



'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


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


if is_vision_available():
						import PIL

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       ["""pixel_values"""]







	def __init__(						self			:     List[Any]		,			__snake_case			:     bool = True		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     PILImageResampling = PILImageResampling.BICUBIC		,			__snake_case			:     bool = True		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     bool = True		,			__snake_case			:     Union[int, float] = 1 / 255		,			__snake_case			:     bool = True		,			__snake_case			:     Optional[Union[float, List[float]]] = None		,			__snake_case			:     Optional[Union[float, List[float]]] = None		,			__snake_case			:     bool = True		,			**__snake_case			:     List[str]		,			)			->		None:
								super().__init__(**__snake_case		)
								UpperCAmelCase :       Any 							=		size if size is not None else {'''shortest_edge''': 224}
								UpperCAmelCase :       int 							=		get_size_dict(__snake_case		,			default_to_square=__snake_case		)
								UpperCAmelCase :       List[Any] 							=		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								UpperCAmelCase :       List[str] 							=		get_size_dict(__snake_case		,			default_to_square=__snake_case		,			param_name='''crop_size'''		)

								UpperCAmelCase :       Any 							=		do_resize
								UpperCAmelCase :       List[Any] 							=		size
								UpperCAmelCase :       str 							=		resample
								UpperCAmelCase :       Tuple 							=		do_center_crop
								UpperCAmelCase :       Any 							=		crop_size
								UpperCAmelCase :       str 							=		do_rescale
								UpperCAmelCase :       int 							=		rescale_factor
								UpperCAmelCase :       Union[str, Any] 							=		do_normalize
								UpperCAmelCase :       Optional[Any] 							=		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								UpperCAmelCase :       Dict 							=		image_std if image_std is not None else OPENAI_CLIP_STD
								UpperCAmelCase :       List[Any] 							=		do_convert_rgb







	def      A	(						self			:     Union[str, Any]		,			__snake_case			:     np.ndarray		,			__snake_case			:     Dict[str, int]		,			__snake_case			:     PILImageResampling = PILImageResampling.BICUBIC		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     List[Any]		,			)			->		np.ndarray:
								UpperCAmelCase :       List[str] 							=		get_size_dict(__snake_case		,			default_to_square=__snake_case		)
								if "shortest_edge" not in size:
															raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""		)
								UpperCAmelCase :       Union[str, Any] 							=		get_resize_output_image_size(__snake_case		,			size=size['''shortest_edge''']		,			default_to_square=__snake_case		)
								return resize(__snake_case		,			size=__snake_case		,			resample=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     Tuple		,			__snake_case			:     np.ndarray		,			__snake_case			:     Dict[str, int]		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     Union[str, Any]		,			)			->		np.ndarray:
								UpperCAmelCase :       Tuple 							=		get_size_dict(__snake_case		)
								if "height" not in size or "width" not in size:
															raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""		)
								return center_crop(__snake_case		,			size=(size['''height'''], size['''width'''])		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     Any		,			__snake_case			:     np.ndarray		,			__snake_case			:     Union[int, float]		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     Optional[Any]		,			)			->		List[str]:
								return rescale(__snake_case		,			scale=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     List[Any]		,			__snake_case			:     np.ndarray		,			__snake_case			:     Union[float, List[float]]		,			__snake_case			:     Union[float, List[float]]		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     Dict		,			)			->		np.ndarray:
								return normalize(__snake_case		,			mean=__snake_case		,			std=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     List[str]		,			__snake_case			:     ImageInput		,			__snake_case			:     bool = None		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     PILImageResampling = None		,			__snake_case			:     bool = None		,			__snake_case			:     int = None		,			__snake_case			:     bool = None		,			__snake_case			:     float = None		,			__snake_case			:     bool = None		,			__snake_case			:     Optional[Union[float, List[float]]] = None		,			__snake_case			:     Optional[Union[float, List[float]]] = None		,			__snake_case			:     bool = None		,			__snake_case			:     Optional[Union[str, TensorType]] = None		,			__snake_case			:     Optional[ChannelDimension] = ChannelDimension.FIRST		,			**__snake_case			:     Optional[int]		,			)			->		PIL.Image.Image:
								UpperCAmelCase :       Any 							=		do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase :       str 							=		size if size is not None else self.size
								UpperCAmelCase :       Dict 							=		get_size_dict(__snake_case		,			param_name='''size'''		,			default_to_square=__snake_case		)
								UpperCAmelCase :       List[str] 							=		resample if resample is not None else self.resample
								UpperCAmelCase :       Tuple 							=		do_center_crop if do_center_crop is not None else self.do_center_crop
								UpperCAmelCase :       Optional[Any] 							=		crop_size if crop_size is not None else self.crop_size
								UpperCAmelCase :       Any 							=		get_size_dict(__snake_case		,			param_name='''crop_size'''		,			default_to_square=__snake_case		)
								UpperCAmelCase :       List[Any] 							=		do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase :       Union[str, Any] 							=		rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase :       Tuple 							=		do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase :       Any 							=		image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase :       Union[str, Any] 							=		image_std if image_std is not None else self.image_std
								UpperCAmelCase :       Any 							=		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								UpperCAmelCase :       List[str] 							=		make_list_of_images(__snake_case		)

