



'''simple docstring'''



import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def 					UpperCAmelCase   (						):
       SCREAMING_SNAKE_CASE					:      List[Any]     					=			'''https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'''
       SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			Image.open(requests.get(A					,		stream=A				).raw				).convert('''RGB'''				)

       return image
def 					UpperCAmelCase   (						A  :				Union[str, Any]				):
       SCREAMING_SNAKE_CASE					:      Optional[int]     					=			[]
       # fmt: off

       # vision encoder
       rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''')				)
       rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''')				)
       rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''')				)
       rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''')				)
       rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''')				)
       rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''')				)

       for i in range(config.vision_config.num_hidden_layers				):
              rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.weight""", F"""vision_model.encoder.layers.{i}.layer_norm1.weight""")				)
              rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.bias""", F"""vision_model.encoder.layers.{i}.layer_norm1.bias""")				)
              rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.weight""", F"""vision_model.encoder.layers.{i}.layer_norm2.weight""")				)
              rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.bias""", F"""vision_model.encoder.layers.{i}.layer_norm2.bias""")				)
              rename_keys.append((F"""visual_encoder.blocks.{i}.attn.qkv.weight""", F"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""")				)
              rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.weight""", F"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",)				)
              rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.bias""", F"""vision_model.encoder.layers.{i}.self_attn.projection.bias""")				)
              rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc1.weight""")				)
              rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc1.bias""")				)
              rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc2.weight""")				)
              rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc2.bias""")				)

       # QFormer
       rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.layernorm.weight''')				)
       rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.layernorm.bias''')				)

       # fmt: on
       return rename_keys
def 					UpperCAmelCase   (						A  :				Tuple					,		A  :				Any					,		A  :				Optional[int]				):
       SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			dct.pop(A				)
       SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			val
def 					UpperCAmelCase   (						A  :				str					,		A  :				str				):
       for i in range(config.vision_config.num_hidden_layers				):
              # read in original q and v biases
              SCREAMING_SNAKE_CASE					:      Dict     					=			state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""				)
              SCREAMING_SNAKE_CASE					:      int     					=			state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""				)

              # next, set bias in the state dict
              SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.cat((q_bias, torch.zeros_like(A					,		requires_grad=A				), v_bias)				)
              SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			qkv_bias
def 					UpperCAmelCase   (						A  :				Any					,		A  :				Union[str, Any]				):
       SCREAMING_SNAKE_CASE					:      Tuple     					=			364 if '''coco''' in model_name else 224
       SCREAMING_SNAKE_CASE					:      Dict     					=			BlipaVisionConfig(image_size=A				).to_dict()

       # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
       # seems like flan-T5 models don't have bos_token_id properly set?
       if "opt-2.7b" in model_name:
              SCREAMING_SNAKE_CASE					:      str     					=			OPTConfig.from_pretrained('''facebook/opt-2.7b'''					,		eos_token_id=A				).to_dict()
       elif "opt-6.7b" in model_name:
              SCREAMING_SNAKE_CASE					:      str     					=			OPTConfig.from_pretrained('''facebook/opt-6.7b'''					,		eos_token_id=A				).to_dict()
       elif "t5-xl" in model_name:
              SCREAMING_SNAKE_CASE					:      Dict     					=			TaConfig.from_pretrained('''google/flan-t5-xl'''					,		dense_act_fn='''gelu'''					,		bos_token_id=1				).to_dict()
       elif "t5-xxl" in model_name:
              SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			TaConfig.from_pretrained('''google/flan-t5-xxl'''					,		dense_act_fn='''gelu'''					,		bos_token_id=1				).to_dict()

       SCREAMING_SNAKE_CASE					:      Tuple     					=			BlipaConfig(vision_config=A					,		text_config=A				)

       return config, image_size
@torch.no_grad()
def 					UpperCAmelCase   (						A  :				Tuple					,		A  :				List[str]=None					,		A  :				List[str]=False				):
       SCREAMING_SNAKE_CASE					:      str     					=			(
           AutoTokenizer.from_pretrained('''facebook/opt-2.7b'''				)
           if '''opt''' in model_name
           else AutoTokenizer.from_pretrained('''google/flan-t5-xl'''				)
       )
       SCREAMING_SNAKE_CASE					:      Tuple     					=			tokenizer('''\n'''					,		add_special_tokens=A				).input_ids[0]
       SCREAMING_SNAKE_CASE					:      Tuple     					=			get_blipa_config(A					,		eos_token_id=A				)

