




'''simple docstring'''


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case : List[Any]  				=						logging.get_logger(__name__)
def 						snake_case_       (UpperCamelCase			:    Dict ):





							'''simple docstring'''


							if "resnet-50" in model_name:
														_a      	=							ResNetConfig.from_pretrained('''microsoft/resnet-50''' )
							elif "resnet-101" in model_name:
														_a      	=							ResNetConfig.from_pretrained('''microsoft/resnet-101''' )
							else:
														raise ValueError('''Model name should include either resnet50 or resnet101''' )

							_a      	=							DetrConfig(use_timm_backbone=UpperCamelCase					,		backbone_config=UpperCamelCase )

							# set label attributes
							_a      	=							'''panoptic''' in model_name
							if is_panoptic:
														_a      	=							250
							else:
														_a      	=							91
														_a      	=							'''huggingface/label-files'''
														_a      	=							'''coco-detection-id2label.json'''
														_a      	=							json.load(open(hf_hub_download(UpperCamelCase					,		UpperCamelCase					,		repo_type='''dataset''' )					,		'''r''' ) )
														_a      	=							{int(UpperCamelCase ): v for k, v in idalabel.items()}
														_a      	=							idalabel
														_a      	=							{v: k for k, v in idalabel.items()}

							return config, is_panoptic
def 						snake_case_       (UpperCamelCase			:    Optional[int] ):





							'''simple docstring'''


							_a      	=							[]

							# stem
							# fmt: off
							rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight''') )
							rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight''') )
							rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias''') )
							rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean''') )
							rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var''') )
							# stages
							for stage_idx in range(len(config.backbone_config.depths ) ):
														for layer_idx in range(config.backbone_config.depths[stage_idx] ):
																					# shortcut
																					if layer_idx == 0:
																												rename_keys.append(
																												    (
																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
																												        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
																												    ) )
																												rename_keys.append(
																												    (
																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
																												        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
																												    ) )
																												rename_keys.append(
																												    (
																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
																												        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
																												    ) )
																												rename_keys.append(
																												    (
																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
																												        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
																												    ) )
																												rename_keys.append(
																												    (
																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
																												        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
																												    ) )
																					# 3 convs
																					for i in range(3 ):
																												rename_keys.append(
																												    (
																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
																												        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
																												    ) )
																												rename_keys.append(
																												    (
																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
																												        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
																												    ) )
																												rename_keys.append(
																												    (
																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
																												        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
																												    ) )
																												rename_keys.append(
																												    (
																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
																												        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
																												    ) )
																												rename_keys.append(
																												    (
																												        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
																												        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
																												    ) )
    # fmt: on

							for i in range(config.encoder_layers ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append(
														    (
														        f'transformer.encoder.layers.{i}.self_attn.out_proj.weight',
														        f'encoder.layers.{i}.self_attn.out_proj.weight',
														    ) )
														rename_keys.append(
														    (f'transformer.encoder.layers.{i}.self_attn.out_proj.bias', f'encoder.layers.{i}.self_attn.out_proj.bias') )
														rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'encoder.layers.{i}.fc1.weight') )
														rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'encoder.layers.{i}.fc1.bias') )
														rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'encoder.layers.{i}.fc2.weight') )
														rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'encoder.layers.{i}.fc2.bias') )
														rename_keys.append(
														    (f'transformer.encoder.layers.{i}.norm1.weight', f'encoder.layers.{i}.self_attn_layer_norm.weight') )
														rename_keys.append(
														    (f'transformer.encoder.layers.{i}.norm1.bias', f'encoder.layers.{i}.self_attn_layer_norm.bias') )
														rename_keys.append(
														    (f'transformer.encoder.layers.{i}.norm2.weight', f'encoder.layers.{i}.final_layer_norm.weight') )
														rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'encoder.layers.{i}.final_layer_norm.bias') )
														# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
														rename_keys.append(
														    (
														        f'transformer.decoder.layers.{i}.self_attn.out_proj.weight',
														        f'decoder.layers.{i}.self_attn.out_proj.weight',
														    ) )
														rename_keys.append(
														    (f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'decoder.layers.{i}.self_attn.out_proj.bias') )
														rename_keys.append(
														    (
														        f'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
														        f'decoder.layers.{i}.encoder_attn.out_proj.weight',
														    ) )
														rename_keys.append(
														    (
														        f'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
														        f'decoder.layers.{i}.encoder_attn.out_proj.bias',
														    ) )
														rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'decoder.layers.{i}.fc1.weight') )
														rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'decoder.layers.{i}.fc1.bias') )
														rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'decoder.layers.{i}.fc2.weight') )
														rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'decoder.layers.{i}.fc2.bias') )
														rename_keys.append(
														    (f'transformer.decoder.layers.{i}.norm1.weight', f'decoder.layers.{i}.self_attn_layer_norm.weight') )
														rename_keys.append(
														    (f'transformer.decoder.layers.{i}.norm1.bias', f'decoder.layers.{i}.self_attn_layer_norm.bias') )
														rename_keys.append(
														    (f'transformer.decoder.layers.{i}.norm2.weight', f'decoder.layers.{i}.encoder_attn_layer_norm.weight') )
														rename_keys.append(
														    (f'transformer.decoder.layers.{i}.norm2.bias', f'decoder.layers.{i}.encoder_attn_layer_norm.bias') )
														rename_keys.append(
														    (f'transformer.decoder.layers.{i}.norm3.weight', f'decoder.layers.{i}.final_layer_norm.weight') )
														rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'decoder.layers.{i}.final_layer_norm.bias') )

							# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
							rename_keys.extend(
							    [
							        ('''input_proj.weight''', '''input_projection.weight'''),
							        ('''input_proj.bias''', '''input_projection.bias'''),
							        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
							        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
							        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
							        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
							        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
							        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
							        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
							        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
							        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
							        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
							        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
							    ] )

							return rename_keys
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    Optional[int]					,		UpperCamelCase			:    List[Any] ):





							'''simple docstring'''


							_a      	=							state_dict.pop(UpperCamelCase )
							_a      	=							val
def 						snake_case_       (UpperCamelCase			:    List[str]					,		UpperCamelCase			:    Union[str, Any]=False ):





							'''simple docstring'''


							_a      	=							''''''
							if is_panoptic:
														_a      	=							'''detr.'''

							# first: transformer encoder
							for i in range(6 ):
														# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
														_a      	=							state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
														_a      	=							state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias' )
														# next, add query, keys and values (in that order) to the state dict
														_a      	=							in_proj_weight[:256, :]
														_a      	=							in_proj_bias[:256]
														_a      	=							in_proj_weight[256:512, :]
														_a      	=							in_proj_bias[256:512]
														_a      	=							in_proj_weight[-256:, :]
														_a      	=							in_proj_bias[-256:]
							# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
							for i in range(6 ):
														# read in weights + bias of input projection layer of self-attention
														_a      	=							state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
														_a      	=							state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
														# next, add query, keys and values (in that order) to the state dict
														_a      	=							in_proj_weight[:256, :]
														_a      	=							in_proj_bias[:256]
														_a      	=							in_proj_weight[256:512, :]
														_a      	=							in_proj_bias[256:512]
														_a      	=							in_proj_weight[-256:, :]
														_a      	=							in_proj_bias[-256:]
														# read in weights + bias of input projection layer of cross-attention
														_a      	=							state_dict.pop(
														    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight' )
														_a      	=							state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias' )
														# next, add query, keys and values (in that order) of cross-attention to the state dict
														_a      	=							in_proj_weight_cross_attn[:256, :]
														_a      	=							in_proj_bias_cross_attn[:256]
														_a      	=							in_proj_weight_cross_attn[256:512, :]
														_a      	=							in_proj_bias_cross_attn[256:512]
														_a      	=							in_proj_weight_cross_attn[-256:, :]
														_a      	=							in_proj_bias_cross_attn[-256:]
def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							_a      	=							Image.open(requests.get(UpperCamelCase					,		stream=UpperCamelCase ).raw )

							return im
@torch.no_grad()
def 						snake_case_       (UpperCamelCase			:    int					,		UpperCamelCase			:    Tuple=None					,		UpperCamelCase			:    int=False ):





