



"""simple docstring"""







import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__		:    int				   =     logging.get_logger(__name__)

lowercase__		:    List[str]				   =     {
    """b0""": efficientnet.EfficientNetBa,
    """b1""": efficientnet.EfficientNetBa,
    """b2""": efficientnet.EfficientNetBa,
    """b3""": efficientnet.EfficientNetBa,
    """b4""": efficientnet.EfficientNetBa,
    """b5""": efficientnet.EfficientNetBa,
    """b6""": efficientnet.EfficientNetBa,
    """b7""": efficientnet.EfficientNetBa,
}

lowercase__		:    Tuple				   =     {
    """b0""": {
        """hidden_dim""": 1_2_8_0,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 2_2_4,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 1_2_8_0,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 2_4_0,
        """dropout_rate""": 0.2,
        """dw_padding""": [1_6],
    },
    """b2""": {
        """hidden_dim""": 1_4_0_8,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 2_6_0,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 1_6],
    },
    """b3""": {
        """hidden_dim""": 1_5_3_6,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 3_0_0,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 1_8],
    },
    """b4""": {
        """hidden_dim""": 1_7_9_2,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 3_8_0,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 2_0_4_8,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 4_5_6,
        """dropout_rate""": 0.4,
        """dw_padding""": [1_3, 2_7],
    },
    """b6""": {
        """hidden_dim""": 2_3_0_4,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 5_2_8,
        """dropout_rate""": 0.5,
        """dw_padding""": [3_1],
    },
    """b7""": {
        """hidden_dim""": 2_5_6_0,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 6_0_0,
        """dropout_rate""": 0.5,
        """dw_padding""": [1_8],
    },
}

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[str]						)						->       str:

  """simple docstring"""




  lowerCAmelCase_       :    List[str]								=			EfficientNetConfig()
  lowerCAmelCase_       :    int								=			CONFIG_MAP[model_name]['hidden_dim']
  lowerCAmelCase_       :    str								=			CONFIG_MAP[model_name]['width_coef']
  lowerCAmelCase_       :    Dict								=			CONFIG_MAP[model_name]['depth_coef']
  lowerCAmelCase_       :    int								=			CONFIG_MAP[model_name]['image_size']
  lowerCAmelCase_       :    Optional[int]								=			CONFIG_MAP[model_name]['dropout_rate']
  lowerCAmelCase_       :    Union[str, Any]								=			CONFIG_MAP[model_name]['dw_padding']

  lowerCAmelCase_       :    Any								=			'huggingface/label-files'
  lowerCAmelCase_       :    List[Any]								=			'imagenet-1k-id2label.json'
  lowerCAmelCase_       :    int								=			1000
  lowerCAmelCase_       :    str								=			json.load(open(hf_hub_download(lowerCAmelCase__   ,       lowerCAmelCase__   ,       repo_type='dataset'						)   ,       'r'						)						)
  lowerCAmelCase_       :    Tuple								=			{int(lowerCAmelCase__						): v for k, v in idalabel.items()}

  lowerCAmelCase_       :    Union[str, Any]								=			idalabel
  lowerCAmelCase_       :    str								=			{v: k for k, v in idalabel.items()}
  return config

def     UpperCamelCase_   (   )						->       List[Any]:

  """simple docstring"""




  lowerCAmelCase_       :    List[str]								=			'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowerCAmelCase_       :    Dict								=			Image.open(requests.get(lowerCAmelCase__   ,       stream=lowerCAmelCase__						).raw						)
  return im

def     UpperCamelCase_   (   lowerCAmelCase__				:    Union[str, Any]						)						->       Any:

  """simple docstring"""




  lowerCAmelCase_       :    Union[str, Any]								=			CONFIG_MAP[model_name]['image_size']
  lowerCAmelCase_       :    int								=			EfficientNetImageProcessor(
      size={'height': size, 'width': size}   ,       image_mean=[0.485, 0.456, 0.406]   ,       image_std=[0.47853944, 0.4732864, 0.47434163]   ,       do_center_crop=lowerCAmelCase__   ,       )
  return preprocessor

def     UpperCamelCase_   (   lowerCAmelCase__				:    Tuple						)						->       Tuple:

