

import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

				if is_vision_available():
								from transformers import MaskaFormerImageProcessor

if is_vision_available():
				from PIL import Image

class 						__UpperCamelCase    :




	def __init__(		self	:						Any		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Optional[Any]=2		,	lowerCAmelCase	:						int=True		,	lowerCAmelCase	:						Union[str, Any]=False		,	lowerCAmelCase	:						Any=10		,	lowerCAmelCase	:						Optional[int]=3		,	lowerCAmelCase	:						List[Any]=32 * 8		,	lowerCAmelCase	:						str=32 * 8		,	lowerCAmelCase	:						List[Any]=4		,	lowerCAmelCase	:						str=64		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			parent
		UpperCAmelCase_       				=			batch_size
		UpperCAmelCase_       				=			is_training
		UpperCAmelCase_       				=			use_auxiliary_loss
		UpperCAmelCase_       				=			num_queries
		UpperCAmelCase_       				=			num_channels
		UpperCAmelCase_       				=			min_size
		UpperCAmelCase_       				=			max_size
		UpperCAmelCase_       				=			num_labels
		UpperCAmelCase_       				=			hidden_dim
		UpperCAmelCase_       				=			hidden_dim




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]							).to(
		    lowerCAmelCase							)

		UpperCAmelCase_       				=			torch.ones([self.batch_size, self.min_size, self.max_size]		,	device=lowerCAmelCase							)

		UpperCAmelCase_       				=			(
		    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]		,	device=lowerCAmelCase							) > 0.5
		).float()
		UpperCAmelCase_       				=			(torch.rand((self.batch_size, self.num_labels)		,	device=lowerCAmelCase							) > 0.5).long()

		UpperCAmelCase_       				=			self.get_config()
		return config, pixel_values, pixel_mask, mask_labels, class_labels




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			MaskaFormerConfig(
		    hidden_size=self.hidden_dim		,	)
		UpperCAmelCase_       				=			self.num_queries
		UpperCAmelCase_       				=			self.num_labels

		UpperCAmelCase_       				=			[1, 1, 1, 1]
		UpperCAmelCase_       				=			self.num_channels

		UpperCAmelCase_       				=			64
		UpperCAmelCase_       				=			128
		UpperCAmelCase_       				=			self.hidden_dim
		UpperCAmelCase_       				=			self.hidden_dim
		UpperCAmelCase_       				=			self.hidden_dim
		return config




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			self.prepare_config_and_inputs()
		UpperCAmelCase_       				=			{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
		return config, inputs_dict




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			output.encoder_hidden_states
		UpperCAmelCase_       				=			output.pixel_decoder_hidden_states
		UpperCAmelCase_       				=			output.transformer_decoder_hidden_states

		self.parent.assertTrue(len(lowerCAmelCase							)		,	len(config.backbone_config.depths							)							)
		self.parent.assertTrue(len(lowerCAmelCase							)		,	len(config.backbone_config.depths							)							)
		self.parent.assertTrue(len(lowerCAmelCase							)		,	config.decoder_layers							)




	def       __A					(		self	:						Dict		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						Union[str, Any]=False							):


		'''simple docstring'''







		with torch.no_grad():
			UpperCAmelCase_       				=			MaskaFormerModel(config=lowerCAmelCase							)
			model.to(lowerCAmelCase							)
			model.eval()

