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,
    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_vision_available, logging


if is_vision_available():
							import PIL


_lowerCamelCase   :				List[Any]     			=						logging.get_logger(__name__)


class   lowercase      (       a       ):
							lowercase__  :    List[str]									=							["""pixel_values"""]




							def __init__(  self				:   List[str]		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Dict[str, int] = None		,							_UpperCamelCase				:   float = None		,							_UpperCamelCase				:   PILImageResampling = PILImageResampling.BILINEAR		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Union[int, float] = 1 / 255		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Optional[Union[float, List[float]]] = None		,							_UpperCamelCase				:   Optional[Union[float, List[float]]] = None		,							**_UpperCamelCase				:   Dict		,							)   ->			None:



												'''simple docstring'''

												super().__init__(**_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    size if size is not None else {"shortest_edge": 384}
												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase		,							default_to_square=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    do_resize
												SCREAMING_SNAKE_CASE           =    size
												# Default value set here for backwards compatibility where the value in config is None
												SCREAMING_SNAKE_CASE           =    crop_pct if crop_pct is not None else 224 / 256
												SCREAMING_SNAKE_CASE           =    resample
												SCREAMING_SNAKE_CASE           =    do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_factor
												SCREAMING_SNAKE_CASE           =    do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else IMAGENET_STANDARD_STD




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   np.ndarray		,							_UpperCamelCase				:   Dict[str, int]		,							_UpperCamelCase				:   float		,							_UpperCamelCase				:   PILImageResampling = PILImageResampling.BICUBIC		,							_UpperCamelCase				:   Optional[Union[str, ChannelDimension]] = None		,							**_UpperCamelCase				:   Optional[int]		,							)   ->			np.ndarray:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase		,							default_to_square=_UpperCamelCase )
												if "shortest_edge" not in size:
																	raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}" )
												SCREAMING_SNAKE_CASE           =    size["shortest_edge"]

												if shortest_edge < 384:
																	# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																	SCREAMING_SNAKE_CASE           =    int(shortest_edge / crop_pct )
																	SCREAMING_SNAKE_CASE           =    get_resize_output_image_size(_UpperCamelCase		,							size=_UpperCamelCase		,							default_to_square=_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    resize(image=_UpperCamelCase		,							size=_UpperCamelCase		,							resample=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )
																	# then crop to (shortest_edge, shortest_edge)
																	return center_crop(image=_UpperCamelCase		,							size=(shortest_edge, shortest_edge)		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )
												else:
																	# warping (no cropping) when evaluated at 384 or larger
																	return resize(
																	    _UpperCamelCase		,							size=(shortest_edge, shortest_edge)		,							resample=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   np.ndarray		,							_UpperCamelCase				:   Union[int, float]		,							_UpperCamelCase				:   Optional[Union[str, ChannelDimension]] = None		,							**_UpperCamelCase				:   Optional[Any]		,							)   ->			List[Any]:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   np.ndarray		,							_UpperCamelCase				:   Union[float, List[float]]		,							_UpperCamelCase				:   Union[float, List[float]]		,							_UpperCamelCase				:   Optional[Union[str, ChannelDimension]] = None		,							**_UpperCamelCase				:   List[Any]		,							)   ->			np.ndarray:



												'''simple docstring'''

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





							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   ImageInput		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   Dict[str, int] = None		,							_UpperCamelCase				:   float = None		,							_UpperCamelCase				:   PILImageResampling = None		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   float = None		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   Optional[Union[float, List[float]]] = None		,							_UpperCamelCase				:   Optional[Union[float, List[float]]] = None		,							_UpperCamelCase				:   Optional[Union[str, TensorType]] = None		,							_UpperCamelCase				:   ChannelDimension = ChannelDimension.FIRST		,							**_UpperCamelCase				:   Any		,							)   ->			PIL.Image.Image:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE           =    crop_pct if crop_pct is not None else self.crop_pct
												SCREAMING_SNAKE_CASE           =    resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE           =    do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE           =    do_normalize if do_normalize is not None else self.do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else self.image_mean
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else self.image_std

