
import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import ViTMAEForPreTraining, ViTMAEModel
					from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor

class      _SCREAMING_SNAKE_CASE				:





				def __init__(		self			:		List[Any]       ,   snake_case_			:		List[Any]       ,   snake_case_			:		Tuple=13       ,   snake_case_			:		int=30       ,   snake_case_			:		Union[str, Any]=2       ,   snake_case_			:		Optional[int]=3       ,   snake_case_			:		Any=True       ,   snake_case_			:		Any=True       ,   snake_case_			:		List[Any]=32       ,   snake_case_			:		Any=5       ,   snake_case_			:		str=4       ,   snake_case_			:		Union[str, Any]=37       ,   snake_case_			:		Tuple="gelu"       ,   snake_case_			:		Optional[Any]=0.1       ,   snake_case_			:		List[Any]=0.1       ,   snake_case_			:		str=10       ,   snake_case_			:		int=0.02       ,   snake_case_			:		Tuple=3       ,   snake_case_			:		List[str]=0.6       ,   snake_case_			:		int=None       ,   ):


								"""simple docstring"""

								A  :	Any				=					parent
								A  :	Tuple				=					batch_size
								A  :	Optional[int]				=					image_size
								A  :	Optional[int]				=					patch_size
								A  :	Any				=					num_channels
								A  :	str				=					is_training
								A  :	Tuple				=					use_labels
								A  :	Union[str, Any]				=					hidden_size
								A  :	str				=					num_hidden_layers
								A  :	Any				=					num_attention_heads
								A  :	Dict				=					intermediate_size
								A  :	List[str]				=					hidden_act
								A  :	List[Any]				=					hidden_dropout_prob
								A  :	int				=					attention_probs_dropout_prob
								A  :	List[Any]				=					type_sequence_label_size
								A  :	List[Any]				=					initializer_range
								A  :	Tuple				=					mask_ratio
								A  :	List[str]				=					scope

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





				def 							_UpperCAmelCase    (		self			:		Any				):


								"""simple docstring"""

								A  :	Union[str, Any]				=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

								A  :	Optional[Any]				=					None
								if self.use_labels:
												A  :	Any				=					ids_tensor([self.batch_size]       ,   self.type_sequence_label_size				)

								A  :	Optional[Any]				=					self.get_config()

								return config, pixel_values, labels





				def 							_UpperCAmelCase    (		self			:		Dict				):


								"""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       ,   hidden_act=self.hidden_act       ,   hidden_dropout_prob=self.hidden_dropout_prob       ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,   is_decoder=A_       ,   initializer_range=self.initializer_range       ,   mask_ratio=self.mask_ratio       ,   )





				def 							_UpperCAmelCase    (		self			:		List[str]       ,   snake_case_			:		Optional[int]       ,   snake_case_			:		List[Any]       ,   snake_case_			:		Optional[Any]				):


								"""simple docstring"""

								A  :	Union[str, Any]				=					ViTMAEModel(config=A_				)
								model.to(A_				)
								model.eval()
								A  :	Dict				=					model(A_				)
								self.parent.assertEqual(result.last_hidden_state.shape       ,   (self.batch_size, self.seq_length, self.hidden_size)				)





				def 							_UpperCAmelCase    (		self			:		Tuple       ,   snake_case_			:		Dict       ,   snake_case_			:		str       ,   snake_case_			:		Optional[Any]				):


								"""simple docstring"""

								A  :	Any				=					ViTMAEForPreTraining(A_				)
								model.to(A_				)
								model.eval()
								A  :	Optional[int]				=					model(A_				)
								A  :	Union[str, Any]				=					(self.image_size // self.patch_size) ** 2
								A  :	List[str]				=					self.patch_size**2 * self.num_channels
								self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, num_patches, expected_num_channels)				)

								# test greyscale images
								A  :	Tuple				=					1
								A  :	Dict				=					ViTMAEForPreTraining(A_				)
								model.to(A_				)
								model.eval()
								A  :	Optional[int]				=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
								A  :	Tuple				=					model(A_				)
								A  :	Union[str, Any]				=					self.patch_size**2
								self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, num_patches, expected_num_channels)				)






				def 							_UpperCAmelCase    (		self			:		Dict				):


								"""simple docstring"""

