

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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD





def      snake_case_				()      ->     int:
   __lowerCAmelCase  :      Tuple    =						"""https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"""
   __lowerCAmelCase  :      int    =						Image.open(requests.get(__A ,       stream=__A					).raw					).convert("""RGB"""					)

   return image





def      snake_case_				(__A					:						Union[str, Any]					)      ->     Tuple:
   __lowerCAmelCase  :      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.embeddings.layernorm.weight""")					)
   rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.bias""", """qformer.embeddings.layernorm.bias""")					)

   # fmt: on
   return rename_keys





def      snake_case_				(__A					:						Union[str, Any] ,       __A					:						Optional[Any] ,       __A					:						Any					)      ->     List[Any]:
   __lowerCAmelCase  :      Optional[Any]    =						dct.pop(__A					)
   __lowerCAmelCase  :      Any    =						val





def      snake_case_				(__A					:						int ,       __A					:						str					)      ->     Dict:
   for i in range(config.vision_config.num_hidden_layers					):
      # read in original q and v biases
      __lowerCAmelCase  :      str    =						state_dict.pop(f'''visual_encoder.blocks.{i}.attn.q_bias'''					)
      __lowerCAmelCase  :      Tuple    =						state_dict.pop(f'''visual_encoder.blocks.{i}.attn.v_bias'''					)

      # next, set bias in the state dict
      __lowerCAmelCase  :      Dict    =						torch.cat((q_bias, torch.zeros_like(__A ,       requires_grad=__A					), v_bias)					)
      __lowerCAmelCase  :      str    =						qkv_bias





def      snake_case_				(__A					:						Any					)      ->     str:
   __lowerCAmelCase  :      int    =						3_6_4 if """coco""" in model_name else 2_2_4
   __lowerCAmelCase  :      Optional[Any]    =						InstructBlipVisionConfig(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 "t5-xl" in model_name:
      __lowerCAmelCase  :      Optional[int]    =						TaConfig.from_pretrained("""google/flan-t5-xl""" ,       dense_act_fn="""gelu""" ,       bos_token_id=1					).to_dict()
   elif "t5-xxl" in model_name:
      __lowerCAmelCase  :      Union[str, Any]    =						TaConfig.from_pretrained("""google/flan-t5-xxl""" ,       dense_act_fn="""gelu""" ,       bos_token_id=1					).to_dict()
   elif "vicuna-7b" in model_name:
      __lowerCAmelCase  :      Any    =						LlamaConfig.from_pretrained("""decapoda-research/llama-7b-hf""" ,       vocab_size=3_2_0_0_1					).to_dict()
   elif "vicuna-13b" in model_name:
      __lowerCAmelCase  :      Optional[Any]    =						LlamaConfig.from_pretrained("""decapoda-research/llama-13b-hf""" ,       vocab_size=3_2_0_0_1					).to_dict()
   else:
      raise ValueError("""Model name not supported"""					)

   # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
   __lowerCAmelCase  :      List[Any]    =						InstructBlipQFormerConfig(vocab_size=3_0_5_2_3					).to_dict()
   __lowerCAmelCase  :      str    =						InstructBlipConfig(vision_config=__A ,       text_config=__A ,       qformer_config=__A					)

   return config, image_size





@torch.no_grad()
def      snake_case_				(__A					:						List[Any] ,       __A					:						Optional[Any]=None ,       __A					:						List[Any]=False					)      ->     int:
   __lowerCAmelCase  :      Any    =						AutoTokenizer.from_pretrained("""bert-base-uncased""" ,       truncation_side="""left"""					)
   qformer_tokenizer.add_special_tokens({"""bos_token""": """[DEC]"""}					)

   if "t5" in model_name:
      __lowerCAmelCase  :      Dict    =						TaTokenizerFast.from_pretrained("""google/flan-t5-xl""" ,       truncation_side="""left"""					)
   elif "vicuna" in model_name:
      # the following was used in the original implementation:
      # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
      # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
      # tokenizer.add_special_tokens({"bos_token": "</s>"})
      # tokenizer.add_special_tokens({"eos_token": "</s>"})
      # tokenizer.add_special_tokens({"unk_token": "</s>"})
      __lowerCAmelCase  :      Tuple    =						LlamaTokenizerFast.from_pretrained(
          """huggyllama/llama-7b""" ,       truncation_side="""left""" ,       bos_token="""</s>""" ,       unk_token="""</s>"""					)
      tokenizer.add_special_tokens({"""pad_token""": """[PAD]"""}					)