												SCREAMING_SNAKE_CASE           =    size if size is not None else self.size
												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase		,							default_to_square=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    make_list_of_images(_UpperCamelCase )

												if not valid_images(_UpperCamelCase ):
																	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_resize and size["shortest_edge"] < 384 and crop_pct is None:
																	raise ValueError("crop_pct must be specified if size < 384." )

												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.
												SCREAMING_SNAKE_CASE           =    [to_numpy_array(_UpperCamelCase ) for image in images]

												if do_resize:
																	SCREAMING_SNAKE_CASE           =    [self.resize(image=_UpperCamelCase		,							size=_UpperCamelCase		,							crop_pct=_UpperCamelCase		,							resample=_UpperCamelCase ) for image in images]

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

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

												SCREAMING_SNAKE_CASE           =    [to_channel_dimension_format(_UpperCamelCase		,							_UpperCamelCase ) for image in images]

												SCREAMING_SNAKE_CASE           =    {"pixel_values": images}
												return BatchFeature(data=_UpperCamelCase		,							tensor_type=_UpperCamelCase )





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 		__snake_case(  self				:   Tuple )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    tempfile.mkdtemp()

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

												SCREAMING_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": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
												    "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
												    "do_convert_rgb": True,
												}
												SCREAMING_SNAKE_CASE           =    os.path.join(self.tmpdirname		,							_UpperCamelCase )
												with open(self.image_processor_file		,							"w"		,							encoding="utf-8" ) as fp:
																	json.dump(_UpperCamelCase		,							_UpperCamelCase )




							def 		__snake_case(  self				:   Union[str, Any]		,							**_UpperCamelCase				:   str )   ->			int:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   List[str]		,							**_UpperCamelCase				:   Tuple )   ->			str:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   List[Any]		,							**_UpperCamelCase				:   Optional[Any] )   ->			Any:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Optional[Any] )   ->			int:



												'''simple docstring'''

												shutil.rmtree(self.tmpdirname )




							def 		__snake_case(  self				:   Dict )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [np.random.randint(255		,							size=(3, 30, 400)		,							dtype=np.uinta )]

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

												return image_inputs




							def 		__snake_case(  self				:   List[str] )   ->			Any:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )
												processor_slow.save_pretrained(self.tmpdirname )
												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,							use_fast=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )
												processor_fast.save_pretrained(self.tmpdirname )
												SCREAMING_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		,							_UpperCamelCase )
												self.assertIsInstance(processor_fast.tokenizer		,							_UpperCamelCase )

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




							def 		__snake_case(  self				:   str )   ->			Tuple:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE           =    self.get_tokenizer(cls_token="(CLS)"		,							sep_token="(SEP)" )
												SCREAMING_SNAKE_CASE           =    self.get_image_processor(do_normalize=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor.from_pretrained(
												    self.tmpdirname		,							cls_token="(CLS)"		,							sep_token="(SEP)"		,							do_normalize=_UpperCamelCase )

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

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




							def 		__snake_case(  self				:   List[Any] )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    self.prepare_image_inputs()

												SCREAMING_SNAKE_CASE           =    image_processor(_UpperCamelCase		,							return_tensors="np" )
												SCREAMING_SNAKE_CASE           =    processor(images=_UpperCamelCase		,							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 		__snake_case(  self				:   List[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

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

												SCREAMING_SNAKE_CASE           =    processor(text=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    tokenizer(_UpperCamelCase )

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




							def 		__snake_case(  self				:   Optional[Any] )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

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

												SCREAMING_SNAKE_CASE           =    processor(text=_UpperCamelCase		,							images=_UpperCamelCase )

												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(_UpperCamelCase ):
																	processor()




							def 		__snake_case(  self				:   Optional[Any] )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

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

												SCREAMING_SNAKE_CASE           =    processor.batch_decode(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    tokenizer.batch_decode(_UpperCamelCase )

												self.assertListEqual(_UpperCamelCase		,							_UpperCamelCase )





							def 		__snake_case(  self				:   int )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

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

												SCREAMING_SNAKE_CASE           =    processor(text=_UpperCamelCase		,							images=_UpperCamelCase )

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




