






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      ):

				"""simple docstring"""




				def __init__(	self		:       List[Any],    lowerCamelCase		:       Union[str, Any],    lowerCamelCase		:       bool = True,    lowerCamelCase		:       Dict[str, int] = None,    lowerCamelCase		:       int = 32,    lowerCamelCase		:       bool = True,    lowerCamelCase		:       Union[int, float] = 1 / 255,    lowerCamelCase		:       bool = True,    lowerCamelCase		:       bool = True,    lowerCamelCase		:       Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073],    lowerCamelCase		:       Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711],    lowerCamelCase		:       bool = True,    lowerCamelCase		:       Dict=7,    lowerCamelCase		:       int=30,    lowerCamelCase		:       Optional[int]=400,    lowerCamelCase		:       Optional[Any]=3,    ):

								'''simple docstring'''
								lowercase__  					=     parent
								lowercase__  					=     do_resize
								lowercase__  					=     size if size is not None else {'''shortest_edge''': 288}
								lowercase__  					=     size_divisor
								lowercase__  					=     do_rescale
								lowercase__  					=     rescale_factor
								lowercase__  					=     do_normalize
								lowercase__  					=     do_center_crop
								lowercase__  					=     image_mean
								lowercase__  					=     image_std
								lowercase__  					=     do_pad
								lowercase__  					=     batch_size
								lowercase__  					=     num_channels
								lowercase__  					=     min_resolution
								lowercase__  					=     max_resolution




				def 							lowercase__  (	self		:       Any    ):

								'''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 							lowercase__  (	self		:       Any,    lowerCamelCase		:       str,    lowerCamelCase		:       str=False    ):

								'''simple docstring'''
								if not batched:
												lowercase__  					=     self.size['''shortest_edge''']
												lowercase__  					=     image_inputs[0]
												if isinstance(lowerCamelCase,    Image.Image    ):
																lowercase__					,		lowercase__  					=     image.size
												else:
																lowercase__					,		lowercase__  					=     image.shape[1], image.shape[2]
												lowercase__  					=     size / min(lowerCamelCase,    lowerCamelCase    )
												if h < w:
																lowercase__					,		lowercase__  					=     size, scale * w
												else:
																lowercase__					,		lowercase__  					=     scale * h, size

												lowercase__  					=     int((1_333 / 800) * size    )
												if max(lowerCamelCase,    lowerCamelCase    ) > max_size:
																lowercase__  					=     max_size / max(lowerCamelCase,    lowerCamelCase    )
																lowercase__  					=     newh * scale
																lowercase__  					=     neww * scale

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

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

								return expected_height, expected_width



@require_torch
@require_vision
class  _UpperCAmelCase   (			A__						,unittest.TestCase      ):

				"""simple docstring"""




				lowercase__											=			BridgeTowerImageProcessor if is_vision_available() else None




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								lowercase__  					=     BridgeTowerImageProcessingTester(self    )




				@property
				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								return self.image_processor_tester.prepare_image_processor_dict()




				def 							lowercase__  (	self		:       Any    ):

								'''simple docstring'''
								lowercase__  					=     self.image_processing_class(**self.image_processor_dict    )
								self.assertTrue(hasattr(lowerCamelCase,    '''image_mean'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''image_std'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''do_normalize'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''do_resize'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''size'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''size_divisor'''    )    )




				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								pass




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								# Initialize image processor
								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

								lowercase__					,		lowercase__  					=     self.image_processor_tester.get_expected_values(lowerCamelCase    )
								self.assertEqual(
								    encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )

								# Test batched
								lowercase__  					=     image_processing(lowerCamelCase,    return_tensors='''pt'''    ).pixel_values

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




				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								# Initialize image processor
								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

								lowercase__					,		lowercase__  					=     self.image_processor_tester.get_expected_values(lowerCamelCase    )
								self.assertEqual(
								    encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )

								# Test batched
								lowercase__  					=     image_processing(lowerCamelCase,    return_tensors='''pt'''    ).pixel_values

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




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								# Initialize image processor
								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

								lowercase__					,		lowercase__  					=     self.image_processor_tester.get_expected_values(lowerCamelCase    )
								self.assertEqual(
								    encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )

								# Test batched
								lowercase__  					=     image_processing(lowerCamelCase,    return_tensors='''pt'''    ).pixel_values

