





'''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   lowerCAmelCase_				( )  ->	Optional[int]:







  """simple docstring"""





  lowerCamelCase__:   Dict    						="""https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"""
  lowerCamelCase__:   Optional[int]    						=Image.open(requests.get(__UpperCamelCase			,					stream=__UpperCamelCase	).raw	).convert("RGB"	)

  return image





def   lowerCAmelCase_				( __a	)  ->	int:







  """simple docstring"""





  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.layernorm.weight")	)
  rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias")	)

  # fmt: on
  return rename_keys





def   lowerCAmelCase_				( __a			,					__a			,					__a	)  ->	int:







  """simple docstring"""





  lowerCamelCase__:   str    						=dct.pop(__UpperCamelCase	)
  lowerCamelCase__:   Optional[int]    						=val





def   lowerCAmelCase_				( __a			,					__a	)  ->	List[Any]:







  """simple docstring"""





  for i in range(config.vision_config.num_hidden_layers	):
    # read in original q and v biases
    lowerCamelCase__:   Tuple    						=state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""	)
    lowerCamelCase__:   Optional[int]    						=state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""	)

    # next, set bias in the state dict
    lowerCamelCase__:   Any    						=torch.cat((q_bias, torch.zeros_like(__UpperCamelCase			,					requires_grad=__UpperCamelCase	), v_bias)	)
    lowerCamelCase__:   Optional[Any]    						=qkv_bias





def   lowerCAmelCase_				( __a			,					__a	)  ->	Optional[int]:







  """simple docstring"""





  lowerCamelCase__:   Optional[Any]    						=364 if """coco""" in model_name else 224
  lowerCamelCase__:   List[Any]    						=BlipaVisionConfig(image_size=__UpperCamelCase	).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:
    lowerCamelCase__:   str    						=OPTConfig.from_pretrained("facebook/opt-2.7b"			,					eos_token_id=__UpperCamelCase	).to_dict()
  elif "opt-6.7b" in model_name:
    lowerCamelCase__:   str    						=OPTConfig.from_pretrained("facebook/opt-6.7b"			,					eos_token_id=__UpperCamelCase	).to_dict()
  elif "t5-xl" in model_name:
    lowerCamelCase__:   Optional[Any]    						=TaConfig.from_pretrained("google/flan-t5-xl"			,					dense_act_fn="gelu"			,					bos_token_id=1	).to_dict()
  elif "t5-xxl" in model_name:
    lowerCamelCase__:   Any    						=TaConfig.from_pretrained("google/flan-t5-xxl"			,					dense_act_fn="gelu"			,					bos_token_id=1	).to_dict()

  lowerCamelCase__:   Tuple    						=BlipaConfig(vision_config=__UpperCamelCase			,					text_config=__UpperCamelCase	)

  return config, image_size





@torch.no_grad()
def   lowerCAmelCase_				( __a			,					__a=None			,					__a=False	)  ->	List[str]:







  """simple docstring"""





  lowerCamelCase__:   Dict    						=(
      AutoTokenizer.from_pretrained("facebook/opt-2.7b"	)
      if """opt""" in model_name
      else AutoTokenizer.from_pretrained("google/flan-t5-xl"	)
  )
  lowerCamelCase__:   Dict    						=tokenizer("\n"			,					add_special_tokens=__UpperCamelCase	).input_ids[0]
  lowerCamelCase__:   List[str]    						=get_blipa_config(__UpperCamelCase			,					eos_token_id=__UpperCamelCase	)

  lowerCamelCase__:   Tuple    						=BlipaForConditionalGeneration(__UpperCamelCase	).eval()

  lowerCamelCase__:   Any    						={
      """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"""),
  }

  lowerCamelCase__:   List[str]    						=model_name_to_original[model_name]

  # load original model
  print("Loading original model..."	)
  lowerCamelCase__:   Optional[Any]    						="""cuda""" if torch.cuda.is_available() else """cpu"""
  lowerCamelCase__:   List[Any]    						=load_model_and_preprocess(
      name=__UpperCamelCase			,					model_type=__UpperCamelCase			,					is_eval=__UpperCamelCase			,					device=__UpperCamelCase	)
  original_model.eval()
  print("Done!"	)

