



'''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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase							:List[str]       = logging.get_logger(__name__)




def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Dict     =					YolosConfig()

			# size of the architecture
			if "yolos_ti" in yolos_name:
						A_     :						Optional[int]     =					1_92
						A_     :						List[str]     =					7_68
						A_     :						int     =					12
						A_     :						List[str]     =					3
						A_     :						Tuple     =					[8_00, 13_33]
						A_     :						Optional[int]     =					False
			elif yolos_name == "yolos_s_dWr":
						A_     :						List[str]     =					3_30
						A_     :						List[str]     =					14
						A_     :						Optional[int]     =					6
						A_     :						Dict     =					13_20
			elif "yolos_s" in yolos_name:
						A_     :						Any     =					3_84
						A_     :						Dict     =					15_36
						A_     :						Union[str, Any]     =					12
						A_     :						int     =					6
			elif "yolos_b" in yolos_name:
						A_     :						Union[str, Any]     =					[8_00, 13_44]

			A_     :						Dict     =					91
			A_     :						int     =					"""huggingface/label-files"""
			A_     :						Dict     =					"""coco-detection-id2label.json"""
			A_     :						List[str]     =					json.load(open(hf_hub_download(lowerCamelCase__						,				lowerCamelCase__						,				repo_type="""dataset"""   )						,				"""r"""   )   )
			A_     :						Optional[int]     =					{int(lowerCamelCase__   ): v for k, v in idalabel.items()}
			A_     :						Dict     =					idalabel
			A_     :						List[Any]     =					{v: k for k, v in idalabel.items()}

			return config




def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__ = False   ):
			'''simple docstring'''





			for i in range(config.num_hidden_layers   ):
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						A_     :						Optional[Any]     =					state_dict.pop(f'blocks.{i}.attn.qkv.weight'   )
						A_     :						Union[str, Any]     =					state_dict.pop(f'blocks.{i}.attn.qkv.bias'   )
						# next, add query, keys and values (in that order) to the state dict
						A_     :						Any     =					in_proj_weight[: config.hidden_size, :]
						A_     :						List[Any]     =					in_proj_bias[: config.hidden_size]
						A_     :						str     =					in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						A_     :						List[Any]     =					in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						A_     :						List[Any]     =					in_proj_weight[-config.hidden_size :, :]
						A_     :						Any     =					in_proj_bias[-config.hidden_size :]




def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			if "backbone" in name:
						A_     :						List[str]     =					name.replace("""backbone"""						,				"""vit"""   )
			if "cls_token" in name:
						A_     :						Tuple     =					name.replace("""cls_token"""						,				"""embeddings.cls_token"""   )
			if "det_token" in name:
						A_     :						Optional[Any]     =					name.replace("""det_token"""						,				"""embeddings.detection_tokens"""   )
			if "mid_pos_embed" in name:
						A_     :						int     =					name.replace("""mid_pos_embed"""						,				"""encoder.mid_position_embeddings"""   )
			if "pos_embed" in name:
						A_     :						Optional[int]     =					name.replace("""pos_embed"""						,				"""embeddings.position_embeddings"""   )
			if "patch_embed.proj" in name:
						A_     :						int     =					name.replace("""patch_embed.proj"""						,				"""embeddings.patch_embeddings.projection"""   )
			if "blocks" in name:
						A_     :						Tuple     =					name.replace("""blocks"""						,				"""encoder.layer"""   )
			if "attn.proj" in name:
						A_     :						List[str]     =					name.replace("""attn.proj"""						,				"""attention.output.dense"""   )
			if "attn" in name:
						A_     :						int     =					name.replace("""attn"""						,				"""attention.self"""   )
			if "norm1" in name:
						A_     :						Any     =					name.replace("""norm1"""						,				"""layernorm_before"""   )
			if "norm2" in name:
						A_     :						int     =					name.replace("""norm2"""						,				"""layernorm_after"""   )
			if "mlp.fc1" in name:
						A_     :						Optional[Any]     =					name.replace("""mlp.fc1"""						,				"""intermediate.dense"""   )
			if "mlp.fc2" in name:
						A_     :						str     =					name.replace("""mlp.fc2"""						,				"""output.dense"""   )
			if "class_embed" in name:
						A_     :						Tuple     =					name.replace("""class_embed"""						,				"""class_labels_classifier"""   )
			if "bbox_embed" in name:
						A_     :						Union[str, Any]     =					name.replace("""bbox_embed"""						,				"""bbox_predictor"""   )
			if "vit.norm" in name:
						A_     :						Tuple     =					name.replace("""vit.norm"""						,				"""vit.layernorm"""   )

