad_token

           # Mask token behave like a normal word, i.e. include the space before it
           lowercase							:					Union[str, Any]	     =				AddedToken(snake_case		,lstrip=snake_case		,rstrip=snake_case				) if isinstance(snake_case		,snake_case				) else mask_token

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

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

           # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
           lowercase							:					Any	     =				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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return len(self.encoder				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return dict(self.encoder		,**self.added_tokens_encoder				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if token in self.cache:
                  return self.cache[token]
           lowercase							:					str	     =				tuple(snake_case				)
           lowercase							:					Union[str, Any]	     =				get_pairs(snake_case				)

           if not pairs:
                  return token

           while True:
                  lowercase							:					Optional[int]	     =				min(snake_case		,key=lambda snake_case				: self.bpe_ranks.get(snake_case		,float("""inf"""				)				)				)
                  if bigram not in self.bpe_ranks:
                         break
                  lowercase     ,      lowercase							:					Any	     =				bigram
                  lowercase							:					Any	     =				[]
                  lowercase							:					Union[str, Any]	     =				0
                  while i < len(snake_case				):
                         try:
                                lowercase							:					Tuple	     =				word.index(snake_case		,snake_case				)
                         except ValueError:
                                new_word.extend(word[i:]				)
                                break
                         else:
                                new_word.extend(word[i:j]				)
                                lowercase							:					Optional[Any]	     =				j

                         if word[i] == first and i < len(snake_case				) - 1 and word[i + 1] == second:
                                new_word.append(first + second				)
                                i += 2
                         else:
                                new_word.append(word[i]				)
                                i += 1
                  lowercase							:					List[str]	     =				tuple(snake_case				)
                  lowercase							:					Any	     =				new_word
                  if len(snake_case				) == 1:
                         break
                  else:
                         lowercase							:					List[str]	     =				get_pairs(snake_case				)
           lowercase							:					Tuple	     =				""" """.join(snake_case				)
           lowercase							:					Dict	     =				word
           return word
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				[]
           for token in re.findall(self.pat		,snake_case				):
                  lowercase							:					int	     =				"""""".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(snake_case				).split(""" """				)				)
           return bpe_tokens
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           return self.encoder.get(snake_case		,self.encoder.get(self.unk_token				)				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           return self.decoder.get(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Any	     =				"""""".join(snake_case				)
           lowercase							:					Optional[int]	     =				bytearray([self.byte_decoder[c] for c in text]				).decode("""utf-8"""		,errors=self.errors				)
           return text
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







           if not os.path.isdir(snake_case				):
                  logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
                  return
           lowercase							:					List[str]	     =				os.path.join(
               snake_case		,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)
           lowercase							:					List[str]	     =				os.path.join(
               snake_case		,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]				)

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

           lowercase							:					List[str]	     =				0
           with open(snake_case		,"""w"""		,encoding="""utf-8"""				) as writer:
                  writer.write("""#version: 0.2\n"""				)
                  for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,key=lambda snake_case				: kv[1]				):
                         if index != token_index:
                                logger.warning(
                                    f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                                    """ Please check that the tokenizer is not corrupted!"""				)
                                lowercase							:					Dict	     =				token_index
                         writer.write(""" """.join(snake_case				) + """\n"""				)
                         index += 1

           return vocab_file, merge_file
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None		,snake_case = False				):



           '''simple docstring'''







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

           if token_ids_a is None:
                  return [1] + ([0] * len(snake_case				)) + [1]
           return [1] + ([0] * len(snake_case				)) + [1, 1] + ([0] * len(snake_case				)) + [1]
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				[self.sep_token_id]
           lowercase							:					List[str]	     =				[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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case=False		,**snake_case				):



           '''simple docstring'''







           lowercase							:					int	     =				kwargs.pop("""add_prefix_space"""		,self.add_prefix_space				)
           if (is_split_into_words or add_prefix_space) and (len(snake_case				) > 0 and not text[0].isspace()):
                  lowercase							:					Optional[int]	     =				""" """ + text
           return (text, kwargs)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







           return token_ids_a + [self.eos_token_id]
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				[]
           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(snake_case				)

           lowercase							:					Tuple	     =				"""  """.join(snake_case				)
           lowercase							:					int	     =				self.encode(snake_case				)
           if len(snake_case				) > self.model_max_length:
                  lowercase							:					List[Any]	     =				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


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor

@require_vision
class __snake_case		(				unittest.TestCase					):
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				tempfile.mkdtemp()

           lowercase							:					List[str]	     =				[
               """[UNK]""",
               """[CLS]""",
               """[SEP]""",
               """[PAD]""",
               """[MASK]""",
               """的""",
               """价""",
               """格""",
               """是""",
               """15""",
               """便""",
               """alex""",
               """##andra""",
               """，""",
               """。""",
               """-""",
               """t""",
               """shirt""",
           ]
           lowercase							:					Dict	     =				os.path.join(self.tmpdirname		,VOCAB_FILES_NAMES["""vocab_file"""]				)
           with open(self.vocab_file		,"""w"""		,encoding="""utf-8"""				) as vocab_writer:
                  vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)

