


import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 YolosImageProcessor
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      def __init__( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Tuple=7  ,							SCREAMING_SNAKE_CASE__       : str=3  ,							SCREAMING_SNAKE_CASE__       : Dict=3_0  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=4_0_0  ,							SCREAMING_SNAKE_CASE__       : List[str]=True  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=None  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=True  ,							SCREAMING_SNAKE_CASE__       : Dict=[0.5, 0.5, 0.5]  ,							SCREAMING_SNAKE_CASE__       : Any=[0.5, 0.5, 0.5]  ,							SCREAMING_SNAKE_CASE__       : List[Any]=True  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=1 / 2_5_5  ,							SCREAMING_SNAKE_CASE__       : List[Any]=True  ,							)	->    List[str]:
             # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
             a_      :	Optional[int]							=						size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
             a_      :	Union[str, Any]							=						parent
             a_      :	Optional[Any]							=						batch_size
             a_      :	Tuple							=						num_channels
             a_      :	List[Any]							=						min_resolution
             a_      :	Any							=						max_resolution
             a_      :	str							=						do_resize
             a_      :	str							=						size
             a_      :	str							=						do_normalize
             a_      :	Tuple							=						image_mean
             a_      :	Union[str, Any]							=						image_std
             a_      :	Tuple							=						do_rescale
             a_      :	List[Any]							=						rescale_factor
             a_      :	List[str]							=						do_pad
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    List[Any]:
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_rescale": self.do_rescale,
                 "rescale_factor": self.rescale_factor,
                 "do_pad": self.do_pad,
             }

      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=False  )	->    Any:
             if not batched:
                    a_      :	str							=						image_inputs[0]
                    if isinstance(SCREAMING_SNAKE_CASE__  ,							Image.Image  ):
                           a_							,	a_      :	List[Any]							=						image.size
                    else:
                           a_							,	a_      :	Union[str, Any]							=						image.shape[1], image.shape[2]
                    if w < h:
                           a_      :	List[str]							=						int(self.size['shortest_edge'] * h / w  )
                           a_      :	str							=						self.size['shortest_edge']
                    elif w > h:
                           a_      :	Optional[Any]							=						self.size['shortest_edge']
                           a_      :	str							=						int(self.size['shortest_edge'] * w / h  )
                    else:
                           a_      :	List[str]							=						self.size['shortest_edge']
                           a_      :	Union[str, Any]							=						self.size['shortest_edge']

             else:
                    a_      :	Dict							=						[]
                    for image in image_inputs:
                           a_							,	a_      :	int							=						self.get_expected_values([image]  )
                           expected_values.append((expected_height, expected_width)  )
                    a_      :	Optional[int]							=						max(SCREAMING_SNAKE_CASE__  ,							key=lambda SCREAMING_SNAKE_CASE__  : item[0]  )[0]
                    a_      :	Optional[int]							=						max(SCREAMING_SNAKE_CASE__  ,							key=lambda SCREAMING_SNAKE_CASE__  : item[1]  )[1]

             return expected_height, expected_width
@require_torch
@require_vision
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					unittest.TestCase    ):
      snake_case__       :  Union[str, Any]       =		YolosImageProcessor if is_vision_available() else None
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    int:
             a_      :	int							=						YolosImageProcessingTester(self  )
      @property
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Dict:
             return self.image_processor_tester.prepare_image_processor_dict()
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Tuple:
             a_      :	Dict							=						self.image_processing_class(**self.image_processor_dict  )
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,							'image_mean'  )  )
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,							'image_std'  )  )
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,							'do_normalize'  )  )
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,							'do_resize'  )  )
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,							'size'  )  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    List[Any]:
             a_      :	Tuple							=						self.image_processing_class.from_dict(self.image_processor_dict  )
             self.assertEqual(image_processor.size  ,							{'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}  )
             self.assertEqual(image_processor.do_pad  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Union[str, Any]							=						self.image_processing_class.from_dict(
                 self.image_processor_dict  ,							size=4_2  ,							max_size=8_4  ,							pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE__  )
             self.assertEqual(image_processor.size  ,							{'shortest_edge': 4_2, 'longest_edge': 8_4}  )
             self.assertEqual(image_processor.do_pad  ,							SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Optional[int]:
             pass
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    int:
             # Initialize image_processing
             a_      :	List[Any]							=						self.image_processing_class(**self.image_processor_dict  )
             # create random PIL images
             a_      :	str							=						prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=SCREAMING_SNAKE_CASE__  )
             for image in image_inputs:
                    self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,							Image.Image  )

             # Test not batched input
             a_      :	int							=						image_processing(image_inputs[0]  ,							return_tensors='pt'  ).pixel_values

             a_							,	a_      :	Optional[Any]							=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__  )

             self.assertEqual(
                 encoded_images.shape  ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,							)

             # Test batched
             a_							,	a_      :	Union[str, Any]							=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__  ,							batched=SCREAMING_SNAKE_CASE__  )

             a_      :	Tuple							=						image_processing(SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  ).pixel_values
             self.assertEqual(
                 encoded_images.shape  ,							(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Any:
             # Initialize image_processing
             a_      :	str							=						self.image_processing_class(**self.image_processor_dict  )
             # create random numpy tensors
             a_      :	Union[str, Any]							=						prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=SCREAMING_SNAKE_CASE__  ,							numpify=SCREAMING_SNAKE_CASE__  )
             for image in image_inputs:
                    self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,							np.ndarray  )

             # Test not batched input
             a_      :	List[str]							=						image_processing(image_inputs[0]  ,							return_tensors='pt'  ).pixel_values

             a_							,	a_      :	int							=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__  )

             self.assertEqual(
                 encoded_images.shape  ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,							)

             # Test batched
             a_      :	int							=						image_processing(SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  ).pixel_values

             a_							,	a_      :	int							=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__  ,							batched=SCREAMING_SNAKE_CASE__  )

             self.assertEqual(
                 encoded_images.shape  ,							(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    int:
             # Initialize image_processing
             a_      :	Dict							=						self.image_processing_class(**self.image_processor_dict  )
             # create random PyTorch tensors
             a_      :	int							=						prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=SCREAMING_SNAKE_CASE__  ,							torchify=SCREAMING_SNAKE_CASE__  )
             for image in image_inputs:
                    self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,							torch.Tensor  )

             # Test not batched input
             a_      :	List[str]							=						image_processing(image_inputs[0]  ,							return_tensors='pt'  ).pixel_values

             a_							,	a_      :	int							=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__  )

             self.assertEqual(
                 encoded_images.shape  ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,							)

             # Test batched
             a_      :	Tuple							=						image_processing(SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  ).pixel_values

             a_							,	a_      :	List[Any]							=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__  ,							batched=SCREAMING_SNAKE_CASE__  )

             self.assertEqual(
                 encoded_images.shape  ,							(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    str:
             # Initialize image_processings
             a_      :	List[str]							=						self.image_processing_class(**self.image_processor_dict  )
             a_      :	Optional[int]							=						self.image_processing_class(do_resize=SCREAMING_SNAKE_CASE__  ,							do_normalize=SCREAMING_SNAKE_CASE__  ,							do_rescale=SCREAMING_SNAKE_CASE__  )
             # create random PyTorch tensors
             a_      :	List[Any]							=						prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=SCREAMING_SNAKE_CASE__  ,							torchify=SCREAMING_SNAKE_CASE__  )
             for image in image_inputs:
                    self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,							torch.Tensor  )

             # Test whether the method "pad" and calling the image processor return the same tensors
             a_      :	Optional[Any]							=						image_processing_a.pad(SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
             a_      :	Optional[Any]							=						image_processing_a(SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )

             self.assertTrue(
                 torch.allclose(encoded_images_with_method['pixel_values']  ,							encoded_images['pixel_values']  ,							atol=1E-4  )  )
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Union[str, Any]:
             # prepare image and target
             a_      :	Any							=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
             with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'  ,							'r'  ) as f:
                    a_      :	Optional[Any]							=						json.loads(f.read()  )

             a_      :	Tuple							=						{'image_id': 3_9_7_6_9, 'annotations': target}

             # encode them
             a_      :	Union[str, Any]							=						YolosImageProcessor.from_pretrained('hustvl/yolos-small'  )
             a_      :	Dict							=						image_processing(images=SCREAMING_SNAKE_CASE__  ,							annotations=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )

             # verify pixel values
             a_      :	List[Any]							=						torch.Size([1, 3, 8_0_0, 1_0_6_6]  )
             self.assertEqual(encoding['pixel_values'].shape  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Tuple							=						torch.tensor([0.2796, 0.3138, 0.3481]  )
             self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-4  )  )

             # verify area
             a_      :	List[str]							=						torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]  )
             self.assertTrue(torch.allclose(encoding['labels'][0]['area']  ,							SCREAMING_SNAKE_CASE__  )  )
             # verify boxes
             a_      :	List[Any]							=						torch.Size([6, 4]  )
             self.assertEqual(encoding['labels'][0]['boxes'].shape  ,							SCREAMING_SNAKE_CASE__  )
             a_      :	Tuple							=						torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]  )
             self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-3  )  )
             # verify image_id
             a_      :	Optional[int]							=						torch.tensor([3_9_7_6_9]  )
             self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']  ,							SCREAMING_SNAKE_CASE__  )  )
             # verify is_crowd
             a_      :	Dict							=						torch.tensor([0, 0, 0, 0, 0, 0]  )
             self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']  ,							SCREAMING_SNAKE_CASE__  )  )
             # verify class_labels
             a_      :	List[Any]							=						torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]  )
             self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']  ,							SCREAMING_SNAKE_CASE__  )  )
             # verify orig_size
             a_      :	Union[str, Any]							=						torch.tensor([4_8_0, 6_4_0]  )
             self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']  ,							SCREAMING_SNAKE_CASE__  )  )
             # verify size
             a_      :	Optional[int]							=						torch.tensor([8_0_0, 1_0_6_6]  )
             self.assertTrue(torch.allclose(encoding['labels'][0]['size']  ,							SCREAMING_SNAKE_CASE__  )  )

      @slow
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[Any]:
             # prepare image, target and masks_path
             a_      :	Any							=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
             with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'  ,							'r'  ) as f:
                    a_      :	Dict							=						json.loads(f.read()  )

             a_      :	Optional[int]							=						{'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

             a_      :	Tuple							=						pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'  )

             # encode them
             a_      :	int							=						YolosImageProcessor(format='coco_panoptic'  )
             a_      :	List[str]							=						image_processing(images=SCREAMING_SNAKE_CASE__  ,							annotations=SCREAMING_SNAKE_CASE__  ,							masks_path=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )

             # verify pixel values
             a_      :	Dict							=						torch.Size([1, 3, 8_0_0, 1_0_6_6]  )
             self.assertEqual(encoding['pixel_values'].shape  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[int]							=						torch.tensor([0.2796, 0.3138, 0.3481]  )
             self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-4  )  )

             # verify area
             a_      :	int							=						torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]  )
             self.assertTrue(torch.allclose(encoding['labels'][0]['area']  ,							SCREAMING_SNAKE_CASE__  )  )
             # verify boxes
             a_      :	Optional[int]							=						torch.Size([6, 4]  )
             self.assertEqual(encoding['labels'][0]['boxes'].shape  ,							SCREAMING_SNAKE_CASE__  )
             a_      :	int							=						torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]  )
             self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-3  )  )
             # verify image_id
             a_      :	Any							=						torch.tensor([3_9_7_6_9]  )
             self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']  ,							SCREAMING_SNAKE_CASE__  )  )
             # verify is_crowd
             a_      :	Any							=						torch.tensor([0, 0, 0, 0, 0, 0]  )
             self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']  ,							SCREAMING_SNAKE_CASE__  )  )
             # verify class_labels
             a_      :	List[str]							=						torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]  )
             self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']  ,							SCREAMING_SNAKE_CASE__  )  )
             # verify masks
             a_      :	Optional[int]							=						8_2_2_8_7_3
             self.assertEqual(encoding['labels'][0]['masks'].sum().item()  ,							SCREAMING_SNAKE_CASE__  )
             # verify orig_size
             a_      :	Tuple							=						torch.tensor([4_8_0, 6_4_0]  )
             self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']  ,							SCREAMING_SNAKE_CASE__  )  )
             # verify size
             a_      :	str							=						torch.tensor([8_0_0, 1_0_6_6]  )
             self.assertTrue(torch.allclose(encoding['labels'][0]['size']  ,							SCREAMING_SNAKE_CASE__  )  )






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					unittest.TestCase    ):
      snake_case__       :  List[str]       =		MvpTokenizer
      snake_case__       :  Dict       =		MvpTokenizerFast
      snake_case__       :  Any       =		True
      snake_case__       :  Optional[int]       =		filter_roberta_detectors
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Optional[int]:
             super().setUp()
             a_      :	Any							=						[
                 'l',
                 'o',
                 'w',
                 'e',
                 'r',
                 's',
                 't',
                 'i',
                 'd',
                 'n',
                 '\u0120',
                 '\u0120l',
                 '\u0120n',
                 '\u0120lo',
                 '\u0120low',
                 'er',
                 '\u0120lowest',
                 '\u0120newer',
                 '\u0120wider',
                 '<unk>',
             ]
             a_      :	int							=						dict(zip(SCREAMING_SNAKE_CASE__  ,							range(len(SCREAMING_SNAKE_CASE__  )  )  )  )
             a_      :	int							=						['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
             a_      :	str							=						{'unk_token': '<unk>'}

             a_      :	Tuple							=						os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['vocab_file']  )
             a_      :	Optional[int]							=						os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['merges_file']  )
             with open(self.vocab_file  ,							'w'  ,							encoding='utf-8'  ) as fp:
                    fp.write(json.dumps(SCREAMING_SNAKE_CASE__  ) + '\n'  )
             with open(self.merges_file  ,							'w'  ,							encoding='utf-8'  ) as fp:
                    fp.write('\n'.join(SCREAMING_SNAKE_CASE__  )  )
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							**SCREAMING_SNAKE_CASE__       : str  )	->    List[Any]:
             kwargs.update(self.special_tokens_map  )
             return self.tokenizer_class.from_pretrained(self.tmpdirname  ,							**SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							**SCREAMING_SNAKE_CASE__       : Optional[Any]  )	->    List[str]:
             kwargs.update(self.special_tokens_map  )
             return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,							**SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Any  )	->    Union[str, Any]:
             return "lower newer", "lower newer"
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    str:
             return MvpTokenizer.from_pretrained('RUCAIBox/mvp'  )
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Optional[int]:
             return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Union[str, Any]:
             a_      :	Union[str, Any]							=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
             a_      :	List[Any]							=						[0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	List[str]							=						tokenizer(SCREAMING_SNAKE_CASE__  ,							max_length=len(SCREAMING_SNAKE_CASE__  )  ,							padding=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
                    self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    self.assertEqual((2, 9)  ,							batch.input_ids.shape  )
                    self.assertEqual((2, 9)  ,							batch.attention_mask.shape  )
                    a_      :	Optional[Any]							=						batch.input_ids.tolist()[0]
                    self.assertListEqual(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    # Test that special tokens are reset
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Optional[Any]:
             a_      :	Any							=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	Optional[Any]							=						tokenizer(SCREAMING_SNAKE_CASE__  ,							padding=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
                    # check if input_ids are returned and no labels
                    self.assertIn('input_ids'  ,							SCREAMING_SNAKE_CASE__  )
                    self.assertIn('attention_mask'  ,							SCREAMING_SNAKE_CASE__  )
                    self.assertNotIn('labels'  ,							SCREAMING_SNAKE_CASE__  )
                    self.assertNotIn('decoder_attention_mask'  ,							SCREAMING_SNAKE_CASE__  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    int:
             a_      :	Union[str, Any]							=						[
                 'Summary of the text.',
                 'Another summary.',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	List[str]							=						tokenizer(text_target=SCREAMING_SNAKE_CASE__  ,							max_length=3_2  ,							padding='max_length'  ,							return_tensors='pt'  )
                    self.assertEqual(3_2  ,							targets['input_ids'].shape[1]  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    List[str]:
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	Dict							=						tokenizer(
                        ['I am a small frog' * 1_0_2_4, 'I am a small frog']  ,							padding=SCREAMING_SNAKE_CASE__  ,							truncation=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
                    self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    self.assertEqual(batch.input_ids.shape  ,							(2, 1_0_2_4)  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Tuple:
             a_      :	Dict							=						['A long paragraph for summarization.']
             a_      :	Dict							=						[
                 'Summary of the text.',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	Union[str, Any]							=						tokenizer(SCREAMING_SNAKE_CASE__  ,							text_target=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
                    a_      :	Dict							=						inputs['input_ids']
                    a_      :	str							=						inputs['labels']
                    self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
                    self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
                    self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
                    self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Union[str, Any]:
             pass

      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    int:
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
                           a_      :	List[str]							=						self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
                           a_      :	Tuple							=						self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
                           a_      :	int							=						'A, <mask> AllenNLP sentence.'
                           a_      :	Optional[Any]							=						tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE__  ,							add_special_tokens=SCREAMING_SNAKE_CASE__  ,							return_token_type_ids=SCREAMING_SNAKE_CASE__  )
                           a_      :	int							=						tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE__  ,							add_special_tokens=SCREAMING_SNAKE_CASE__  ,							return_token_type_ids=SCREAMING_SNAKE_CASE__  )

                           # token_type_ids should put 0 everywhere
                           self.assertEqual(sum(tokens_r['token_type_ids']  )  ,							sum(tokens_p['token_type_ids']  )  )

                           # attention_mask should put 1 everywhere, so sum over length should be 1
                           self.assertEqual(
                               sum(tokens_r['attention_mask']  ) / len(tokens_r['attention_mask']  )  ,							sum(tokens_p['attention_mask']  ) / len(tokens_p['attention_mask']  )  ,							)

                           a_      :	str							=						tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']  )
                           a_      :	Any							=						tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']  )

                           # Rust correctly handles the space before the mask while python doesnt
                           self.assertSequenceEqual(tokens_p['input_ids']  ,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]  )
                           self.assertSequenceEqual(tokens_r['input_ids']  ,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]  )

                           self.assertSequenceEqual(
                               SCREAMING_SNAKE_CASE__  ,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )
                           self.assertSequenceEqual(
                               SCREAMING_SNAKE_CASE__  ,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )


