import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor





def 		__lowerCamelCase							(UpperCAmelCase__       :							Any					):
					SCREAMING_SNAKE_CASE           =    SwinaSRConfig()

					if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
										SCREAMING_SNAKE_CASE           =    4
					elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
										SCREAMING_SNAKE_CASE           =    4
										SCREAMING_SNAKE_CASE           =    4_8
										SCREAMING_SNAKE_CASE           =    "pixelshuffle_aux"
					elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
										SCREAMING_SNAKE_CASE           =    [6, 6, 6, 6]
										SCREAMING_SNAKE_CASE           =    6_0
										SCREAMING_SNAKE_CASE           =    [6, 6, 6, 6]
										SCREAMING_SNAKE_CASE           =    "pixelshuffledirect"
					elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
										SCREAMING_SNAKE_CASE           =    4
										SCREAMING_SNAKE_CASE           =    "nearest+conv"
					elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
										SCREAMING_SNAKE_CASE           =    1
										SCREAMING_SNAKE_CASE           =    1
										SCREAMING_SNAKE_CASE           =    1_2_6
										SCREAMING_SNAKE_CASE           =    7
										SCREAMING_SNAKE_CASE           =    255.0
										SCREAMING_SNAKE_CASE           =    ""

					return config





def 		__lowerCamelCase							(UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							str					):
					if "patch_embed.proj" in name and "layers" not in name:
										SCREAMING_SNAKE_CASE           =    name.replace("patch_embed.proj" ,	"embeddings.patch_embeddings.projection"					)
					if "patch_embed.norm" in name:
										SCREAMING_SNAKE_CASE           =    name.replace("patch_embed.norm" ,	"embeddings.patch_embeddings.layernorm"					)
					if "layers" in name:
										SCREAMING_SNAKE_CASE           =    name.replace("layers" ,	"encoder.stages"					)
					if "residual_group.blocks" in name:
										SCREAMING_SNAKE_CASE           =    name.replace("residual_group.blocks" ,	"layers"					)
					if "attn.proj" in name:
										SCREAMING_SNAKE_CASE           =    name.replace("attn.proj" ,	"attention.output.dense"					)
					if "attn" in name:
										SCREAMING_SNAKE_CASE           =    name.replace("attn" ,	"attention.self"					)
					if "norm1" in name:
										SCREAMING_SNAKE_CASE           =    name.replace("norm1" ,	"layernorm_before"					)
					if "norm2" in name:
										SCREAMING_SNAKE_CASE           =    name.replace("norm2" ,	"layernorm_after"					)
					if "mlp.fc1" in name:
										SCREAMING_SNAKE_CASE           =    name.replace("mlp.fc1" ,	"intermediate.dense"					)
					if "mlp.fc2" in name:
										SCREAMING_SNAKE_CASE           =    name.replace("mlp.fc2" ,	"output.dense"					)
					if "q_bias" in name:
										SCREAMING_SNAKE_CASE           =    name.replace("q_bias" ,	"query.bias"					)
					if "k_bias" in name:
										SCREAMING_SNAKE_CASE           =    name.replace("k_bias" ,	"key.bias"					)
					if "v_bias" in name:
										SCREAMING_SNAKE_CASE           =    name.replace("v_bias" ,	"value.bias"					)
					if "cpb_mlp" in name:
										SCREAMING_SNAKE_CASE           =    name.replace("cpb_mlp" ,	"continuous_position_bias_mlp"					)
					if "patch_embed.proj" in name:
										SCREAMING_SNAKE_CASE           =    name.replace("patch_embed.proj" ,	"patch_embed.projection"					)

					if name == "norm.weight":
										SCREAMING_SNAKE_CASE           =    "layernorm.weight"
					if name == "norm.bias":
										SCREAMING_SNAKE_CASE           =    "layernorm.bias"

					if "conv_first" in name:
										SCREAMING_SNAKE_CASE           =    name.replace("conv_first" ,	"first_convolution"					)

					if (
					    "upsample" in name
					    or "conv_before_upsample" in name
					    or "conv_bicubic" in name
					    or "conv_up" in name
					    or "conv_hr" in name
					    or "conv_last" in name
					    or "aux" in name
					):
										# heads
										if "conv_last" in name:
															SCREAMING_SNAKE_CASE           =    name.replace("conv_last" ,	"final_convolution"					)
										if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
															if "conv_before_upsample.0" in name:
																				SCREAMING_SNAKE_CASE           =    name.replace("conv_before_upsample.0" ,	"conv_before_upsample"					)
															if "upsample.0" in name:
																				SCREAMING_SNAKE_CASE           =    name.replace("upsample.0" ,	"upsample.convolution_0"					)
															if "upsample.2" in name:
																				SCREAMING_SNAKE_CASE           =    name.replace("upsample.2" ,	"upsample.convolution_1"					)
															SCREAMING_SNAKE_CASE           =    "upsample." + name
										elif config.upsampler == "pixelshuffledirect":
															SCREAMING_SNAKE_CASE           =    name.replace("upsample.0.weight" ,	"upsample.conv.weight"					)
															SCREAMING_SNAKE_CASE           =    name.replace("upsample.0.bias" ,	"upsample.conv.bias"					)
										else:
															pass
					else:
										SCREAMING_SNAKE_CASE           =    "swin2sr." + name

					return name





def 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							int					):
					for key in orig_state_dict.copy().keys():
										SCREAMING_SNAKE_CASE           =    orig_state_dict.pop(UpperCAmelCase__					)

										if "qkv" in key:
															SCREAMING_SNAKE_CASE           =    key.split("."					)
															SCREAMING_SNAKE_CASE           =    int(key_split[1]					)
															SCREAMING_SNAKE_CASE           =    int(key_split[4]					)
															SCREAMING_SNAKE_CASE           =    config.embed_dim

															if "weight" in key:
																				SCREAMING_SNAKE_CASE           =    val[:dim, :]
																				SCREAMING_SNAKE_CASE           =    val[dim : dim * 2, :]
																				SCREAMING_SNAKE_CASE           =    val[-dim:, :]
															else:
																				SCREAMING_SNAKE_CASE           =    val[:dim]
																				SCREAMING_SNAKE_CASE           =    val[dim : dim * 2]
																				SCREAMING_SNAKE_CASE           =    val[-dim:]
															pass
										else:
															SCREAMING_SNAKE_CASE           =    val

					return orig_state_dict





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							Optional[Any]					):
					SCREAMING_SNAKE_CASE           =    get_config(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    SwinaSRForImageSuperResolution(UpperCAmelCase__					)
					model.eval()

					SCREAMING_SNAKE_CASE           =    torch.hub.load_state_dict_from_url(UpperCAmelCase__ ,	map_location="cpu"					)
					SCREAMING_SNAKE_CASE           =    convert_state_dict(UpperCAmelCase__ ,	UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    model.load_state_dict(UpperCAmelCase__ ,	strict=UpperCAmelCase__					)

					if len(UpperCAmelCase__					) > 0:
										raise ValueError("Missing keys when converting: {}".format(UpperCAmelCase__					)					)
					for key in unexpected_keys:
										if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
															raise ValueError(F"Unexpected key {key} in state_dict"					)

    # verify values
					SCREAMING_SNAKE_CASE           =    "https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"
					SCREAMING_SNAKE_CASE           =    Image.open(requests.get(UpperCAmelCase__ ,	stream=UpperCAmelCase__					).raw					).convert("RGB"					)
					SCREAMING_SNAKE_CASE           =    SwinaSRImageProcessor()
					# pixel_values = processor(image, return_tensors="pt").pixel_values

					SCREAMING_SNAKE_CASE           =    1_2_6 if "Jpeg" in checkpoint_url else 2_5_6
					SCREAMING_SNAKE_CASE           =    Compose(
					    [
					        Resize((image_size, image_size)					),
					        ToTensor(),
					        Normalize(mean=[0.485, 0.456, 0.406] ,	std=[0.229, 0.224, 0.225]					),
					    ]					)
					SCREAMING_SNAKE_CASE           =    transforms(UpperCAmelCase__					).unsqueeze(0					)

					if config.num_channels == 1:
										SCREAMING_SNAKE_CASE           =    pixel_values[:, 0, :, :].unsqueeze(1					)

					SCREAMING_SNAKE_CASE           =    model(UpperCAmelCase__					)

					# assert values
					if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
										SCREAMING_SNAKE_CASE           =    torch.Size([1, 3, 5_1_2, 5_1_2]					)
										SCREAMING_SNAKE_CASE           =    torch.tensor(
										    [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]]					)
					elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
										SCREAMING_SNAKE_CASE           =    torch.Size([1, 3, 1_0_2_4, 1_0_2_4]					)
										SCREAMING_SNAKE_CASE           =    torch.tensor(
										    [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]]					)
					elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
										# TODO values didn't match exactly here
										SCREAMING_SNAKE_CASE           =    torch.Size([1, 3, 1_0_2_4, 1_0_2_4]					)
										SCREAMING_SNAKE_CASE           =    torch.tensor(
										    [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]]					)
					elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
										SCREAMING_SNAKE_CASE           =    torch.Size([1, 3, 5_1_2, 5_1_2]					)
										SCREAMING_SNAKE_CASE           =    torch.tensor(
										    [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]]					)
					elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
										SCREAMING_SNAKE_CASE           =    torch.Size([1, 3, 1_0_2_4, 1_0_2_4]					)
										SCREAMING_SNAKE_CASE           =    torch.tensor(
										    [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]]					)

					assert (
					    outputs.reconstruction.shape == expected_shape
					), F"Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"
					assert torch.allclose(outputs.reconstruction[0, 0, :3, :3] ,	UpperCAmelCase__ ,	atol=1e-3					)
					print("Looks ok!"					)

					SCREAMING_SNAKE_CASE           =    {
					    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth": (
					        "swin2SR-classical-sr-x2-64"
					    ),
					    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth": (
					        "swin2SR-classical-sr-x4-64"
					    ),
					    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth": (
					        "swin2SR-compressed-sr-x4-48"
					    ),
					    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth": (
					        "swin2SR-lightweight-x2-64"
					    ),
					    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth": (
					        "swin2SR-realworld-sr-x4-64-bsrgan-psnr"
					    ),
					}
					SCREAMING_SNAKE_CASE           =    url_to_name[checkpoint_url]

					if pytorch_dump_folder_path is not None:
										print(F"Saving model {model_name} to {pytorch_dump_folder_path}"					)
										model.save_pretrained(UpperCAmelCase__					)
										print(F"Saving image processor to {pytorch_dump_folder_path}"					)
										processor.save_pretrained(UpperCAmelCase__					)

					if push_to_hub:
										model.push_to_hub(F"caidas/{model_name}"					)
										processor.push_to_hub(F"caidas/{model_name}"					)


if __name__ == "__main__":
							_lowerCamelCase   :				int     			=						argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--checkpoint_url''',
							    default='''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''',
							    type=str,
							    help='''URL of the original Swin2SR checkpoint you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
							)
							parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the converted model to the hub.''')

							_lowerCamelCase   :				Any     			=						parser.parse_args()
							convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





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 )