       SCREAMING_SNAKE_CASE					:      Dict     					=			BlipaForConditionalGeneration(A				).eval()

       SCREAMING_SNAKE_CASE					:      int     					=			{
           '''blip2-opt-2.7b''': ('''blip2_opt''', '''pretrain_opt2.7b'''),
           '''blip2-opt-6.7b''': ('''blip2_opt''', '''pretrain_opt6.7b'''),
           '''blip2-opt-2.7b-coco''': ('''blip2_opt''', '''caption_coco_opt2.7b'''),
           '''blip2-opt-6.7b-coco''': ('''blip2_opt''', '''caption_coco_opt6.7b'''),
           '''blip2-flan-t5-xl''': ('''blip2_t5''', '''pretrain_flant5xl'''),
           '''blip2-flan-t5-xl-coco''': ('''blip2_t5''', '''caption_coco_flant5xl'''),
           '''blip2-flan-t5-xxl''': ('''blip2_t5''', '''pretrain_flant5xxl'''),
       }

       SCREAMING_SNAKE_CASE					:      Optional[int]     					=			model_name_to_original[model_name]

       # load original model
       print('''Loading original model...'''				)
       SCREAMING_SNAKE_CASE					:      Tuple     					=			'''cuda''' if torch.cuda.is_available() else '''cpu'''
       SCREAMING_SNAKE_CASE					:      Optional[int]     					=			load_model_and_preprocess(
           name=A					,		model_type=A					,		is_eval=A					,		device=A				)
       original_model.eval()
       print('''Done!'''				)

       # update state dict keys
       SCREAMING_SNAKE_CASE					:      List[str]     					=			original_model.state_dict()
       SCREAMING_SNAKE_CASE					:      List[str]     					=			create_rename_keys(A				)
       for src, dest in rename_keys:
              rename_key(A					,		A					,		A				)

       # some keys can be renamed efficiently
       for key, val in state_dict.copy().items():
              SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			state_dict.pop(A				)
              if key.startswith('''Qformer.bert'''				):
                     SCREAMING_SNAKE_CASE					:      Optional[int]     					=			key.replace('''Qformer.bert'''					,		'''qformer'''				)
              if "attention.self" in key:
                     SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			key.replace('''self'''					,		'''attention'''				)
              if "opt_proj" in key:
                     SCREAMING_SNAKE_CASE					:      str     					=			key.replace('''opt_proj'''					,		'''language_projection'''				)
              if "t5_proj" in key:
                     SCREAMING_SNAKE_CASE					:      Tuple     					=			key.replace('''t5_proj'''					,		'''language_projection'''				)
              if key.startswith('''opt'''				):
                     SCREAMING_SNAKE_CASE					:      Tuple     					=			key.replace('''opt'''					,		'''language'''				)
              if key.startswith('''t5'''				):
                     SCREAMING_SNAKE_CASE					:      Dict     					=			key.replace('''t5'''					,		'''language'''				)
              SCREAMING_SNAKE_CASE					:      Optional[int]     					=			val

       # read in qv biases
       read_in_q_v_bias(A					,		A				)

       SCREAMING_SNAKE_CASE					:      Optional[int]     					=			hf_model.load_state_dict(A					,		strict=A				)
       assert len(A				) == 0
       assert unexpected_keys == ["qformer.embeddings.position_ids"]

       SCREAMING_SNAKE_CASE					:      str     					=			load_demo_image()
       SCREAMING_SNAKE_CASE					:      Optional[int]     					=			vis_processors['''eval'''](A				).unsqueeze(0				).to(A				)
       SCREAMING_SNAKE_CASE					:      int     					=			tokenizer(['''\n''']					,		return_tensors='''pt'''				).input_ids.to(A				)