			return name




def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			for key in orig_state_dict.copy().keys():
						A_     :						Optional[Any]     =					orig_state_dict.pop(lowerCamelCase__   )

						if "qkv" in key:
									A_     :						Dict     =					key.split("""."""   )
									A_     :						Optional[Any]     =					int(key_split[2]   )
									A_     :						str     =					model.vit.encoder.layer[layer_num].attention.attention.all_head_size
									if "weight" in key:
												A_     :						Tuple     =					val[:dim, :]
												A_     :						Optional[Any]     =					val[
												    dim : dim * 2, :
												]
												A_     :						Dict     =					val[-dim:, :]
									else:
												A_     :						int     =					val[:dim]
												A_     :						List[Any]     =					val[dim : dim * 2]
												A_     :						Any     =					val[-dim:]
						else:
									A_     :						Dict     =					val

			return orig_state_dict




def      a      (			):
			'''simple docstring'''





			A_     :						Dict     =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			A_     :						Dict     =					Image.open(requests.get(lowerCamelCase__						,				stream=lowerCamelCase__   ).raw   )
			return im




@torch.no_grad()
def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__ = False   ):
			'''simple docstring'''





			A_     :						Any     =					get_yolos_config(lowerCamelCase__   )

			# load original state_dict
			A_     :						Any     =					torch.load(lowerCamelCase__						,				map_location="""cpu"""   )["""model"""]

			# load 🤗 model
			A_     :						Dict     =					YolosForObjectDetection(lowerCamelCase__   )
			model.eval()
			A_     :						Any     =					convert_state_dict(lowerCamelCase__						,				lowerCamelCase__   )
			model.load_state_dict(lowerCamelCase__   )

			# Check outputs on an image, prepared by YolosImageProcessor
			A_     :						str     =					8_00 if yolos_name != """yolos_ti""" else 5_12
			A_     :						Optional[Any]     =					YolosImageProcessor(format="""coco_detection"""						,				size=lowerCamelCase__   )
			A_     :						List[str]     =					image_processor(images=prepare_img()						,				return_tensors="""pt"""   )
			A_     :						Any     =					model(**lowerCamelCase__   )
			A_     :						str     =					outputs.logits, outputs.pred_boxes

