code: 
  
  
  
  
  import argparse
  import collections
  
  import jax
  import jax.numpy as jnp
  import torch
  import torch.nn as nn
  from clip.model import CLIP
  from flax.training import checkpoints
  from huggingface_hub import Repository
  
  from transformers import (
      CLIPTokenizer,
      OwlViTConfig,
      OwlViTForObjectDetection,
      OwlViTImageProcessor,
      OwlViTModel,
      OwlViTProcessor,
  )
  
  
  SCREAMING_SNAKE_CASE__    :    Optional[Any] 						=					{
      '''vit_b32''': {
          '''embed_dim''': 5_12,
          '''image_resolution''': 7_68,
          '''context_length''': 16,
          '''vocab_size''': 4_94_08,
          '''vision_layers''': 12,
          '''vision_width''': 7_68,
          '''vision_patch_size''': 32,
          '''transformer_width''': 5_12,
          '''transformer_heads''': 8,
          '''transformer_layers''': 12,
      },
      '''vit_b16''': {
          '''embed_dim''': 5_12,
          '''image_resolution''': 7_68,
          '''context_length''': 16,
          '''vocab_size''': 4_94_08,
          '''vision_layers''': 12,
          '''vision_width''': 7_68,
          '''vision_patch_size''': 16,
          '''transformer_width''': 5_12,
          '''transformer_heads''': 8,
          '''transformer_layers''': 12,
      },
      '''vit_l14''': {
          '''embed_dim''': 7_68,
          '''image_resolution''': 8_40,
          '''context_length''': 16,
          '''vocab_size''': 4_94_08,
          '''vision_layers''': 24,
          '''vision_width''': 10_24,
          '''vision_patch_size''': 14,
          '''transformer_width''': 7_68,
          '''transformer_heads''': 12,
          '''transformer_layers''': 12,
      },
  }
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE=""					,						SCREAMING_SNAKE_CASE="/"   ) ->       str:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	[]
  
  			for k, v in params.items():
  						SCREAMING_SNAKE_CASE_			=	parent_key + sep + k if parent_key else k
  
  						if isinstance(snake_case__					,						collections.MutableMapping   ):
  									items.extend(flatten_nested_dict(snake_case__					,						snake_case__					,						sep=snake_case__   ).items()   )
  						else:
  									items.append((new_key, v)   )
  			return dict(snake_case__   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Optional[Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			return jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE   : x.astype(jnp.floataa   ) if x.dtype == jnp.bfloataa else x					,						snake_case__   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       int:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	pt_attn_layer.in_proj_weight.chunk(3					,						dim=0   )
  			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	pt_attn_layer.in_proj_bias.chunk(3					,						dim=0   )
  
  			SCREAMING_SNAKE_CASE_			=	pt_attn_layer.out_proj.weight
  			SCREAMING_SNAKE_CASE_			=	pt_attn_layer.out_proj.bias
  
  			SCREAMING_SNAKE_CASE_			=	q_proj
  			SCREAMING_SNAKE_CASE_			=	q_proj_bias
  
  			SCREAMING_SNAKE_CASE_			=	k_proj
  			SCREAMING_SNAKE_CASE_			=	k_proj_bias
  
  			SCREAMING_SNAKE_CASE_			=	v_proj
  			SCREAMING_SNAKE_CASE_			=	v_proj_bias
  
  			SCREAMING_SNAKE_CASE_			=	out_proj_weights
  			SCREAMING_SNAKE_CASE_			=	out_proj_bias
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       int:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			copy_linear(hf_mlp.fca					,						pt_mlp.c_fc   )
  			copy_linear(hf_mlp.fca					,						pt_mlp.c_proj   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       int:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	pt_linear.weight
  			SCREAMING_SNAKE_CASE_			=	pt_linear.bias
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Optional[int]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			copy_linear(hf_layer.layer_norma					,						pt_layer.ln_a   )
  			copy_linear(hf_layer.layer_norma					,						pt_layer.ln_a   )
  
