


'''simple docstring'''



import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase      :      Tuple            =					logging.get_logger(__name__)


def 				lowerCamelCase__				(	A       :      str					):




						'''simple docstring'''






						UpperCAmelCase					    =     SwinConfig.from_pretrained(
						    '''microsoft/swin-tiny-patch4-window7-224'''				,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']					)
						UpperCAmelCase					    =     MaskFormerConfig(backbone_config=A					)

						UpperCAmelCase					    =     '''huggingface/label-files'''
						if "ade20k-full" in model_name:
												# this should be ok
												UpperCAmelCase					    =     8_47
												UpperCAmelCase					    =     '''maskformer-ade20k-full-id2label.json'''
						elif "ade" in model_name:
												# this should be ok
												UpperCAmelCase					    =     1_50
												UpperCAmelCase					    =     '''ade20k-id2label.json'''
						elif "coco-stuff" in model_name:
												# this should be ok
												UpperCAmelCase					    =     1_71
												UpperCAmelCase					    =     '''maskformer-coco-stuff-id2label.json'''
						elif "coco" in model_name:
												# TODO
												UpperCAmelCase					    =     1_33
												UpperCAmelCase					    =     '''coco-panoptic-id2label.json'''
						elif "cityscapes" in model_name:
												# this should be ok
												UpperCAmelCase					    =     19
												UpperCAmelCase					    =     '''cityscapes-id2label.json'''
						elif "vistas" in model_name:
												# this should be ok
												UpperCAmelCase					    =     65
												UpperCAmelCase					    =     '''mapillary-vistas-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()}

						return config


def 				lowerCamelCase__				(	A       :      List[Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     []
						# stem
						# fmt: off
						rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')					)
						rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')					)
						rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')					)
						rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.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.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")					)
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")					)
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")					)
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")					)
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")					)
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")					)
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")					)
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")					)
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")					)
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")					)
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")					)
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")					)

												if i < 3:
																		rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")					)
																		rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")					)
																		rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")					)
												rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")					)
												rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")					)

						# FPN
						rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')					)
						rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')					)
						rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')					)
						for source_index, target_index in zip(range(3				,       0				,       -1					)				,       range(0				,       3					)					):
												rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")					)
												rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")					)
												rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")					)
												rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")					)
												rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")					)
												rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")					)
						rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')					)
						rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')					)

						# Transformer decoder
						for idx in range(config.decoder_config.decoder_layers					):
												# self-attention out projection
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")					)
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")					)
												# cross-attention out projection
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")					)
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")					)
												# MLP 1
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")					)
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")					)
												# MLP 2
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")					)
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")					)
												# layernorm 1 (self-attention layernorm)
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")					)
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")					)
												# layernorm 2 (cross-attention layernorm)
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")					)
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")					)
												# layernorm 3 (final layernorm)
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")					)
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")					)

						rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')					)
						rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')					)

						# heads on top
						rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')					)

						rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')					)
						rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')					)

						rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')					)
						rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')					)

						for i in range(3					):
												rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""")					)
												rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""")					)
						# fmt: on

						return rename_keys


def 				lowerCamelCase__				(	A       :      Union[str, Any]				,       A       :      List[str]				,       A       :      Union[str, Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     dct.pop(A					)
						UpperCAmelCase					    =     val


def 				lowerCamelCase__				(	A       :      int				,       A       :      Dict					):




						'''simple docstring'''