       # create processor
       SCREAMING_SNAKE_CASE					:      Dict     					=			BlipImageProcessor(
           size={'''height''': image_size, '''width''': image_size}					,		image_mean=A					,		image_std=A				)
       SCREAMING_SNAKE_CASE					:      int     					=			BlipaProcessor(image_processor=A					,		tokenizer=A				)
       SCREAMING_SNAKE_CASE					:      str     					=			processor(images=A					,		return_tensors='''pt'''				).pixel_values.to(A				)

       # make sure processor creates exact same pixel values
       assert torch.allclose(A					,		A				)

       original_model.to(A				)
       hf_model.to(A				)
       with torch.no_grad():
              if "opt" in model_name:
                     SCREAMING_SNAKE_CASE					:      Any     					=			original_model({'''image''': original_pixel_values, '''text_input''': ['''''']}				).logits
                     SCREAMING_SNAKE_CASE					:      List[Any]     					=			hf_model(A					,		A				).logits
              else:
                     SCREAMING_SNAKE_CASE					:      Any     					=			original_model(
                         {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']}				).logits
                     SCREAMING_SNAKE_CASE					:      str     					=			input_ids.masked_fill(input_ids == tokenizer.pad_token_id					,		-100				)
                     SCREAMING_SNAKE_CASE					:      Tuple     					=			hf_model(A					,		A					,		labels=A				).logits

       assert original_logits.shape == logits.shape
       print('''First values of original logits:'''					,		original_logits[0, :3, :3]				)
       print('''First values of HF logits:'''					,		logits[0, :3, :3]				)

       # assert values
       if model_name == "blip2-flan-t5-xl":
              SCREAMING_SNAKE_CASE					:      int     					=			torch.tensor(
                  [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]					,		device=A				)
              assert torch.allclose(logits[0, :3, :3]					,		A					,		atol=1e-4				)
       elif model_name == "blip2-flan-t5-xl-coco":
              SCREAMING_SNAKE_CASE					:      Any     					=			torch.tensor(
                  [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]					,		device=A				)
       else:
              # cast to same type
              SCREAMING_SNAKE_CASE					:      Tuple     					=			logits.dtype
              assert torch.allclose(original_logits.to(A				)					,		A					,		atol=1e-2				)
       print('''Looks ok!'''				)

       print('''Generating a caption...'''				)
       SCREAMING_SNAKE_CASE					:      Any     					=			''''''
       SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer(A					,		return_tensors='''pt'''				).input_ids.to(A				)

       SCREAMING_SNAKE_CASE					:      Dict     					=			original_model.generate({'''image''': original_pixel_values}				)
       SCREAMING_SNAKE_CASE					:      Optional[int]     					=			hf_model.generate(
           A					,		A					,		do_sample=A					,		num_beams=5					,		max_length=30					,		min_length=1					,		top_p=0.9					,		repetition_penalty=1.0					,		length_penalty=1.0					,		temperature=1					,		)
       print('''Original generation:'''					,		A				)
       SCREAMING_SNAKE_CASE					:      Optional[int]     					=			input_ids.shape[1]
       SCREAMING_SNAKE_CASE					:      Optional[int]     					=			processor.batch_decode(outputs[:, prompt_length:]					,		skip_special_tokens=A				)
       SCREAMING_SNAKE_CASE					:      Tuple     					=			[text.strip() for text in output_text]
       print('''HF generation:'''					,		A				)

       if pytorch_dump_folder_path is not None:
              processor.save_pretrained(A				)
              hf_model.save_pretrained(A				)

       if push_to_hub:
              processor.push_to_hub(F"""nielsr/{model_name}"""				)
              hf_model.push_to_hub(F"""nielsr/{model_name}"""				)


if __name__ == "__main__":
       lowerCAmelCase_     :       List[Any]								=						argparse.ArgumentParser()
       lowerCAmelCase_     :       Union[str, Any]								=						[
           'blip2-opt-2.7b',
           'blip2-opt-6.7b',
           'blip2-opt-2.7b-coco',
           'blip2-opt-6.7b-coco',
           'blip2-flan-t5-xl',
           'blip2-flan-t5-xl-coco',
           'blip2-flan-t5-xxl',
       ]
       parser.add_argument(
           '--model_name',
           default='blip2-opt-2.7b',
           choices=choices,
           type=str,
           help='Path to hf config.json of model to convert',
       )
       parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
       parser.add_argument(
           '--push_to_hub',
           action='store_true',
           help='Whether to push the model and processor to the hub after converting',
       )

       lowerCAmelCase_     :       Optional[Any]								=						parser.parse_args()

       convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''