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

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

  # read in qv biases
  read_in_q_v_bias(__UpperCamelCase			,					__UpperCamelCase	)

  lowerCamelCase__:   Any    						=hf_model.load_state_dict(__UpperCamelCase			,					strict=__UpperCamelCase	)
  assert len(__UpperCamelCase	) == 0
  assert unexpected_keys == ["qformer.embeddings.position_ids"]

  lowerCamelCase__:   List[str]    						=load_demo_image()
  lowerCamelCase__:   Any    						=vis_processors["""eval"""](__UpperCamelCase	).unsqueeze(0	).to(__UpperCamelCase	)
  lowerCamelCase__:   Dict    						=tokenizer(["\n"]			,					return_tensors="pt"	).input_ids.to(__UpperCamelCase	)

  # create processor
  lowerCamelCase__:   int    						=BlipImageProcessor(
      size={"height": image_size, "width": image_size}			,					image_mean=__UpperCamelCase			,					image_std=__UpperCamelCase	)
  lowerCamelCase__:   List[str]    						=BlipaProcessor(image_processor=__UpperCamelCase			,					tokenizer=__UpperCamelCase	)
  lowerCamelCase__:   List[Any]    						=processor(images=__UpperCamelCase			,					return_tensors="pt"	).pixel_values.to(__UpperCamelCase	)

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

  original_model.to(__UpperCamelCase	)
  hf_model.to(__UpperCamelCase	)
  with torch.no_grad():
    if "opt" in model_name:
      lowerCamelCase__:   List[Any]    						=original_model({"image": original_pixel_values, "text_input": [""]}	).logits
      lowerCamelCase__:   Any    						=hf_model(__UpperCamelCase			,					__UpperCamelCase	).logits
    else:
      lowerCamelCase__:   Tuple    						=original_model(
          {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}	).logits
      lowerCamelCase__:   List[Any]    						=input_ids.masked_fill(input_ids == tokenizer.pad_token_id			,					-100	)
      lowerCamelCase__:   int    						=hf_model(__UpperCamelCase			,					__UpperCamelCase			,					labels=__UpperCamelCase	).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":
    lowerCamelCase__:   Optional[Any]    						=torch.tensor(
        [[-4_1.5_8_5_0, -4.4_4_4_0, -8.9_9_2_2], [-4_7.4_3_2_2, -5.9_1_4_3, -1.7_3_4_0]]			,					device=__UpperCamelCase	)
    assert torch.allclose(logits[0, :3, :3]			,					__UpperCamelCase			,					atol=1e-4	)
  elif model_name == "blip2-flan-t5-xl-coco":
    lowerCamelCase__:   Any    						=torch.tensor(
        [[-5_7.0_1_0_9, -9.8_9_6_7, -1_2.6_2_8_0], [-6_8.6_5_7_8, -1_2.7_1_9_1, -1_0.5_0_6_5]]			,					device=__UpperCamelCase	)
  else:
    # cast to same type
    lowerCamelCase__:   List[Any]    						=logits.dtype
    assert torch.allclose(original_logits.to(__UpperCamelCase	)			,					__UpperCamelCase			,					atol=1e-2	)
  print("Looks ok!"	)

  print("Generating a caption..."	)
  lowerCamelCase__:   Optional[int]    						=""""""
  lowerCamelCase__:   int    						=tokenizer(__UpperCamelCase			,					return_tensors="pt"	).input_ids.to(__UpperCamelCase	)

  lowerCamelCase__:   Any    						=original_model.generate({"image": original_pixel_values}	)
  lowerCamelCase__:   Optional[int]    						=hf_model.generate(
      __UpperCamelCase			,					__UpperCamelCase			,					do_sample=__UpperCamelCase			,					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:"			,					__UpperCamelCase	)
  lowerCamelCase__:   Optional[int]    						=input_ids.shape[1]
  lowerCamelCase__:   Union[str, Any]    						=processor.batch_decode(outputs[:, prompt_length:]			,					skip_special_tokens=__UpperCamelCase	)
  lowerCamelCase__:   Union[str, Any]    						=[text.strip() for text in output_text]
  print("HF generation:"			,					__UpperCamelCase	)

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

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


if __name__ == "__main__":
     __A             =       argparse.ArgumentParser()
     __A             =       [
         '''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",
     )

     __A             =       parser.parse_args()

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








from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
     import PIL

__A             =       logging.get_logger(__name__)





def   lowerCAmelCase_				( __a	)  ->	List[List[ImageInput]]:







  """simple docstring"""





  if isinstance(__a			,					(list, tuple)	) and isinstance(videos[0]			,					(list, tuple)	) and is_valid_image(videos[0][0]	):
    return videos

  elif isinstance(__a			,					(list, tuple)	) and is_valid_image(videos[0]	):
    return [videos]

  elif is_valid_image(__a	):
    return [[videos]]

  raise ValueError(F"""Could not make batched video from {videos}"""	)






class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					):







 '''simple docstring'''
 lowercase_	=    ["pixel_values"]

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



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)
   lowerCamelCase__:   Optional[int]    						=size if size is not None else {"shortest_edge": 256}
   lowerCamelCase__:   Any    						=get_size_dict(UpperCAmelCase_ ,				default_to_square=UpperCAmelCase_)
   lowerCamelCase__:   Tuple    						=crop_size if crop_size is not None else {"height": 224, "width": 224}
   lowerCamelCase__:   Any    						=get_size_dict(UpperCAmelCase_ ,				param_name="crop_size")

   lowerCamelCase__:   str    						=do_resize
   lowerCamelCase__:   Any    						=size
   lowerCamelCase__:   Any    						=do_center_crop
   lowerCamelCase__:   int    						=crop_size
   lowerCamelCase__:   int    						=resample
   lowerCamelCase__:   Optional[int]    						=do_rescale
   lowerCamelCase__:   int    						=rescale_factor
   lowerCamelCase__:   Dict    						=offset
   lowerCamelCase__:   List[Any]    						=do_normalize
   lowerCamelCase__:   Optional[int]    						=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   lowerCamelCase__:   List[str]    						=image_std if image_std is not None else IMAGENET_STANDARD_STD

 def 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any] ,				UpperCAmelCase_       :     np.ndarray ,				UpperCAmelCase_       :     Dict[str, int] ,				UpperCAmelCase_       :     PILImageResampling = PILImageResampling.BILINEAR ,				UpperCAmelCase_       :     Optional[Union[str, ChannelDimension]] = None ,				**UpperCAmelCase_       :     List[Any] ,				) ->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__:   Dict    						=get_size_dict(UpperCAmelCase_ ,				default_to_square=UpperCAmelCase_)
   if "shortest_edge" in size:
     lowerCamelCase__:   Union[str, Any]    						=get_resize_output_image_size(UpperCAmelCase_ ,				size["shortest_edge"] ,				default_to_square=UpperCAmelCase_)
   elif "height" in size and "width" in size:
     lowerCamelCase__:   int    						=(size["height"], size["width"])
   else:
     raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""")
   return resize(UpperCAmelCase_ ,				size=UpperCAmelCase_ ,				resample=UpperCAmelCase_ ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     str ,				UpperCAmelCase_       :     np.ndarray ,				UpperCAmelCase_       :     Dict[str, int] ,				UpperCAmelCase_       :     Optional[Union[str, ChannelDimension]] = None ,				**UpperCAmelCase_       :     List[Any] ,				) ->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__:   List[str]    						=get_size_dict(UpperCAmelCase_)
   if "height" not in size or "width" not in size:
     raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}""")
   return center_crop(UpperCAmelCase_ ,				size=(size["height"], size["width"]) ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any] ,				UpperCAmelCase_       :     np.ndarray ,				UpperCAmelCase_       :     Union[int, float] ,				UpperCAmelCase_       :     bool = True ,				UpperCAmelCase_       :     Optional[Union[str, ChannelDimension]] = None ,				**UpperCAmelCase_       :     Union[str, Any] ,				) ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:   str    						=image.astype(np.floataa)
   if offset:
     lowerCamelCase__:   List[Any]    						=image - (scale / 2)
   return rescale(UpperCAmelCase_ ,				scale=UpperCAmelCase_ ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)

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



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int] ,				UpperCAmelCase_       :     ImageInput ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     Dict[str, int] = None ,				UpperCAmelCase_       :     PILImageResampling = None ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     Dict[str, int] = None ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     float = None ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     Optional[Union[float, List[float]]] = None ,				UpperCAmelCase_       :     Optional[Union[float, List[float]]] = None ,				UpperCAmelCase_       :     Optional[ChannelDimension] = ChannelDimension.FIRST ,				) ->np.ndarray:



   '''simple docstring'''



   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_center_crop and crop_size is None:
     raise ValueError("Crop size must be specified if do_center_crop is True.")

   if do_rescale and rescale_factor is None:
     raise ValueError("Rescale factor must be specified if do_rescale is True.")

   if do_normalize and (image_mean is None or image_std is None):
     raise ValueError("Image mean and std must be specified if do_normalize is True.")

   if offset and not do_rescale:
     raise ValueError("For offset, do_rescale must also be set to True.")