   __lowerCAmelCase   ,__lowerCAmelCase  :      int    =						get_blipa_config(__A					)
   __lowerCAmelCase  :      str    =						InstructBlipForConditionalGeneration(__A					).eval()

   __lowerCAmelCase  :      Optional[int]    =						{
       """instructblip-vicuna-7b""": ("""blip2_vicuna_instruct""", """vicuna7b"""),
       """instructblip-vicuna-13b""": ("""blip2_vicuna_instruct""", """vicuna13b"""),
       """instructblip-flan-t5-xl""": ("""blip2_t5_instruct""", """flant5xl"""),
       """instructblip-flan-t5-xxl""": ("""blip2_t5_instruct""", """flant5xxl"""),
   }

   __lowerCAmelCase   ,__lowerCAmelCase  :      List[str]    =						model_name_to_original[model_name]

   # load original model
   print("""Loading original model..."""					)
   __lowerCAmelCase  :      Union[str, Any]    =						"""cuda:1""" if torch.cuda.is_available() else """cpu"""
   __lowerCAmelCase  :      str    =						"""cuda:2""" if torch.cuda.is_available() else """cpu"""
   __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      Union[str, Any]    =						load_model_and_preprocess(
       name=__A ,       model_type=__A ,       is_eval=__A ,       device=__A					)
   original_model.eval()
   print("""Done!"""					)

   # update state dict keys
   __lowerCAmelCase  :      List[str]    =						original_model.state_dict()
   __lowerCAmelCase  :      Dict    =						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():
      __lowerCAmelCase  :      List[str]    =						state_dict.pop(__A					)
      if key.startswith("""Qformer.bert"""					):
         __lowerCAmelCase  :      Any    =						key.replace("""Qformer.bert""" ,       """qformer"""					)
      if "attention.self" in key:
         __lowerCAmelCase  :      Dict    =						key.replace("""self""" ,       """attention"""					)
      if "llm_proj" in key:
         __lowerCAmelCase  :      List[str]    =						key.replace("""llm_proj""" ,       """language_projection"""					)
      if "t5_proj" in key:
         __lowerCAmelCase  :      str    =						key.replace("""t5_proj""" ,       """language_projection"""					)
      if key.startswith("""llm_model"""					):
         __lowerCAmelCase  :      int    =						key.replace("""llm_model""" ,       """language_model"""					)
      if key.startswith("""t5"""					):
         __lowerCAmelCase  :      Dict    =						key.replace("""t5""" ,       """language"""					)
      __lowerCAmelCase  :      str    =						val

   # read in qv biases
   read_in_q_v_bias(__A ,       __A					)

   # note: weights get loaded in torch.float32 by default
   hf_model.load_state_dict(__A ,       strict=__A					)

   __lowerCAmelCase  :      Optional[int]    =						load_demo_image()
   __lowerCAmelCase  :      List[Any]    =						"""What is unusual about this image?"""

   # create processor
   __lowerCAmelCase  :      int    =						BlipImageProcessor(
       size={"""height""": image_size, """width""": image_size} ,       image_mean=__A ,       image_std=__A					)
   __lowerCAmelCase  :      Any    =						InstructBlipProcessor(
       image_processor=__A ,       tokenizer=__A ,       qformer_tokenizer=__A ,       )
   __lowerCAmelCase  :      Union[str, Any]    =						processor(images=__A ,       text=__A ,       return_tensors="""pt"""					).to(__A					)

   # make sure processor creates exact same pixel values
   __lowerCAmelCase  :      Optional[Any]    =						vis_processors["""eval"""](__A					).unsqueeze(0					).to(__A					)
   __lowerCAmelCase  :      Any    =						inputs.pixel_values
   assert torch.allclose(original_pixel_values.to(pixel_values.device					) ,       __A					)

   original_model.to(__A					)
   hf_model.to(__A					)
   with torch.no_grad():
      if "vicuna" in model_name:
         __lowerCAmelCase  :      Dict    =						original_model({"""image""": original_pixel_values, """text_input""": [prompt]}					).logits
         __lowerCAmelCase  :      str    =						hf_model(**__A					).logits
      else:
         __lowerCAmelCase  :      Optional[int]    =						original_model(
             {"""image""": original_pixel_values, """text_input""": [prompt], """text_output""": ["""\n"""]}					).logits
         __lowerCAmelCase  :      Union[str, Any]    =						tokenizer("""\n""" ,       return_tensors="""pt"""					).input_ids.to(__A					)
         __lowerCAmelCase  :      Optional[Any]    =						label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id ,       -1_0_0					)
         __lowerCAmelCase  :      Tuple    =						hf_model(**__A ,       labels=__A					).logits

   print("""First values of original logits:""" ,       original_logits[0, :3, :3]					)
   print("""First values of HF logits:""" ,       logits[0, :3, :3]					)

   # assert values
   assert original_logits.shape == logits.shape
   __lowerCAmelCase  :      str    =						1e-4 if """vicuna""" in model_name else 1e-5
   assert torch.allclose(original_logits.to(logits.device					) ,       __A ,       atol=__A					)
   print("""Looks ok!"""					)

   print("""Generating with original model..."""					)
   __lowerCAmelCase  :      List[Any]    =						original_model.generate({"""image""": original_pixel_values, """prompt""": prompt} ,       num_beams=5					)

   # important: we need to cast the weights of the HF model to the appropriate type
   print("""Generating with HF model..."""					)
   __lowerCAmelCase  :      Optional[int]    =						hf_model.generate(
       **__A ,       do_sample=__A ,       num_beams=5 ,       max_length=2_5_6 ,       min_length=1 ,       top_p=0.9 ,       repetition_penalty=1.5 ,       length_penalty=1.0 ,       temperature=1 ,       )
   if "vicuna" in model_name:
      # convert output id 0 to 2 (eos_token_id)
      # TODO add this in the generate method?
      __lowerCAmelCase  :      Optional[int]    =						2
   print("""Original generation:""" ,       __A					)
   __lowerCAmelCase  :      int    =						processor.batch_decode(__A ,       skip_special_tokens=__A					)
   __lowerCAmelCase  :      int    =						[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'''Salesforce/{model_name}'''					)
      hf_model.push_to_hub(f'''Salesforce/{model_name}'''					)


if __name__ == "__main__":
   __UpperCAmelCase								=			argparse.ArgumentParser()
   __UpperCAmelCase								=			[
       """instructblip-vicuna-7b""",
       """instructblip-vicuna-13b""",
       """instructblip-flan-t5-xl""",
       """instructblip-flan-t5-xxl""",
   ]
   parser.add_argument(
       """--model_name""",
       default="""instructblip-flan-t5-xl""",
       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""",
   )

