

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

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 BridgeTowerImageProcessor







class  lowercase				(       unittest.TestCase			):




				def __init__(					self      ,   snake_case      ,   snake_case = True      ,   snake_case = None      ,   snake_case = 32      ,   snake_case = True      ,   snake_case = 1 / 255      ,   snake_case = True      ,   snake_case = True      ,   snake_case = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]      ,   snake_case = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]      ,   snake_case = True      ,   snake_case=7      ,   snake_case=30      ,   snake_case=400      ,   snake_case=3      ,   ):
											snake_case_							=    parent
											snake_case_							=    do_resize
											snake_case_							=    size if size is not None else {'shortest_edge': 288}
											snake_case_							=    size_divisor
											snake_case_							=    do_rescale
											snake_case_							=    rescale_factor
											snake_case_							=    do_normalize
											snake_case_							=    do_center_crop
											snake_case_							=    image_mean
											snake_case_							=    image_std
											snake_case_							=    do_pad
											snake_case_							=    batch_size
											snake_case_							=    num_channels
											snake_case_							=    min_resolution
											snake_case_							=    max_resolution




				def     a							(					self       ):
											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     a							(					self      ,   snake_case      ,   snake_case=False       ):
											if not batched:
																		snake_case_							=    self.size['shortest_edge']
																		snake_case_							=    image_inputs[0]
																		if isinstance(snake_case      ,   Image.Image       ):
																									snake_case_				,    snake_case_							=    image.size
																		else:
																									snake_case_				,    snake_case_							=    image.shape[1], image.shape[2]
																		snake_case_							=    size / min(snake_case      ,   snake_case       )
																		if h < w:
																									snake_case_				,    snake_case_							=    size, scale * w
																		else:
																									snake_case_				,    snake_case_							=    scale * h, size

																		snake_case_							=    int((1333 / 800) * size       )
																		if max(snake_case      ,   snake_case       ) > max_size:
																									snake_case_							=    max_size / max(snake_case      ,   snake_case       )
																									snake_case_							=    newh * scale
																									snake_case_							=    neww * scale

																		snake_case_				,    snake_case_							=    int(newh + 0.5       ), int(neww + 0.5       )
																		snake_case_				,    snake_case_							=    (
																		    newh // self.size_divisor * self.size_divisor,
																		    neww // self.size_divisor * self.size_divisor,
																		)

											else:
																		snake_case_							=    []
																		for image in image_inputs:
																									snake_case_				,    snake_case_							=    self.get_expected_values([image]       )
																									expected_values.append((expected_height, expected_width)       )
																		snake_case_							=    max(snake_case      ,   key=lambda snake_case       : item[0]       )[0]
																		snake_case_							=    max(snake_case      ,   key=lambda snake_case       : item[1]       )[1]

											return expected_height, expected_width







@require_torch
@require_vision
class  lowercase				(       lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: Dict       			=			BridgeTowerImageProcessor if is_vision_available() else None




				def     a							(					self       ):
											snake_case_							=    BridgeTowerImageProcessingTester(self       )




				@property
				def     a							(					self       ):
											return self.image_processor_tester.prepare_image_processor_dict()




				def     a							(					self       ):
											snake_case_							=    self.image_processing_class(**self.image_processor_dict       )
											self.assertTrue(hasattr(snake_case      ,   'image_mean'       )       )
											self.assertTrue(hasattr(snake_case      ,   'image_std'       )       )
											self.assertTrue(hasattr(snake_case      ,   'do_normalize'       )       )
											self.assertTrue(hasattr(snake_case      ,   'do_resize'       )       )
											self.assertTrue(hasattr(snake_case      ,   'size'       )       )
											self.assertTrue(hasattr(snake_case      ,   'size_divisor'       )       )




				def     a							(					self       ):
											pass




				def     a							(					self       ):
											# Initialize image processor
											snake_case_							=    self.image_processing_class(**self.image_processor_dict       )
											# create random PIL images
											snake_case_							=    prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=snake_case       )
											for image in image_inputs:
																		self.assertIsInstance(snake_case      ,   Image.Image       )