  """simple docstring"""




  lowerCAmelCase_       :    Any								=			[v.split('_'						)[0].split('block'						)[1] for v in original_param_names if v.startswith('block'						)]
  lowerCAmelCase_       :    str								=			sorted(set(lowerCAmelCase__						)						)
  lowerCAmelCase_       :    Any								=			len(lowerCAmelCase__						)
  lowerCAmelCase_       :    Tuple								=			{b: str(lowerCAmelCase__						) for b, i in zip(lowerCAmelCase__   ,       range(lowerCAmelCase__						)						)}

  lowerCAmelCase_       :    Any								=			[]
  rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')						)
  rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')						)
  rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')						)
  rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')						)
  rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')						)

  for b in block_names:
    lowerCAmelCase_       :    int								=			block_name_mapping[b]
    rename_keys.append((f"block{b}_expand_conv/kernel:0", f"encoder.blocks.{hf_b}.expansion.expand_conv.weight")						)
    rename_keys.append((f"block{b}_expand_bn/gamma:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.weight")						)
    rename_keys.append((f"block{b}_expand_bn/beta:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.bias")						)
    rename_keys.append(
        (f"block{b}_expand_bn/moving_mean:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean")						)
    rename_keys.append(
        (f"block{b}_expand_bn/moving_variance:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_var")						)
    rename_keys.append(
        (f"block{b}_dwconv/depthwise_kernel:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight")						)
    rename_keys.append((f"block{b}_bn/gamma:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight")						)
    rename_keys.append((f"block{b}_bn/beta:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias")						)
    rename_keys.append(
        (f"block{b}_bn/moving_mean:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean")						)
    rename_keys.append(
        (f"block{b}_bn/moving_variance:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var")						)

    rename_keys.append((f"block{b}_se_reduce/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight")						)
    rename_keys.append((f"block{b}_se_reduce/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias")						)
    rename_keys.append((f"block{b}_se_expand/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.weight")						)
    rename_keys.append((f"block{b}_se_expand/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.bias")						)
    rename_keys.append(
        (f"block{b}_project_conv/kernel:0", f"encoder.blocks.{hf_b}.projection.project_conv.weight")						)
    rename_keys.append((f"block{b}_project_bn/gamma:0", f"encoder.blocks.{hf_b}.projection.project_bn.weight")						)
    rename_keys.append((f"block{b}_project_bn/beta:0", f"encoder.blocks.{hf_b}.projection.project_bn.bias")						)
    rename_keys.append(
        (f"block{b}_project_bn/moving_mean:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_mean")						)
    rename_keys.append(
        (f"block{b}_project_bn/moving_variance:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_var")						)

  rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight')						)
  rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight')						)
  rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias')						)
  rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean')						)
  rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var')						)

  lowerCAmelCase_       :    str								=			{}
  for item in rename_keys:
    if item[0] in original_param_names:
      lowerCAmelCase_       :    str								=			'efficientnet.' + item[1]

  lowerCAmelCase_       :    Tuple								=			'classifier.weight'
  lowerCAmelCase_       :    str								=			'classifier.bias'
  return key_mapping

def     UpperCamelCase_   (   lowerCAmelCase__				:    Union[str, Any]   ,       lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    Optional[Any]						)						->       List[str]:

  """simple docstring"""




  for key, value in tf_params.items():
    if "normalization" in key:
      continue

    lowerCAmelCase_       :    int								=			key_mapping[key]
    if "_conv" in key and "kernel" in key:
      lowerCAmelCase_       :    Optional[int]								=			torch.from_numpy(lowerCAmelCase__						).permute(3   ,       2   ,       0   ,       1						)
    elif "depthwise_kernel" in key:
      lowerCAmelCase_       :    List[Any]								=			torch.from_numpy(lowerCAmelCase__						).permute(2   ,       3   ,       0   ,       1						)
    elif "kernel" in key:
      lowerCAmelCase_       :    Optional[Any]								=			torch.from_numpy(np.transpose(lowerCAmelCase__						)						)
    else:
      lowerCAmelCase_       :    Any								=			torch.from_numpy(lowerCAmelCase__						)

    # Replace HF parameters with original TF model parameters
    assert hf_params[hf_key].shape == new_hf_value.shape
    hf_params[hf_key].copy_(lowerCAmelCase__						)

@torch.no_grad()
def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[Any]   ,       lowerCAmelCase__				:    Union[str, Any]   ,       lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    List[Any]						)						->       str:

  """simple docstring"""




  lowerCAmelCase_       :    Union[str, Any]								=			model_classes[model_name](
      include_top=lowerCAmelCase__   ,       weights='imagenet'   ,       input_tensor=lowerCAmelCase__   ,       input_shape=lowerCAmelCase__   ,       pooling=lowerCAmelCase__   ,       classes=1000   ,       classifier_activation='softmax'   ,       )

  lowerCAmelCase_       :    Optional[int]								=			original_model.trainable_variables
  lowerCAmelCase_       :    Any								=			original_model.non_trainable_variables
  lowerCAmelCase_       :    int								=			{param.name: param.numpy() for param in tf_params}
  for param in tf_non_train_params:
    lowerCAmelCase_       :    Tuple								=			param.numpy()
  lowerCAmelCase_       :    Dict								=			list(tf_params.keys()						)

