"""simple docstring"""
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


SCREAMING_SNAKE_CASE__      						=  get_tests_dir("fixtures/test_sentencepiece_no_bos.model")



@require_sentencepiece
@require_tokenizers
class   lowerCAmelCase_    (			lowerCAmelCase     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Tuple			  =						PegasusTokenizer
				_lowerCAmelCase     :    int			  =						PegasusTokenizerFast
				_lowerCAmelCase     :    Union[str, Any]			  =						True
				_lowerCAmelCase     :    List[str]			  =						True

				def 		snake_case (				self    ):






										"""simple docstring"""

										super().setUp()

										# We have a SentencePiece fixture for testing
										snake_case								=			PegasusTokenizer(lowerCAmelCase    )
										tokenizer.save_pretrained(self.tmpdirname    )

				@cached_property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return PegasusTokenizer.from_pretrained('google/pegasus-large'    )

				def 		snake_case (				self		,    **lowerCAmelCase    ):






										"""simple docstring"""

										return PegasusTokenizer.from_pretrained(self.tmpdirname		,    **lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										return ("This is a test", "This is a test")

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			'</s>'
										snake_case								=			1

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase    )		,    lowerCAmelCase    )
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase    )		,    lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			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    )		,    11_03    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										self.assertEqual(self.get_tokenizer().vocab_size		,    11_03    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
										snake_case								=			self.tokenizer_class.from_pretrained(self.tmpdirname    )
										snake_case								=			(
										    '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>'
										)
										snake_case								=			rust_tokenizer([raw_input_str]		,    return_tensors=lowerCAmelCase		,    add_special_tokens=lowerCAmelCase    ).input_ids[0]
										snake_case								=			py_tokenizer([raw_input_str]		,    return_tensors=lowerCAmelCase		,    add_special_tokens=lowerCAmelCase    ).input_ids[0]
										self.assertListEqual(lowerCAmelCase		,    lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self._large_tokenizer
										# <mask_1> masks whole sentence while <mask_2> masks single word
										snake_case								=			'<mask_1> To ensure a <mask_2> flow of bank resolutions.'
										snake_case								=			[2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
										snake_case								=			tokenizer([raw_input_str]		,    return_tensors=lowerCAmelCase    ).input_ids[0]
										self.assertListEqual(lowerCAmelCase		,    lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self._large_tokenizer
										# The tracebacks for the following asserts are **better** without messages or self.assertEqual
										assert tokenizer.vocab_size == 9_61_03
										assert tokenizer.pad_token_id == 0
										assert tokenizer.eos_token_id == 1
										assert tokenizer.offset == 1_03
										assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
										assert tokenizer.unk_token == "<unk>"
										assert tokenizer.model_max_length == 10_24
										snake_case								=			'To ensure a smooth flow of bank resolutions.'
										snake_case								=			[4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
										snake_case								=			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 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			['This is going to be way too long.' * 1_50, 'short example']
										snake_case								=			['not super long but more than 5 tokens', 'tiny']
										snake_case								=			self._large_tokenizer(lowerCAmelCase		,    padding=lowerCAmelCase		,    truncation=lowerCAmelCase		,    return_tensors='pt'    )
										snake_case								=			self._large_tokenizer(
										    text_target=lowerCAmelCase		,    max_length=5		,    padding=lowerCAmelCase		,    truncation=lowerCAmelCase		,    return_tensors='pt'    )

										assert batch.input_ids.shape == (2, 10_24)
										assert batch.attention_mask.shape == (2, 10_24)
										assert targets["input_ids"].shape == (2, 5)
										assert len(lowerCAmelCase    ) == 2  # input_ids, attention_mask.

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			{'input_ids': [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 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_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 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   lowerCAmelCase_    (			lowerCAmelCase     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Dict			  =						PegasusTokenizer
				_lowerCAmelCase     :    Tuple			  =						PegasusTokenizerFast
				_lowerCAmelCase     :    str			  =						True
				_lowerCAmelCase     :    List[Any]			  =						True

				def 		snake_case (				self    ):






										"""simple docstring"""

										super().setUp()

										# We have a SentencePiece fixture for testing
										snake_case								=			PegasusTokenizer(lowerCAmelCase		,    offset=0		,    mask_token_sent=lowerCAmelCase		,    mask_token='[MASK]'    )
										tokenizer.save_pretrained(self.tmpdirname    )