  			# copy MLP
  			copy_mlp(hf_layer.mlp					,						pt_layer.mlp   )
  
  			# copy attn
  			copy_attn_layer(hf_layer.self_attn					,						pt_layer.attn   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Any:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			for hf_layer, pt_layer in zip(snake_case__					,						snake_case__   ):
  						copy_layer(snake_case__					,						snake_case__   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       int:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	pt_model.token_embedding.weight
  			SCREAMING_SNAKE_CASE_			=	pt_model.positional_embedding
  
  			# copy layer norm
  			copy_linear(hf_encoder.final_layer_norm					,						pt_model.ln_final   )
  
  			# copy hidden layers
  			copy_layers(hf_encoder.encoder.layers					,						pt_model.transformer.resblocks   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Optional[int]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	pt_model.text_projection.data.T
  
  			# copy text encoder
  			copy_encoder(hf_model.text_model					,						snake_case__   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Any:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	pt_model.visual.proj.data.T
  
  			# copy layer norms
  			copy_linear(hf_model.vision_model.pre_layernorm					,						pt_model.visual.ln_pre   )
  			copy_linear(hf_model.vision_model.post_layernorm					,						pt_model.visual.ln_post   )
  
  			# copy embeds
  			SCREAMING_SNAKE_CASE_			=	pt_model.visual.conva.weight.data
  			SCREAMING_SNAKE_CASE_			=	pt_model.visual.class_embedding
  			SCREAMING_SNAKE_CASE_			=	pt_model.visual.positional_embedding.data
  
  			# copy encoder
  			copy_layers(hf_model.vision_model.encoder.layers					,						pt_model.visual.transformer.resblocks   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Any:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	flatten_nested_dict(flax_params['backbone']['merged_class_token']   )
  
  			SCREAMING_SNAKE_CASE_			=	torch.from_numpy(flax_class_token_params['scale']   )
  			SCREAMING_SNAKE_CASE_			=	torch.from_numpy(flax_class_token_params['bias']   )
  			SCREAMING_SNAKE_CASE_			=	nn.Parameter(snake_case__   )
  			SCREAMING_SNAKE_CASE_			=	nn.Parameter(snake_case__   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Dict:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	hf_model.state_dict()
  			SCREAMING_SNAKE_CASE_			=	{}
  
  			# Rename class prediction head flax params to pytorch HF
  			SCREAMING_SNAKE_CASE_			=	flatten_nested_dict(flax_params['class_head']   )
  
  			for flax_key, v in flax_class_params.items():
  						SCREAMING_SNAKE_CASE_			=	flax_key.replace('/'					,						'.'   )
  						SCREAMING_SNAKE_CASE_			=	torch_key.replace('.kernel'					,						'.weight'   )
  						SCREAMING_SNAKE_CASE_			=	torch_key.replace('Dense_0'					,						'dense0'   )
  						SCREAMING_SNAKE_CASE_			=	'class_head.' + torch_key
  
  						if "weight" in torch_key and v.ndim == 2:
  									SCREAMING_SNAKE_CASE_			=	v.T
  
  						SCREAMING_SNAKE_CASE_			=	nn.Parameter(torch.from_numpy(snake_case__   )   )
  
  			# Rename box prediction box flax params to pytorch HF
  			SCREAMING_SNAKE_CASE_			=	flatten_nested_dict(flax_params['obj_box_head']   )
  
  			for flax_key, v in flax_box_params.items():
  						SCREAMING_SNAKE_CASE_			=	flax_key.replace('/'					,						'.'   )
  						SCREAMING_SNAKE_CASE_			=	torch_key.replace('.kernel'					,						'.weight'   )
  						SCREAMING_SNAKE_CASE_			=	torch_key.replace('_'					,						''   ).lower()
  						SCREAMING_SNAKE_CASE_			=	'box_head.' + torch_key
  
  						if "weight" in torch_key and v.ndim == 2:
  									SCREAMING_SNAKE_CASE_			=	v.T
  
  						SCREAMING_SNAKE_CASE_			=	nn.Parameter(torch.from_numpy(snake_case__   )   )
  