			A_     :						Tuple     =					None, None
			if yolos_name == "yolos_ti":
						A_     :						Dict     =					torch.tensor(
						    [[-39.5_022, -11.9_820, -17.6_888], [-29.9_574, -9.9_769, -17.7_691], [-42.3_281, -20.7_200, -30.6_294]]   )
						A_     :						Dict     =					torch.tensor(
						    [[0.4_021, 0.0_836, 0.7_979], [0.0_184, 0.2_609, 0.0_364], [0.1_781, 0.2_004, 0.2_095]]   )
			elif yolos_name == "yolos_s_200_pre":
						A_     :						Optional[Any]     =					torch.tensor(
						    [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]   )
						A_     :						int     =					torch.tensor(
						    [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]   )
			elif yolos_name == "yolos_s_300_pre":
						A_     :						List[str]     =					torch.tensor(
						    [[-36.2_220, -14.4_385, -23.5_457], [-35.6_970, -14.7_583, -21.3_935], [-31.5_939, -13.6_042, -16.8_049]]   )
						A_     :						Union[str, Any]     =					torch.tensor(
						    [[0.7_614, 0.2_316, 0.4_728], [0.7_168, 0.4_495, 0.3_855], [0.4_996, 0.1_466, 0.9_996]]   )
			elif yolos_name == "yolos_s_dWr":
						A_     :						Tuple     =					torch.tensor(
						    [[-42.8_668, -24.1_049, -41.1_690], [-34.7_456, -14.1_274, -24.9_194], [-33.7_898, -12.1_946, -25.6_495]]   )
						A_     :						str     =					torch.tensor(
						    [[0.5_587, 0.2_773, 0.0_605], [0.5_004, 0.3_014, 0.9_994], [0.4_999, 0.1_548, 0.9_994]]   )
			elif yolos_name == "yolos_base":
						A_     :						Dict     =					torch.tensor(
						    [[-40.6_064, -24.3_084, -32.6_447], [-55.1_990, -30.7_719, -35.5_877], [-51.4_311, -33.3_507, -35.6_462]]   )
						A_     :						Union[str, Any]     =					torch.tensor(
						    [[0.5_555, 0.2_794, 0.0_655], [0.9_049, 0.2_664, 0.1_894], [0.9_183, 0.1_984, 0.1_635]]   )
			else:
						raise ValueError(f'Unknown yolos_name: {yolos_name}'   )

			assert torch.allclose(logits[0, :3, :3]						,				lowerCamelCase__						,				atol=1E-4   )
			assert torch.allclose(pred_boxes[0, :3, :3]						,				lowerCamelCase__						,				atol=1E-4   )

			Path(lowerCamelCase__   ).mkdir(exist_ok=lowerCamelCase__   )
			print(f'Saving model {yolos_name} to {pytorch_dump_folder_path}'   )
			model.save_pretrained(lowerCamelCase__   )
			print(f'Saving image processor to {pytorch_dump_folder_path}'   )
			image_processor.save_pretrained(lowerCamelCase__   )

			if push_to_hub:
						A_     :						Optional[int]     =					{
						    """yolos_ti""": """yolos-tiny""",
						    """yolos_s_200_pre""": """yolos-small""",
						    """yolos_s_300_pre""": """yolos-small-300""",
						    """yolos_s_dWr""": """yolos-small-dwr""",
						    """yolos_base""": """yolos-base""",
						}

						print("""Pushing to the hub..."""   )
						A_     :						int     =					model_mapping[yolos_name]
						image_processor.push_to_hub(lowerCamelCase__						,				organization="""hustvl"""   )
						model.push_to_hub(lowerCamelCase__						,				organization="""hustvl"""   )


if __name__ == "__main__":
						lowerCamelCase							:Union[str, Any]       = argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--yolos_name''',
						    default='''yolos_s_200_pre''',
						    type=str,
						    help=(
						        '''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
						        ''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
						    ),
						)
						parser.add_argument(
						    '''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
						)
						parser.add_argument(
						    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
						)

						lowerCamelCase							:List[str]       = parser.parse_args()
						convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, 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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
						import PIL

if is_torch_available():
						import torch


lowerCamelCase							:str       = logging.get_logger(__name__)







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Tuple		      = ['pixel_values']


	def __init__(self	,				lowercase = True	,				lowercase = None	,				lowercase = PILImageResampling.BILINEAR	,				lowercase = True	,				lowercase = 1 / 255	,				lowercase = True	,				lowercase = None	,				lowercase = True	,				**lowercase	,				):
				super().__init__(**lowercase							)
				A_     :						Union[str, Any]     =					size if size is not None else {"""shortest_edge""": 224}
				A_     :						Union[str, Any]     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				A_     :						Optional[Any]     =					crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
				A_     :						Tuple     =					get_size_dict(lowercase	,				param_name="""crop_size"""							)

