




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        __lowerCAmelCase					(			__magic_name__     ,							unittest.TestCase					):

			"""simple docstring"""







			snake_case_  						=			MvpTokenizer
			snake_case_  						=			MvpTokenizerFast
			snake_case_  						=			True
			snake_case_  						=			filter_roberta_detectors


			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							super().setUp()
							__lowerCamelCase									=       [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							__lowerCamelCase									=       dict(zip(lowerCamelCase__     ,		range(len(lowerCamelCase__				)				)				)				)
							__lowerCamelCase									=       ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							__lowerCamelCase									=       {'unk_token': '<unk>'}

							__lowerCamelCase									=       os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES['vocab_file']				)
							__lowerCamelCase									=       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(lowerCamelCase__				) + '\n'				)
							with open(self.merges_file     ,		'w'     ,		encoding='utf-8'				) as fp:
											fp.write('\n'.join(lowerCamelCase__				)				)


			def  lowercase_       ( self     ,		**lowerCamelCase__				)       -> Union[str, Any]:



							'''simple docstring'''
							kwargs.update(self.special_tokens_map				)
							return self.tokenizer_class.from_pretrained(self.tmpdirname     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		**lowerCamelCase__				)       -> List[str]:



							'''simple docstring'''
							kwargs.update(self.special_tokens_map				)
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> Optional[Any]:



							'''simple docstring'''
							return "lower newer", "lower newer"


			@cached_property
			def  lowercase_       ( self				)       -> List[str]:



							'''simple docstring'''
							return MvpTokenizer.from_pretrained('RUCAIBox/mvp'				)


			@cached_property
			def  lowercase_       ( self				)       -> List[Any]:



							'''simple docstring'''
							return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'				)


			@require_torch
			def  lowercase_       ( self				)       -> Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase									=       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							__lowerCamelCase									=       [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowerCamelCase									=       tokenizer(lowerCamelCase__     ,		max_length=len(lowerCamelCase__				)     ,		padding=lowerCamelCase__     ,		return_tensors='pt'				)
											self.assertIsInstance(lowerCamelCase__     ,		lowerCamelCase__				)

											self.assertEqual((2, 9)     ,		batch.input_ids.shape				)
											self.assertEqual((2, 9)     ,		batch.attention_mask.shape				)
											__lowerCamelCase									=       batch.input_ids.tolist()[0]
											self.assertListEqual(lowerCamelCase__     ,		lowerCamelCase__				)
											# Test that special tokens are reset


			@require_torch
			def  lowercase_       ( self				)       -> List[str]:



							'''simple docstring'''
							__lowerCamelCase									=       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowerCamelCase									=       tokenizer(lowerCamelCase__     ,		padding=lowerCamelCase__     ,		return_tensors='pt'				)
											# check if input_ids are returned and no labels
											self.assertIn('input_ids'     ,		lowerCamelCase__				)
											self.assertIn('attention_mask'     ,		lowerCamelCase__				)
											self.assertNotIn('labels'     ,		lowerCamelCase__				)
											self.assertNotIn('decoder_attention_mask'     ,		lowerCamelCase__				)


			@require_torch
			def  lowercase_       ( self				)       -> Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase									=       [
							    'Summary of the text.',
							    'Another summary.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowerCamelCase									=       tokenizer(text_target=lowerCamelCase__     ,		max_length=32     ,		padding='max_length'     ,		return_tensors='pt'				)
											self.assertEqual(32     ,		targets['input_ids'].shape[1]				)


			@require_torch
			def  lowercase_       ( self				)       -> Optional[Any]:



							'''simple docstring'''
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowerCamelCase									=       tokenizer(
											    ['I am a small frog' * 1_024, 'I am a small frog']     ,		padding=lowerCamelCase__     ,		truncation=lowerCamelCase__     ,		return_tensors='pt'				)
											self.assertIsInstance(lowerCamelCase__     ,		lowerCamelCase__				)
											self.assertEqual(batch.input_ids.shape     ,		(2, 1_024)				)


			@require_torch
			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       ['A long paragraph for summarization.']
							__lowerCamelCase									=       [
							    'Summary of the text.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowerCamelCase									=       tokenizer(lowerCamelCase__     ,		text_target=lowerCamelCase__     ,		return_tensors='pt'				)
											__lowerCamelCase									=       inputs['input_ids']
											__lowerCamelCase									=       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  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							pass






			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
															__lowerCamelCase									=       self.rust_tokenizer_class.from_pretrained(lowerCamelCase__     ,		**lowerCamelCase__				)
															__lowerCamelCase									=       self.tokenizer_class.from_pretrained(lowerCamelCase__     ,		**lowerCamelCase__				)
															__lowerCamelCase									=       'A, <mask> AllenNLP sentence.'
															__lowerCamelCase									=       tokenizer_r.encode_plus(lowerCamelCase__     ,		add_special_tokens=lowerCamelCase__     ,		return_token_type_ids=lowerCamelCase__				)
															__lowerCamelCase									=       tokenizer_p.encode_plus(lowerCamelCase__     ,		add_special_tokens=lowerCamelCase__     ,		return_token_type_ids=lowerCamelCase__				)

															# 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']				)     ,		)

															__lowerCamelCase									=       tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']				)
															__lowerCamelCase									=       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, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)
															self.assertSequenceEqual(tokens_r['input_ids']     ,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)