  			# Copy flax params to PyTorch params
  			for name, param in new_params.items():
  						if name in pt_params.keys():
  									pt_params[name].copy_(snake_case__   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Dict:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			for k, v in flax_attn_params.items():
  						if k.startswith('transformer'   ):
  									SCREAMING_SNAKE_CASE_			=	k.replace('transformer.resblocks'					,						'text_model.encoder.layers'   )
  						else:
  									SCREAMING_SNAKE_CASE_			=	k.replace('visual.transformer.resblocks'					,						'vision_model.encoder.layers'   )
  
  						SCREAMING_SNAKE_CASE_			=	torch_key.replace('attn'					,						'self_attn'   )
  						SCREAMING_SNAKE_CASE_			=	torch_key.replace('key'					,						'k_proj'   )
  						SCREAMING_SNAKE_CASE_			=	torch_key.replace('value'					,						'v_proj'   )
  						SCREAMING_SNAKE_CASE_			=	torch_key.replace('query'					,						'q_proj'   )
  						SCREAMING_SNAKE_CASE_			=	torch_key.replace('out'					,						'out_proj'   )
  
  						if "bias" in torch_key and v.ndim == 2:
  									SCREAMING_SNAKE_CASE_			=	v.shape[0] * v.shape[1]
  									SCREAMING_SNAKE_CASE_			=	v.reshape(snake_case__   )
  
  						if "weight" in torch_key and "out" in torch_key:
  									SCREAMING_SNAKE_CASE_			=	(v.shape[0] * v.shape[1], v.shape[2])
  									SCREAMING_SNAKE_CASE_			=	v.reshape(snake_case__   ).T
  
  						if "weight" in torch_key and "out" not in torch_key:
  									SCREAMING_SNAKE_CASE_			=	(v.shape[0], v.shape[1] * v.shape[2])
  									SCREAMING_SNAKE_CASE_			=	v.reshape(snake_case__   ).T
  
  						# Copy flax CLIP attn params to HF PyTorch params
  						SCREAMING_SNAKE_CASE_			=	torch.from_numpy(snake_case__   )
  						hf_backbone.state_dict()[torch_key].copy_(snake_case__   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       List[str]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	{}
  			SCREAMING_SNAKE_CASE_			=	[]
  
  			for k, v in params.items():
  						if "attn." in k:
  									SCREAMING_SNAKE_CASE_			=	k[: k.rindex('attn.'   ) + 5]
  									if base in processed_attn_layers:
  												continue
  
  									processed_attn_layers.append(snake_case__   )
  									SCREAMING_SNAKE_CASE_			=	params[base + 'out.weight'].shape[-1]
  									SCREAMING_SNAKE_CASE_			=	params[base + 'out.weight'].reshape(snake_case__					,						snake_case__   ).T
  									SCREAMING_SNAKE_CASE_			=	params[base + 'out.bias']
  						else:
  									SCREAMING_SNAKE_CASE_			=	v
  			return new_params
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       str:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	CLIP(**snake_case__   )
  			torch_model.eval()
  			SCREAMING_SNAKE_CASE_			=	torch_model.state_dict()
  
  			SCREAMING_SNAKE_CASE_			=	flatten_nested_dict(flax_params['backbone']['clip']   )
  			SCREAMING_SNAKE_CASE_			=	{}
  
  			for flax_key, v in flax_clip_params.items():
  						SCREAMING_SNAKE_CASE_			=	flax_key.replace('/'					,						'.'   )
  						SCREAMING_SNAKE_CASE_			=	torch_key.replace('text.token_embedding.embedding'					,						'token_embedding.kernel'   )
  
  						if (
  						    torch_key.startswith('text.transformer'   )
  						    or torch_key.startswith('text.text_projection'   )
  						    or torch_key.startswith('text.ln_final'   )
  						    or torch_key.startswith('text.positional_embedding'   )
  						):
  									SCREAMING_SNAKE_CASE_			=	torch_key[5:]
  
  						SCREAMING_SNAKE_CASE_			=	torch_key.replace('text_projection.kernel'					,						'text_projection'   )
  						SCREAMING_SNAKE_CASE_			=	torch_key.replace('visual.proj.kernel'					,						'visual.proj'   )
  						SCREAMING_SNAKE_CASE_			=	torch_key.replace('.scale'					,						'.weight'   )
  						SCREAMING_SNAKE_CASE_			=	torch_key.replace('.kernel'					,						'.weight'   )
  
  						if "conv" in torch_key or "downsample.0.weight" in torch_key:
  									SCREAMING_SNAKE_CASE_			=	v.transpose(3					,						2					,						0					,						1   )
  
  						elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
  									# Fully connected layers are transposed, embeddings are not
  									SCREAMING_SNAKE_CASE_			=	v.T
  