						UpperCAmelCase					    =     [int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
						for i in range(len(backbone_config.depths					)					):
												UpperCAmelCase					    =     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)
																		UpperCAmelCase					    =     state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""					)
																		UpperCAmelCase					    =     state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""					)
																		# next, add query, keys and values (in that order) to the state dict
																		UpperCAmelCase					    =     in_proj_weight[:dim, :]
																		UpperCAmelCase					    =     in_proj_bias[: dim]
																		UpperCAmelCase					    =     in_proj_weight[
																		    dim : dim * 2, :
																		]
																		UpperCAmelCase					    =     in_proj_bias[
																		    dim : dim * 2
																		]
																		UpperCAmelCase					    =     in_proj_weight[
																		    -dim :, :
																		]
																		UpperCAmelCase					    =     in_proj_bias[-dim :]
																		# fmt: on


def 				lowerCamelCase__				(	A       :      Any				,       A       :      List[Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     config.decoder_config.hidden_size
						for idx in range(config.decoder_config.decoder_layers					):
												# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
												UpperCAmelCase					    =     state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""					)
												UpperCAmelCase					    =     state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""					)
												# next, add query, keys and values (in that order) to the state dict
												UpperCAmelCase					    =     in_proj_weight[: hidden_size, :]
												UpperCAmelCase					    =     in_proj_bias[:config.hidden_size]
												UpperCAmelCase					    =     in_proj_weight[hidden_size : hidden_size * 2, :]
												UpperCAmelCase					    =     in_proj_bias[hidden_size : hidden_size * 2]
												UpperCAmelCase					    =     in_proj_weight[-hidden_size :, :]
												UpperCAmelCase					    =     in_proj_bias[-hidden_size :]
												# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
												UpperCAmelCase					    =     state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""					)
												UpperCAmelCase					    =     state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""					)
												# next, add query, keys and values (in that order) to the state dict
												UpperCAmelCase					    =     in_proj_weight[: hidden_size, :]
												UpperCAmelCase					    =     in_proj_bias[:config.hidden_size]
												UpperCAmelCase					    =     in_proj_weight[hidden_size : hidden_size * 2, :]
												UpperCAmelCase					    =     in_proj_bias[hidden_size : hidden_size * 2]
												UpperCAmelCase					    =     in_proj_weight[-hidden_size :, :]
												UpperCAmelCase					    =     in_proj_bias[-hidden_size :]
						# fmt: on


def 				lowerCamelCase__				(	):




						'''simple docstring'''






						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       :      str				,       A       :      str				,       A       :      str				,       A       :      bool = False					):




						'''simple docstring'''






						UpperCAmelCase					    =     get_maskformer_config(A					)

						# load original state_dict
						with open(A				,       '''rb'''					) as f:
												UpperCAmelCase					    =     pickle.load(A					)
						UpperCAmelCase					    =     data['''model''']

						# for name, param in state_dict.items():
						#     print(name, param.shape)

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

						# update to torch tensors
						for key, value in state_dict.items():
												UpperCAmelCase					    =     torch.from_numpy(A					)

						# load 🤗 model
						UpperCAmelCase					    =     MaskFormerForInstanceSegmentation(A					)
						model.eval()

						for name, param in model.named_parameters():
												print(A				,       param.shape					)

						UpperCAmelCase    ,    UpperCAmelCase					    =     model.load_state_dict(A				,       strict=A					)
						assert missing_keys == [
						    "model.pixel_level_module.encoder.model.layernorm.weight",
						    "model.pixel_level_module.encoder.model.layernorm.bias",
						]
						assert len(A					) == 0, f"""Unexpected keys: {unexpected_keys}"""

						# verify results
						UpperCAmelCase					    =     prepare_img()
						if "vistas" in model_name:
												UpperCAmelCase					    =     65
						elif "cityscapes" in model_name:
												UpperCAmelCase					    =     6_55_35
						else:
												UpperCAmelCase					    =     2_55
						UpperCAmelCase					    =     True if '''ade''' in model_name else False
						UpperCAmelCase					    =     MaskFormerImageProcessor(ignore_index=A				,       reduce_labels=A					)

						UpperCAmelCase					    =     image_processor(A				,       return_tensors='''pt'''					)

