





import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_pytesseract_available():
		from PIL import Image

		from transformers import LayoutLMvaImageProcessor


class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						def __init__(    self			:		Optional[int] ,				lowerCamelCase_			:		Any ,				lowerCamelCase_			:		Tuple=7 ,				lowerCamelCase_			:		List[Any]=3 ,				lowerCamelCase_			:		Dict=18 ,				lowerCamelCase_			:		Optional[Any]=30 ,				lowerCamelCase_			:		str=4_00 ,				lowerCamelCase_			:		int=True ,				lowerCamelCase_			:		List[Any]=None ,				lowerCamelCase_			:		Tuple=True ,				):





										'''simple docstring'''







										_snake_case :			Dict  						=   size if size is not None else {'height': 18, 'width': 18}
										_snake_case :			Union[str, Any]  						=   parent
										_snake_case :			Union[str, Any]  						=   batch_size
										_snake_case :			Any  						=   num_channels
										_snake_case :			Tuple  						=   image_size
										_snake_case :			Optional[Any]  						=   min_resolution
										_snake_case :			List[str]  						=   max_resolution
										_snake_case :			List[Any]  						=   do_resize
										_snake_case :			Dict  						=   size
										_snake_case :			str  						=   apply_ocr







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}



@require_torch
@require_pytesseract
class 			lowercase				(							snake_case__,			unittest.TestCase     ):



						"""simple docstring"""

						_UpperCamelCase  :	List[Any]								=	LayoutLMvaImageProcessor if is_pytesseract_available() else None







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   LayoutLMvaImageProcessingTester(self					)







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





										'''simple docstring'''







										return self.image_processor_tester.prepare_image_processor_dict()







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										_snake_case :			Optional[Any]  						=   self.image_processing_class(**self.image_processor_dict					)
										self.assertTrue(hasattr(_A ,				'do_resize'					)					)
										self.assertTrue(hasattr(_A ,				'size'					)					)
										self.assertTrue(hasattr(_A ,				'apply_ocr'					)					)







						def 		__UpperCAmelCase       (    self			:		List[str]					):





										'''simple docstring'''







										_snake_case :			List[str]  						=   self.image_processing_class.from_dict(self.image_processor_dict					)
										self.assertEqual(image_processor.size ,				{'height': 18, 'width': 18}					)

										_snake_case :			List[str]  						=   self.image_processing_class.from_dict(self.image_processor_dict ,				size=42					)
										self.assertEqual(image_processor.size ,				{'height': 42, 'width': 42}					)







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										pass







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   self.image_processing_class(**self.image_processor_dict					)
										# create random PIL images
										_snake_case :			str  						=   prepare_image_inputs(self.image_processor_tester ,				equal_resolution=_A					)
										for image in image_inputs:
														self.assertIsInstance(_A ,				Image.Image					)

										# Test not batched input
										_snake_case :			Dict  						=   image_processing(image_inputs[0] ,				return_tensors='pt'					)
										self.assertEqual(
										    encoding.pixel_values.shape ,				(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    ) ,				)

										self.assertIsInstance(encoding.words ,				_A					)
										self.assertIsInstance(encoding.boxes ,				_A					)

										# Test batched
										_snake_case :			Optional[Any]  						=   image_processing(_A ,				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'],
										    ) ,				)







						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   self.image_processing_class(**self.image_processor_dict					)
										# create random numpy tensors
										_snake_case :			Optional[Any]  						=   prepare_image_inputs(self.image_processor_tester ,				equal_resolution=_A ,				numpify=_A					)
										for image in image_inputs:
														self.assertIsInstance(_A ,				np.ndarray					)

										# Test not batched input
										_snake_case :			List[str]  						=   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 :			int  						=   image_processing(_A ,				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'],
										    ) ,				)







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   self.image_processing_class(**self.image_processor_dict					)
										# create random PyTorch tensors
										_snake_case :			int  						=   prepare_image_inputs(self.image_processor_tester ,				equal_resolution=_A ,				torchify=_A					)
										for image in image_inputs:
														self.assertIsInstance(_A ,				torch.Tensor					)

										# Test not batched input
										_snake_case :			Any  						=   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 :			Tuple  						=   image_processing(_A ,				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'],
										    ) ,				)