								A  :	List[Any]				=					self.prepare_config_and_inputs()
								A			,							A			,							A  :	Union[str, Any]				=					config_and_inputs
								A  :	Union[str, Any]				=					{'''pixel_values''': pixel_values}
								return config, inputs_dict



@require_torch
class      _SCREAMING_SNAKE_CASE				(		snake_case,     snake_case,     unittest.TestCase				):
				lowerCamelCase_	  =  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
				lowerCamelCase_	  =  {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

				lowerCamelCase_	  =  False
				lowerCamelCase_	  =  False
				lowerCamelCase_	  =  False
				lowerCamelCase_	  =  False





				def 							_UpperCAmelCase    (		self			:		List[Any]				):


								"""simple docstring"""

								A  :	str				=					ViTMAEModelTester(self				)
								A  :	Tuple				=					ConfigTester(self       ,   config_class=A_       ,   has_text_modality=A_       ,   hidden_size=37				)





				def 							_UpperCAmelCase    (		self			:		List[str]				):


								"""simple docstring"""

								self.config_tester.run_common_tests()





				@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''				)
				def 							_UpperCAmelCase    (		self			:		Optional[int]				):


								"""simple docstring"""

								pass





				def 							_UpperCAmelCase    (		self			:		List[str]				):


								"""simple docstring"""

								A			,							A  :	Dict				=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												A  :	Optional[int]				=					model_class(A_				)
												self.assertIsInstance(model.get_input_embeddings()       ,   (nn.Module)				)
												A  :	List[str]				=					model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(A_       ,   nn.Linear				)				)





				def 							_UpperCAmelCase    (		self			:		int				):


								"""simple docstring"""

								A			,							A  :	Optional[int]				=					self.model_tester.prepare_config_and_inputs_for_common()

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

												A  :	str				=					['''pixel_values''']
												self.assertListEqual(arg_names[:1]       ,   A_				)





				def 							_UpperCAmelCase    (		self			:		Dict				):


								"""simple docstring"""

								A  :	str				=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A_				)





				def 							_UpperCAmelCase    (		self			:		Any				):


								"""simple docstring"""

								A  :	Tuple				=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*A_				)





				def 							_UpperCAmelCase    (		self			:		List[Any]       ,   snake_case_			:		str       ,   snake_case_			:		Dict       ,   snake_case_			:		Union[str, Any]				):


								"""simple docstring"""

								np.random.seed(2				)

								A  :	str				=					int((pt_model.config.image_size // pt_model.config.patch_size) ** 2				)
								A  :	Dict				=					np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)
								A  :	Tuple				=					torch.from_numpy(A_				)

								# Add `noise` argument.
								# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
								A  :	str				=					pt_noise

								super().check_pt_tf_models(A_       ,   A_       ,   A_				)





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


								"""simple docstring"""

								A			,							A  :	Optional[int]				=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												A  :	int				=					model_class(A_				)
												model.to(A_				)
												model.eval()
												# make random mask reproducible
												torch.manual_seed(2				)
												with torch.no_grad():
																A  :	Dict				=					model(**self._prepare_for_class(A_       ,   A_				)				)

												A  :	List[str]				=					outputs[0].cpu().numpy()
												A  :	Tuple				=					0

												with tempfile.TemporaryDirectory() as tmpdirname:
																model.save_pretrained(A_				)
																A  :	str				=					model_class.from_pretrained(A_				)
																model.to(A_				)
																# make random mask reproducible
																torch.manual_seed(2				)
																with torch.no_grad():
																				A  :	Optional[int]				=					model(**self._prepare_for_class(A_       ,   A_				)				)

																# Make sure we don't have nans
																A  :	str				=					after_outputs[0].cpu().numpy()
																A  :	List[Any]				=					0
																A  :	List[str]				=					np.amax(np.abs(out_a - out_a				)				)
																self.assertLessEqual(A_       ,   1E-5				)





				@unittest.skip(
				    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''				)
				def 							_UpperCAmelCase    (		self			:		Union[str, Any]				):


								"""simple docstring"""

								pass





				@unittest.skip(
				    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''				)
				def 							_UpperCAmelCase    (		self			:		Dict				):


								"""simple docstring"""

								pass





				@unittest.skip(
				    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''				)
				def 							_UpperCAmelCase    (		self			:		Tuple				):


								"""simple docstring"""

								pass





				@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''				)
				def 							_UpperCAmelCase    (		self			:		List[str]				):