			UpperCAmelCase_       				=			model(pixel_values=lowerCAmelCase		,	pixel_mask=lowerCAmelCase							)
			UpperCAmelCase_       				=			model(lowerCAmelCase		,	output_hidden_states=lowerCAmelCase							)

		self.parent.assertEqual(
		    output.transformer_decoder_last_hidden_state.shape		,	(self.batch_size, self.num_queries, self.hidden_dim)		,	)
		# let's ensure the other two hidden state exists
		self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None							)
		self.parent.assertTrue(output.encoder_last_hidden_state is not None							)

		if output_hidden_states:
			self.check_output_hidden_state(lowerCAmelCase		,	lowerCAmelCase							)




	def       __A					(		self	:						int		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			MaskaFormerForUniversalSegmentation(config=lowerCAmelCase							)
		model.to(lowerCAmelCase							)
		model.eval()

		def comm_check_on_output(lowerCAmelCase	:						int							):
			# let's still check that all the required stuff is there
			self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None							)
			self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None							)
			self.parent.assertTrue(result.encoder_last_hidden_state is not None							)
			# okay, now we need to check the logits shape
			# due to the encoder compression, masks have a //4 spatial size
			self.parent.assertEqual(
			    result.masks_queries_logits.shape		,	(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)		,	)
			# + 1 for null class
			self.parent.assertEqual(
			    result.class_queries_logits.shape		,	(self.batch_size, self.num_queries, self.num_labels + 1)							)

		with torch.no_grad():
			UpperCAmelCase_       				=			model(pixel_values=lowerCAmelCase		,	pixel_mask=lowerCAmelCase							)
			UpperCAmelCase_       				=			model(lowerCAmelCase							)

			comm_check_on_output(lowerCAmelCase							)

			UpperCAmelCase_       				=			model(
			    pixel_values=lowerCAmelCase		,	pixel_mask=lowerCAmelCase		,	mask_labels=lowerCAmelCase		,	class_labels=lowerCAmelCase							)

		comm_check_on_output(lowerCAmelCase							)

		self.parent.assertTrue(result.loss is not None							)
		self.parent.assertEqual(result.loss.shape		,	torch.Size([1]							)							)


@require_torch
class 						__UpperCamelCase    (					lowercase     ,     lowercase     ,     unittest.TestCase						):
	SCREAMING_SNAKE_CASE__    				=     (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
	SCREAMING_SNAKE_CASE__    				=     {'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

	SCREAMING_SNAKE_CASE__    				=     False
	SCREAMING_SNAKE_CASE__    				=     False
	SCREAMING_SNAKE_CASE__    				=     False
	SCREAMING_SNAKE_CASE__    				=     False




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			MaskaFormerModelTester(self							)
		UpperCAmelCase_       				=			ConfigTester(self		,	config_class=lowerCAmelCase		,	has_text_modality=lowerCAmelCase							)




	def       __A					(		self	:						Union[str, Any]							):


		'''simple docstring'''







		self.config_tester.run_common_tests()




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs_for_common()
		self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase		,	**lowerCAmelCase		,	output_hidden_states=lowerCAmelCase							)




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCAmelCase							)




	@unittest.skip(reason="Mask2Former does not use inputs_embeds"							)
	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		pass




	@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"							)
	def       __A					(		self	:						Any							):


		'''simple docstring'''







		pass




	@unittest.skip(reason="Mask2Former is not a generative model"							)
	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		pass




	@unittest.skip(reason="Mask2Former does not use token embeddings"							)
	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		pass




	@require_torch_multi_gpu
	@unittest.skip(
	    reason="Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"							)
	def       __A					(		self	:						str							):


		'''simple docstring'''







		pass




	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."							)
	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		pass




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			UpperCAmelCase_       				=			model_class(lowerCAmelCase							)
			UpperCAmelCase_       				=			inspect.signature(model.forward							)
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			UpperCAmelCase_       				=			[*signature.parameters.keys()]

			UpperCAmelCase_       				=			["pixel_values"]
			self.assertListEqual(arg_names[:1]		,	lowerCAmelCase							)




	@slow
	def       __A					(		self	:						int							):


		'''simple docstring'''







		for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
			UpperCAmelCase_       				=			MaskaFormerModel.from_pretrained(lowerCAmelCase							)
			self.assertIsNotNone(lowerCAmelCase							)