  						SCREAMING_SNAKE_CASE_			=	v
  
  			SCREAMING_SNAKE_CASE_			=	_convert_attn_layers(snake_case__   )
  			new_torch_params.update(snake_case__   )
  			SCREAMING_SNAKE_CASE_			=	{}
  
  			# Copy flax CLIP backbone params to PyTorch params
  			for name, param in new_torch_params.items():
  						if name in torch_clip_params.keys():
  									SCREAMING_SNAKE_CASE_			=	torch.from_numpy(new_torch_params[name]   )
  									torch_clip_params[name].copy_(snake_case__   )
  						else:
  									SCREAMING_SNAKE_CASE_			=	param
  
  			return torch_clip_params, torch_model, attn_params
  
  @torch.no_grad()
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE=None   ) ->       Optional[Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	Repository(snake_case__					,						clone_from=F'google/{pytorch_dump_folder_path}'   )
  			repo.git_pull()
  
  			if config_path is not None:
  						SCREAMING_SNAKE_CASE_			=	OwlViTConfig.from_pretrained(snake_case__   )
  			else:
  						SCREAMING_SNAKE_CASE_			=	OwlViTConfig()
  
  			SCREAMING_SNAKE_CASE_			=	OwlViTModel(snake_case__   ).eval()
  			SCREAMING_SNAKE_CASE_			=	OwlViTForObjectDetection(snake_case__   ).eval()
  
  			copy_text_model_and_projection(snake_case__					,						snake_case__   )
  			copy_vision_model_and_projection(snake_case__					,						snake_case__   )
  			SCREAMING_SNAKE_CASE_			=	pt_backbone.logit_scale
  			copy_flax_attn_params(snake_case__					,						snake_case__   )
  
  			SCREAMING_SNAKE_CASE_			=	hf_backbone
  			copy_class_merge_token(snake_case__					,						snake_case__   )
  			copy_class_box_heads(snake_case__					,						snake_case__   )
  
  			# Save HF model
  			hf_model.save_pretrained(repo.local_dir   )
  
  			# Initialize image processor
  			SCREAMING_SNAKE_CASE_			=	OwlViTImageProcessor(
  			    size=config.vision_config.image_size					,						crop_size=config.vision_config.image_size   )
  			# Initialize tokenizer
  			SCREAMING_SNAKE_CASE_			=	CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'					,						pad_token='!'					,						model_max_length=16   )
  
  			# Initialize processor
  			SCREAMING_SNAKE_CASE_			=	OwlViTProcessor(image_processor=snake_case__					,						tokenizer=snake_case__   )
  			image_processor.save_pretrained(repo.local_dir   )
  			processor.save_pretrained(repo.local_dir   )
  
  			repo.git_add()
  			repo.git_commit('Upload model and processor'   )
  			repo.git_push()
  
  
  if __name__ == "__main__":
  				SCREAMING_SNAKE_CASE__    :    int 						=					argparse.ArgumentParser()
  				# Required parameters
  				parser.add_argument(
  				    "--owlvit_version",
  				    default=None,
  				    type=str,
  				    required=True,
  				    help="OWL-ViT model name [clip_b16, clip_b32, clip_l14].",
  				)
  				parser.add_argument(
  				    "--owlvit_checkpoint", default=None, type=str, required=True, help="Path to flax model checkpoint."
  				)
  				parser.add_argument("--hf_config", default=None, type=str, required=True, help="Path to HF model config.")
  				parser.add_argument(
  				    "--pytorch_dump_folder_path", default="hf_model", type=str, help="Path to the output PyTorch model."
  				)
  				SCREAMING_SNAKE_CASE__    :    str 						=					parser.parse_args()
  