				@cached_property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'    )

				def 		snake_case (				self		,    **lowerCAmelCase    ):






										"""simple docstring"""

										return PegasusTokenizer.from_pretrained(self.tmpdirname		,    **lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										return ("This is a test", "This is a test")

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
										snake_case								=			self.tokenizer_class.from_pretrained(self.tmpdirname    )
										snake_case								=			(
										    'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
										    ' <pad> <pad> <pad>'
										)
										snake_case								=			rust_tokenizer([raw_input_str]		,    return_tensors=lowerCAmelCase		,    add_special_tokens=lowerCAmelCase    ).input_ids[0]
										snake_case								=			py_tokenizer([raw_input_str]		,    return_tensors=lowerCAmelCase		,    add_special_tokens=lowerCAmelCase    ).input_ids[0]
										self.assertListEqual(lowerCAmelCase		,    lowerCAmelCase    )

				@require_torch
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			['This is going to be way too long.' * 10_00, 'short example']
										snake_case								=			['not super long but more than 5 tokens', 'tiny']
										snake_case								=			self._large_tokenizer(lowerCAmelCase		,    padding=lowerCAmelCase		,    truncation=lowerCAmelCase		,    return_tensors='pt'    )
										snake_case								=			self._large_tokenizer(
										    text_target=lowerCAmelCase		,    max_length=5		,    padding=lowerCAmelCase		,    truncation=lowerCAmelCase		,    return_tensors='pt'    )

										assert batch.input_ids.shape == (2, 40_96)
										assert batch.attention_mask.shape == (2, 40_96)
										assert targets["input_ids"].shape == (2, 5)
										assert len(lowerCAmelCase    ) == 2  # input_ids, attention_mask.

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			(
										    'This is an example string that is used to test the original TF implementation against the HF'
										    ' implementation'
										)

										snake_case								=			self._large_tokenizer(lowerCAmelCase    ).input_ids

										self.assertListEqual(
										    lowerCAmelCase		,    [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]		,    )



"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor



class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=7		,    lowerCAmelCase=3		,    lowerCAmelCase=18		,    lowerCAmelCase=30		,    lowerCAmelCase=4_00		,    lowerCAmelCase=True		,    lowerCAmelCase=None		,    lowerCAmelCase=True		,    lowerCAmelCase=False		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=[0.5, 0.5, 0.5]		,    lowerCAmelCase=[0.5, 0.5, 0.5]		,    ):






										"""simple docstring"""

										snake_case								=			parent
										snake_case								=			batch_size
										snake_case								=			num_channels
										snake_case								=			image_size
										snake_case								=			min_resolution
										snake_case								=			max_resolution
										snake_case								=			do_resize
										snake_case								=			size if size is not None else {'height': 18, 'width': 20}
										snake_case								=			do_thumbnail
										snake_case								=			do_align_axis
										snake_case								=			do_pad
										snake_case								=			do_normalize
										snake_case								=			image_mean
										snake_case								=			image_std

				def 		snake_case (				self    ):






										"""simple docstring"""

										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_thumbnail": self.do_thumbnail,
										    "do_align_long_axis": self.do_align_axis,
										    "do_pad": self.do_pad,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										}





@require_torch
@require_vision
class   lowerCAmelCase_    (			lowerCAmelCase     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    str			  =						DonutImageProcessor if is_vision_available() else None

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			DonutImageProcessingTester(self    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return self.image_processor_tester.prepare_image_processor_dict()

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_resize'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'size'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_thumbnail'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_align_long_axis'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_pad'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_normalize'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'image_mean'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'image_std'    )    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict    )
										self.assertEqual(image_processor.size		,    {'height': 18, 'width': 20}    )

										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict		,    size=42    )
										self.assertEqual(image_processor.size		,    {'height': 42, 'width': 42}    )

										# Previous config had dimensions in (width, height) order
										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict		,    size=(42, 84)    )
										self.assertEqual(image_processor.size		,    {'height': 84, 'width': 42}    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										pass

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

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

										# Test not batched input
										snake_case								=			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.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

										# Test batched
										snake_case								=			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.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										# create random numpy tensors
										snake_case								=			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
										snake_case								=			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.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

										# Test batched
										snake_case								=			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.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										# create random PyTorch tensors
										snake_case								=			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
										snake_case								=			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.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

										# Test batched
										snake_case								=			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.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )