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

								if do_resize and size is None:
															raise ValueError('''Size must be specified if do_resize is True.'''		)

								if do_center_crop and crop_size is None:
															raise ValueError('''Crop size must be specified if do_center_crop is True.'''		)

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

								if do_normalize and (image_mean is None or image_std is None):
															raise ValueError('''Image mean and std must be specified if do_normalize is True.'''		)

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
															UpperCAmelCase :       str 							=		[convert_to_rgb(__snake_case		) for image in images]

								# All transformations expect numpy arrays.
								UpperCAmelCase :       Union[str, Any] 							=		[to_numpy_array(__snake_case		) for image in images]

								if do_resize:
															UpperCAmelCase :       int 							=		[self.resize(image=__snake_case		,			size=__snake_case		,			resample=__snake_case		) for image in images]

								if do_center_crop:
															UpperCAmelCase :       Optional[Any] 							=		[self.center_crop(image=__snake_case		,			size=__snake_case		) for image in images]

								if do_rescale:
															UpperCAmelCase :       Optional[int] 							=		[self.rescale(image=__snake_case		,			scale=__snake_case		) for image in images]

								if do_normalize:
															UpperCAmelCase :       int 							=		[self.normalize(image=__snake_case		,			mean=__snake_case		,			std=__snake_case		) for image in images]

								UpperCAmelCase :       List[Any] 							=		[to_channel_dimension_format(__snake_case		,			__snake_case		) for image in images]

								UpperCAmelCase :       Tuple 							=		{'''pixel_values''': images}
								return BatchFeature(data=__snake_case		,			tensor_type=__snake_case		)









import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase		       =							get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')





@require_sentencepiece
@require_tokenizers
class   __a			(    __UpperCamelCase       ,       unittest.TestCase      ):
 __lowercase :							int        = PegasusTokenizer
 __lowercase :							Any        = PegasusTokenizerFast
 __lowercase :							Optional[int]        = True
 __lowercase :							Tuple        = True
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Any:




        '''simple docstring'''
        super().setUp()

        # We have a SentencePiece fixture for testing
        lowercase__:   List[str]  			=   PegasusTokenizer(lowerCAmelCase__ )
        tokenizer.save_pretrained(self.tmpdirname )
 @cached_property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        return PegasusTokenizer.from_pretrained('google/pegasus-large' )
 def 		SCREAMING_SNAKE_CASE__   (   self				, **lowerCAmelCase__ )					->				PegasusTokenizer:




        '''simple docstring'''
        return PegasusTokenizer.from_pretrained(self.tmpdirname				, **lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        return ("This is a test", "This is a test")
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   Optional[Any]  			=   '</s>'
        lowercase__:   Union[str, Any]  			=   1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__ )				, lowerCAmelCase__ )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__ )				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




        '''simple docstring'''
        lowercase__:   Tuple  			=   list(self.get_tokenizer().get_vocab().keys() )

        self.assertEqual(vocab_keys[0]				, '<pad>' )
        self.assertEqual(vocab_keys[1]				, '</s>' )
        self.assertEqual(vocab_keys[-1]				, 'v' )
        self.assertEqual(len(lowerCAmelCase__ )				, 1_103 )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Any:




        '''simple docstring'''
        self.assertEqual(self.get_tokenizer().vocab_size				, 1_103 )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




        '''simple docstring'''
        lowercase__:   Dict  			=   self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
        lowercase__:   Optional[int]  			=   self.tokenizer_class.from_pretrained(self.tmpdirname )
        lowercase__:   Optional[Any]  			=   (
            'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
            ' </s> <pad> <pad> <pad>'
        )
        lowercase__:   Dict  			=   rust_tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__				, add_special_tokens=lowerCAmelCase__ ).input_ids[0]
        lowercase__:   Tuple  			=   py_tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__				, add_special_tokens=lowerCAmelCase__ ).input_ids[0]
        self.assertListEqual(lowerCAmelCase__				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        lowercase__:   int  			=   self._large_tokenizer
        # <mask_1> masks whole sentence while <mask_2> masks single word
        lowercase__:   Any  			=   '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
        lowercase__:   Union[str, Any]  			=   [2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
        lowercase__:   int  			=   tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__ ).input_ids[0]
        self.assertListEqual(lowerCAmelCase__				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   self._large_tokenizer
        # The tracebacks for the following asserts are **better** without messages or self.assertEqual
        assert tokenizer.vocab_size == 96_103
        assert tokenizer.pad_token_id == 0
        assert tokenizer.eos_token_id == 1
        assert tokenizer.offset == 103
        assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
        assert tokenizer.unk_token == "<unk>"
        assert tokenizer.model_max_length == 1_024
        lowercase__:   int  			=   'To ensure a smooth flow of bank resolutions.'
        lowercase__:   Any  			=   [413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
        lowercase__:   str  			=   tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__ ).input_ids[0]
        self.assertListEqual(lowerCAmelCase__				, lowerCAmelCase__ )
        assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
 @require_torch
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict:




