

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


_UpperCAmelCase	:     Optional[int]		       = logging.get_logger(__name__)







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Dict     =    ['''pixel_values''']




				def __init__(      self   ,     snake_case = True   ,     snake_case = None   ,     snake_case = PILImageResampling.BICUBIC   ,     snake_case = True   ,     snake_case = 1 / 255   ,     snake_case = True   ,     snake_case = None   ,     snake_case = None   ,     snake_case = True   ,     **snake_case   ,     ):
											super().__init__(**snake_case )
											snake_case_           =					size if size is not None else {'height': 384, 'width': 384}
											snake_case_           =					get_size_dict(snake_case   ,     default_to_square=snake_case )

											snake_case_           =					do_resize
											snake_case_           =					size
											snake_case_           =					resample
											snake_case_           =					do_rescale
											snake_case_           =					rescale_factor
											snake_case_           =					do_normalize
											snake_case_           =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
											snake_case_           =					image_std if image_std is not None else OPENAI_CLIP_STD
											snake_case_           =					do_convert_rgb




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case = PILImageResampling.BICUBIC   ,     snake_case = None   ,     **snake_case   ,     ):
											snake_case_           =					get_size_dict(snake_case   ,     default_to_square=snake_case )
											if "height" not in size or "width" not in size:
																		raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''' )
											snake_case_           =					(size['height'], size['width'])
											return resize(snake_case   ,     size=snake_case   ,     resample=snake_case   ,     data_format=snake_case   ,     **snake_case )




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case = None   ,     **snake_case   ,     ):
											return rescale(snake_case   ,     scale=snake_case   ,     data_format=snake_case   ,     **snake_case )




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case = None   ,     **snake_case   ,     ):
											return normalize(snake_case   ,     mean=snake_case   ,     std=snake_case   ,     data_format=snake_case   ,     **snake_case )




				def 					a							(      self   ,     snake_case   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = ChannelDimension.FIRST   ,     **snake_case   ,     ):
											snake_case_           =					do_resize if do_resize is not None else self.do_resize
											snake_case_           =					resample if resample is not None else self.resample
											snake_case_           =					do_rescale if do_rescale is not None else self.do_rescale
											snake_case_           =					rescale_factor if rescale_factor is not None else self.rescale_factor
											snake_case_           =					do_normalize if do_normalize is not None else self.do_normalize
											snake_case_           =					image_mean if image_mean is not None else self.image_mean
											snake_case_           =					image_std if image_std is not None else self.image_std
											snake_case_           =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

											snake_case_           =					size if size is not None else self.size
											snake_case_           =					get_size_dict(snake_case   ,     default_to_square=snake_case )

											snake_case_           =					make_list_of_images(snake_case )

											if not valid_images(snake_case ):
																		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 or resample is None:
																		raise ValueError('Size and resample must be specified if do_resize 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.' )

											# PIL RGBA images are converted to RGB
											if do_convert_rgb:
																		snake_case_           =					[convert_to_rgb(snake_case ) for image in images]

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

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

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

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

											snake_case_           =					[to_channel_dimension_format(snake_case   ,     snake_case ) for image in images]

											snake_case_           =					BatchFeature(data={'pixel_values': images}   ,     tensor_type=snake_case )

											return encoded_outputs









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 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    MvpTokenizer
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    MvpTokenizerFast
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    True
				__SCREAMING_SNAKE_CASE      :				Tuple     =    filter_roberta_detectors




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

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




				def 					a							(      self   ,     **snake_case ):
											kwargs.update(self.special_tokens_map )
											return self.tokenizer_class.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     **snake_case ):
											kwargs.update(self.special_tokens_map )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     snake_case ):
											return "lower newer", "lower newer"




				@cached_property
				def 					a							(      self ):
											return MvpTokenizer.from_pretrained('RUCAIBox/mvp' )




				@cached_property
				def 					a							(      self ):
											return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp' )




				@require_torch
				def 					a							(      self ):
											snake_case_           =					['A long paragraph for summarization.', 'Another paragraph for summarization.']
											snake_case_           =					[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

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

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




				@require_torch
				def 					a							(      self ):
											snake_case_           =					['A long paragraph for summarization.', 'Another paragraph for summarization.']
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_           =					tokenizer(snake_case   ,     padding=snake_case   ,     return_tensors='pt' )
																		# check if input_ids are returned and no labels
																		self.assertIn('input_ids'   ,     snake_case )
																		self.assertIn('attention_mask'   ,     snake_case )
																		self.assertNotIn('labels'   ,     snake_case )
																		self.assertNotIn('decoder_attention_mask'   ,     snake_case )




				@require_torch
				def 					a							(      self ):
											snake_case_           =					[
											    'Summary of the text.',
											    'Another summary.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_           =					tokenizer(text_target=snake_case   ,     max_length=32   ,     padding='max_length'   ,     return_tensors='pt' )
																		self.assertEqual(32   ,     targets['input_ids'].shape[1] )




				@require_torch
				def 					a							(      self ):
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_           =					tokenizer(
																		    ['I am a small frog' * 1024, 'I am a small frog']   ,     padding=snake_case   ,     truncation=snake_case   ,     return_tensors='pt' )
																		self.assertIsInstance(snake_case   ,     snake_case )
																		self.assertEqual(batch.input_ids.shape   ,     (2, 1024) )




				@require_torch
				def 					a							(      self ):
											snake_case_           =					['A long paragraph for summarization.']
											snake_case_           =					[
											    'Summary of the text.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_           =					tokenizer(snake_case   ,     text_target=snake_case   ,     return_tensors='pt' )
																		snake_case_           =					inputs['input_ids']
																		snake_case_           =					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 					a							(      self ):
											pass




				def 					a							(      self ):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
																									snake_case_           =					self.rust_tokenizer_class.from_pretrained(snake_case   ,     **snake_case )
																									snake_case_           =					self.tokenizer_class.from_pretrained(snake_case   ,     **snake_case )
																									snake_case_           =					'A, <mask> AllenNLP sentence.'
																									snake_case_           =					tokenizer_r.encode_plus(snake_case   ,     add_special_tokens=snake_case   ,     return_token_type_ids=snake_case )
																									snake_case_           =					tokenizer_p.encode_plus(snake_case   ,     add_special_tokens=snake_case   ,     return_token_type_ids=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'] )   ,     )

																									snake_case_           =					tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
																									snake_case_           =					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, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
																									self.assertSequenceEqual(tokens_r['input_ids']   ,     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )

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




