






"""simple docstring"""



import unittest

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 OwlViTImageProcessor

class 			a	(  unittest.TestCase      ):







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase=7				,						_lowerCamelCase=3				,						_lowerCamelCase=1_8				,						_lowerCamelCase=3_0				,						_lowerCamelCase=4_0_0				,						_lowerCamelCase=True				,						_lowerCamelCase=None				,						_lowerCamelCase=True				,						_lowerCamelCase=None				,						_lowerCamelCase=True				,						_lowerCamelCase=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]				,						_lowerCamelCase=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]				,						_lowerCamelCase=True				,						):
      lowercase				       =			parent
      lowercase				       =			batch_size
      lowercase				       =			num_channels
      lowercase				       =			image_size
      lowercase				       =			min_resolution
      lowercase				       =			max_resolution
      lowercase				       =			do_resize
      lowercase				       =			size if size is not None else {'height': 1_8, 'width': 1_8}
      lowercase				       =			do_center_crop
      lowercase				       =			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
      lowercase				       =			do_normalize
      lowercase				       =			image_mean
      lowercase				       =			image_std
      lowercase				       =			do_convert_rgb







    def      UpperCamelCase_ (     self			):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_convert_rgb": self.do_convert_rgb,
      }


@require_torch
@require_vision
class 			a	(  a_,							unittest.TestCase      ):
    UpperCAmelCase_					:	Tuple      	=OwlViTImageProcessor if is_vision_available() else None







    def      UpperCamelCase_ (     self			):
      lowercase				       =			OwlViTImageProcessingTester(self			)







    @property
    def      UpperCamelCase_ (     self			):
      return self.image_processor_tester.prepare_image_processor_dict()







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'do_resize'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'size'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'do_center_crop'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'center_crop'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'do_normalize'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'image_mean'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'image_std'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'do_convert_rgb'			)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size				,						{'height': 1_8, 'width': 1_8}			)
      self.assertEqual(image_processor.crop_size				,						{'height': 1_8, 'width': 1_8}			)

      lowercase				       =			self.image_processing_class.from_dict(self.image_processor_dict				,						size=4_2				,						crop_size=8_4			)
      self.assertEqual(image_processor.size				,						{'height': 4_2, 'width': 4_2}			)
      self.assertEqual(image_processor.crop_size				,						{'height': 8_4, 'width': 8_4}			)







    def      UpperCamelCase_ (     self			):
      # Initialize image_processing
      lowercase				       =			self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      lowercase				       =			prepare_image_inputs(self.image_processor_tester				,						equal_resolution=_lowerCamelCase			)

      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase				,						Image.Image			)

      # Test not batched input
      lowercase				       =			image_processing(image_inputs[0]				,						return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape				,						(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,						)

      # Test batched
      lowercase				       =			image_processing(_lowerCamelCase				,						return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape				,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,						)







    def      UpperCamelCase_ (     self			):
      # Initialize image_processing
      lowercase				       =			self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      lowercase				       =			prepare_image_inputs(self.image_processor_tester				,						equal_resolution=_lowerCamelCase				,						numpify=_lowerCamelCase			)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase				,						np.ndarray			)

      # Test not batched input
      lowercase				       =			image_processing(image_inputs[0]				,						return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape				,						(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,						)

      # Test batched
      lowercase				       =			image_processing(_lowerCamelCase				,						return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape				,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,						)







    def      UpperCamelCase_ (     self			):
      # Initialize image_processing
      lowercase				       =			self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      lowercase				       =			prepare_image_inputs(self.image_processor_tester				,						equal_resolution=_lowerCamelCase				,						torchify=_lowerCamelCase			)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase				,						torch.Tensor			)

      # Test not batched input
      lowercase				       =			image_processing(image_inputs[0]				,						return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape				,						(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,						)

      # Test batched
      lowercase				       =			image_processing(_lowerCamelCase				,						return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape				,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,						)






'''simple docstring'''




import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase	:		Tuple		  =						logging.get_logger(__name__)

_lowerCAmelCase	:		Dict		  =						{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

_lowerCAmelCase	:		Optional[int]		  =						{
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
}

_lowerCAmelCase	:		Tuple		  =						{
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}

@lru_cache()
def       __UpperCamelCase     (     )     ->   Any:




	"""simple docstring"""





	lowerCAmelCase       :     Any   				=			(
	    list(range(ord('!'					)       , ord('~'					) + 1					)					) + list(range(ord('¡'					)       , ord('¬'					) + 1					)					) + list(range(ord('®'					)       , ord('ÿ'					) + 1					)					)
	)
	lowerCAmelCase       :     Optional[int]   				=			bs[:]
	lowerCAmelCase       :     Optional[int]   				=			0
	for b in range(2**8					):
		if b not in bs:
			bs.append(_A					)
			cs.append(2**8 + n					)
			n += 1
	lowerCAmelCase       :     int   				=			[chr(_A					) for n in cs]
	return dict(zip(_A       , _A					)					)

def       __UpperCamelCase     (     _A     :					Union[str, Any]					)     ->   Any:




	"""simple docstring"""





	lowerCAmelCase       :     Tuple   				=			set()
	lowerCAmelCase       :     List[Any]   				=			word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)					)
		lowerCAmelCase       :     Dict   				=			char
	return pairs






class 			lowerCAmelCase					(					a				):
	_lowerCamelCase :       int         =			VOCAB_FILES_NAMES
	_lowerCamelCase :       Any         =			PRETRAINED_VOCAB_FILES_MAP
	_lowerCamelCase :       int         =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCamelCase :       Optional[Any]         =			["""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__			,       ):
		lowerCAmelCase       :     Optional[Any]   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else bos_token
		lowerCAmelCase       :     List[Any]   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else eos_token
		lowerCAmelCase       :     Any   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else sep_token
		lowerCAmelCase       :     str   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else cls_token
		lowerCAmelCase       :     Tuple   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else unk_token
		lowerCAmelCase       :     List[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
		lowerCAmelCase       :     List[str]   				=			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:
			lowerCAmelCase       :     Tuple   				=			json.load(snake_case__     )
		lowerCAmelCase       :     Union[str, Any]   				=			{v: k for k, v in self.encoder.items()}
		lowerCAmelCase       :     List[str]   				=			errors  # how to handle errors in decoding
		lowerCAmelCase       :     Tuple   				=			bytes_to_unicode()
		lowerCAmelCase       :     Dict   				=			{v: k for k, v in self.byte_encoder.items()}
		with open(snake_case__			,       encoding='utf-8'     ) as merges_handle:
			lowerCAmelCase       :     List[str]   				=			merges_handle.read().split('\n'     )[1:-1]
		lowerCAmelCase       :     Tuple   				=			[tuple(merge.split()     ) for merge in bpe_merges]
		lowerCAmelCase       :     Optional[Any]   				=			dict(zip(snake_case__			,       range(len(snake_case__     )     )     )     )
		lowerCAmelCase       :     List[str]   				=			{}
		lowerCAmelCase       :     Dict   				=			add_prefix_space

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



	@property
	def 					lowercase  (       self     ):
		return len(self.encoder     )



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



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

		if not pairs:
			return token

		while True:
			lowerCAmelCase       :     str   				=			min(snake_case__			,       key=lambda snake_case__     : self.bpe_ranks.get(snake_case__			,       float('inf'     )     )     )
			if bigram not in self.bpe_ranks:
				break
			lowerCAmelCase				, lowerCAmelCase       :     List[Any]   				=			bigram
			lowerCAmelCase       :     Optional[int]   				=			[]
			lowerCAmelCase       :     Union[str, Any]   				=			0
			while i < len(snake_case__     ):
				try:
					lowerCAmelCase       :     Union[str, Any]   				=			word.index(snake_case__			,       snake_case__     )
				except ValueError:
					new_word.extend(word[i:]     )
					break
				else:
					new_word.extend(word[i:j]     )
					lowerCAmelCase       :     List[str]   				=			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
			lowerCAmelCase       :     int   				=			tuple(snake_case__     )
			lowerCAmelCase       :     List[Any]   				=			new_word
			if len(snake_case__     ) == 1:
				break
			else:
				lowerCAmelCase       :     Union[str, Any]   				=			get_pairs(snake_case__     )
		lowerCAmelCase       :     Optional[Any]   				=			' '.join(snake_case__     )
		lowerCAmelCase       :     Any   				=			word
		return word



	def 					lowercase  (       self			,       snake_case__     ):
		lowerCAmelCase       :     List[Any]   				=			[]
		for token in re.findall(self.pat			,       snake_case__     ):
			lowerCAmelCase       :     Dict   				=			''.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 					lowercase  (       self			,       snake_case__     ):
		return self.encoder.get(snake_case__			,       self.encoder.get(self.unk_token     )     )



	def 					lowercase  (       self			,       snake_case__     ):
		return self.decoder.get(snake_case__     )



	def 					lowercase  (       self			,       snake_case__     ):
		lowerCAmelCase       :     Union[str, Any]   				=			''.join(snake_case__     )
		lowerCAmelCase       :     Any   				=			bytearray([self.byte_decoder[c] for c in text]     ).decode('utf-8'			,       errors=self.errors     )
		return text



	def 					lowercase  (       self			,       snake_case__			,       snake_case__ = None     ):
		if not os.path.isdir(snake_case__     ):
			logger.error(f"Vocabulary path ({save_directory}) should be a directory"     )
			return
		lowerCAmelCase       :     Union[str, Any]   				=			os.path.join(
		    snake_case__			,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
		lowerCAmelCase       :     int   				=			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'     )

		lowerCAmelCase       :     List[Any]   				=			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!'     )
					lowerCAmelCase       :     int   				=			token_index
				writer.write(' '.join(snake_case__     ) + '\n'     )
				index += 1

		return vocab_file, merge_file



	def 					lowercase  (       self			,       snake_case__			,       snake_case__ = None     ):
		if token_ids_a is None:
			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
		lowerCAmelCase       :     Optional[Any]   				=			[self.cls_token_id]
		lowerCAmelCase       :     List[str]   				=			[self.sep_token_id]
		return cls + token_ids_a + sep + sep + token_ids_a + sep



	def 					lowercase  (       self			,       snake_case__			,       snake_case__ = None			,       snake_case__ = False     ):
		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 					lowercase  (       self			,       snake_case__			,       snake_case__ = None     ):
		lowerCAmelCase       :     Union[str, Any]   				=			[self.sep_token_id]
		lowerCAmelCase       :     Tuple   				=			[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 					lowercase  (       self			,       snake_case__			,       snake_case__=False			,       **snake_case__     ):
		lowerCAmelCase       :     List[str]   				=			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()):
			lowerCAmelCase       :     Optional[Any]   				=			' ' + text
		return (text, kwargs)