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

											snake_case_				,    snake_case_							=    self.image_processor_tester.get_expected_values(snake_case       )
											self.assertEqual(
											    encoded_images.shape      ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,   )

											# Test batched
											snake_case_							=    image_processing(snake_case      ,   return_tensors='pt'       ).pixel_values

											snake_case_				,    snake_case_							=    self.image_processor_tester.get_expected_values(snake_case      ,   batched=snake_case       )
											self.assertEqual(
											    encoded_images.shape      ,   (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )      ,   )




				def     a							(					self       ):
											# Initialize image processor
											snake_case_							=    self.image_processing_class(**self.image_processor_dict       )
											# create random numpy tensors
											snake_case_							=    prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=snake_case      ,   numpify=snake_case       )
											for image in image_inputs:
																		self.assertIsInstance(snake_case      ,   np.ndarray       )

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

											snake_case_				,    snake_case_							=    self.image_processor_tester.get_expected_values(snake_case       )
											self.assertEqual(
											    encoded_images.shape      ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,   )

											# Test batched
											snake_case_							=    image_processing(snake_case      ,   return_tensors='pt'       ).pixel_values

											snake_case_				,    snake_case_							=    self.image_processor_tester.get_expected_values(snake_case      ,   batched=snake_case       )
											self.assertEqual(
											    encoded_images.shape      ,   (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )      ,   )




				def     a							(					self       ):
											# Initialize image processor
											snake_case_							=    self.image_processing_class(**self.image_processor_dict       )
											# create random PyTorch tensors
											snake_case_							=    prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=snake_case      ,   torchify=snake_case       )
											for image in image_inputs:
																		self.assertIsInstance(snake_case      ,   torch.Tensor       )

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

											snake_case_				,    snake_case_							=    self.image_processor_tester.get_expected_values(snake_case       )
											self.assertEqual(
											    encoded_images.shape      ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,   )

											# Test batched
											snake_case_							=    image_processing(snake_case      ,   return_tensors='pt'       ).pixel_values

											snake_case_				,    snake_case_							=    self.image_processor_tester.get_expected_values(snake_case      ,   batched=snake_case       )
											self.assertEqual(
											    encoded_images.shape      ,   (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )      ,   )







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor







@require_vision
class  lowercase				(       unittest.TestCase			):




				def     a							(					self       ):
											snake_case_							=    tempfile.mkdtemp()

											snake_case_							=    [
											    '[UNK]',
											    '[CLS]',
											    '[SEP]',
											    '[PAD]',
											    '[MASK]',
											    '的',
											    '价',
											    '格',
											    '是',
											    '15',
											    '便',
											    'alex',
											    '##andra',
											    '，',
											    '。',
											    '-',
											    't',
											    'shirt',
											]
											snake_case_							=    os.path.join(self.tmpdirname      ,   VOCAB_FILES_NAMES['vocab_file']       )
											with open(self.vocab_file      ,   'w'      ,   encoding='utf-8'       ) as vocab_writer:
																		vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )

											snake_case_							=    {
											    'do_resize': True,
											    'size': {'height': 224, 'width': 224},
											    'do_center_crop': True,
											    'crop_size': {'height': 18, 'width': 18},
											    'do_normalize': True,
											    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
											    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
											    'do_convert_rgb': True,
											}
											snake_case_							=    os.path.join(self.tmpdirname      ,   snake_case       )
											with open(self.image_processor_file      ,   'w'      ,   encoding='utf-8'       ) as fp:
																		json.dump(snake_case      ,   snake_case       )




				def     a							(					self      ,   **snake_case       ):
											return BertTokenizer.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self      ,   **snake_case       ):
											return BertTokenizerFast.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self      ,   **snake_case       ):
											return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self       ):
											shutil.rmtree(self.tmpdirname       )




				def     a							(					self       ):
											snake_case_							=    [np.random.randint(255      ,   size=(3, 30, 400)      ,   dtype=np.uinta       )]

											snake_case_							=    [Image.fromarray(np.moveaxis(snake_case      ,   0      ,   -1       )       ) for x in image_inputs]

											return image_inputs




				def     a							(					self       ):
											snake_case_							=    self.get_tokenizer()
											snake_case_							=    self.get_rust_tokenizer()
											snake_case_							=    self.get_image_processor()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )
											processor_slow.save_pretrained(self.tmpdirname       )
											snake_case_							=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname      ,   use_fast=snake_case       )