							'''simple docstring'''


							_a	,				_a      	=							get_detr_config(UpperCamelCase )

							# load original model from torch hub
							_a      	=							{
							    '''detr-resnet-50''': '''detr_resnet50''',
							    '''detr-resnet-101''': '''detr_resnet101''',
							}
							logger.info(f'Converting model {model_name}...' )
							_a      	=							torch.hub.load('''facebookresearch/detr'''					,		model_name_to_original_name[model_name]					,		pretrained=UpperCamelCase ).eval()
							_a      	=							detr.state_dict()
							# rename keys
							for src, dest in create_rename_keys(UpperCamelCase ):
														if is_panoptic:
																					_a      	=							'''detr.''' + src
														rename_key(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
							# query, key and value matrices need special treatment
							read_in_q_k_v(UpperCamelCase					,		is_panoptic=UpperCamelCase )
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							_a      	=							'''detr.model.''' if is_panoptic else '''model.'''
							for key in state_dict.copy().keys():
														if is_panoptic:
																					if (
																					    key.startswith('''detr''' )
																					    and not key.startswith('''class_labels_classifier''' )
																					    and not key.startswith('''bbox_predictor''' )
																					):
																												_a      	=							state_dict.pop(UpperCamelCase )
																												_a      	=							val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												_a      	=							state_dict.pop(UpperCamelCase )
																												_a      	=							val
																					elif key.startswith('''bbox_attention''' ) or key.startswith('''mask_head''' ):
																												continue
																					else:
																												_a      	=							state_dict.pop(UpperCamelCase )
																												_a      	=							val
														else:
																					if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
																												_a      	=							state_dict.pop(UpperCamelCase )
																												_a      	=							val

    # finally, create HuggingFace model and load state dict
							_a      	=							DetrForSegmentation(UpperCamelCase ) if is_panoptic else DetrForObjectDetection(UpperCamelCase )
							model.load_state_dict(UpperCamelCase )
							model.eval()

							# verify our conversion on an image
							_a      	=							'''coco_panoptic''' if is_panoptic else '''coco_detection'''
							_a      	=							DetrImageProcessor(format=UpperCamelCase )

							_a      	=							processor(images=prepare_img()					,		return_tensors='''pt''' )
							_a      	=							encoding['''pixel_values''']

							_a      	=							detr(UpperCamelCase )
							_a      	=							model(UpperCamelCase )

							assert torch.allclose(outputs.logits					,		original_outputs['''pred_logits''']					,		atol=1e-3 )
							assert torch.allclose(outputs.pred_boxes					,		original_outputs['''pred_boxes''']					,		atol=1e-3 )
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks					,		original_outputs['''pred_masks''']					,		atol=1e-4 )
							print('''Looks ok!''' )

							if pytorch_dump_folder_path is not None:
														# Save model and image processor
														logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
														Path(UpperCamelCase ).mkdir(exist_ok=UpperCamelCase )
														model.save_pretrained(UpperCamelCase )
														processor.save_pretrained(UpperCamelCase )

							if push_to_hub:
														# Upload model and image processor to the hub
														logger.info('''Uploading PyTorch model and image processor to the hub...''' )
														model.push_to_hub(f'nielsr/{model_name}' )
														processor.push_to_hub(f'nielsr/{model_name}' )


if __name__ == "__main__":
				_snake_case : Optional[int]  				=						argparse.ArgumentParser()

				parser.add_argument(
				    '--model_name',
				    default='detr-resnet-50',
				    type=str,
				    choices=['detr-resnet-50', 'detr-resnet-101'],
				    help='Name of the DETR model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
				)
				parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
				_snake_case : Optional[int]  				=						parser.parse_args()
				convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''


from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
				import torch


_snake_case : Tuple  				=						logging.get_logger(__name__)

class      A       (						_a       ):
							lowercase_							     =							['pixel_values']







							def __init__(    self			:    str				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Dict[str, int]] = None				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BILINEAR				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Union[int, float] = 1 / 2_55				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     **lowerCAmelCase_			:    Any				,     )     ->				None:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_   )
														_a      	=							size if size is not None else {'''shortest_edge''': 2_56}
														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_   )
														_a      	=							crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														_a      	=							get_size_dict(lowerCAmelCase_				,     param_name='''crop_size'''   )
														_a      	=							do_resize
														_a      	=							size
														_a      	=							resample
														_a      	=							do_center_crop
														_a      	=							crop_size
														_a      	=							do_rescale
														_a      	=							rescale_factor
														_a      	=							do_normalize
														_a      	=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														_a      	=							image_std if image_std is not None else IMAGENET_STANDARD_STD