  				# Initialize PyToch clip model
  				SCREAMING_SNAKE_CASE__    :    Union[str, Any] 						=					args.owlvit_version
  				if model_name == "clip_b16":
  								SCREAMING_SNAKE_CASE__    :    Union[str, Any] 						=					CONFIGS['''vit_b16''']
  				elif model_name == "clip_b32":
  								SCREAMING_SNAKE_CASE__    :    Optional[Any] 						=					CONFIGS['''vit_b32''']
  				elif model_name == "clip_l14":
  								SCREAMING_SNAKE_CASE__    :    str 						=					CONFIGS['''vit_l14''']
  
  				# Load from checkpoint and convert params to float-32
  				SCREAMING_SNAKE_CASE__    :    List[str] 						=					checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['''optimizer''']['''target''']
  				SCREAMING_SNAKE_CASE__    :    Dict 						=					jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
  				del variables
  
  				# Convert CLIP backbone
  				SCREAMING_SNAKE_CASE__    :    Any 						=					convert_clip_backbone(flax_params, torch_config)
  
  				convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)
  
style_context: 
  
  
  
  
  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 (
      get_resize_output_image_size,
      normalize,
      pad,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      get_image_size,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, logging
  from ...utils.import_utils import is_vision_available
  
  
  SCREAMING_SNAKE_CASE__    :    Tuple								=					logging.get_logger(__name__)
  
  
  if is_vision_available():
  				import PIL
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  	A						       =   ['''pixel_values''']
  
  
  
  
  
  	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = False			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 1 / 255			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				None:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__(**SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	size if size is not None else {'height': 2560, 'width': 1920}
  				if isinstance(SCREAMING_SNAKE_CASE			,    (tuple, list)			):
  							# The previous feature extractor size parameter was in (width, height) format
  							SCREAMING_SNAKE_CASE_			=	size[::-1]
  				SCREAMING_SNAKE_CASE_			=	get_size_dict(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	do_resize
  				SCREAMING_SNAKE_CASE_			=	size
  				SCREAMING_SNAKE_CASE_			=	resample
  				SCREAMING_SNAKE_CASE_			=	do_thumbnail
  				SCREAMING_SNAKE_CASE_			=	do_align_long_axis
  				SCREAMING_SNAKE_CASE_			=	do_pad
  				SCREAMING_SNAKE_CASE_			=	do_rescale
  				SCREAMING_SNAKE_CASE_			=	rescale_factor
  				SCREAMING_SNAKE_CASE_			=	do_normalize
  				SCREAMING_SNAKE_CASE_			=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
  				SCREAMING_SNAKE_CASE_			=	image_std if image_std is not None else IMAGENET_STANDARD_STD
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			)     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	get_image_size(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	size['height'], size['width']
  
  				if (output_width < output_height and input_width > input_height) or (
  				    output_width > output_height and input_width < input_height
  				):
  							SCREAMING_SNAKE_CASE_			=	np.rotaa(SCREAMING_SNAKE_CASE			,    3			)
  
  				if data_format is not None:
  							SCREAMING_SNAKE_CASE_			=	to_channel_dimension_format(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  				return image
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = False			,    SCREAMING_SNAKE_CASE = None			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	size['height'], size['width']
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	get_image_size(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	output_width - input_width
  				SCREAMING_SNAKE_CASE_			=	output_height - input_height
  
  				if random_padding:
  							SCREAMING_SNAKE_CASE_			=	np.random.randint(low=0			,    high=delta_height + 1			)
  							SCREAMING_SNAKE_CASE_			=	np.random.randint(low=0			,    high=delta_width + 1			)
  				else:
  							SCREAMING_SNAKE_CASE_			=	delta_height // 2
  							SCREAMING_SNAKE_CASE_			=	delta_width // 2
  
  				SCREAMING_SNAKE_CASE_			=	delta_height - pad_top
  				SCREAMING_SNAKE_CASE_			=	delta_width - pad_left
  
  				SCREAMING_SNAKE_CASE_			=	((pad_top, pad_bottom), (pad_left, pad_right))
  				return pad(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    *SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'			)
  				return self.pad_image(*SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	get_image_size(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	size['height'], size['width']
  