								"""simple docstring"""

								pass





				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
				def 							_UpperCAmelCase    (		self			:		Optional[Any]				):


								"""simple docstring"""

								pass






				@slow
				def 							_UpperCAmelCase    (		self			:		int				):


								"""simple docstring"""

								for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A  :	List[Any]				=					ViTMAEModel.from_pretrained(A_				)
												self.assertIsNotNone(A_				)





def 				_lowerCamelCase      (       ):



				'''simple docstring'''







				A  :	str				=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
				return image






@require_torch
@require_vision
class      _SCREAMING_SNAKE_CASE				(		unittest.TestCase				):





				@cached_property
				def 							_UpperCAmelCase    (		self			:		Dict				):


								"""simple docstring"""

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






				@slow
				def 							_UpperCAmelCase    (		self			:		Union[str, Any]				):


								"""simple docstring"""

								np.random.seed(2				)

								A  :	List[Any]				=					ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''				).to(A_				)

								A  :	str				=					self.default_image_processor
								A  :	Dict				=					prepare_img()
								A  :	int				=					image_processor(images=A_       ,   return_tensors='''pt'''				).to(A_				)

								# 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)
								A  :	Union[str, Any]				=					ViTMAEConfig()
								A  :	Any				=					int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2				)
								A  :	Union[str, Any]				=					np.random.uniform(size=(1, num_patches)				)

								# forward pass
								with torch.no_grad():
												A  :	str				=					model(**A_       ,   noise=torch.from_numpy(A_				).to(device=A_				)				)

								# verify the logits
								A  :	int				=					torch.Size((1, 196, 768)				)
								self.assertEqual(outputs.logits.shape       ,   A_				)

								A  :	int				=					torch.tensor(
								    [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]				)

								self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]       ,   expected_slice.to(A_				)       ,   atol=1E-4				)				)



"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


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





def       a_ (	lowercase__ :List[str]     ):
			__lowerCamelCase          =      SwinConfig(
			    embed_dim=192,			depths=(2, 2, 18, 2),			num_heads=(6, 12, 24, 48),			window_size=12,			out_features=["""stage2""", """stage3""", """stage4"""],			)

			__lowerCamelCase          =      DetaConfig(
			    backbone_config=lowercase__,			num_queries=900,			encoder_ffn_dim=2048,			decoder_ffn_dim=2048,			num_feature_levels=5,			assign_first_stage=lowercase__,			with_box_refine=lowercase__,			two_stage=lowercase__,			)

			# set labels
			__lowerCamelCase          =      """huggingface/label-files"""
			if "o365" in model_name:
						__lowerCamelCase          =      366
						__lowerCamelCase          =      """object365-id2label.json"""
			else:
						__lowerCamelCase          =      91
						__lowerCamelCase          =      """coco-detection-id2label.json"""

			__lowerCamelCase          =      num_labels
			__lowerCamelCase          =      json.load(open(cached_download(hf_hub_url(lowercase__,			lowercase__,			repo_type="""dataset"""     )     ),			"""r"""     )     )
			__lowerCamelCase          =      {int(lowercase__     ): v for k, v in idalabel.items()}
			__lowerCamelCase          =      idalabel
			__lowerCamelCase          =      {v: k for k, v in idalabel.items()}

			return config





def       a_ (	lowercase__ :List[str]     ):
			__lowerCamelCase          =      []

			# stem
			# fmt: off
			rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""")     )
			rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""")     )
			rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""")     )
			rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.bias""")     )
			# stages
			for i in range(len(config.backbone_config.depths     )     ):
						for j in range(config.backbone_config.depths[i]     ):
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')     )

						if i < 3:
									rename_keys.append((f'backbone.0.body.layers.{i}.downsample.reduction.weight', f'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.weight', f'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.bias', f'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')     )

			rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""")     )
			rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""")     )
			rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""")     )
			rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""")     )
			rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""")     )
			rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""")     )

			# transformer encoder
			for i in range(config.encoder_layers     ):
						rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', f'model.encoder.layers.{i}.self_attn.sampling_offsets.weight')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', f'model.encoder.layers.{i}.self_attn.sampling_offsets.bias')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', f'model.encoder.layers.{i}.self_attn.attention_weights.weight')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', f'model.encoder.layers.{i}.self_attn.attention_weights.bias')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.weight', f'model.encoder.layers.{i}.self_attn.value_proj.weight')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.bias', f'model.encoder.layers.{i}.self_attn.value_proj.bias')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.weight', f'model.encoder.layers.{i}.self_attn.output_proj.weight')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.bias', f'model.encoder.layers.{i}.self_attn.output_proj.bias')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.norm1.weight', f'model.encoder.layers.{i}.self_attn_layer_norm.weight')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'model.encoder.layers.{i}.self_attn_layer_norm.bias')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'model.encoder.layers.{i}.fc1.weight')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'model.encoder.layers.{i}.fc1.bias')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'model.encoder.layers.{i}.fc2.weight')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'model.encoder.layers.{i}.fc2.bias')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'model.encoder.layers.{i}.final_layer_norm.weight')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'model.encoder.layers.{i}.final_layer_norm.bias')     )

			# transformer decoder
			for i in range(config.decoder_layers     ):
						rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', f'model.decoder.layers.{i}.encoder_attn.attention_weights.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', f'model.decoder.layers.{i}.encoder_attn.attention_weights.bias')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', f'model.decoder.layers.{i}.encoder_attn.value_proj.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', f'model.decoder.layers.{i}.encoder_attn.value_proj.bias')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', f'model.decoder.layers.{i}.encoder_attn.output_proj.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', f'model.decoder.layers.{i}.encoder_attn.output_proj.bias')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.norm1.weight', f'model.decoder.layers.{i}.encoder_attn_layer_norm.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'model.decoder.layers.{i}.encoder_attn_layer_norm.bias')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'model.decoder.layers.{i}.self_attn.out_proj.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'model.decoder.layers.{i}.self_attn.out_proj.bias')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.norm2.weight', f'model.decoder.layers.{i}.self_attn_layer_norm.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.norm2.bias', f'model.decoder.layers.{i}.self_attn_layer_norm.bias')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'model.decoder.layers.{i}.fc1.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'model.decoder.layers.{i}.fc1.bias')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'model.decoder.layers.{i}.fc2.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'model.decoder.layers.{i}.fc2.bias')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'model.decoder.layers.{i}.final_layer_norm.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'model.decoder.layers.{i}.final_layer_norm.bias')     )

			# fmt: on

			return rename_keys





def       a_ (	lowercase__ :Optional[Any],			lowercase__ :str,			lowercase__ :Optional[int]     ):
			__lowerCamelCase          =      dct.pop(lowercase__     )
			__lowerCamelCase          =      val





def       a_ (	lowercase__ :List[Any],			lowercase__ :Dict     ):
			__lowerCamelCase          =      [int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
			for i in range(len(backbone_config.depths     )     ):
						__lowerCamelCase          =      num_features[i]
						for j in range(backbone_config.depths[i]     ):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									__lowerCamelCase          =      state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'     )
									__lowerCamelCase          =      state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'     )
									# next, add query, keys and values (in that order) to the state dict
									__lowerCamelCase          =      in_proj_weight[:dim, :]
									__lowerCamelCase          =      in_proj_bias[: dim]
									__lowerCamelCase          =      in_proj_weight[
									    dim : dim * 2, :
									]
									__lowerCamelCase          =      in_proj_bias[
									    dim : dim * 2
									]
									__lowerCamelCase          =      in_proj_weight[
									    -dim :, :
									]
									__lowerCamelCase          =      in_proj_bias[-dim :]
									# fmt: on





def       a_ (	lowercase__ :int,			lowercase__ :Optional[int]     ):
			# transformer decoder self-attention layers
			__lowerCamelCase          =      config.d_model
			for i in range(config.decoder_layers     ):
						# read in weights + bias of input projection layer of self-attention
						__lowerCamelCase          =      state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight'     )
						__lowerCamelCase          =      state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_bias'     )
						# next, add query, keys and values (in that order) to the state dict
						__lowerCamelCase          =      in_proj_weight[:hidden_size, :]
						__lowerCamelCase          =      in_proj_bias[:hidden_size]
						__lowerCamelCase          =      in_proj_weight[
						    hidden_size : hidden_size * 2, :
						]
						__lowerCamelCase          =      in_proj_bias[hidden_size : hidden_size * 2]
						__lowerCamelCase          =      in_proj_weight[-hidden_size:, :]
						__lowerCamelCase          =      in_proj_bias[-hidden_size:]





def       a_ (	):
			__lowerCamelCase          =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
			__lowerCamelCase          =      Image.open(requests.get(lowercase__,			stream=lowercase__     ).raw     )

			return im





@torch.no_grad()
def       a_ (	lowercase__ :Optional[int],			lowercase__ :Any,			lowercase__ :Dict     ):
			__lowerCamelCase          =      get_deta_config(lowercase__     )