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )
											processor_fast.save_pretrained(self.tmpdirname       )
											snake_case_							=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname       )

											self.assertEqual(processor_slow.tokenizer.get_vocab()      ,   tokenizer_slow.get_vocab()       )
											self.assertEqual(processor_fast.tokenizer.get_vocab()      ,   tokenizer_fast.get_vocab()       )
											self.assertEqual(tokenizer_slow.get_vocab()      ,   tokenizer_fast.get_vocab()       )
											self.assertIsInstance(processor_slow.tokenizer      ,   snake_case       )
											self.assertIsInstance(processor_fast.tokenizer      ,   snake_case       )

											self.assertEqual(processor_slow.image_processor.to_json_string()      ,   image_processor.to_json_string()       )
											self.assertEqual(processor_fast.image_processor.to_json_string()      ,   image_processor.to_json_string()       )
											self.assertIsInstance(processor_slow.image_processor      ,   snake_case       )
											self.assertIsInstance(processor_fast.image_processor      ,   snake_case       )




				def     a							(					self       ):
											snake_case_							=    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()      ,   image_processor=self.get_image_processor()       )
											processor.save_pretrained(self.tmpdirname       )

											snake_case_							=    self.get_tokenizer(cls_token='(CLS)'      ,   sep_token='(SEP)'       )
											snake_case_							=    self.get_image_processor(do_normalize=snake_case       )

											snake_case_							=    ChineseCLIPProcessor.from_pretrained(
											    self.tmpdirname      ,   cls_token='(CLS)'      ,   sep_token='(SEP)'      ,   do_normalize=snake_case       )

											self.assertEqual(processor.tokenizer.get_vocab()      ,   tokenizer_add_kwargs.get_vocab()       )
											self.assertIsInstance(processor.tokenizer      ,   snake_case       )

											self.assertEqual(processor.image_processor.to_json_string()      ,   image_processor_add_kwargs.to_json_string()       )
											self.assertIsInstance(processor.image_processor      ,   snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    self.prepare_image_inputs()

											snake_case_							=    image_processor(snake_case      ,   return_tensors='np'       )
											snake_case_							=    processor(images=snake_case      ,   return_tensors='np'       )

											for key in input_feat_extract.keys():
																		self.assertAlmostEqual(input_feat_extract[key].sum()      ,   input_processor[key].sum()      ,   delta=1e-2       )




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    'Alexandra，T-shirt的价格是15便士。'

											snake_case_							=    processor(text=snake_case       )

											snake_case_							=    tokenizer(snake_case       )

											for key in encoded_tok.keys():
																		self.assertListEqual(encoded_tok[key]      ,   encoded_processor[key]       )




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    'Alexandra，T-shirt的价格是15便士。'
											snake_case_							=    self.prepare_image_inputs()

											snake_case_							=    processor(text=snake_case      ,   images=snake_case       )

											self.assertListEqual(list(inputs.keys()       )      ,   ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']       )

											# test if it raises when no input is passed
											with pytest.raises(snake_case       ):
																		processor()




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											snake_case_							=    processor.batch_decode(snake_case       )
											snake_case_							=    tokenizer.batch_decode(snake_case       )

											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    'Alexandra，T-shirt的价格是15便士。'
											snake_case_							=    self.prepare_image_inputs()

											snake_case_							=    processor(text=snake_case      ,   images=snake_case       )

											self.assertListEqual(list(inputs.keys()       )      ,   processor.model_input_names       )