  				# We always resize to the smallest of either the input or output size.
  				SCREAMING_SNAKE_CASE_			=	min(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	min(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  				if height == input_height and width == input_width:
  							return image
  
  				if input_height > input_width:
  							SCREAMING_SNAKE_CASE_			=	int(input_width * height / input_height			)
  				elif input_width > input_height:
  							SCREAMING_SNAKE_CASE_			=	int(input_height * width / input_width			)
  
  				return resize(
  				    SCREAMING_SNAKE_CASE			,    size=(height, width)			,    resample=SCREAMING_SNAKE_CASE			,    reducing_gap=2.0			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	get_size_dict(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	min(size['height']			,    size['width']			)
  				SCREAMING_SNAKE_CASE_			=	get_resize_output_image_size(SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			,    default_to_square=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	resize(SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			,    resample=SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  				return resized_image
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return rescale(SCREAMING_SNAKE_CASE			,    scale=SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return normalize(SCREAMING_SNAKE_CASE			,    mean=SCREAMING_SNAKE_CASE			,    std=SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = False			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = ChannelDimension.FIRST			,    **SCREAMING_SNAKE_CASE			,    )     ->				PIL.Image.Image:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	do_resize if do_resize is not None else self.do_resize
  				SCREAMING_SNAKE_CASE_			=	size if size is not None else self.size
  				if isinstance(SCREAMING_SNAKE_CASE			,    (tuple, list)			):
  							# Previous feature extractor had size in (width, height) format
  							SCREAMING_SNAKE_CASE_			=	size[::-1]
  				SCREAMING_SNAKE_CASE_			=	get_size_dict(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	resample if resample is not None else self.resample
  				SCREAMING_SNAKE_CASE_			=	do_thumbnail if do_thumbnail is not None else self.do_thumbnail
  				SCREAMING_SNAKE_CASE_			=	do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
  				SCREAMING_SNAKE_CASE_			=	do_pad if do_pad is not None else self.do_pad
  				SCREAMING_SNAKE_CASE_			=	do_rescale if do_rescale is not None else self.do_rescale
  				SCREAMING_SNAKE_CASE_			=	rescale_factor if rescale_factor is not None else self.rescale_factor
  				SCREAMING_SNAKE_CASE_			=	do_normalize if do_normalize is not None else self.do_normalize
  				SCREAMING_SNAKE_CASE_			=	image_mean if image_mean is not None else self.image_mean
  				SCREAMING_SNAKE_CASE_			=	image_std if image_std is not None else self.image_std
  
  				SCREAMING_SNAKE_CASE_			=	make_list_of_images(SCREAMING_SNAKE_CASE			)
  
  				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_rescale and rescale_factor is None:
  							raise ValueError('Rescale factor must be specified if do_rescale is True.'			)
  
  				if do_pad and size is None:
  							raise ValueError('Size must be specified if do_pad 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.'			)
  
  				# All transformations expect numpy arrays.
  				SCREAMING_SNAKE_CASE_			=	[to_numpy_array(SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_align_long_axis:
  							SCREAMING_SNAKE_CASE_			=	[self.align_long_axis(SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_resize:
  							SCREAMING_SNAKE_CASE_			=	[self.resize(image=SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			,    resample=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_thumbnail:
  							SCREAMING_SNAKE_CASE_			=	[self.thumbnail(image=SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_pad:
  							SCREAMING_SNAKE_CASE_			=	[self.pad_image(image=SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			,    random_padding=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_rescale:
  							SCREAMING_SNAKE_CASE_			=	[self.rescale(image=SCREAMING_SNAKE_CASE			,    scale=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_normalize:
  							SCREAMING_SNAKE_CASE_			=	[self.normalize(image=SCREAMING_SNAKE_CASE			,    mean=SCREAMING_SNAKE_CASE			,    std=SCREAMING_SNAKE_CASE			) for image in images]
  
  				SCREAMING_SNAKE_CASE_			=	[to_channel_dimension_format(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for image in images]
  
  				SCREAMING_SNAKE_CASE_			=	{'pixel_values': images}
  				return BatchFeature(data=SCREAMING_SNAKE_CASE			,    tensor_type=SCREAMING_SNAKE_CASE			)
  