			# load original state dict
			if model_name == "deta-swin-large":
						__lowerCamelCase          =      hf_hub_download(repo_id="""nielsr/deta-checkpoints""",			filename="""adet_swin_ft.pth"""     )
			elif model_name == "deta-swin-large-o365":
						__lowerCamelCase          =      hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365""",			filename="""deta_swin_pt_o365.pth"""     )
			else:
						raise ValueError(f'Model name {model_name} not supported'     )

			__lowerCamelCase          =      torch.load(lowercase__,			map_location="""cpu"""     )["""model"""]

			# original state dict
			for name, param in state_dict.items():
						print(lowercase__,			param.shape     )

			# rename keys
			__lowerCamelCase          =      create_rename_keys(lowercase__     )
			for src, dest in rename_keys:
						rename_key(lowercase__,			lowercase__,			lowercase__     )
			read_in_swin_q_k_v(lowercase__,			config.backbone_config     )
			read_in_decoder_q_k_v(lowercase__,			lowercase__     )

			# fix some prefixes
			for key in state_dict.copy().keys():
						if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
									__lowerCamelCase          =      state_dict.pop(lowercase__     )
									__lowerCamelCase          =      val
						if "input_proj" in key:
									__lowerCamelCase          =      state_dict.pop(lowercase__     )
									__lowerCamelCase          =      val
						if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
									__lowerCamelCase          =      state_dict.pop(lowercase__     )
									__lowerCamelCase          =      val

    # finally, create HuggingFace model and load state dict
			__lowerCamelCase          =      DetaForObjectDetection(lowercase__     )
			model.load_state_dict(lowercase__     )
			model.eval()

			__lowerCamelCase          =      """cuda""" if torch.cuda.is_available() else """cpu"""
			model.to(lowercase__     )

			# load image processor
			__lowerCamelCase          =      DetaImageProcessor(format="""coco_detection"""     )

			# verify our conversion on image
			__lowerCamelCase          =      prepare_img()
			__lowerCamelCase          =      processor(images=lowercase__,			return_tensors="""pt"""     )
			__lowerCamelCase          =      encoding["""pixel_values"""]
			__lowerCamelCase          =      model(pixel_values.to(lowercase__     )     )

			# verify logits
			print("""Logits:""",			outputs.logits[0, :3, :3]     )
			print("""Boxes:""",			outputs.pred_boxes[0, :3, :3]     )
			if model_name == "deta-swin-large":
						__lowerCamelCase          =      torch.tensor(
						    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]     )
						__lowerCamelCase          =      torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]     )
			elif model_name == "deta-swin-large-o365":
						__lowerCamelCase          =      torch.tensor(
						    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]     )
						__lowerCamelCase          =      torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]     )
			assert torch.allclose(outputs.logits[0, :3, :3],			expected_logits.to(lowercase__     ),			atol=1e-4     )
			assert torch.allclose(outputs.pred_boxes[0, :3, :3],			expected_boxes.to(lowercase__     ),			atol=1e-4     )
			print("""Everything ok!"""     )

			if pytorch_dump_folder_path:
						# Save model and processor
						logger.info(f'Saving PyTorch model and processor to {pytorch_dump_folder_path}...'     )
						Path(lowercase__     ).mkdir(exist_ok=lowercase__     )
						model.save_pretrained(lowercase__     )
						processor.save_pretrained(lowercase__     )

			# Push to hub
			if push_to_hub:
						print("""Pushing model and processor to hub..."""     )
						model.push_to_hub(f'jozhang97/{model_name}'     )
						processor.push_to_hub(f'jozhang97/{model_name}'     )


if __name__ == "__main__":
					__magic_name__						: Union[str, Any]    =				argparse.ArgumentParser()

					parser.add_argument(
					    '--model_name',
					    type=str,
					    default='deta-swin-large',
					    choices=['deta-swin-large', 'deta-swin-large-o365'],
					    help='Name of the 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 or not to push the converted model to the 🤗 hub.'
					)
					__magic_name__						: str    =				parser.parse_args()
					convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




