





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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    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


UpperCamelCase__   : Optional[Any]					  = logging.get_logger(__name__)


if is_vision_available():
				import PIL







class   _UpperCamelCase  (       snake_case__  ):







					'''simple docstring'''

					lowerCamelCase     :     Optional[int]			    =       ['pixel_values']





					def __init__(						self    :  Optional[int] ,					__lowercase    :  List[Any] = True ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  List[str] = PILImageResampling.BICUBIC ,					__lowercase    :  Optional[Any] = True ,					__lowercase    :  str = None ,					__lowercase    :  str = True ,					__lowercase    :  List[Any] = 1 / 2_55 ,					__lowercase    :  Union[str, Any] = True ,					__lowercase    :  Tuple = None ,					__lowercase    :  Dict = None ,					__lowercase    :  Dict = True ,					**__lowercase    :  List[str] ,					):






						'''simple docstring'''







						super().__init__(**lowercase_		)
						UpperCAmelCase_								=     size if size is not None else {"shortest_edge": 2_24}
						UpperCAmelCase_								=     get_size_dict(lowercase_ ,					default_to_square=lowercase_		)
						UpperCAmelCase_								=     crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
						UpperCAmelCase_								=     get_size_dict(lowercase_ ,					default_to_square=lowercase_ ,					param_name="""crop_size"""		)

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





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int] ,					__lowercase    :  List[str] ,					__lowercase    :  List[Any] ,					__lowercase    :  Optional[Any] = PILImageResampling.BICUBIC ,					__lowercase    :  int = None ,					**__lowercase    :  Union[str, Any] ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     get_size_dict(lowercase_ ,					default_to_square=lowercase_		)
						if "shortest_edge" not in size:
							raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""		)
						UpperCAmelCase_								=     get_resize_output_image_size(lowercase_ ,					size=size["""shortest_edge"""] ,					default_to_square=lowercase_		)
						return resize(lowercase_ ,					size=lowercase_ ,					resample=lowercase_ ,					data_format=lowercase_ ,					**lowercase_		)





					def        SCREAMING_SNAKE_CASE							(						self    :  int ,					__lowercase    :  str ,					__lowercase    :  Optional[int] ,					__lowercase    :  int = None ,					**__lowercase    :  Optional[int] ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     get_size_dict(lowercase_		)
						if "height" not in size or "width" not in size:
							raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""		)
						return center_crop(lowercase_ ,					size=(size["""height"""], size["""width"""]) ,					data_format=lowercase_ ,					**lowercase_		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  Dict ,					__lowercase    :  List[Any] ,					__lowercase    :  Union[str, Any] = None ,					**__lowercase    :  List[str] ,					):






						'''simple docstring'''







						return rescale(lowercase_ ,					scale=lowercase_ ,					data_format=lowercase_ ,					**lowercase_		)





					def        SCREAMING_SNAKE_CASE							(						self    :  int ,					__lowercase    :  Optional[int] ,					__lowercase    :  Optional[Any] ,					__lowercase    :  Optional[int] ,					__lowercase    :  List[str] = None ,					**__lowercase    :  Optional[Any] ,					):






						'''simple docstring'''







						return normalize(lowercase_ ,					mean=lowercase_ ,					std=lowercase_ ,					data_format=lowercase_ ,					**lowercase_		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  List[str] ,					__lowercase    :  Any = None ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  Union[str, Any] = None ,					__lowercase    :  Dict = None ,					__lowercase    :  List[Any] = None ,					__lowercase    :  Tuple = None ,					__lowercase    :  Dict = None ,					__lowercase    :  Optional[Any] = None ,					__lowercase    :  str = None ,					__lowercase    :  List[Any] = None ,					__lowercase    :  str = None ,					__lowercase    :  List[str] = None ,					__lowercase    :  List[str] = ChannelDimension.FIRST ,					**__lowercase    :  Optional[Any] ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     do_resize if do_resize is not None else self.do_resize
						UpperCAmelCase_								=     size if size is not None else self.size
						UpperCAmelCase_								=     get_size_dict(lowercase_ ,					param_name="""size""" ,					default_to_square=lowercase_		)
						UpperCAmelCase_								=     resample if resample is not None else self.resample
						UpperCAmelCase_								=     do_center_crop if do_center_crop is not None else self.do_center_crop
						UpperCAmelCase_								=     crop_size if crop_size is not None else self.crop_size
						UpperCAmelCase_								=     get_size_dict(lowercase_ ,					param_name="""crop_size""" ,					default_to_square=lowercase_		)
						UpperCAmelCase_								=     do_rescale if do_rescale is not None else self.do_rescale
						UpperCAmelCase_								=     rescale_factor if rescale_factor is not None else self.rescale_factor
						UpperCAmelCase_								=     do_normalize if do_normalize is not None else self.do_normalize
						UpperCAmelCase_								=     image_mean if image_mean is not None else self.image_mean
						UpperCAmelCase_								=     image_std if image_std is not None else self.image_std
						UpperCAmelCase_								=     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						UpperCAmelCase_								=     make_list_of_images(lowercase_		)

						if not valid_images(lowercase_		):
							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."""		)

