

from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor

class 						__UpperCamelCase    :




	def __init__(		self	:						str		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						List[str]=13		,	lowerCAmelCase	:						Optional[int]=30		,	lowerCAmelCase	:						Tuple=2		,	lowerCAmelCase	:						List[Any]=3		,	lowerCAmelCase	:						Any=True		,	lowerCAmelCase	:						Optional[int]=True		,	lowerCAmelCase	:						Any=32		,	lowerCAmelCase	:						Union[str, Any]=2		,	lowerCAmelCase	:						str=4		,	lowerCAmelCase	:						Tuple=37		,	lowerCAmelCase	:						Optional[int]="gelu"		,	lowerCAmelCase	:						Dict=0.1		,	lowerCAmelCase	:						str=0.1		,	lowerCAmelCase	:						List[Any]=10		,	lowerCAmelCase	:						Union[str, Any]=0.02		,	lowerCAmelCase	:						Tuple=3		,	lowerCAmelCase	:						List[Any]=0.6		,	lowerCAmelCase	:						List[Any]=None		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			parent
		UpperCAmelCase_       				=			batch_size
		UpperCAmelCase_       				=			image_size
		UpperCAmelCase_       				=			patch_size
		UpperCAmelCase_       				=			num_channels
		UpperCAmelCase_       				=			is_training
		UpperCAmelCase_       				=			use_labels
		UpperCAmelCase_       				=			hidden_size
		UpperCAmelCase_       				=			num_hidden_layers
		UpperCAmelCase_       				=			num_attention_heads
		UpperCAmelCase_       				=			intermediate_size
		UpperCAmelCase_       				=			hidden_act
		UpperCAmelCase_       				=			hidden_dropout_prob
		UpperCAmelCase_       				=			attention_probs_dropout_prob
		UpperCAmelCase_       				=			type_sequence_label_size
		UpperCAmelCase_       				=			initializer_range
		UpperCAmelCase_       				=			mask_ratio
		UpperCAmelCase_       				=			scope

		# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
		# (we add 1 for the [CLS] token)
		UpperCAmelCase_       				=			(image_size // patch_size) ** 2
		UpperCAmelCase_       				=			int(math.ceil((1 - mask_ratio) * (num_patches + 1)							)							)




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

		UpperCAmelCase_       				=			None
		if self.use_labels:
			UpperCAmelCase_       				=			ids_tensor([self.batch_size]		,	self.type_sequence_label_size							)

		UpperCAmelCase_       				=			self.get_config()

		return config, pixel_values, labels




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		return ViTMAEConfig(
		    image_size=self.image_size		,	patch_size=self.patch_size		,	num_channels=self.num_channels		,	hidden_size=self.hidden_size		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	intermediate_size=self.intermediate_size		,	decoder_hidden_size=self.hidden_size		,	decoder_num_hidden_layers=self.num_hidden_layers		,	decoder_num_attention_heads=self.num_attention_heads		,	decoder_intermediate_size=self.intermediate_size		,	hidden_act=self.hidden_act		,	hidden_dropout_prob=self.hidden_dropout_prob		,	attention_probs_dropout_prob=self.attention_probs_dropout_prob		,	is_decoder=lowerCAmelCase		,	initializer_range=self.initializer_range		,	mask_ratio=self.mask_ratio		,	)




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


		'''simple docstring'''







		UpperCAmelCase_       				=			TFViTMAEModel(config=lowerCAmelCase							)
		UpperCAmelCase_       				=			model(lowerCAmelCase		,	training=lowerCAmelCase							)
		self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size)							)




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


		'''simple docstring'''







		UpperCAmelCase_       				=			TFViTMAEForPreTraining(lowerCAmelCase							)
		UpperCAmelCase_       				=			model(lowerCAmelCase		,	training=lowerCAmelCase							)
		# expected sequence length = num_patches
		UpperCAmelCase_       				=			(self.image_size // self.patch_size) ** 2
		UpperCAmelCase_       				=			self.patch_size**2 * self.num_channels
		self.parent.assertEqual(result.logits.shape		,	(self.batch_size, num_patches, expected_num_channels)							)

		# test greyscale images
		UpperCAmelCase_       				=			1
		UpperCAmelCase_       				=			TFViTMAEForPreTraining(lowerCAmelCase							)

		UpperCAmelCase_       				=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
		UpperCAmelCase_       				=			model(lowerCAmelCase		,	training=lowerCAmelCase							)
		UpperCAmelCase_       				=			self.patch_size**2
		self.parent.assertEqual(result.logits.shape		,	(self.batch_size, num_patches, expected_num_channels)							)




	def       __A					(		self	:						Any							):


		'''simple docstring'''







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


@require_tf
class 						__UpperCamelCase    (					lowercase     ,     lowercase     ,     unittest.TestCase						):
	SCREAMING_SNAKE_CASE__    				=     (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
	SCREAMING_SNAKE_CASE__    				=     {'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