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			(self.model_tester.min_size,) * 2
		UpperCAmelCase_       				=			{
		    "pixel_values": torch.randn((2, 3, *size)		,	device=lowerCAmelCase							),
		    "mask_labels": torch.randn((2, 10, *size)		,	device=lowerCAmelCase							),
		    "class_labels": torch.zeros(2		,	10		,	device=lowerCAmelCase							).long(),
		}
		UpperCAmelCase_       				=			self.model_tester.get_config()

		UpperCAmelCase_       				=			MaskaFormerForUniversalSegmentation(lowerCAmelCase							).to(lowerCAmelCase							)
		UpperCAmelCase_       				=			model(**lowerCAmelCase							)
		self.assertTrue(outputs.loss is not None							)




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs_for_common()
		self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase		,	**lowerCAmelCase		,	output_hidden_states=lowerCAmelCase							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			UpperCAmelCase_       				=			model_class(lowerCAmelCase							).to(lowerCAmelCase							)
			UpperCAmelCase_       				=			model(**lowerCAmelCase		,	output_attentions=lowerCAmelCase							)
			self.assertTrue(outputs.attentions is not None							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		if not self.model_tester.is_training:
			return

		UpperCAmelCase_       				=			self.all_model_classes[1]
		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()

		UpperCAmelCase_       				=			model_class(lowerCAmelCase							)
		model.to(lowerCAmelCase							)
		model.train()

		UpperCAmelCase_       				=			model(lowerCAmelCase		,	mask_labels=lowerCAmelCase		,	class_labels=lowerCAmelCase							).loss
		loss.backward()




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.all_model_classes[1]
		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		UpperCAmelCase_       				=			True
		UpperCAmelCase_       				=			True

		UpperCAmelCase_       				=			model_class(lowerCAmelCase							).to(lowerCAmelCase							)
		model.train()

		UpperCAmelCase_       				=			model(lowerCAmelCase		,	mask_labels=lowerCAmelCase		,	class_labels=lowerCAmelCase							)

		UpperCAmelCase_       				=			outputs.encoder_hidden_states[0]
		encoder_hidden_states.retain_grad()

		UpperCAmelCase_       				=			outputs.pixel_decoder_hidden_states[0]
		pixel_decoder_hidden_states.retain_grad()

		UpperCAmelCase_       				=			outputs.transformer_decoder_hidden_states[0]
		transformer_decoder_hidden_states.retain_grad()

		UpperCAmelCase_       				=			outputs.attentions[0]
		attentions.retain_grad()

		outputs.loss.backward(retain_graph=lowerCAmelCase							)

		self.assertIsNotNone(encoder_hidden_states.grad							)
		self.assertIsNotNone(pixel_decoder_hidden_states.grad							)
		self.assertIsNotNone(transformer_decoder_hidden_states.grad							)
		self.assertIsNotNone(attentions.grad							)


_a:    Tuple 							=			1e-4



def       __lowerCAmelCase      (						):
	UpperCAmelCase_       				=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
	return image




@require_vision
@slow
class 						__UpperCamelCase    (					unittest.TestCase						):




	@cached_property
	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		return "facebook/mask2former-swin-small-coco-instance"




	@cached_property
	def       __A					(		self	:						Any							):


		'''simple docstring'''







		return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints							) if is_vision_available() else None




	def       __A					(		self	:						Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			MaskaFormerModel.from_pretrained(self.model_checkpoints							).to(lowerCAmelCase							)
		UpperCAmelCase_       				=			self.default_image_processor
		UpperCAmelCase_       				=			prepare_img()
		UpperCAmelCase_       				=			image_processor(lowerCAmelCase		,	return_tensors="pt"							).to(lowerCAmelCase							)
		UpperCAmelCase_       				=			inputs["pixel_values"].shape
		# check size is divisible by 32
		self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0							)
		# check size
		self.assertEqual(lowerCAmelCase		,	(1, 3, 384, 384)							)

		with torch.no_grad():
			UpperCAmelCase_       				=			model(**lowerCAmelCase							)

