


"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase				=						logging.get_logger(__name__)


UpperCAmelCase				=						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

UpperCAmelCase				=						{
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

UpperCAmelCase				=						{"""facebook/blenderbot_small-90M""": 512}

def 				_snake_case      (   __snake_case							:      Tuple   ):




   """simple docstring"""







   _lowerCamelCase	:					Optional[Any]   =		set()
   _lowerCamelCase	:					Any   =		word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)   )
      _lowerCamelCase	:					Tuple   =		char

   _lowerCamelCase	:					Tuple   =		set(__snake_case   )
   return pairs





class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					VOCAB_FILES_NAMES
   __UpperCAmelCase							       =					PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase							       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase							       =					['''input_ids''', '''attention_mask''']





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE="__start__"      ,   SCREAMING_SNAKE_CASE="__end__"      ,   SCREAMING_SNAKE_CASE="__unk__"      ,   SCREAMING_SNAKE_CASE="__null__"      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  List[str]:
      super().__init__(unk_token=SCREAMING_SNAKE_CASE      ,   bos_token=SCREAMING_SNAKE_CASE      ,   eos_token=SCREAMING_SNAKE_CASE      ,   pad_token=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)

      with open(SCREAMING_SNAKE_CASE      ,   encoding="""utf-8""") as vocab_handle:
         _lowerCamelCase	:					List[str]   =		json.load(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Tuple   =		{v: k for k, v in self.encoder.items()}
      with open(SCREAMING_SNAKE_CASE      ,   encoding="""utf-8""") as merges_handle:
         _lowerCamelCase	:					Dict   =		merges_handle.read().split("""\n""")[1:-1]
      _lowerCamelCase	:					Optional[int]   =		[tuple(merge.split()) for merge in merges]
      _lowerCamelCase	:					int   =		dict(zip(SCREAMING_SNAKE_CASE      ,   range(len(SCREAMING_SNAKE_CASE))))
      _lowerCamelCase	:					Dict   =		{}





   @property
   def 		UpperCamelCase_  (							self)  ->  int:
      return len(self.encoder)





   def 		UpperCamelCase_  (							self)  ->  Dict:
      return dict(self.encoder      ,   **self.added_tokens_encoder)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  str:
      if token in self.cache:
         return self.cache[token]
      _lowerCamelCase	:					List[Any]   =		re.sub("""([.,!?()])"""      ,   r""" \1"""      ,   SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[int]   =		re.sub("""(')"""      ,   r""" \1 """      ,   SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		re.sub(r"""\s{2,}"""      ,   """ """      ,   SCREAMING_SNAKE_CASE)
      if "\n" in token:
         _lowerCamelCase	:					Tuple   =		token.replace("""\n"""      ,   """ __newln__""")

      _lowerCamelCase	:					Any   =		token.split(""" """)
      _lowerCamelCase	:					str   =		[]
      for token in tokens:
         if not len(SCREAMING_SNAKE_CASE):
            continue

         _lowerCamelCase	:					str   =		token.lower()
         _lowerCamelCase	:					Any   =		tuple(SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Any   =		tuple(list(word[:-1]) + [word[-1] + """</w>"""])
         _lowerCamelCase	:					Tuple   =		get_pairs(SCREAMING_SNAKE_CASE)

         if not pairs:
            words.append(SCREAMING_SNAKE_CASE)
            continue

         while True:
            _lowerCamelCase	:					Optional[Any]   =		min(SCREAMING_SNAKE_CASE      ,   key=lambda SCREAMING_SNAKE_CASE: self.bpe_ranks.get(SCREAMING_SNAKE_CASE      ,   float("""inf""")))
            if bigram not in self.bpe_ranks:
               break
            _lowerCamelCase   ,    _lowerCamelCase	:					Tuple   =		bigram
            _lowerCamelCase	:					Dict   =		[]
            _lowerCamelCase	:					Union[str, Any]   =		0

            while i < len(SCREAMING_SNAKE_CASE):
               try:
                  _lowerCamelCase	:					List[Any]   =		word.index(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)
                  new_word.extend(word[i:j])
                  _lowerCamelCase	:					List[str]   =		j
               except ValueError:
                  new_word.extend(word[i:])
                  break

               if word[i] == first and i < len(SCREAMING_SNAKE_CASE) - 1 and word[i + 1] == second:
                  new_word.append(first + second)
                  i += 2
               else:
                  new_word.append(word[i])
                  i += 1
            _lowerCamelCase	:					Any   =		tuple(SCREAMING_SNAKE_CASE)
            _lowerCamelCase	:					Any   =		new_word
            if len(SCREAMING_SNAKE_CASE) == 1:
               break
            else:
               _lowerCamelCase	:					Optional[int]   =		get_pairs(SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					int   =		"""@@ """.join(SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					List[Any]   =		word[:-4]

         _lowerCamelCase	:					Dict   =		word
         words.append(SCREAMING_SNAKE_CASE)
      return " ".join(SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					Any   =		[]

      _lowerCamelCase	:					Any   =		re.findall(r"""\S+\n?"""      ,   SCREAMING_SNAKE_CASE)

      for token in words:
         split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE).split(""" """)))
      return split_tokens





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					Tuple   =		token.lower()
      return self.encoder.get(SCREAMING_SNAKE_CASE      ,   self.encoder.get(self.unk_token))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  str:
      return self.decoder.get(SCREAMING_SNAKE_CASE      ,   self.unk_token)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  str:
      _lowerCamelCase	:					str   =		""" """.join(SCREAMING_SNAKE_CASE).replace("""@@ """      ,   """""").strip()
      return out_string