   __UpperCAmelCase								=			parser.parse_args()

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





import unittest

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 MobileViTImageProcessor



class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def __init__(      self			:   List[Any]	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Optional[Any]=7	,							lowerCAmelCase			:   List[str]=3	,							lowerCAmelCase			:   int=18	,							lowerCAmelCase			:   int=30	,							lowerCAmelCase			:   Optional[int]=4_00	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   Dict=None	,							lowerCAmelCase			:   List[str]=True	,							lowerCAmelCase			:   Tuple=None	,							lowerCAmelCase			:   Any=True	,							)							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						size if size is not None else {"""shortest_edge""": 20}
     __lowerCAmelCase  :      Any    =						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
     __lowerCAmelCase  :      str    =						parent
     __lowerCAmelCase  :      List[str]    =						batch_size
     __lowerCAmelCase  :      int    =						num_channels
     __lowerCAmelCase  :      List[str]    =						image_size
     __lowerCAmelCase  :      Optional[int]    =						min_resolution
     __lowerCAmelCase  :      List[str]    =						max_resolution
     __lowerCAmelCase  :      List[Any]    =						do_resize
     __lowerCAmelCase  :      Optional[int]    =						size
     __lowerCAmelCase  :      List[Any]    =						do_center_crop
     __lowerCAmelCase  :      Optional[Any]    =						crop_size
     __lowerCAmelCase  :      int    =						do_flip_channel_order







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Union[str, Any]:






     """simple docstring"""







     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "do_flip_channel_order": self.do_flip_channel_order,
     }






@require_torch
@require_vision
class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      List[str]										=MobileViTImageProcessor if is_vision_available() else None







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						MobileViTImageProcessingTester(self   )







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Dict:






     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()







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






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						self.image_processing_class(**self.image_processor_dict   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_resize"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""size"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_center_crop"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""center_crop"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_flip_channel_order"""   )   )







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






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.image_processing_class.from_dict(self.image_processor_dict   )
     self.assertEqual(image_processor.size	,							{"""shortest_edge""": 20}   )
     self.assertEqual(image_processor.crop_size	,							{"""height""": 18, """width""": 18}   )

     __lowerCAmelCase  :      Optional[Any]    =						self.image_processing_class.from_dict(self.image_processor_dict	,							size=42	,							crop_size=84   )
     self.assertEqual(image_processor.size	,							{"""shortest_edge""": 42}   )
     self.assertEqual(image_processor.crop_size	,							{"""height""": 84, """width""": 84}   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     int:






     """simple docstring"""







     pass







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.image_processing_class(**self.image_processor_dict   )
     # create random PIL images
     __lowerCAmelCase  :      Union[str, Any]    =						prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCAmelCase   )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase	,							Image.Image   )

     # Test not batched input
     __lowerCAmelCase  :      List[Any]    =						image_processing(image_inputs[0]	,							return_tensors="""pt"""   ).pixel_values
     self.assertEqual(
         encoded_images.shape	,							(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)

     # Test batched
     __lowerCAmelCase  :      str    =						image_processing(lowerCAmelCase	,							return_tensors="""pt"""   ).pixel_values
     self.assertEqual(
         encoded_images.shape	,							(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.image_processing_class(**self.image_processor_dict   )
     # create random numpy tensors
     __lowerCAmelCase  :      Any    =						prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCAmelCase	,							numpify=lowerCAmelCase   )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase	,							np.ndarray   )

     # Test not batched input
     __lowerCAmelCase  :      Optional[Any]    =						image_processing(image_inputs[0]	,							return_tensors="""pt"""   ).pixel_values
     self.assertEqual(
         encoded_images.shape	,							(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)

     # Test batched
     __lowerCAmelCase  :      Tuple    =						image_processing(lowerCAmelCase	,							return_tensors="""pt"""   ).pixel_values
     self.assertEqual(
         encoded_images.shape	,							(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)







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






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						self.image_processing_class(**self.image_processor_dict   )
     # create random PyTorch tensors
     __lowerCAmelCase  :      str    =						prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCAmelCase	,							torchify=lowerCAmelCase   )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase	,							torch.Tensor   )

     # Test not batched input
     __lowerCAmelCase  :      Optional[int]    =						image_processing(image_inputs[0]	,							return_tensors="""pt"""   ).pixel_values
     self.assertEqual(
         encoded_images.shape	,							(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)

     # Test batched
     __lowerCAmelCase  :      Tuple    =						image_processing(lowerCAmelCase	,							return_tensors="""pt"""   ).pixel_values
     self.assertEqual(
         encoded_images.shape	,							(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)