   # All transformations expect numpy arrays.
   lowerCamelCase__:   List[str]    						=to_numpy_array(UpperCAmelCase_)

   if do_resize:
     lowerCamelCase__:   Optional[Any]    						=self.resize(image=UpperCAmelCase_ ,				size=UpperCAmelCase_ ,				resample=UpperCAmelCase_)

   if do_center_crop:
     lowerCamelCase__:   str    						=self.center_crop(UpperCAmelCase_ ,				size=UpperCAmelCase_)

   if do_rescale:
     lowerCamelCase__:   List[str]    						=self.rescale(image=UpperCAmelCase_ ,				scale=UpperCAmelCase_ ,				offset=UpperCAmelCase_)

   if do_normalize:
     lowerCamelCase__:   Dict    						=self.normalize(image=UpperCAmelCase_ ,				mean=UpperCAmelCase_ ,				std=UpperCAmelCase_)

   lowerCamelCase__:   Any    						=to_channel_dimension_format(UpperCAmelCase_ ,				UpperCAmelCase_)
   return image







 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[Any] ,				UpperCAmelCase_       :     ImageInput ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     Dict[str, int] = None ,				UpperCAmelCase_       :     PILImageResampling = None ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     Dict[str, int] = None ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     float = None ,				UpperCAmelCase_       :     bool = 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_       :     List[str] ,				) ->PIL.Image.Image:



   '''simple docstring'''



   lowerCamelCase__:   Union[str, Any]    						=do_resize if do_resize is not None else self.do_resize
   lowerCamelCase__:   Optional[int]    						=resample if resample is not None else self.resample
   lowerCamelCase__:   Optional[int]    						=do_center_crop if do_center_crop is not None else self.do_center_crop
   lowerCamelCase__:   List[str]    						=do_rescale if do_rescale is not None else self.do_rescale
   lowerCamelCase__:   Dict    						=rescale_factor if rescale_factor is not None else self.rescale_factor
   lowerCamelCase__:   Union[str, Any]    						=offset if offset is not None else self.offset
   lowerCamelCase__:   Dict    						=do_normalize if do_normalize is not None else self.do_normalize
   lowerCamelCase__:   Optional[int]    						=image_mean if image_mean is not None else self.image_mean
   lowerCamelCase__:   List[Any]    						=image_std if image_std is not None else self.image_std

   lowerCamelCase__:   List[Any]    						=size if size is not None else self.size
   lowerCamelCase__:   Optional[int]    						=get_size_dict(UpperCAmelCase_ ,				default_to_square=UpperCAmelCase_)
   lowerCamelCase__:   str    						=crop_size if crop_size is not None else self.crop_size
   lowerCamelCase__:   Optional[Any]    						=get_size_dict(UpperCAmelCase_ ,				param_name="crop_size")

   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.")

   lowerCamelCase__:   str    						=make_batched(UpperCAmelCase_)

   lowerCamelCase__:   Any    						=[
       [
           self._preprocess_image(
               image=UpperCAmelCase_ ,				do_resize=UpperCAmelCase_ ,				size=UpperCAmelCase_ ,				resample=UpperCAmelCase_ ,				do_center_crop=UpperCAmelCase_ ,				crop_size=UpperCAmelCase_ ,				do_rescale=UpperCAmelCase_ ,				rescale_factor=UpperCAmelCase_ ,				offset=UpperCAmelCase_ ,				do_normalize=UpperCAmelCase_ ,				image_mean=UpperCAmelCase_ ,				image_std=UpperCAmelCase_ ,				data_format=UpperCAmelCase_ ,				)
           for img in video
       ]
       for video in videos
   ]

   lowerCamelCase__:   int    						={"pixel_values": videos}
   return BatchFeature(data=UpperCAmelCase_ ,				tensor_type=UpperCAmelCase_)