		UpperCAmelCase_       				=			torch.tensor(
		    [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]							).to(lowerCAmelCase							)
		self.assertTrue(
		    torch.allclose(
		        outputs.encoder_last_hidden_state[0, 0, :3, :3]		,	lowerCAmelCase		,	atol=lowerCAmelCase							)							)

		UpperCAmelCase_       				=			torch.tensor(
		    [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]							).to(lowerCAmelCase							)
		self.assertTrue(
		    torch.allclose(
		        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]		,	lowerCAmelCase		,	atol=lowerCAmelCase							)							)

		UpperCAmelCase_       				=			torch.tensor(
		    [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]							).to(lowerCAmelCase							)
		self.assertTrue(
		    torch.allclose(
		        outputs.transformer_decoder_last_hidden_state[0, :3, :3]		,	lowerCAmelCase		,	atol=lowerCAmelCase							)							)




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints							).to(lowerCAmelCase							).eval()
		UpperCAmelCase_       				=			self.default_image_processor
		UpperCAmelCase_       				=			prepare_img()
		UpperCAmelCase_       				=			image_processor(lowerCAmelCase		,	return_tensors="pt"							).to(lowerCAmelCase							)
		UpperCAmelCase_       				=			inputs["pixel_values"].shape
		# check size is divisible by 32
		self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0							)
		# check size
		self.assertEqual(lowerCAmelCase		,	(1, 3, 384, 384)							)

		with torch.no_grad():
			UpperCAmelCase_       				=			model(**lowerCAmelCase							)
		# masks_queries_logits
		UpperCAmelCase_       				=			outputs.masks_queries_logits
		self.assertEqual(
		    masks_queries_logits.shape		,	(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)							)
		UpperCAmelCase_       				=			[
		    [-8.7_839, -9.0_056, -8.8_121],
		    [-7.4_104, -7.0_313, -6.5_401],
		    [-6.6_105, -6.3_427, -6.4_675],
		]
		UpperCAmelCase_       				=			torch.tensor(lowerCAmelCase							).to(lowerCAmelCase							)
		self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]		,	lowerCAmelCase		,	atol=lowerCAmelCase							)							)
		# class_queries_logits
		UpperCAmelCase_       				=			outputs.class_queries_logits
		self.assertEqual(class_queries_logits.shape		,	(1, model.config.num_queries, model.config.num_labels + 1)							)
		UpperCAmelCase_       				=			torch.tensor(
		    [
		        [1.8_324, -8.0_835, -4.1_922],
		        [0.8_450, -9.0_050, -3.6_053],
		        [0.3_045, -7.7_293, -3.0_275],
		    ]							).to(lowerCAmelCase							)
		self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]		,	lowerCAmelCase		,	atol=lowerCAmelCase							)							)




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints							).to(lowerCAmelCase							).eval()
		UpperCAmelCase_       				=			self.default_image_processor

		UpperCAmelCase_       				=			image_processor(
		    [np.zeros((3, 800, 1_333)							), np.zeros((3, 800, 1_333)							)]		,	segmentation_maps=[np.zeros((384, 384)							).astype(np.floataa							), np.zeros((384, 384)							).astype(np.floataa							)]		,	return_tensors="pt"		,	)

		UpperCAmelCase_       				=			inputs["pixel_values"].to(lowerCAmelCase							)
		UpperCAmelCase_       				=			[el.to(lowerCAmelCase							) for el in inputs["mask_labels"]]
		UpperCAmelCase_       				=			[el.to(lowerCAmelCase							) for el in inputs["class_labels"]]

		with torch.no_grad():
			UpperCAmelCase_       				=			model(**lowerCAmelCase							)

		self.assertTrue(outputs.loss is not None							)