  # Load HuggingFace model
  lowerCAmelCase_       :    str								=			get_efficientnet_config(lowerCAmelCase__						)
  lowerCAmelCase_       :    Any								=			EfficientNetForImageClassification(lowerCAmelCase__						).eval()
  lowerCAmelCase_       :    Dict								=			hf_model.state_dict()

  # Create src-to-dst parameter name mapping dictionary
  print('Converting parameters...'						)
  lowerCAmelCase_       :    Dict								=			rename_keys(lowerCAmelCase__						)
  replace_params(lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)

  # Initialize preprocessor and preprocess input image
  lowerCAmelCase_       :    int								=			convert_image_processor(lowerCAmelCase__						)
  lowerCAmelCase_       :    Union[str, Any]								=			preprocessor(images=prepare_img()   ,       return_tensors='pt'						)

  # HF model inference
  hf_model.eval()
  with torch.no_grad():
    lowerCAmelCase_       :    Dict								=			hf_model(**lowerCAmelCase__						)
  lowerCAmelCase_       :    Tuple								=			outputs.logits.detach().numpy()

  # Original model inference
  lowerCAmelCase_       :    List[str]								=			False
  lowerCAmelCase_       :    Optional[Any]								=			CONFIG_MAP[model_name]['image_size']
  lowerCAmelCase_       :    Any								=			prepare_img().resize((image_size, image_size)   ,       resample=PIL.Image.NEAREST						)
  lowerCAmelCase_       :    Union[str, Any]								=			image.img_to_array(lowerCAmelCase__						)
  lowerCAmelCase_       :    str								=			np.expand_dims(lowerCAmelCase__   ,       axis=0						)
  lowerCAmelCase_       :    Optional[Any]								=			original_model.predict(lowerCAmelCase__						)

  # Check whether original and HF model outputs match  -> np.allclose
  assert np.allclose(lowerCAmelCase__   ,       lowerCAmelCase__   ,       atol=1e-3						), "The predicted logits are not the same."
  print('Model outputs match!'						)

  if save_model:
    # Create folder to save model
    if not os.path.isdir(lowerCAmelCase__						):
      os.mkdir(lowerCAmelCase__						)
    # Save converted model and image processor
    hf_model.save_pretrained(lowerCAmelCase__						)
    preprocessor.save_pretrained(lowerCAmelCase__						)

  if push_to_hub:
    # Push model and image processor to hub
    print(f"Pushing converted {model_name} to the hub..."						)
    lowerCAmelCase_       :    Any								=			f"efficientnet-{model_name}"
    preprocessor.push_to_hub(lowerCAmelCase__						)
    hf_model.push_to_hub(lowerCAmelCase__						)


if __name__ == "__main__":
     lowercase__		:    Optional[Any]				   =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--model_name""",
         default="""b0""",
         type=str,
         help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default="""hf_model""",
         type=str,
         help="""Path to the output PyTorch model directory.""",
     )
     parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
     parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")

     lowercase__		:    Optional[int]				   =     parser.parse_args()
     convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)










"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase__		:    Tuple				   =     logging.get_logger(__name__)







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					["""pixel_values"""]



      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				Optional[Dict[str, int]] = None   ,      SCREAMING_SNAKE_CASE_    :				PILImageResampling = PILImageResampling.BICUBIC   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				Union[int, float] = 1 / 2_5_5   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int] = None   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      **SCREAMING_SNAKE_CASE_    :				int   ,      ):
        super().__init__(**SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Dict								=			size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
        lowerCAmelCase_       :    List[Any]								=			get_size_dict(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Tuple								=			crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
        lowerCAmelCase_       :    List[str]								=			get_size_dict(SCREAMING_SNAKE_CASE_   ,      default_to_square=SCREAMING_SNAKE_CASE_   ,      param_name='crop_size'	)

        lowerCAmelCase_       :    List[Any]								=			do_resize
        lowerCAmelCase_       :    Any								=			do_rescale
        lowerCAmelCase_       :    int								=			do_normalize
        lowerCAmelCase_       :    List[str]								=			do_center_crop
        lowerCAmelCase_       :    Dict								=			crop_size
        lowerCAmelCase_       :    Optional[Any]								=			size
        lowerCAmelCase_       :    Tuple								=			resample
        lowerCAmelCase_       :    Optional[int]								=			rescale_factor
        lowerCAmelCase_       :    Optional[Any]								=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        lowerCAmelCase_       :    List[str]								=			image_std if image_std is not None else IMAGENET_DEFAULT_STD