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












import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_pegasus import PegasusTokenizer
else:
							A__      :     Optional[Any]       =					None


A__      :     str       =					logging.get_logger(__name__)


A__      :     Optional[int]       =					'▁'

A__      :     List[str]       =					{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A__      :     Dict       =					{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

A__      :     int       =					{
    'google/pegasus-xsum': 5_12,
}
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			VOCAB_FILES_NAMES
				lowercase__											=			PRETRAINED_VOCAB_FILES_MAP
				lowercase__											=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__											=			PegasusTokenizer
				lowercase__											=			["""input_ids""", """attention_mask"""]




				def __init__(	self		:       Dict,    lowerCamelCase		:       str=None,    lowerCamelCase		:       List[str]=None,    lowerCamelCase		:       int="<pad>",    lowerCamelCase		:       str="</s>",    lowerCamelCase		:       Optional[Any]="<unk>",    lowerCamelCase		:       Any="<mask_2>",    lowerCamelCase		:       Optional[Any]="<mask_1>",    lowerCamelCase		:       int=None,    lowerCamelCase		:       Dict=103,    **lowerCamelCase		:       List[Any],    ):

								'''simple docstring'''
								lowercase__  					=     offset

								if additional_special_tokens is not None:
												if not isinstance(lowerCamelCase,    lowerCamelCase    ):
																raise TypeError(
																    F"""additional_special_tokens should be of type {type(lowerCamelCase    )}, but is"""
																    F""" {type(lowerCamelCase    )}"""    )

												lowercase__  					=     (
												    ([mask_token_sent] + additional_special_tokens)
												    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
												    else additional_special_tokens
												)
												# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
												additional_special_tokens_extended += [
												    F"""<unk_{i}>""" for i in range(len(lowerCamelCase    ),    self.offset - 1    )
												]

												if len(set(lowerCamelCase    )    ) != len(lowerCamelCase    ):
																raise ValueError(
																    '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
																    F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""    )
												lowercase__  					=     additional_special_tokens_extended
								else:
												lowercase__  					=     [mask_token_sent] if mask_token_sent is not None else []
												additional_special_tokens += [F"""<unk_{i}>""" for i in range(2,    self.offset    )]

								super().__init__(
								    lowerCamelCase,    tokenizer_file=lowerCamelCase,    pad_token=lowerCamelCase,    eos_token=lowerCamelCase,    unk_token=lowerCamelCase,    mask_token=lowerCamelCase,    mask_token_sent=lowerCamelCase,    offset=lowerCamelCase,    additional_special_tokens=lowerCamelCase,    **lowerCamelCase,    )
								lowercase__  					=     vocab_file
								lowercase__  					=     False if not self.vocab_file else True




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       int    ):

								'''simple docstring'''
								lowercase__  					=     set(self.all_special_ids    )  # call it once instead of inside list comp
								all_special_ids.remove(self.unk_token_id    )  # <unk> is only sometimes special

								if all_special_ids != set(range(len(self.additional_special_tokens    ) + 3    )    ):
												raise ValueError(
												    '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
												    F""" {len(self.additional_special_tokens    )} additional_special_tokens, but got {all_special_ids}"""    )

								return [1 if x in all_special_ids else 0 for x in seq]




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       List,    lowerCamelCase		:       Optional[List] = None,    lowerCamelCase		:       bool = False    ):

								'''simple docstring'''
								if already_has_special_tokens:
												return self._special_token_mask(lowerCamelCase    )
								elif token_ids_a is None:
												return self._special_token_mask(lowerCamelCase    ) + [1]
								else:
												return self._special_token_mask(token_ids_a + token_ids_a    ) + [1]




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       Optional[int],    lowerCamelCase		:       Optional[Any]=None    ):

								'''simple docstring'''
								if token_ids_a is None:
												return token_ids_a + [self.eos_token_id]
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return token_ids_a + token_ids_a + [self.eos_token_id]




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       str,    lowerCamelCase		:       Optional[str] = None    ):

								'''simple docstring'''
								if not self.can_save_slow_tokenizer:
												raise ValueError(
												    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
												    '''tokenizer.'''    )

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

								if os.path.abspath(self.vocab_file    ) != os.path.abspath(lowerCamelCase    ):
												copyfile(self.vocab_file,    lowerCamelCase    )

								return (out_vocab_file,)




