




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)






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			)
