



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ViltImageProcessor

class 						lowerCamelCase_     (			unittest.TestCase    ):






						'''simple docstring'''







						def __init__(						self   :      Dict						,						__lowerCamelCase   :      List[str]						,						__lowerCamelCase   :      Tuple=7						,						__lowerCamelCase   :      Union[str, Any]=3						,						__lowerCamelCase   :      Tuple=18						,						__lowerCamelCase   :      Optional[Any]=30						,						__lowerCamelCase   :      Optional[Any]=4_00						,						__lowerCamelCase   :      List[Any]=True						,						__lowerCamelCase   :      List[Any]=None						,						__lowerCamelCase   :      str=2						,						__lowerCamelCase   :      Optional[Any]=True						,						__lowerCamelCase   :      str=[0.5, 0.5, 0.5]						,						__lowerCamelCase   :      int=[0.5, 0.5, 0.5]						,						)   ->					str:
									A				:       Optional[int]														=    size if size is not None else {"shortest_edge": 30}
									A				:       Optional[Any]														=    parent
									A				:       Optional[int]														=    batch_size
									A				:       Dict														=    num_channels
									A				:       Union[str, Any]														=    image_size
									A				:       str														=    min_resolution
									A				:       Dict														=    max_resolution
									A				:       Tuple														=    do_resize
									A				:       Dict														=    size
									A				:       List[Any]														=    size_divisor
									A				:       List[Any]														=    do_normalize
									A				:       Optional[Any]														=    image_mean
									A				:       Optional[Any]														=    image_std







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					str:
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "size_divisor": self.size_divisor,
									}







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]						,						__lowerCamelCase   :      Optional[int]						,						__lowerCamelCase   :      Any=False			)   ->					Tuple:
									if not batched:
												A				:       Tuple														=    self.size["shortest_edge"]
												A				:       List[str]														=    image_inputs[0]
												if isinstance(__lowerCamelCase						,						Image.Image			):
															A							,			A				:       Any														=    image.size
												else:
															A							,			A				:       int														=    image.shape[1], image.shape[2]
												A				:       Dict														=    size / min(__lowerCamelCase						,						__lowerCamelCase			)
												if h < w:
															A							,			A				:       Dict														=    size, scale * w
												else:
															A							,			A				:       List[str]														=    scale * h, size

												A				:       Dict														=    int((13_33 / 8_00) * size			)
												if max(__lowerCamelCase						,						__lowerCamelCase			) > max_size:
															A				:       Any														=    max_size / max(__lowerCamelCase						,						__lowerCamelCase			)
															A				:       List[Any]														=    newh * scale
															A				:       int														=    neww * scale

												A							,			A				:       Any														=    int(newh + 0.5			), int(neww + 0.5			)
												A							,			A				:       int														=    (
												    newh // self.size_divisor * self.size_divisor,
												    neww // self.size_divisor * self.size_divisor,
												)

									else:
												A				:       Optional[int]														=    []
												for image in image_inputs:
															A							,			A				:       int														=    self.get_expected_values([image]			)
															expected_values.append((expected_height, expected_width)			)
												A				:       Union[str, Any]														=    max(__lowerCamelCase						,						key=lambda __lowerCamelCase			: item[0]			)[0]
												A				:       Tuple														=    max(__lowerCamelCase						,						key=lambda __lowerCamelCase			: item[1]			)[1]

									return expected_height, expected_width







@require_torch
@require_vision
class 						lowerCamelCase_     (			_A    ,unittest.TestCase    ):






						'''simple docstring'''
						a__ =	ViltImageProcessor if is_vision_available() else None







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Union[str, Any]:
									A				:       Any														=    ViltImageProcessingTester(self			)







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Tuple:
									return self.image_processor_tester.prepare_image_processor_dict()







						def       SCREAMING_SNAKE_CASE__		(						self   :      int			)   ->					List[str]:
									A				:       Optional[int]														=    self.image_processing_class(**self.image_processor_dict			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"image_mean"			)			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"image_std"			)			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"do_normalize"			)			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"do_resize"			)			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"size"			)			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"size_divisor"			)			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					List[str]:
									A				:       List[str]														=    self.image_processing_class.from_dict(self.image_processor_dict			)
									self.assertEqual(image_processor.size						,						{"shortest_edge": 30}			)

									A				:       int														=    self.image_processing_class.from_dict(self.image_processor_dict						,						size=42			)
									self.assertEqual(image_processor.size						,						{"shortest_edge": 42}			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					List[str]:
									pass







						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					Optional[Any]:
									# Initialize image_processing
									A				:       Optional[int]														=    self.image_processing_class(**self.image_processor_dict			)
									# create random PIL images
									A				:       Optional[Any]														=    prepare_image_inputs(self.image_processor_tester						,						equal_resolution=__lowerCamelCase			)
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase						,						Image.Image			)

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

									A							,			A				:       str														=    self.image_processor_tester.get_expected_values(__lowerCamelCase			)
									self.assertEqual(
									    encoded_images.shape						,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,						)

									# Test batched
									A				:       Any														=    image_processing(__lowerCamelCase						,						return_tensors="pt"			).pixel_values

									A							,			A				:       List[str]														=    self.image_processor_tester.get_expected_values(__lowerCamelCase						,						batched=__lowerCamelCase			)
									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   :      Optional[int]			)   ->					Dict:
									# Initialize image_processing
									A				:       Optional[Any]														=    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=__lowerCamelCase						,						numpify=__lowerCamelCase			)
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase						,						np.ndarray			)

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

									A							,			A				:       Tuple														=    self.image_processor_tester.get_expected_values(__lowerCamelCase			)
									self.assertEqual(
									    encoded_images.shape						,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,						)

									# Test batched
									A				:       Tuple														=    image_processing(__lowerCamelCase						,						return_tensors="pt"			).pixel_values