						UpperCAmelCase					    =     model(**A					)

						print('''Logits:'''				,       outputs.class_queries_logits[0, :3, :3]					)

						if model_name == "maskformer-swin-tiny-ade":
												UpperCAmelCase					    =     torch.tensor(
												    [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]					)
						assert torch.allclose(outputs.class_queries_logits[0, :3, :3]				,       A				,       atol=1E-4					)
						print('''Looks ok!'''					)

						if pytorch_dump_folder_path is not None:
												print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""					)
												Path(A					).mkdir(exist_ok=A					)
												model.save_pretrained(A					)
												image_processor.save_pretrained(A					)

						if push_to_hub:
												print('''Pushing model and image processor to the hub...'''					)
												model.push_to_hub(f"""nielsr/{model_name}"""					)
												image_processor.push_to_hub(f"""nielsr/{model_name}"""					)


if __name__ == "__main__":
	_lowercase      :      Any            =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""maskformer-swin-tiny-ade""",
	    type=str,
	    help=("""Name of the MaskFormer model you'd like to convert""",),
	)
	parser.add_argument(
	    """--checkpoint_path""",
	    default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
	    type=str,
	    help="""Path to the original state dict (.pth file).""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
	)
	parser.add_argument(
	    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
	)

	_lowercase      :      List[Any]            =					parser.parse_args()
	convert_maskformer_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)









'''simple docstring'''



import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
	import torch

	from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin






class 	UpperCamelCase__:




				def __init__(		self      :			List[str]				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			Optional[int]=None				,   lowerCAmelCase      :			Optional[int]=None				,   lowerCAmelCase      :			List[str]=None				,   lowerCAmelCase      :			int="resnet50"				,   lowerCAmelCase      :			Optional[int]=3				,   lowerCAmelCase      :			Union[str, Any]=32				,   lowerCAmelCase      :			List[Any]=3				,   lowerCAmelCase      :			Tuple=True				,   lowerCAmelCase      :			str=True				,   )->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     parent
										UpperCAmelCase					    =     out_indices if out_indices is not None else [4]
										UpperCAmelCase					    =     stage_names
										UpperCAmelCase					    =     out_features
										UpperCAmelCase					    =     backbone
										UpperCAmelCase					    =     batch_size
										UpperCAmelCase					    =     image_size
										UpperCAmelCase					    =     num_channels
										UpperCAmelCase					    =     use_pretrained_backbone
										UpperCAmelCase					    =     is_training




				def 					a__(		self      :			Optional[Any]				)->					Optional[int]:

										"""simple docstring"""




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

										return config, pixel_values




				def 					a__(		self      :			int				)->					Optional[Any]:

										"""simple docstring"""




										return TimmBackboneConfig(
										    image_size=self.image_size				,   num_channels=self.num_channels				,   out_features=self.out_features				,   out_indices=self.out_indices				,   stage_names=self.stage_names				,   use_pretrained_backbone=self.use_pretrained_backbone				,   backbone=self.backbone				,   )




				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Dict				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     TimmBackbone(config=lowerCAmelCase				)
										model.to(lowerCAmelCase				)
										model.eval()
										with torch.no_grad():
																UpperCAmelCase					    =     model(lowerCAmelCase				)
										self.parent.assertEqual(
										    result.feature_map[-1].shape				,   (self.batch_size, model.channels[-1], 14, 14)				,   )





				def 					a__(		self      :			str				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.prepare_config_and_inputs()
										UpperCAmelCase    ,    UpperCAmelCase					    =     config_and_inputs
										UpperCAmelCase					    =     {'''pixel_values''': pixel_values}
										return config, inputs_dict