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int]   ,      SCREAMING_SNAKE_CASE_    :				PILImageResampling = PILImageResampling.BILINEAR   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				str   ,      ):
        lowerCAmelCase_       :    str								=			get_size_dict(SCREAMING_SNAKE_CASE_	)

        if "shortest_edge" in size:
          lowerCAmelCase_       :    Any								=			get_resize_output_image_size(SCREAMING_SNAKE_CASE_   ,      size=size['shortest_edge']   ,      default_to_square=SCREAMING_SNAKE_CASE_	)
          # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
          lowerCAmelCase_       :    Union[str, Any]								=			(size['height'], size['width'])
        else:
          raise ValueError(F"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"	)
        return resize(SCREAMING_SNAKE_CASE_   ,      size=SCREAMING_SNAKE_CASE_   ,      resample=SCREAMING_SNAKE_CASE_   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				int   ,      ):
        lowerCAmelCase_       :    Any								=			get_size_dict(SCREAMING_SNAKE_CASE_	)
        if "height" not in size or "width" not in size:
          raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}"	)
        return center_crop(SCREAMING_SNAKE_CASE_   ,      size=(size['height'], size['width'])   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				float   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				Optional[int]	):
        return rescale(SCREAMING_SNAKE_CASE_   ,      scale=SCREAMING_SNAKE_CASE_   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				Union[float, List[float]]   ,      SCREAMING_SNAKE_CASE_    :				Union[float, List[float]]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				str   ,      ):
        return normalize(SCREAMING_SNAKE_CASE_   ,      mean=SCREAMING_SNAKE_CASE_   ,      std=SCREAMING_SNAKE_CASE_   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				ImageInput   ,      SCREAMING_SNAKE_CASE_    :				Optional[bool] = None   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int] = None   ,      SCREAMING_SNAKE_CASE_    :				PILImageResampling = None   ,      SCREAMING_SNAKE_CASE_    :				bool = None   ,      SCREAMING_SNAKE_CASE_    :				int = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[bool] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[float] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[bool] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, TensorType]] = None   ,      SCREAMING_SNAKE_CASE_    :				Union[str, ChannelDimension] = ChannelDimension.FIRST   ,      **SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      ):
        lowerCAmelCase_       :    Optional[int]								=			do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase_       :    Optional[int]								=			do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase_       :    Optional[Any]								=			do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase_       :    Dict								=			do_center_crop if do_center_crop is not None else self.do_center_crop
        lowerCAmelCase_       :    Dict								=			crop_size if crop_size is not None else self.crop_size
        lowerCAmelCase_       :    Optional[Any]								=			get_size_dict(SCREAMING_SNAKE_CASE_   ,      param_name='crop_size'   ,      default_to_square=SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Union[str, Any]								=			resample if resample is not None else self.resample
        lowerCAmelCase_       :    Optional[Any]								=			rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase_       :    str								=			image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase_       :    List[str]								=			image_std if image_std is not None else self.image_std

        lowerCAmelCase_       :    Tuple								=			size if size is not None else self.size
        lowerCAmelCase_       :    str								=			get_size_dict(SCREAMING_SNAKE_CASE_	)

        if not is_batched(SCREAMING_SNAKE_CASE_	):
          lowerCAmelCase_       :    List[Any]								=			[images]

        if not valid_images(SCREAMING_SNAKE_CASE_	):
          raise ValueError(
              'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
              'torch.Tensor, tf.Tensor or jax.ndarray.'	)

        if do_resize and size is None:
          raise ValueError('Size 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.'	)

        # All transformations expect numpy arrays.
        lowerCAmelCase_       :    str								=			[to_numpy_array(SCREAMING_SNAKE_CASE_	) for image in images]

        if do_resize:
          lowerCAmelCase_       :    Any								=			[self.resize(image=SCREAMING_SNAKE_CASE_   ,      size=SCREAMING_SNAKE_CASE_   ,      resample=SCREAMING_SNAKE_CASE_	) for image in images]

        if do_center_crop:
          lowerCAmelCase_       :    Optional[int]								=			[self.center_crop(image=SCREAMING_SNAKE_CASE_   ,      size=SCREAMING_SNAKE_CASE_	) for image in images]

        if do_rescale:
          lowerCAmelCase_       :    Tuple								=			[self.rescale(image=SCREAMING_SNAKE_CASE_   ,      scale=SCREAMING_SNAKE_CASE_	) for image in images]

        if do_normalize:
          lowerCAmelCase_       :    str								=			[self.normalize(image=SCREAMING_SNAKE_CASE_   ,      mean=SCREAMING_SNAKE_CASE_   ,      std=SCREAMING_SNAKE_CASE_	) for image in images]

        lowerCAmelCase_       :    Any								=			[to_channel_dimension_format(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	) for image in images]

        lowerCAmelCase_       :    Optional[Any]								=			{'pixel_values': images}
        return BatchFeature(data=SCREAMING_SNAKE_CASE_   ,      tensor_type=SCREAMING_SNAKE_CASE_	)