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

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

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

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

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

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

						UpperCAmelCase_								=     [to_channel_dimension_format(lowercase_ ,					lowercase_		) for image in images]

						UpperCAmelCase_								=     {"pixel_values": images}
						return BatchFeature(data=lowercase_ ,					tensor_type=lowercase_		)









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   _UpperCamelCase  (       unittest.TestCase  ):







					'''simple docstring'''

					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     tempfile.mkdtemp()

						UpperCAmelCase_								=     [
						    """[UNK]""",
						    """[CLS]""",
						    """[SEP]""",
						    """[PAD]""",
						    """[MASK]""",
						    """的""",
						    """价""",
						    """格""",
						    """是""",
						    """15""",
						    """便""",
						    """alex""",
						    """##andra""",
						    """，""",
						    """。""",
						    """-""",
						    """t""",
						    """shirt""",
						]
						UpperCAmelCase_								=     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]		)		)

						UpperCAmelCase_								=     {
						    """do_resize""": True,
						    """size""": {"""height""": 2_24, """width""": 2_24},
						    """do_center_crop""": True,
						    """crop_size""": {"""height""": 18, """width""": 18},
						    """do_normalize""": True,
						    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
						    """image_std""": [0.26862954, 0.26130258, 0.27577711],
						    """do_convert_rgb""": True,
						}
						UpperCAmelCase_								=     os.path.join(self.tmpdirname ,					__lowercase		)
						with open(self.image_processor_file ,					"""w""" ,					encoding="""utf-8"""		) as fp:
							json.dump(__lowercase ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					**__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						return BertTokenizer.from_pretrained(self.tmpdirname ,					**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any] ,					**__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						return BertTokenizerFast.from_pretrained(self.tmpdirname ,					**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  int ,					**__lowercase    :  int		):






						'''simple docstring'''







						return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname ,					**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						shutil.rmtree(self.tmpdirname		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_								=     [np.random.randint(2_55 ,					size=(3, 30, 4_00) ,					dtype=np.uinta		)]

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

						return image_inputs





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_tokenizer()
						UpperCAmelCase_								=     self.get_rust_tokenizer()
						UpperCAmelCase_								=     self.get_image_processor()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)
						processor_slow.save_pretrained(self.tmpdirname		)
						UpperCAmelCase_								=     ChineseCLIPProcessor.from_pretrained(self.tmpdirname ,					use_fast=__lowercase		)

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)
						processor_fast.save_pretrained(self.tmpdirname		)
						UpperCAmelCase_								=     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 ,					__lowercase		)
						self.assertIsInstance(processor_fast.tokenizer ,					__lowercase		)

						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 ,					__lowercase		)
						self.assertIsInstance(processor_fast.image_processor ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  int		):






						'''simple docstring'''







						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=self.get_tokenizer() ,					image_processor=self.get_image_processor()		)
						processor.save_pretrained(self.tmpdirname		)

						UpperCAmelCase_								=     self.get_tokenizer(cls_token="""(CLS)""" ,					sep_token="""(SEP)"""		)
						UpperCAmelCase_								=     self.get_image_processor(do_normalize=__lowercase		)

						UpperCAmelCase_								=     ChineseCLIPProcessor.from_pretrained(
						    self.tmpdirname ,					cls_token="""(CLS)""" ,					sep_token="""(SEP)""" ,					do_normalize=__lowercase		)

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

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





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     self.prepare_image_inputs()

						UpperCAmelCase_								=     image_processor(__lowercase ,					return_tensors="""np"""		)
						UpperCAmelCase_								=     processor(images=__lowercase ,					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        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     """Alexandra，T-shirt的价格是15便士。"""

						UpperCAmelCase_								=     processor(text=__lowercase		)

						UpperCAmelCase_								=     tokenizer(__lowercase		)

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





					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     """Alexandra，T-shirt的价格是15便士。"""
						UpperCAmelCase_								=     self.prepare_image_inputs()

						UpperCAmelCase_								=     processor(text=__lowercase ,					images=__lowercase		)

						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(__lowercase		):
							processor()





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

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

						UpperCAmelCase_								=     processor.batch_decode(__lowercase		)
						UpperCAmelCase_								=     tokenizer.batch_decode(__lowercase		)

						self.assertListEqual(__lowercase ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     """Alexandra，T-shirt的价格是15便士。"""
						UpperCAmelCase_								=     self.prepare_image_inputs()

						UpperCAmelCase_								=     processor(text=__lowercase ,					images=__lowercase		)

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