									A							,			A				:       Tuple														=    self.image_processor_tester.get_expected_values(__lowerCamelCase						,						batched=__lowerCamelCase			)
									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[Any]			)   ->					int:
									# Initialize image_processing
									A				:       List[str]														=    self.image_processing_class(**self.image_processor_dict			)
									# create random PyTorch tensors
									A				:       Tuple														=    prepare_image_inputs(self.image_processor_tester						,						equal_resolution=__lowerCamelCase						,						torchify=__lowerCamelCase			)
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase						,						torch.Tensor			)

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

									A							,			A				:       Tuple														=    self.image_processor_tester.get_expected_values(__lowerCamelCase			)
									self.assertEqual(
									    encoded_images.shape						,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,						)

									# Test batched
									A				:       Optional[Any]														=    image_processing(__lowerCamelCase						,						return_tensors="pt"			).pixel_values

									A							,			A				:       Dict														=    self.image_processor_tester.get_expected_values(__lowerCamelCase						,						batched=__lowerCamelCase			)
									self.assertEqual(
									    encoded_images.shape						,						(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )						,						)




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     =	{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE     =	{
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
    },
}

class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def __init__(						self   :      Dict						,						__lowerCamelCase   :      str=None						,						__lowerCamelCase   :      int=None						,						__lowerCamelCase   :      Optional[Any]=False						,						__lowerCamelCase   :      List[Any]=True						,						__lowerCamelCase   :      int=False						,						__lowerCamelCase   :      Optional[int]="<s>"						,						__lowerCamelCase   :      Union[str, Any]="</s>"						,						__lowerCamelCase   :      Dict="<unk>"						,						__lowerCamelCase   :      List[str]="<sep>"						,						__lowerCamelCase   :      Any="<pad>"						,						__lowerCamelCase   :      Optional[Any]="<cls>"						,						__lowerCamelCase   :      Union[str, Any]="<mask>"						,						__lowerCamelCase   :      List[str]=["<eop>", "<eod>"]						,						**__lowerCamelCase   :      Optional[Any]						,						)   ->					Optional[int]:
									A				:       Any														=    AddedToken(__lowerCamelCase						,						lstrip=__lowerCamelCase						,						rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase						,						__lowerCamelCase			) else mask_token

									super().__init__(
									    vocab_file=__lowerCamelCase						,						tokenizer_file=__lowerCamelCase						,						do_lower_case=__lowerCamelCase						,						remove_space=__lowerCamelCase						,						keep_accents=__lowerCamelCase						,						bos_token=__lowerCamelCase						,						eos_token=__lowerCamelCase						,						unk_token=__lowerCamelCase						,						sep_token=__lowerCamelCase						,						pad_token=__lowerCamelCase						,						cls_token=__lowerCamelCase						,						mask_token=__lowerCamelCase						,						additional_special_tokens=__lowerCamelCase						,						**__lowerCamelCase						,						)

									A				:       str														=    3
									A				:       str														=    do_lower_case
									A				:       int														=    remove_space
									A				:       Optional[int]														=    keep_accents
									A				:       Tuple														=    vocab_file
									A				:       int														=    False if not self.vocab_file else True

									try:
												import jieba
									except ModuleNotFoundError as error:
												raise error.__class__(
												    "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
												    "See https://pypi.org/project/jieba/ for installation."			)
									A				:       str														=    jieba
									A				:       Optional[Any]														=    str.maketrans(" \n"						,						"\u2582\u2583"			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]						,						__lowerCamelCase   :      List[int]						,						__lowerCamelCase   :      Optional[List[int]] = None			)   ->					List[int]:
									A				:       Dict														=    [self.sep_token_id]
									A				:       Tuple														=    [self.cls_token_id]
									if token_ids_a is None:
												return token_ids_a + sep + cls
									return token_ids_a + sep + token_ids_a + sep + cls







						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      List[int]						,						__lowerCamelCase   :      Optional[List[int]] = None			)   ->					List[int]:
									A				:       Tuple														=    [self.sep_token_id]
									A				:       Dict														=    [2]

									if token_ids_a is None:
												return len(token_ids_a + sep			) * [0] + cls_segment_id
									return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1] + cls_segment_id







						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      str						,						__lowerCamelCase   :      Optional[str] = None			)   ->					Tuple[str]:
									if not self.can_save_slow_tokenizer:
												raise ValueError(
												    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
												    "tokenizer."			)

									if not os.path.isdir(__lowerCamelCase			):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
												return
									A				:       List[Any]														=    os.path.join(
									    __lowerCamelCase						,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

									if os.path.abspath(self.vocab_file			) != os.path.abspath(__lowerCamelCase			):
												copyfile(self.vocab_file						,						__lowerCamelCase			)

									return (out_vocab_file,)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any						,						__lowerCamelCase   :      Dict						,						*__lowerCamelCase   :      List[str]						,						**__lowerCamelCase   :      Tuple			)   ->					str:
									A				:       Union[str, Any]														=    [
									    " ".join([x.translate(self.translator			) for x in self.jieba.cut(__lowerCamelCase						,						cut_all=__lowerCamelCase			)]			)
									    for text in batch_text_or_text_pairs
									]
									return super()._batch_encode_plus(__lowerCamelCase						,						*__lowerCamelCase						,						**__lowerCamelCase			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any						,						*__lowerCamelCase   :      Optional[Any]						,						**__lowerCamelCase   :      Optional[int]			)   ->					Tuple:
									A				:       Optional[Any]														=    super()._decode(*__lowerCamelCase						,						**__lowerCamelCase			)
									A				:       Tuple														=    text.replace(" "						,						""			).replace("\u2582"						,						" "			).replace("\u2583"						,						"\n"			)
									return text