






"""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


lowerCAmelCase_	:    Optional[Any]            =      logging.get_logger(__name__)


lowerCAmelCase_	:    List[str]            =      {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCAmelCase_	:    List[Any]            =      {
    '''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'''
        )
    },
}

lowerCAmelCase_	:    List[str]            =      {'''facebook/blenderbot_small-90M''': 5_1_2}






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			set()
       UpperCAmelCase	    =			word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)		)
              UpperCAmelCase	    =			char

       UpperCAmelCase	    =			set(lowerCAmelCase		)
       return pairs





class   UpperCamelCase_ (       a_  ):
  _A       :					Union[str, Any]				=	VOCAB_FILES_NAMES
  _A       :					Tuple				=	PRETRAINED_VOCAB_FILES_MAP
  _A       :					List[Any]				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _A       :					List[Any]				=	['input_ids', 'attention_mask']



  def __init__(      self ,		snake_case__ ,		snake_case__ ,		snake_case__="__start__" ,		snake_case__="__end__" ,		snake_case__="__unk__" ,		snake_case__="__null__" ,		**snake_case__ ,		)			->			List[Any]:



         """simple docstring"""





         super().__init__(unk_token=snake_case__ ,		bos_token=snake_case__ ,		eos_token=snake_case__ ,		pad_token=snake_case__ ,		**snake_case__				)

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



  @property
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         return len(self.encoder				)



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         return dict(self.encoder ,		**self.added_tokens_encoder				)



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			str:



         """simple docstring"""





         if token in self.cache:
                return self.cache[token]
         UpperCAmelCase	    =			re.sub("""([.,!?()])""" ,		R""" \1""" ,		snake_case__				)
         UpperCAmelCase	    =			re.sub("""(')""" ,		R""" \1 """ ,		snake_case__				)
         UpperCAmelCase	    =			re.sub(R"""\s{2,}""" ,		""" """ ,		snake_case__				)
         if "\n" in token:
                UpperCAmelCase	    =			token.replace("""\n""" ,		""" __newln__"""				)

         UpperCAmelCase	    =			token.split(""" """				)
         UpperCAmelCase	    =			[]
         for token in tokens:
                if not len(snake_case__				):
                       continue

                UpperCAmelCase	    =			token.lower()
                UpperCAmelCase	    =			tuple(snake_case__				)
                UpperCAmelCase	    =			tuple(list(word[:-1]				) + [word[-1] + """</w>"""]				)
                UpperCAmelCase	    =			get_pairs(snake_case__				)

                if not pairs:
                       words.append(snake_case__				)
                       continue

                while True:
                       UpperCAmelCase	    =			min(snake_case__ ,		key=lambda snake_case__				: self.bpe_ranks.get(snake_case__ ,		float("""inf"""				)				)				)
                       if bigram not in self.bpe_ranks:
                              break
                       UpperCAmelCase    ,       UpperCAmelCase	    =			bigram
                       UpperCAmelCase	    =			[]
                       UpperCAmelCase	    =			0

                       while i < len(snake_case__				):
                              try:
                                     UpperCAmelCase	    =			word.index(snake_case__ ,		snake_case__				)
                                     new_word.extend(word[i:j]				)
                                     UpperCAmelCase	    =			j
                              except ValueError:
                                     new_word.extend(word[i:]				)
                                     break

                              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
                       UpperCAmelCase	    =			tuple(snake_case__				)
                       UpperCAmelCase	    =			new_word
                       if len(snake_case__				) == 1:
                              break
                       else:
                              UpperCAmelCase	    =			get_pairs(snake_case__				)
                UpperCAmelCase	    =			"""@@ """.join(snake_case__				)
                UpperCAmelCase	    =			word[:-4]

                UpperCAmelCase	    =			word
                words.append(snake_case__				)
         return " ".join(snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			[]

         UpperCAmelCase	    =			re.findall(R"""\S+\n?""" ,		snake_case__				)

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



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			token.lower()
         return self.encoder.get(snake_case__ ,		self.encoder.get(self.unk_token				)				)



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			str:



         """simple docstring"""





         return self.decoder.get(snake_case__ ,		self.unk_token				)



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			""" """.join(snake_case__				).replace("""@@ """ ,		""""""				).strip()
         return out_string



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None				)			->			Tuple[str]:



         """simple docstring"""





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

         UpperCAmelCase	    =			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!"""				)
                              UpperCAmelCase	    =			token_index
                       writer.write(""" """.join(snake_case__				) + """\n"""				)
                       index += 1

         return vocab_file, merge_file








"""simple docstring"""







import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import BridgeTowerImageProcessor





class   UpperCamelCase_ (       unittest.TestCase  ):