				A_     :						List[Any]     =					do_resize
				A_     :						List[str]     =					size
				A_     :						Dict     =					resample
				A_     :						int     =					do_rescale
				A_     :						str     =					rescale_factor
				A_     :						Tuple     =					do_center_crop
				A_     :						Tuple     =					crop_size
				A_     :						List[str]     =					do_flip_channel_order


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = PIL.Image.BILINEAR	,				lowercase = None	,				**lowercase	,				):
				A_     :						Any     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				if "shortest_edge" not in size:
							raise ValueError(F'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'							)
				A_     :						Any     =					get_resize_output_image_size(lowercase	,				size=size["""shortest_edge"""]	,				default_to_square=lowercase							)
				return resize(lowercase	,				size=lowercase	,				resample=lowercase	,				data_format=lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = None	,				**lowercase	,				):
				A_     :						Tuple     =					get_size_dict(lowercase							)
				if "height" not in size or "width" not in size:
							raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'							)
				return center_crop(lowercase	,				size=(size["""height"""], size["""width"""])	,				data_format=lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = None	,				**lowercase	,				):
				return rescale(lowercase	,				scale=lowercase	,				data_format=lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None							):
				return flip_channel_order(lowercase	,				data_format=lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = ChannelDimension.FIRST	,				**lowercase	,				):
				A_     :						str     =					do_resize if do_resize is not None else self.do_resize
				A_     :						Optional[int]     =					resample if resample is not None else self.resample
				A_     :						str     =					do_rescale if do_rescale is not None else self.do_rescale
				A_     :						str     =					rescale_factor if rescale_factor is not None else self.rescale_factor
				A_     :						str     =					do_center_crop if do_center_crop is not None else self.do_center_crop
				A_     :						Dict     =					(
				    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
				)

				A_     :						Union[str, Any]     =					size if size is not None else self.size
				A_     :						Dict     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				A_     :						Any     =					crop_size if crop_size is not None else self.crop_size
				A_     :						Union[str, Any]     =					get_size_dict(lowercase	,				param_name="""crop_size"""							)

				A_     :						Union[str, Any]     =					make_list_of_images(lowercase							)

				if not valid_images(lowercase							):
							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_center_crop and crop_size is None:
							raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

				# All transformations expect numpy arrays.
				A_     :						Optional[Any]     =					[to_numpy_array(lowercase							) for image in images]

				if do_resize:
							A_     :						List[Any]     =					[self.resize(image=lowercase	,				size=lowercase	,				resample=lowercase							) for image in images]

				if do_center_crop:
							A_     :						str     =					[self.center_crop(image=lowercase	,				size=lowercase							) for image in images]

				if do_rescale:
							A_     :						Dict     =					[self.rescale(image=lowercase	,				scale=lowercase							) for image in images]

				# the pretrained checkpoints assume images are BGR, not RGB
				if do_flip_channel_order:
							A_     :						List[str]     =					[self.flip_channel_order(image=lowercase							) for image in images]

				A_     :						List[str]     =					[to_channel_dimension_format(lowercase	,				lowercase							) for image in images]

				A_     :						str     =					{"""pixel_values""": images}
				return BatchFeature(data=lowercase	,				tensor_type=lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						Optional[Any]     =					outputs.logits

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

							if is_torch_tensor(lowercase							):
										A_     :						Dict     =					target_sizes.numpy()

							A_     :						Union[str, Any]     =					[]

							for idx in range(len(lowercase							)							):
										A_     :						Union[str, Any]     =					torch.nn.functional.interpolate(
										    logits[idx].unsqueeze(dim=0							)	,				size=target_sizes[idx]	,				mode="""bilinear"""	,				align_corners=lowercase							)
										A_     :						Any     =					resized_logits[0].argmax(dim=0							)
										semantic_segmentation.append(lowercase							)
				else:
							A_     :						str     =					logits.argmax(dim=1							)
							A_     :						Optional[Any]     =					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

				return semantic_segmentation