        '''simple docstring'''
        lowercase__:   Any  			=   ['This is going to be way too long.' * 150, 'short example']
        lowercase__:   Tuple  			=   ['not super long but more than 5 tokens', 'tiny']
        lowercase__:   Dict  			=   self._large_tokenizer(lowerCAmelCase__				, padding=lowerCAmelCase__				, truncation=lowerCAmelCase__				, return_tensors='pt' )
        lowercase__:   Any  			=   self._large_tokenizer(
            text_target=lowerCAmelCase__				, max_length=5				, padding=lowerCAmelCase__				, truncation=lowerCAmelCase__				, return_tensors='pt' )

        assert batch.input_ids.shape == (2, 1_024)
        assert batch.attention_mask.shape == (2, 1_024)
        assert targets["input_ids"].shape == (2, 5)
        assert len(lowerCAmelCase__ ) == 2  # input_ids, attention_mask.
 @slow
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        # fmt: off
        lowercase__:   List[str]  			=   {'input_ids': [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=lowerCAmelCase__				, model_name='google/bigbird-pegasus-large-arxiv'				, revision='ba85d0851d708441f91440d509690f1ab6353415'				, )





@require_sentencepiece
@require_tokenizers
class   __a			(    __UpperCamelCase       ,       unittest.TestCase      ):
 __lowercase :							int        = PegasusTokenizer
 __lowercase :							Any        = PegasusTokenizerFast
 __lowercase :							Any        = True
 __lowercase :							Dict        = True
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        super().setUp()

        # We have a SentencePiece fixture for testing
        lowercase__:   Union[str, Any]  			=   PegasusTokenizer(lowerCAmelCase__				, offset=0				, mask_token_sent=lowerCAmelCase__				, mask_token='[MASK]' )
        tokenizer.save_pretrained(self.tmpdirname )
 @cached_property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv' )
 def 		SCREAMING_SNAKE_CASE__   (   self				, **lowerCAmelCase__ )					->				PegasusTokenizer:




        '''simple docstring'''
        return PegasusTokenizer.from_pretrained(self.tmpdirname				, **lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Optional[int]:




        '''simple docstring'''
        return ("This is a test", "This is a test")
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




        '''simple docstring'''
        lowercase__:   str  			=   self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
        lowercase__:   str  			=   self.tokenizer_class.from_pretrained(self.tmpdirname )
        lowercase__:   Tuple  			=   (
            'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
            ' <pad> <pad> <pad>'
        )
        lowercase__:   List[Any]  			=   rust_tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__				, add_special_tokens=lowerCAmelCase__ ).input_ids[0]
        lowercase__:   Any  			=   py_tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__				, add_special_tokens=lowerCAmelCase__ ).input_ids[0]
        self.assertListEqual(lowerCAmelCase__				, lowerCAmelCase__ )
 @require_torch
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        lowercase__:   List[Any]  			=   ['This is going to be way too long.' * 1_000, 'short example']
        lowercase__:   str  			=   ['not super long but more than 5 tokens', 'tiny']
        lowercase__:   Tuple  			=   self._large_tokenizer(lowerCAmelCase__				, padding=lowerCAmelCase__				, truncation=lowerCAmelCase__				, return_tensors='pt' )
        lowercase__:   Dict  			=   self._large_tokenizer(
            text_target=lowerCAmelCase__				, max_length=5				, padding=lowerCAmelCase__				, truncation=lowerCAmelCase__				, return_tensors='pt' )

        assert batch.input_ids.shape == (2, 4_096)
        assert batch.attention_mask.shape == (2, 4_096)
        assert targets["input_ids"].shape == (2, 5)
        assert len(lowerCAmelCase__ ) == 2  # input_ids, attention_mask.
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   str  			=   (
            'This is an example string that is used to test the original TF implementation against the HF'
            ' implementation'
        )

        lowercase__:   Optional[int]  			=   self._large_tokenizer(lowerCAmelCase__ ).input_ids

        self.assertListEqual(
            lowerCAmelCase__				, [182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1]				, )