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None)  ->  Tuple[str]:
      if not os.path.isdir(SCREAMING_SNAKE_CASE):
         logger.error(F'Vocabulary path ({save_directory}) should be a directory')
         return
      _lowerCamelCase	:					int   =		os.path.join(
          SCREAMING_SNAKE_CASE      ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
      _lowerCamelCase	:					Tuple   =		os.path.join(
          SCREAMING_SNAKE_CASE      ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""])

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

      _lowerCamelCase	:					List[Any]   =		0
      with open(SCREAMING_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 SCREAMING_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!""")
               _lowerCamelCase	:					int   =		token_index
            writer.write(""" """.join(SCREAMING_SNAKE_CASE) + """\n""")
            index += 1

      return vocab_file, merge_file









import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import (
        AutoProcessor,
        BertTokenizerFast,
        BlipImageProcessor,
        GPTaTokenizer,
        InstructBlipProcessor,
        PreTrainedTokenizerFast,
    )






@require_vision
class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def 				UpperCamelCase__						(				self	:List[str]):



          """simple docstring"""

          _lowercase              =tempfile.mkdtemp()

          _lowercase              =BlipImageProcessor()
          _lowercase              =GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model')
          _lowercase              =BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert')

          _lowercase              =InstructBlipProcessor(snake_case,      snake_case,      snake_case)

          processor.save_pretrained(self.tmpdirname)





       def 				UpperCamelCase__						(				self	:List[str],      **snake_case	:str):



          """simple docstring"""

          return AutoProcessor.from_pretrained(self.tmpdirname,      **snake_case).tokenizer





       def 				UpperCamelCase__						(				self	:Optional[Any],      **snake_case	:List[Any]):



          """simple docstring"""

          return AutoProcessor.from_pretrained(self.tmpdirname,      **snake_case).image_processor





       def 				UpperCamelCase__						(				self	:Tuple,      **snake_case	:Any):



          """simple docstring"""

          return AutoProcessor.from_pretrained(self.tmpdirname,      **snake_case).qformer_tokenizer





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          shutil.rmtree(self.tmpdirname)





       def 				UpperCamelCase__						(				self	:List[str]):



          """simple docstring"""

          _lowercase              =[np.random.randint(255,      size=(3, 30, 400),      dtype=np.uinta)]

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

          return image_inputs





       def 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

          _lowercase              =InstructBlipProcessor(
              tokenizer=self.get_tokenizer(),      image_processor=self.get_image_processor(),      qformer_tokenizer=self.get_qformer_tokenizer(),      )
          processor.save_pretrained(self.tmpdirname)

          _lowercase              =self.get_tokenizer(bos_token='(BOS)',      eos_token='(EOS)')
          _lowercase              =self.get_image_processor(do_normalize=snake_case,      padding_value=1.0)

          _lowercase              =InstructBlipProcessor.from_pretrained(
              self.tmpdirname,      bos_token='(BOS)',      eos_token='(EOS)',      do_normalize=snake_case,      padding_value=1.0)

          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)
          self.assertIsInstance(processor.qformer_tokenizer,      snake_case)





       def 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

          _lowercase              =self.get_image_processor()
          _lowercase              =self.get_tokenizer()
          _lowercase              =self.get_qformer_tokenizer()

          _lowercase              =InstructBlipProcessor(
              tokenizer=snake_case,      image_processor=snake_case,      qformer_tokenizer=snake_case)

          _lowercase              =self.prepare_image_inputs()

          _lowercase              =image_processor(snake_case,      return_tensors='np')
          _lowercase              =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 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

          _lowercase              =self.get_image_processor()
          _lowercase              =self.get_tokenizer()
          _lowercase              =self.get_qformer_tokenizer()

          _lowercase              =InstructBlipProcessor(
              tokenizer=snake_case,      image_processor=snake_case,      qformer_tokenizer=snake_case)

          _lowercase              ='lower newer'

          _lowercase              =processor(text=snake_case)

          _lowercase              =tokenizer(snake_case,      return_token_type_ids=snake_case)
          _lowercase              =qformer_tokenizer(snake_case,      return_token_type_ids=snake_case)

          for key in encoded_tokens.keys():
             self.assertListEqual(encoded_tokens[key],      encoded_processor[key])

          for key in encoded_tokens_qformer.keys():
             self.assertListEqual(encoded_tokens_qformer[key],      encoded_processor['qformer_' + key])





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =self.get_image_processor()
          _lowercase              =self.get_tokenizer()
          _lowercase              =self.get_qformer_tokenizer()

          _lowercase              =InstructBlipProcessor(
              tokenizer=snake_case,      image_processor=snake_case,      qformer_tokenizer=snake_case)

          _lowercase              ='lower newer'
          _lowercase              =self.prepare_image_inputs()

          _lowercase              =processor(text=snake_case,      images=snake_case)

          self.assertListEqual(
              list(inputs.keys()),      ['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values'],      )

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





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          _lowercase              =self.get_image_processor()
          _lowercase              =self.get_tokenizer()
          _lowercase              =self.get_qformer_tokenizer()

          _lowercase              =InstructBlipProcessor(
              tokenizer=snake_case,      image_processor=snake_case,      qformer_tokenizer=snake_case)

          _lowercase              =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          _lowercase              =processor.batch_decode(snake_case)
          _lowercase              =tokenizer.batch_decode(snake_case)

          self.assertListEqual(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              =self.get_image_processor()
          _lowercase              =self.get_tokenizer()
          _lowercase              =self.get_qformer_tokenizer()

          _lowercase              =InstructBlipProcessor(
              tokenizer=snake_case,      image_processor=snake_case,      qformer_tokenizer=snake_case)

          _lowercase              ='lower newer'
          _lowercase              =self.prepare_image_inputs()

          _lowercase              =processor(text=snake_case,      images=snake_case)

          self.assertListEqual(
              list(inputs.keys()),      ['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values'],      )