  def __init__(      self ,		snake_case__ ,		snake_case__ = True ,		snake_case__ = None ,		snake_case__ = 32 ,		snake_case__ = True ,		snake_case__ = 1 / 2_55 ,		snake_case__ = True ,		snake_case__ = True ,		snake_case__ = [0.48_145_466, 0.4_578_275, 0.40_821_073] ,		snake_case__ = [0.26_862_954, 0.26_130_258, 0.27_577_711] ,		snake_case__ = True ,		snake_case__=7 ,		snake_case__=30 ,		snake_case__=4_00 ,		snake_case__=3 ,		)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			parent
         UpperCAmelCase	    =			do_resize
         UpperCAmelCase	    =			size if size is not None else {"""shortest_edge""": 2_88}
         UpperCAmelCase	    =			size_divisor
         UpperCAmelCase	    =			do_rescale
         UpperCAmelCase	    =			rescale_factor
         UpperCAmelCase	    =			do_normalize
         UpperCAmelCase	    =			do_center_crop
         UpperCAmelCase	    =			image_mean
         UpperCAmelCase	    =			image_std
         UpperCAmelCase	    =			do_pad
         UpperCAmelCase	    =			batch_size
         UpperCAmelCase	    =			num_channels
         UpperCAmelCase	    =			min_resolution
         UpperCAmelCase	    =			max_resolution



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "size_divisor": self.size_divisor,
         }



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__=False				)			->			Optional[int]:



         """simple docstring"""





         if not batched:
                UpperCAmelCase	    =			self.size["""shortest_edge"""]
                UpperCAmelCase	    =			image_inputs[0]
                if isinstance(snake_case__ ,		Image.Image				):
                       UpperCAmelCase    ,       UpperCAmelCase	    =			image.size
                else:
                       UpperCAmelCase    ,       UpperCAmelCase	    =			image.shape[1], image.shape[2]
                UpperCAmelCase	    =			size / min(snake_case__ ,		snake_case__				)
                if h < w:
                       UpperCAmelCase    ,       UpperCAmelCase	    =			size, scale * w
                else:
                       UpperCAmelCase    ,       UpperCAmelCase	    =			scale * h, size

                UpperCAmelCase	    =			int((13_33 / 8_00) * size				)
                if max(snake_case__ ,		snake_case__				) > max_size:
                       UpperCAmelCase	    =			max_size / max(snake_case__ ,		snake_case__				)
                       UpperCAmelCase	    =			newh * scale
                       UpperCAmelCase	    =			neww * scale

                UpperCAmelCase    ,       UpperCAmelCase	    =			int(newh + 0.5				), int(neww + 0.5				)
                UpperCAmelCase    ,       UpperCAmelCase	    =			(
                    newh // self.size_divisor * self.size_divisor,
                    neww // self.size_divisor * self.size_divisor,
                )

         else:
                UpperCAmelCase	    =			[]
                for image in image_inputs:
                       UpperCAmelCase    ,       UpperCAmelCase	    =			self.get_expected_values([image]				)
                       expected_values.append((expected_height, expected_width)				)
                UpperCAmelCase	    =			max(snake_case__ ,		key=lambda snake_case__				: item[0]				)[0]
                UpperCAmelCase	    =			max(snake_case__ ,		key=lambda snake_case__				: item[1]				)[1]

         return expected_height, expected_width





@require_torch
@require_vision
class   UpperCamelCase_ (       a_   ,					unittest.TestCase  ):
  _A       :					int				=	BridgeTowerImageProcessor if is_vision_available() else None



  def 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			BridgeTowerImageProcessingTester(self				)



  @property
  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         return self.image_processor_tester.prepare_image_processor_dict()



  def 		UpperCamelCase_				(      self				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class(**self.image_processor_dict				)
         self.assertTrue(hasattr(snake_case__ ,		"""image_mean"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""image_std"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""do_normalize"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""do_resize"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""size"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""size_divisor"""				)				)



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



         """simple docstring"""





         pass



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



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class(**self.image_processor_dict				)
         # create random PIL images
         UpperCAmelCase	    =			prepare_image_inputs(self.image_processor_tester ,		equal_resolution=snake_case__				)
         for image in image_inputs:
                self.assertIsInstance(snake_case__ ,		Image.Image				)

         # Test not batched input
         UpperCAmelCase	    =			image_processing(image_inputs[0] ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,		)

         # Test batched
         UpperCAmelCase	    =			image_processing(snake_case__ ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__ ,		batched=snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             ) ,		)



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class(**self.image_processor_dict				)
         # create random numpy tensors
         UpperCAmelCase	    =			prepare_image_inputs(self.image_processor_tester ,		equal_resolution=snake_case__ ,		numpify=snake_case__				)
         for image in image_inputs:
                self.assertIsInstance(snake_case__ ,		np.ndarray				)

         # Test not batched input
         UpperCAmelCase	    =			image_processing(image_inputs[0] ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,		)

         # Test batched
         UpperCAmelCase	    =			image_processing(snake_case__ ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__ ,		batched=snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             ) ,		)



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class(**self.image_processor_dict				)
         # create random PyTorch tensors
         UpperCAmelCase	    =			prepare_image_inputs(self.image_processor_tester ,		equal_resolution=snake_case__ ,		torchify=snake_case__				)
         for image in image_inputs:
                self.assertIsInstance(snake_case__ ,		torch.Tensor				)

         # Test not batched input
         UpperCAmelCase	    =			image_processing(image_inputs[0] ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,		)

         # Test batched
         UpperCAmelCase	    =			image_processing(snake_case__ ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__ ,		batched=snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             ) ,		)