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Dict[str, int]				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BICUBIC				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    int				,     )     ->				np.ndarray:


														"""simple docstring"""


														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_   )
														if "shortest_edge" not in size:
																					raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'   )
														_a      	=							get_resize_output_image_size(lowerCAmelCase_				,     size=size['''shortest_edge''']				,     default_to_square=lowerCAmelCase_   )
														return resize(lowerCAmelCase_				,     size=lowerCAmelCase_				,     resample=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







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


														"""simple docstring"""


														_a      	=							get_size_dict(lowerCAmelCase_   )
														if "height" not in size or "width" not in size:
																					raise ValueError(F'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'   )
														return center_crop(lowerCAmelCase_				,     size=(size['''height'''], size['''width'''])				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







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


														"""simple docstring"""


														return rescale(lowerCAmelCase_				,     scale=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Union[float, List[float]]				,     lowerCAmelCase_			:    Union[float, List[float]]				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    int				,     )     ->				np.ndarray:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    ImageInput				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    PILImageResampling = None				,     lowerCAmelCase_			:    bool = None				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Optional[float] = None				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[str, TensorType]] = None				,     lowerCAmelCase_			:    Union[str, ChannelDimension] = ChannelDimension.FIRST				,     **lowerCAmelCase_			:    Union[str, Any]				,     )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							do_resize if do_resize is not None else self.do_resize
														_a      	=							size if size is not None else self.size
														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_   )
														_a      	=							resample if resample is not None else self.resample
														_a      	=							do_center_crop if do_center_crop is not None else self.do_center_crop
														_a      	=							crop_size if crop_size is not None else self.crop_size
														_a      	=							get_size_dict(lowerCAmelCase_				,     param_name='''crop_size'''   )
														_a      	=							do_rescale if do_rescale is not None else self.do_rescale
														_a      	=							rescale_factor if rescale_factor is not None else self.rescale_factor
														_a      	=							do_normalize if do_normalize is not None else self.do_normalize
														_a      	=							image_mean if image_mean is not None else self.image_mean
														_a      	=							image_std if image_std is not None else self.image_std

														_a      	=							make_list_of_images(lowerCAmelCase_   )

														if not valid_images(lowerCAmelCase_   ):
																					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.'''   )

														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.
														_a      	=							[to_numpy_array(lowerCAmelCase_   ) for image in images]

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

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

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

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

														_a      	=							[to_channel_dimension_format(lowerCAmelCase_				,     lowerCAmelCase_   ) for image in images]

														_a      	=							{'''pixel_values''': images}
														return BatchFeature(data=lowerCAmelCase_				,     tensor_type=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    Optional[int]				,     lowerCAmelCase_			:    List[Tuple] = None   )     ->				Any:


														"""simple docstring"""


														_a      	=							outputs.logits

														# Resize logits and compute semantic segmentation maps
														if target_sizes is not None:
																					if len(lowerCAmelCase_   ) != len(lowerCAmelCase_   ):
																												raise ValueError(
																												    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''   )

																					if is_torch_tensor(lowerCAmelCase_   ):
																												_a      	=							target_sizes.numpy()

																					_a      	=							[]

																					for idx in range(len(lowerCAmelCase_   )   ):
																												_a      	=							torch.nn.functional.interpolate(
																												    logits[idx].unsqueeze(dim=0   )				,     size=target_sizes[idx]				,     mode='''bilinear'''				,     align_corners=lowerCAmelCase_   )
																												_a      	=							resized_logits[0].argmax(dim=0   )
																												semantic_segmentation.append(lowerCAmelCase_   )
														else:
																					_a      	=							logits.argmax(dim=1   )
																					_a      	=							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

														return semantic_segmentation