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										_snake_case :			Dict  						=   LayoutLMvaImageProcessor()

										from datasets import load_dataset

										_snake_case :			Tuple  						=   load_dataset('hf-internal-testing/fixtures_docvqa' ,				split='test'					)

										_snake_case :			str  						=   Image.open(ds[0]['file']					).convert('RGB'					)

										_snake_case :			Optional[int]  						=   image_processing(_A ,				return_tensors='pt'					)

										self.assertEqual(encoding.pixel_values.shape ,				(1, 3, 2_24, 2_24)					)
										self.assertEqual(len(encoding.words					) ,				len(encoding.boxes					)					)

										# fmt: off
										# the words and boxes were obtained with Tesseract 4.1.1
										_snake_case :			Optional[int]  						=   [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']]  # noqa: E231
										_snake_case :			Optional[int]  						=   [[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]]  # noqa: E231
										# fmt: on

										self.assertListEqual(encoding.words ,				_A					)
										self.assertListEqual(encoding.boxes ,				_A					)

										# with apply_OCR = False
										_snake_case :			str  						=   LayoutLMvaImageProcessor(apply_ocr=_A					)

										_snake_case :			int  						=   image_processing(_A ,				return_tensors='pt'					)

										self.assertEqual(encoding.pixel_values.shape ,				(1, 3, 2_24, 2_24)					)













import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


lowercase_		:				Dict				=						'''bert-base-cased'''
lowercase_		:				Any				=						'''google/pegasus-xsum'''
lowercase_		:				str				=						[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowercase_		:				Tuple				=						['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowercase_		:				Any				=						'''patrickvonplaten/t5-tiny-random'''
lowercase_		:				List[Any]				=						'''sshleifer/bart-tiny-random'''
lowercase_		:				Dict				=						'''sshleifer/tiny-mbart'''
lowercase_		:				str				=						'''sshleifer/tiny-marian-en-de'''

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			str  						=   '\n'.join(__lowerCAmelCase )
				Path(__lowerCAmelCase ).open('w' ).writelines(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(__lowerCAmelCase	,			F'''{split}.source''' )	,			__lowerCAmelCase )
								_dump_articles(os.path.join(__lowerCAmelCase	,			F'''{split}.target''' )	,			__lowerCAmelCase )
				return tmp_dir


class 			lowercase				(							a_     ):



						"""simple docstring"""

						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ] ,				)
						@slow
						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		int					):





										'''simple docstring'''







										_snake_case :			Optional[Any]  						=   AutoTokenizer.from_pretrained(lowerCamelCase_					)
										_snake_case :			Dict  						=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
										_snake_case :			Optional[int]  						=   max(len(tokenizer.encode(lowerCamelCase_					)					) for a in ARTICLES					)
										_snake_case :			Any  						=   max(len(tokenizer.encode(lowerCamelCase_					)					) for a in SUMMARIES					)
										_snake_case :			Dict  						=   4
										_snake_case :			Any  						=   8
										assert max_len_target > max_src_len  # Will be truncated
										assert max_len_source > max_src_len  # Will be truncated
										_snake_case						,			_snake_case :			Optional[Any]  						=   'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
										_snake_case :			int  						=   SeqaSeqDataset(
										    lowerCamelCase_ ,				data_dir=lowerCamelCase_ ,				type_path='train' ,				max_source_length=lowerCamelCase_ ,				max_target_length=lowerCamelCase_ ,				src_lang=lowerCamelCase_ ,				tgt_lang=lowerCamelCase_ ,				)
										_snake_case :			List[str]  						=   DataLoader(lowerCamelCase_ ,				batch_size=2 ,				collate_fn=train_dataset.collate_fn					)
										for batch in dataloader:
														assert isinstance(lowerCamelCase_ ,				lowerCamelCase_					)
														assert batch["attention_mask"].shape == batch["input_ids"].shape
														# show that articles were trimmed.
														assert batch["input_ids"].shape[1] == max_src_len
														# show that targets are the same len
														assert batch["labels"].shape[1] == max_tgt_len
														if tok_name != MBART_TINY:
																		continue
														# check language codes in correct place
														_snake_case :			List[Any]  						=   shift_tokens_right(batch['labels'] ,				tokenizer.pad_token_id					)
														assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
														assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
														assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
														assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

														break  # No need to test every batch







						@parameterized.expand([BART_TINY, BERT_BASE_CASED]					)
						def 		__UpperCAmelCase       (    self			:		Any ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   AutoTokenizer.from_pretrained(lowerCamelCase_					)
										_snake_case :			List[Any]  						=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
										_snake_case :			Dict  						=   max(len(tokenizer.encode(lowerCamelCase_					)					) for a in ARTICLES					)
										_snake_case :			Union[str, Any]  						=   max(len(tokenizer.encode(lowerCamelCase_					)					) for a in SUMMARIES					)
										_snake_case :			Union[str, Any]  						=   4
										_snake_case :			Optional[int]  						=   LegacySeqaSeqDataset(
										    lowerCamelCase_ ,				data_dir=lowerCamelCase_ ,				type_path='train' ,				max_source_length=20 ,				max_target_length=lowerCamelCase_ ,				)
										_snake_case :			Dict  						=   DataLoader(lowerCamelCase_ ,				batch_size=2 ,				collate_fn=train_dataset.collate_fn					)
										for batch in dataloader:
														assert batch["attention_mask"].shape == batch["input_ids"].shape
														# show that articles were trimmed.
														assert batch["input_ids"].shape[1] == max_len_source
														assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
														# show that targets were truncated
														assert batch["labels"].shape[1] == trunc_target  # Truncated
														assert max_len_target > trunc_target  # Truncated
														break  # No need to test every batch







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										_snake_case :			int  						=   AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'					)

										_snake_case :			List[str]  						=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
										_snake_case :			Any  						=   tmp_dir.joinpath('train.source'					).open().readlines()
										_snake_case :			Optional[Any]  						=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
										pack_data_dir(lowerCamelCase_ ,				lowerCamelCase_ ,				1_28 ,				lowerCamelCase_					)
										_snake_case :			Tuple  						=   {x.name for x in tmp_dir.iterdir()}
										_snake_case :			Dict  						=   {x.name for x in save_dir.iterdir()}
										_snake_case :			str  						=   save_dir.joinpath('train.source'					).open().readlines()
										# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
										# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
										assert len(lowerCamelCase_					) < len(lowerCamelCase_					)
										assert len(lowerCamelCase_					) == 1
										assert len(packed_examples[0]					) == sum(len(lowerCamelCase_					) for x in orig_examples					)
										assert orig_paths == new_paths







						@pytest.mark.skipif(not FAIRSEQ_AVAILABLE ,				reason='This test requires fairseq'					)
						def 		__UpperCAmelCase       (    self			:		List[str]					):





										'''simple docstring'''







										if not FAIRSEQ_AVAILABLE:
														return
										_snake_case						,			_snake_case						,			_snake_case :			int  						=   self._get_dataset(max_len=64					)
										_snake_case :			List[str]  						=   64
										_snake_case :			str  						=   ds.make_dynamic_sampler(lowerCamelCase_ ,				required_batch_size_multiple=lowerCamelCase_					)
										_snake_case :			Optional[Any]  						=   [len(lowerCamelCase_					) for x in batch_sampler]
										assert len(set(lowerCamelCase_					)					) > 1  # it's not dynamic batch size if every batch is the same length
										assert sum(lowerCamelCase_					) == len(lowerCamelCase_					)  # no dropped or added examples
										_snake_case :			Union[str, Any]  						=   DataLoader(lowerCamelCase_ ,				batch_sampler=lowerCamelCase_ ,				collate_fn=ds.collate_fn ,				num_workers=2					)
										_snake_case :			List[Any]  						=   []
										_snake_case :			List[Any]  						=   []
										for batch in data_loader:
														_snake_case :			Any  						=   batch['input_ids'].shape
														_snake_case :			str  						=   src_shape[0]
														assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
														_snake_case :			int  						=   np.product(batch['input_ids'].shape					)
														num_src_per_batch.append(lowerCamelCase_					)
														if num_src_tokens > (max_tokens * 1.1):
																		failures.append(lowerCamelCase_					)
										assert num_src_per_batch[0] == max(lowerCamelCase_					)
										if failures:
														raise AssertionError(f'''too many tokens in {len(lowerCamelCase_					)} batches'''					)







						def 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''simple docstring'''







										_snake_case						,			_snake_case						,			_snake_case :			str  						=   self._get_dataset(max_len=5_12					)
										_snake_case :			Optional[Any]  						=   2
										_snake_case :			Dict  						=   ds.make_sortish_sampler(lowerCamelCase_ ,				shuffle=lowerCamelCase_					)

										_snake_case :			int  						=   DataLoader(lowerCamelCase_ ,				batch_size=lowerCamelCase_ ,				collate_fn=ds.collate_fn ,				num_workers=2					)
										_snake_case :			str  						=   DataLoader(lowerCamelCase_ ,				batch_size=lowerCamelCase_ ,				collate_fn=ds.collate_fn ,				num_workers=2 ,				sampler=lowerCamelCase_					)

										_snake_case :			Tuple  						=   tokenizer.pad_token_id

										def count_pad_tokens(lowerCamelCase_			:		List[str] ,				lowerCamelCase_			:		Any="input_ids"					):
														return [batch[k].eq(lowerCamelCase_					).sum().item() for batch in data_loader]

										assert sum(count_pad_tokens(lowerCamelCase_ ,				k='labels'					)					) < sum(count_pad_tokens(lowerCamelCase_ ,				k='labels'					)					)
										assert sum(count_pad_tokens(lowerCamelCase_					)					) < sum(count_pad_tokens(lowerCamelCase_					)					)
										assert len(lowerCamelCase_					) == len(lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Tuple ,				lowerCamelCase_			:		Tuple=10_00 ,				lowerCamelCase_			:		Tuple=1_28					):





										'''simple docstring'''







										if os.getenv('USE_REAL_DATA' ,				lowerCamelCase_					):
														_snake_case :			Dict  						=   'examples/seq2seq/wmt_en_ro'
														_snake_case :			List[Any]  						=   max_len * 2 * 64
														if not Path(lowerCamelCase_					).joinpath('train.len'					).exists():
																		save_len_file(lowerCamelCase_ ,				lowerCamelCase_					)
										else:
														_snake_case :			Union[str, Any]  						=   'examples/seq2seq/test_data/wmt_en_ro'
														_snake_case :			List[Any]  						=   max_len * 4
														save_len_file(lowerCamelCase_ ,				lowerCamelCase_					)

										_snake_case :			Union[str, Any]  						=   AutoTokenizer.from_pretrained(lowerCamelCase_					)
										_snake_case :			str  						=   SeqaSeqDataset(
										    lowerCamelCase_ ,				data_dir=lowerCamelCase_ ,				type_path='train' ,				max_source_length=lowerCamelCase_ ,				max_target_length=lowerCamelCase_ ,				n_obs=lowerCamelCase_ ,				)
										return ds, max_tokens, tokenizer







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





										'''simple docstring'''







										_snake_case						,			_snake_case						,			_snake_case :			Any  						=   self._get_dataset()
										_snake_case :			List[str]  						=   set(DistributedSortishSampler(lowerCamelCase_ ,				2_56 ,				num_replicas=2 ,				rank=0 ,				add_extra_examples=lowerCamelCase_					)					)
										_snake_case :			Optional[Any]  						=   set(DistributedSortishSampler(lowerCamelCase_ ,				2_56 ,				num_replicas=2 ,				rank=1 ,				add_extra_examples=lowerCamelCase_					)					)
										assert idsa.intersection(lowerCamelCase_					) == set()







						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ] ,				)
						def 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		Optional[int]					):





										'''simple docstring'''







										_snake_case :			List[str]  						=   AutoTokenizer.from_pretrained(lowerCamelCase_ ,				use_fast=lowerCamelCase_					)
										if tok_name == MBART_TINY:
														_snake_case :			int  						=   SeqaSeqDataset(
														    lowerCamelCase_ ,				data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					) ,				type_path='train' ,				max_source_length=4 ,				max_target_length=8 ,				src_lang='EN' ,				tgt_lang='FR' ,				)
														_snake_case :			Optional[Any]  						=   train_dataset.dataset_kwargs
														assert "src_lang" in kwargs and "tgt_lang" in kwargs
										else:
														_snake_case :			Tuple  						=   SeqaSeqDataset(
														    lowerCamelCase_ ,				data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					) ,				type_path='train' ,				max_source_length=4 ,				max_target_length=8 ,				)
														_snake_case :			List[Any]  						=   train_dataset.dataset_kwargs
														assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
														assert len(lowerCamelCase_					) == 1 if tok_name == BART_TINY else len(lowerCamelCase_					) == 0