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








from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
					import torch


__A								=       logging.get_logger(__name__)







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			['''pixel_values''']


			def __init__( self     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = PILImageResampling.BILINEAR     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = 1 / 255     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> None:



							'''simple docstring'''
							super().__init__(**lowerCamelCase__				)
							__lowerCamelCase									=       size if size is not None else {'shortest_edge': 256}
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		default_to_square=lowerCamelCase__				)
							__lowerCamelCase									=       crop_size if crop_size is not None else {'height': 224, 'width': 224}
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		param_name='crop_size'				)
							__lowerCamelCase									=       do_resize
							__lowerCamelCase									=       size
							__lowerCamelCase									=       resample
							__lowerCamelCase									=       do_center_crop
							__lowerCamelCase									=       crop_size
							__lowerCamelCase									=       do_rescale
							__lowerCamelCase									=       rescale_factor
							__lowerCamelCase									=       do_normalize
							__lowerCamelCase									=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							__lowerCamelCase									=       image_std if image_std is not None else IMAGENET_STANDARD_STD


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = PILImageResampling.BICUBIC     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> np.ndarray:



							'''simple docstring'''
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		default_to_square=lowerCamelCase__				)
							if "shortest_edge" not in size:
											raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
							__lowerCamelCase									=       get_resize_output_image_size(lowerCamelCase__     ,		size=size['shortest_edge']     ,		default_to_square=lowerCamelCase__				)
							return resize(lowerCamelCase__     ,		size=lowerCamelCase__     ,		resample=lowerCamelCase__     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> np.ndarray:



							'''simple docstring'''
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__				)
							if "height" not in size or "width" not in size:
											raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""				)
							return center_crop(lowerCamelCase__     ,		size=(size['height'], size['width'])     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__				)       -> np.ndarray:



							'''simple docstring'''
							return rescale(lowerCamelCase__     ,		scale=lowerCamelCase__     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> np.ndarray:



							'''simple docstring'''
							return normalize(lowerCamelCase__     ,		mean=lowerCamelCase__     ,		std=lowerCamelCase__     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = ChannelDimension.FIRST     ,		**lowerCamelCase__     ,		)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       do_resize if do_resize is not None else self.do_resize
							__lowerCamelCase									=       size if size is not None else self.size
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		default_to_square=lowerCamelCase__				)
							__lowerCamelCase									=       resample if resample is not None else self.resample
							__lowerCamelCase									=       do_center_crop if do_center_crop is not None else self.do_center_crop
							__lowerCamelCase									=       crop_size if crop_size is not None else self.crop_size
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		param_name='crop_size'				)
							__lowerCamelCase									=       do_rescale if do_rescale is not None else self.do_rescale
							__lowerCamelCase									=       rescale_factor if rescale_factor is not None else self.rescale_factor
							__lowerCamelCase									=       do_normalize if do_normalize is not None else self.do_normalize
							__lowerCamelCase									=       image_mean if image_mean is not None else self.image_mean
							__lowerCamelCase									=       image_std if image_std is not None else self.image_std

							__lowerCamelCase									=       make_list_of_images(lowerCamelCase__				)

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

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

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

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

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

							# All transformations expect numpy arrays.
							__lowerCamelCase									=       [to_numpy_array(lowerCamelCase__				) for image in images]

							if do_resize:
											__lowerCamelCase									=       [self.resize(image=lowerCamelCase__     ,		size=lowerCamelCase__     ,		resample=lowerCamelCase__				) for image in images]

							if do_center_crop:
											__lowerCamelCase									=       [self.center_crop(image=lowerCamelCase__     ,		size=lowerCamelCase__				) for image in images]

							if do_rescale:
											__lowerCamelCase									=       [self.rescale(image=lowerCamelCase__     ,		scale=lowerCamelCase__				) for image in images]

							if do_normalize:
											__lowerCamelCase									=       [self.normalize(image=lowerCamelCase__     ,		mean=lowerCamelCase__     ,		std=lowerCamelCase__				) for image in images]

							__lowerCamelCase									=       [to_channel_dimension_format(lowerCamelCase__     ,		lowerCamelCase__				) for image in images]

							__lowerCamelCase									=       {'pixel_values': images}
							return BatchFeature(data=lowerCamelCase__     ,		tensor_type=lowerCamelCase__				)






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
											if len(lowerCamelCase__				) != len(lowerCamelCase__				):
															raise ValueError(
															    'Make sure that you pass in as many target sizes as the batch dimension of the logits'				)

											if is_torch_tensor(lowerCamelCase__				):
															__lowerCamelCase									=       target_sizes.numpy()

											__lowerCamelCase									=       []

											for idx in range(len(lowerCamelCase__				)				):
															__lowerCamelCase									=       torch.nn.functional.interpolate(
															    logits[idx].unsqueeze(dim=0				)     ,		size=target_sizes[idx]     ,		mode='bilinear'     ,		align_corners=lowerCamelCase__				)
															__lowerCamelCase									=       resized_logits[0].argmax(dim=0				)
															semantic_segmentation.append(lowerCamelCase__				)
							else:
											__lowerCamelCase									=       logits.argmax(dim=1				)
											__lowerCamelCase									=       [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

							return semantic_segmentation


