


"""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





'''simple docstring'''

from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowerCamelCase				:  Optional[int]    		=      logging.get_logger(__name__)
class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      lowerCAmelCase__   :	Optional[int]       =				["""pixel_values"""]



      def __init__(self       :       str  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       Union[int, float] = 1 / 255  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       int = 8  ,		**UpperCamelCase       :       Dict  ,		):

             '''simple docstring'''







             super().__init__(**UpperCamelCase					)

             lowercase__      =					do_rescale
             lowercase__      =					rescale_factor
             lowercase__      =					do_pad
             lowercase__      =					pad_size



      def  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       np.ndarray  ,		UpperCamelCase       :       float  ,		UpperCamelCase       :       Optional[Union[str, ChannelDimension]] = None  ,		**UpperCamelCase       :       Tuple					):

             '''simple docstring'''







             return rescale(UpperCamelCase  ,		scale=UpperCamelCase  ,		data_format=UpperCamelCase  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Tuple  ,		UpperCamelCase       :       np.ndarray  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       Optional[Union[str, ChannelDimension]] = None					):

             '''simple docstring'''







             lowercase__     ,lowercase__      =					get_image_size(UpperCamelCase					)
             lowercase__      =					(old_height // size + 1) * size - old_height
             lowercase__      =					(old_width // size + 1) * size - old_width

             return pad(UpperCamelCase  ,		((0, pad_height), (0, pad_width))  ,		mode='''symmetric'''  ,		data_format=UpperCamelCase					)






      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       ImageInput  ,		UpperCamelCase       :       Optional[bool] = None  ,		UpperCamelCase       :       Optional[float] = None  ,		UpperCamelCase       :       Optional[bool] = None  ,		UpperCamelCase       :       Optional[int] = None  ,		UpperCamelCase       :       Optional[Union[str, TensorType]] = None  ,		UpperCamelCase       :       Union[str, ChannelDimension] = ChannelDimension.FIRST  ,		**UpperCamelCase       :       int  ,		):

             '''simple docstring'''







             lowercase__      =					do_rescale if do_rescale is not None else self.do_rescale
             lowercase__      =					rescale_factor if rescale_factor is not None else self.rescale_factor
             lowercase__      =					do_pad if do_pad is not None else self.do_pad
             lowercase__      =					pad_size if pad_size is not None else self.pad_size

             lowercase__      =					make_list_of_images(UpperCamelCase					)

             if not valid_images(UpperCamelCase					):
                    raise ValueError(
                        '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                        '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)

             if do_rescale and rescale_factor is None:
                    raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

             # All transformations expect numpy arrays.
             lowercase__      =					[to_numpy_array(UpperCamelCase					) for image in images]

             if do_rescale:
                    lowercase__      =					[self.rescale(image=UpperCamelCase  ,		scale=UpperCamelCase					) for image in images]

             if do_pad:
                    lowercase__      =					[self.pad(UpperCamelCase  ,		size=UpperCamelCase					) for image in images]

             lowercase__      =					[to_channel_dimension_format(UpperCamelCase  ,		UpperCamelCase					) for image in images]

             lowercase__      =					{'''pixel_values''': images}
             return BatchFeature(data=UpperCamelCase  ,		tensor_type=UpperCamelCase					)