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 	lowerCamelCase_							(					unittest.TestCase			):







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tempfile.mkdtemp()

													SCREAMING_SNAKE_CASE					:      int     					=			[
													    '''[UNK]''',
													    '''[CLS]''',
													    '''[SEP]''',
													    '''[PAD]''',
													    '''[MASK]''',
													    '''的''',
													    '''价''',
													    '''格''',
													    '''是''',
													    '''15''',
													    '''便''',
													    '''alex''',
													    '''##andra''',
													    '''，''',
													    '''。''',
													    '''-''',
													    '''t''',
													    '''shirt''',
													]
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			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					:      Optional[Any]     					=			{
													    '''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''': [0.4814_5466, 0.457_8275, 0.4082_1073],
													    '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
													    '''do_convert_rgb''': True,
													}
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			os.path.join(self.tmpdirname				,    lowerCAmelCase__				)
													with open(self.image_processor_file				,    '''w'''				,    encoding='''utf-8'''				) as fp:
																				json.dump(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      str				,    **lowerCAmelCase__   :      int				):

													"""simple docstring"""
													return BertTokenizer.from_pretrained(self.tmpdirname				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Any				,    **lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													return BertTokenizerFast.from_pretrained(self.tmpdirname				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				,    **lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													shutil.rmtree(self.tmpdirname				)







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			[np.random.randint(2_55				,    size=(3, 30, 4_00)				,    dtype=np.uinta				)]

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			[Image.fromarray(np.moveaxis(lowerCAmelCase__				,    0				,    -1				)				) for x in image_inputs]

													return image_inputs







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.get_tokenizer()
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_rust_tokenizer()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_image_processor()

													SCREAMING_SNAKE_CASE					:      Dict     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)
													processor_slow.save_pretrained(self.tmpdirname				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			ChineseCLIPProcessor.from_pretrained(self.tmpdirname				,    use_fast=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)
													processor_fast.save_pretrained(self.tmpdirname				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			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				,    lowerCAmelCase__				)
													self.assertIsInstance(processor_fast.tokenizer				,    lowerCAmelCase__				)

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







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			ChineseCLIPProcessor(tokenizer=self.get_tokenizer()				,    image_processor=self.get_image_processor()				)
													processor.save_pretrained(self.tmpdirname				)

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.get_tokenizer(cls_token='''(CLS)'''				,    sep_token='''(SEP)'''				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.get_image_processor(do_normalize=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			ChineseCLIPProcessor.from_pretrained(
													    self.tmpdirname				,    cls_token='''(CLS)'''				,    sep_token='''(SEP)'''				,    do_normalize=lowerCAmelCase__				)

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

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







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.prepare_image_inputs()

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			image_processor(lowerCAmelCase__				,    return_tensors='''np'''				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			processor(images=lowerCAmelCase__				,    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 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			'''Alexandra，T-shirt的价格是15便士。'''

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			processor(text=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer(lowerCAmelCase__				)

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







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      str     					=			'''Alexandra，T-shirt的价格是15便士。'''
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.prepare_image_inputs()

													SCREAMING_SNAKE_CASE					:      List[str]     					=			processor(text=lowerCAmelCase__				,    images=lowerCAmelCase__				)

													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(lowerCAmelCase__				):
																				processor()







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

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

													SCREAMING_SNAKE_CASE					:      Any     					=			processor.batch_decode(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			tokenizer.batch_decode(lowerCAmelCase__				)

													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Dict     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			'''Alexandra，T-shirt的价格是15便士。'''
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.prepare_image_inputs()

													SCREAMING_SNAKE_CASE					:      Any     					=			processor(text=lowerCAmelCase__				,    images=lowerCAmelCase__				)

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