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




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFViTMAEModelTester(self							)
		UpperCAmelCase_       				=			ConfigTester(self		,	config_class=lowerCAmelCase		,	has_text_modality=lowerCAmelCase		,	hidden_size=37							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		self.config_tester.run_common_tests()




	@unittest.skip(reason="ViTMAE does not use inputs_embeds"							)
	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		pass




	def       __A					(		self	:						Optional[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							)
			self.assertIsInstance(model.get_input_embeddings()		,	(tf.keras.layers.Layer)							)
			UpperCAmelCase_       				=			model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(lowerCAmelCase		,	tf.keras.layers.Layer							)							)




	def       __A					(		self	:						Optional[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.call							)
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			UpperCAmelCase_       				=			[*signature.parameters.keys()]

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




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







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




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







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




	def       __A					(		self	:						str							):


		'''simple docstring'''







		np.random.seed(2							)

		UpperCAmelCase_				,   UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs_for_common()

		UpperCAmelCase_       				=			int((config.image_size // config.patch_size) ** 2							)
		UpperCAmelCase_       				=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

		for model_class in self.all_model_classes:
			UpperCAmelCase_       				=			model_class(lowerCAmelCase							)
			UpperCAmelCase_       				=			self._prepare_for_class(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	noise=lowerCAmelCase							)

			UpperCAmelCase_       				=			copy.deepcopy(self._prepare_for_class(lowerCAmelCase		,	lowerCAmelCase							)							)
			UpperCAmelCase_       				=			model(**lowerCAmelCase		,	noise=lowerCAmelCase							)
			UpperCAmelCase_       				=			outputs_dict[0].numpy()
			UpperCAmelCase_       				=			outputs_keywords[0].numpy()

			self.assertLess(np.sum(np.abs(output_dict - output_keywords							)							)		,	1e-6							)




	def       __A					(		self	:						int							):


		'''simple docstring'''







		np.random.seed(2							)

		UpperCAmelCase_				,   UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs_for_common()

		UpperCAmelCase_       				=			int((config.image_size // config.patch_size) ** 2							)
		UpperCAmelCase_       				=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

		def prepare_numpy_arrays(lowerCAmelCase	:						Dict							):
			UpperCAmelCase_       				=			{}
			for k, v in inputs_dict.items():
				if tf.is_tensor(lowerCAmelCase							):
					UpperCAmelCase_       				=			v.numpy()
				else:
					UpperCAmelCase_       				=			np.array(lowerCAmelCase							)

			return inputs_np_dict

		for model_class in self.all_model_classes:
			UpperCAmelCase_       				=			model_class(lowerCAmelCase							)

			UpperCAmelCase_       				=			self._prepare_for_class(lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			prepare_numpy_arrays(lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	noise=lowerCAmelCase							)
			UpperCAmelCase_       				=			model(**lowerCAmelCase		,	noise=lowerCAmelCase							)
			self.assert_outputs_same(lowerCAmelCase		,	lowerCAmelCase							)




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


		'''simple docstring'''







		np.random.seed(2							)

		UpperCAmelCase_       				=			int((tf_model.config.image_size // tf_model.config.patch_size) ** 2							)
		UpperCAmelCase_       				=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
		UpperCAmelCase_       				=			tf.constant(lowerCAmelCase							)

		# Add `noise` argument.
		# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
		UpperCAmelCase_       				=			tf_noise

		super().check_pt_tf_models(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		np.random.seed(2							)

		UpperCAmelCase_				,   UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs_for_common()

		UpperCAmelCase_       				=			{
		    module_member
		    for model_class in self.all_model_classes
		    for module in (import_module(model_class.__module__							),)
		    for module_member_name in dir(lowerCAmelCase							)
		    if module_member_name.endswith("MainLayer"							)
		    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
		    and module_member_name[: -len("MainLayer"							)] == model_class.__name__[: -len("Model"							)]
		    for module_member in (getattr(lowerCAmelCase		,	lowerCAmelCase							),)
		    if isinstance(lowerCAmelCase		,	lowerCAmelCase							)
		    and tf.keras.layers.Layer in module_member.__bases__
		    and getattr(lowerCAmelCase		,	"_keras_serializable"		,	lowerCAmelCase							)
		}

		UpperCAmelCase_       				=			int((config.image_size // config.patch_size) ** 2							)
		UpperCAmelCase_       				=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
		UpperCAmelCase_       				=			tf.convert_to_tensor(lowerCAmelCase							)
		inputs_dict.update({"noise": noise}							)

		for main_layer_class in tf_main_layer_classes:
			UpperCAmelCase_       				=			main_layer_class(lowerCAmelCase							)

			UpperCAmelCase_       				=			{
			    name: tf.keras.Input(tensor.shape[1:]		,	dtype=tensor.dtype							) for name, tensor in inputs_dict.items()
			}

			UpperCAmelCase_       				=			tf.keras.Model(lowerCAmelCase		,	outputs=main_layer(lowerCAmelCase							)							)
			UpperCAmelCase_       				=			model(lowerCAmelCase							)

			with tempfile.TemporaryDirectory() as tmpdirname:
				UpperCAmelCase_       				=			os.path.join(lowerCAmelCase		,	"keras_model.h5"							)
				model.save(lowerCAmelCase							)
				UpperCAmelCase_       				=			tf.keras.models.load_model(
				    lowerCAmelCase		,	custom_objects={main_layer_class.__name__: main_layer_class}							)
				assert isinstance(lowerCAmelCase		,	tf.keras.Model							)
				UpperCAmelCase_       				=			model(lowerCAmelCase							)
				self.assert_outputs_same(lowerCAmelCase		,	lowerCAmelCase							)




	@slow
	def       __A					(		self	:						Any							):


		'''simple docstring'''







		np.random.seed(2							)

		UpperCAmelCase_				,   UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs_for_common()

		UpperCAmelCase_       				=			int((config.image_size // config.patch_size) ** 2							)
		UpperCAmelCase_       				=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

		for model_class in self.all_model_classes:
			UpperCAmelCase_       				=			model_class(lowerCAmelCase							)
			UpperCAmelCase_       				=			self._prepare_for_class(lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			model(lowerCAmelCase		,	noise=lowerCAmelCase							)

			if model_class.__name__ == "TFViTMAEModel":
				UpperCAmelCase_       				=			outputs.last_hidden_state.numpy()
				UpperCAmelCase_       				=			0
			else:
				UpperCAmelCase_       				=			outputs.logits.numpy()
				UpperCAmelCase_       				=			0

			with tempfile.TemporaryDirectory() as tmpdirname:
				model.save_pretrained(lowerCAmelCase		,	saved_model=lowerCAmelCase							)
				UpperCAmelCase_       				=			model_class.from_pretrained(lowerCAmelCase							)
				UpperCAmelCase_       				=			model(lowerCAmelCase		,	noise=lowerCAmelCase							)

				if model_class.__name__ == "TFViTMAEModel":
					UpperCAmelCase_       				=			after_outputs["last_hidden_state"].numpy()
					UpperCAmelCase_       				=			0
				else:
					UpperCAmelCase_       				=			after_outputs["logits"].numpy()
					UpperCAmelCase_       				=			0

				UpperCAmelCase_       				=			np.amax(np.abs(out_a - out_a							)							)
				self.assertLessEqual(lowerCAmelCase		,	1e-5							)




	def       __A					(		self	:						str							):


		'''simple docstring'''







		np.random.seed(2							)

		UpperCAmelCase_				,   UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs_for_common()

		UpperCAmelCase_       				=			int((config.image_size // config.patch_size) ** 2							)
		UpperCAmelCase_       				=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

		for model_class in self.all_model_classes:
			UpperCAmelCase_       				=			model_class(lowerCAmelCase							)
			UpperCAmelCase_       				=			self._prepare_for_class(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	noise=lowerCAmelCase							)
			UpperCAmelCase_       				=			model.get_config()
			# make sure that returned config is jsonifiable, which is required by keras
			json.dumps(lowerCAmelCase							)
			UpperCAmelCase_       				=			model_class.from_config(model.get_config()							)
			# make sure it also accepts a normal config
			UpperCAmelCase_       				=			model_class.from_config(model.config							)
			UpperCAmelCase_       				=			new_model(lowerCAmelCase							)  # Build model
			new_model.set_weights(model.get_weights()							)
			UpperCAmelCase_       				=			new_model(lowerCAmelCase		,	noise=lowerCAmelCase							)

			self.assert_outputs_same(lowerCAmelCase		,	lowerCAmelCase							)




	@unittest.skip(
	    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."							)
	def       __A					(		self	:						Any							):


		'''simple docstring'''







		pass




	@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"							)
	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		pass




	@slow
	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"							)
		self.assertIsNotNone(lowerCAmelCase							)



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




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




	@cached_property
	def       __A					(		self	:						Any							):


		'''simple docstring'''







		return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"							) if is_vision_available() else None




	@slow
	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		np.random.seed(2							)

		UpperCAmelCase_       				=			TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"							)

		UpperCAmelCase_       				=			self.default_image_processor
		UpperCAmelCase_       				=			prepare_img()
		UpperCAmelCase_       				=			image_processor(images=lowerCAmelCase		,	return_tensors="tf"							)

		# prepare a noise vector that will be also used for testing the TF model
		# (this way we can ensure that the PT and TF models operate on the same inputs)
		UpperCAmelCase_       				=			ViTMAEConfig()
		UpperCAmelCase_       				=			int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
		UpperCAmelCase_       				=			np.random.uniform(size=(1, num_patches)							)

		# forward pass
		UpperCAmelCase_       				=			model(**lowerCAmelCase		,	noise=lowerCAmelCase							)

		# verify the logits
		UpperCAmelCase_       				=			tf.convert_to_tensor([1, 196, 768]							)
		self.assertEqual(outputs.logits.shape		,	lowerCAmelCase							)

		UpperCAmelCase_       				=			tf.convert_to_tensor(
		    [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]							)

		tf.debugging.assert_near(outputs.logits[0, :3, :3]		,	lowerCAmelCase		,	atol=1e-4							)


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)