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()
_a:    Optional[Any] 							=			logging.get_logger(__name__)



def       __lowerCAmelCase      (						A    ):
	# initialize config
	if "resnet-50" in model_name:
		UpperCAmelCase_       				=			ResNetConfig.from_pretrained("microsoft/resnet-50"    )
	elif "resnet-101" in model_name:
		UpperCAmelCase_       				=			ResNetConfig.from_pretrained("microsoft/resnet-101"    )
	else:
		raise ValueError("Model name should include either resnet50 or resnet101"    )

	UpperCAmelCase_       				=			DetrConfig(use_timm_backbone=A       ,     backbone_config=A    )

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

	return config, is_panoptic



def       __lowerCAmelCase      (						A    ):
	# here we list all keys to be renamed (original name on the left, our name on the right)
	UpperCAmelCase_       				=			[]

	# 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       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			state_dict.pop(A    )
	UpperCAmelCase_       				=			val



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_       				=			""
	if is_panoptic:
		UpperCAmelCase_       				=			"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)
		UpperCAmelCase_       				=			state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"    )
		UpperCAmelCase_       				=			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
		UpperCAmelCase_       				=			in_proj_weight[:256, :]
		UpperCAmelCase_       				=			in_proj_bias[:256]
		UpperCAmelCase_       				=			in_proj_weight[256:512, :]
		UpperCAmelCase_       				=			in_proj_bias[256:512]
		UpperCAmelCase_       				=			in_proj_weight[-256:, :]
		UpperCAmelCase_       				=			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
		UpperCAmelCase_       				=			state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"    )
		UpperCAmelCase_       				=			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
		UpperCAmelCase_       				=			in_proj_weight[:256, :]
		UpperCAmelCase_       				=			in_proj_bias[:256]
		UpperCAmelCase_       				=			in_proj_weight[256:512, :]
		UpperCAmelCase_       				=			in_proj_bias[256:512]
		UpperCAmelCase_       				=			in_proj_weight[-256:, :]
		UpperCAmelCase_       				=			in_proj_bias[-256:]
		# read in weights + bias of input projection layer of cross-attention
		UpperCAmelCase_       				=			state_dict.pop(
		    F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"    )
		UpperCAmelCase_       				=			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
		UpperCAmelCase_       				=			in_proj_weight_cross_attn[:256, :]
		UpperCAmelCase_       				=			in_proj_bias_cross_attn[:256]
		UpperCAmelCase_       				=			in_proj_weight_cross_attn[256:512, :]
		UpperCAmelCase_       				=			in_proj_bias_cross_attn[256:512]
		UpperCAmelCase_       				=			in_proj_weight_cross_attn[-256:, :]
		UpperCAmelCase_       				=			in_proj_bias_cross_attn[-256:]



def       __lowerCAmelCase      (						):
	UpperCAmelCase_       				=			"http://images.cocodataset.org/val2017/000000039769.jpg"
	UpperCAmelCase_       				=			Image.open(requests.get(A       ,     stream=A    ).raw    )

	return im



@torch.no_grad()
def       __lowerCAmelCase      (						A       ,     A=None       ,     A=False    ):
	UpperCAmelCase_				,   UpperCAmelCase_       				=			get_detr_config(A    )

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

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

	# verify our conversion on an image
	UpperCAmelCase_       				=			"coco_panoptic" if is_panoptic else "coco_detection"
	UpperCAmelCase_       				=			DetrImageProcessor(format=A    )

	UpperCAmelCase_       				=			processor(images=prepare_img()       ,     return_tensors="pt"    )
	UpperCAmelCase_       				=			encoding["pixel_values"]

	UpperCAmelCase_       				=			detr(A    )
	UpperCAmelCase_       				=			model(A    )

	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(A    ).mkdir(exist_ok=A    )
		model.save_pretrained(A    )
		processor.save_pretrained(A    )

	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__":
				_a:    List[str] 							=			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.""")
				_a:    str 							=			parser.parse_args()
				convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)