           lowercase							:					Any	     =				{
               """do_resize""": True,
               """size""": {"""height""": 224, """width""": 224},
               """do_center_crop""": True,
               """crop_size""": {"""height""": 18, """width""": 18},
               """do_normalize""": True,
               """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
               """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
               """do_convert_rgb""": True,
           }
           lowercase							:					List[str]	     =				os.path.join(self.tmpdirname		,snake_case				)
           with open(self.image_processor_file		,"""w"""		,encoding="""utf-8"""				) as fp:
                  json.dump(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           return BertTokenizer.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           return BertTokenizerFast.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           shutil.rmtree(self.tmpdirname				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				[np.random.randint(255		,size=(3, 30, 400)		,dtype=np.uinta				)]

           lowercase							:					str	     =				[Image.fromarray(np.moveaxis(snake_case		,0		,-1				)				) for x in image_inputs]

           return image_inputs
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				self.get_tokenizer()
           lowercase							:					Dict	     =				self.get_rust_tokenizer()
           lowercase							:					Union[str, Any]	     =				self.get_image_processor()

           lowercase							:					Optional[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)
           processor_slow.save_pretrained(self.tmpdirname				)
           lowercase							:					Any	     =				ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,use_fast=snake_case				)

           lowercase							:					List[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)
           processor_fast.save_pretrained(self.tmpdirname				)
           lowercase							:					Tuple	     =				ChineseCLIPProcessor.from_pretrained(self.tmpdirname				)

           self.assertEqual(processor_slow.tokenizer.get_vocab()		,tokenizer_slow.get_vocab()				)
           self.assertEqual(processor_fast.tokenizer.get_vocab()		,tokenizer_fast.get_vocab()				)
           self.assertEqual(tokenizer_slow.get_vocab()		,tokenizer_fast.get_vocab()				)
           self.assertIsInstance(processor_slow.tokenizer		,snake_case				)
           self.assertIsInstance(processor_fast.tokenizer		,snake_case				)

           self.assertEqual(processor_slow.image_processor.to_json_string()		,image_processor.to_json_string()				)
           self.assertEqual(processor_fast.image_processor.to_json_string()		,image_processor.to_json_string()				)
           self.assertIsInstance(processor_slow.image_processor		,snake_case				)
           self.assertIsInstance(processor_fast.image_processor		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				ChineseCLIPProcessor(tokenizer=self.get_tokenizer()		,image_processor=self.get_image_processor()				)
           processor.save_pretrained(self.tmpdirname				)

           lowercase							:					int	     =				self.get_tokenizer(cls_token="""(CLS)"""		,sep_token="""(SEP)"""				)
           lowercase							:					Dict	     =				self.get_image_processor(do_normalize=snake_case				)

           lowercase							:					Optional[Any]	     =				ChineseCLIPProcessor.from_pretrained(
               self.tmpdirname		,cls_token="""(CLS)"""		,sep_token="""(SEP)"""		,do_normalize=snake_case				)

           self.assertEqual(processor.tokenizer.get_vocab()		,tokenizer_add_kwargs.get_vocab()				)
           self.assertIsInstance(processor.tokenizer		,snake_case				)

           self.assertEqual(processor.image_processor.to_json_string()		,image_processor_add_kwargs.to_json_string()				)
           self.assertIsInstance(processor.image_processor		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.get_image_processor()
           lowercase							:					List[str]	     =				self.get_tokenizer()

           lowercase							:					Optional[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					Optional[int]	     =				self.prepare_image_inputs()

           lowercase							:					Tuple	     =				image_processor(snake_case		,return_tensors="""np"""				)
           lowercase							:					Optional[int]	     =				processor(images=snake_case		,return_tensors="""np"""				)

           for key in input_feat_extract.keys():
                  self.assertAlmostEqual(input_feat_extract[key].sum()		,input_processor[key].sum()		,delta=1e-2				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.get_image_processor()
           lowercase							:					Union[str, Any]	     =				self.get_tokenizer()

           lowercase							:					Any	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					Any	     =				"""Alexandra，T-shirt的价格是15便士。"""

           lowercase							:					int	     =				processor(text=snake_case				)

           lowercase							:					Dict	     =				tokenizer(snake_case				)

           for key in encoded_tok.keys():
                  self.assertListEqual(encoded_tok[key]		,encoded_processor[key]				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				self.get_image_processor()
           lowercase							:					Optional[Any]	     =				self.get_tokenizer()

           lowercase							:					Dict	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					List[str]	     =				"""Alexandra，T-shirt的价格是15便士。"""
           lowercase							:					Any	     =				self.prepare_image_inputs()

           lowercase							:					Optional[Any]	     =				processor(text=snake_case		,images=snake_case				)

           self.assertListEqual(list(inputs.keys()				)		,["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]				)

           # test if it raises when no input is passed
           with pytest.raises(snake_case				):
                  processor()
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.get_image_processor()
           lowercase							:					str	     =				self.get_tokenizer()

           lowercase							:					Dict	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					str	     =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           lowercase							:					Any	     =				processor.batch_decode(snake_case				)
           lowercase							:					str	     =				tokenizer.batch_decode(snake_case				)

           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Dict	     =				self.get_image_processor()
           lowercase							:					Dict	     =				self.get_tokenizer()

           lowercase							:					List[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					str	     =				"""Alexandra，T-shirt的价格是15便士。"""
           lowercase							:					Optional[Any]	     =				self.prepare_image_inputs()

           lowercase							:					Dict	     =				processor(text=snake_case		,images=snake_case				)

           self.assertListEqual(list(inputs.keys()				)		,processor.model_input_names				)