@require_torch
@require_timm
class 	UpperCamelCase__(       lowerCAmelCase						,    lowerCAmelCase						,    lowerCAmelCase						,    unittest.TestCase	):
				__magic_name__    :				List[str]  		=	(TimmBackbone,) if is_torch_available() else ()
				__magic_name__    :				Any  		=	{"feature-extraction": TimmBackbone} if is_torch_available() else {}
				__magic_name__    :				Union[str, Any]  		=	False
				__magic_name__    :				int  		=	False
				__magic_name__    :				Tuple  		=	False
				__magic_name__    :				List[str]  		=	False




				def 					a__(		self      :			int				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     TimmBackboneModelTester(self				)
										UpperCAmelCase					    =     ConfigTester(self				,   config_class=lowerCAmelCase				,   has_text_modality=lowerCAmelCase				)




				def 					a__(		self      :			List[str]				)->					Dict:

										"""simple docstring"""




										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()




				def 					a__(		self      :			List[Any]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     '''resnet18'''
										UpperCAmelCase					    =     '''microsoft/resnet-18'''

										UpperCAmelCase					    =     AutoBackbone.from_pretrained(lowerCAmelCase				,   use_timm_backbone=lowerCAmelCase				)
										UpperCAmelCase					    =     AutoBackbone.from_pretrained(lowerCAmelCase				)

										self.assertEqual(len(timm_model.out_features				)				,   len(transformers_model.out_features				)				)
										self.assertEqual(len(timm_model.stage_names				)				,   len(transformers_model.stage_names				)				)
										self.assertEqual(timm_model.channels				,   transformers_model.channels				)
										# Out indices are set to the last layer by default. For timm models, we don't know
										# the number of layers in advance, so we set it to (-1,), whereas for transformers
										# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
										self.assertEqual(timm_model.out_indices				,   (-1,)				)
										self.assertEqual(transformers_model.out_indices				,   [len(timm_model.stage_names				) - 1]				)

										UpperCAmelCase					    =     AutoBackbone.from_pretrained(lowerCAmelCase				,   use_timm_backbone=lowerCAmelCase				,   out_indices=[1, 2, 3]				)
										UpperCAmelCase					    =     AutoBackbone.from_pretrained(lowerCAmelCase				,   out_indices=[1, 2, 3]				)

										self.assertEqual(timm_model.out_indices				,   transformers_model.out_indices				)
										self.assertEqual(len(timm_model.out_features				)				,   len(transformers_model.out_features				)				)
										self.assertEqual(timm_model.channels				,   transformers_model.channels				)




				@unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''				)
				def 					a__(		self      :			Union[str, Any]				)->					Tuple:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''				)
				def 					a__(		self      :			Dict				)->					Dict:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone initialization is managed on the timm side'''				)
				def 					a__(		self      :			Tuple				)->					List[Any]:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''				)
				def 					a__(		self      :			List[str]				)->					Optional[Any]:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''				)
				def 					a__(		self      :			Optional[Any]				)->					Optional[Any]:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''				)
				def 					a__(		self      :			Any				)->					Optional[int]:

										"""simple docstring"""




										pass




				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''				)
				def 					a__(		self      :			Optional[int]				)->					Optional[int]:

										"""simple docstring"""




										pass




				@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''				)
				def 					a__(		self      :			str				)->					str:

										"""simple docstring"""




										pass




				@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''				)
				def 					a__(		self      :			Any				)->					int:

										"""simple docstring"""




										pass




				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''				)
				def 					a__(		self      :			List[Any]				)->					int:

										"""simple docstring"""




										pass




				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''				)
				def 					a__(		self      :			List[Any]				)->					List[str]:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''				)
				def 					a__(		self      :			List[str]				)->					Dict:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''				)
				def 					a__(		self      :			str				)->					Tuple:

										"""simple docstring"""




										pass




				@unittest.skip('''Safetensors is not supported by timm.'''				)
				def 					a__(		self      :			List[Any]				)->					List[Any]:

										"""simple docstring"""




										pass




				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
				def 					a__(		self      :			int				)->					Tuple:

										"""simple docstring"""




										pass




				def 					a__(		self      :			Dict				)->					Union[str, 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				)




				def 					a__(		self      :			Union[str, Any]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()
										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     self.has_attentions

										# no need to test all models as different heads yield the same functionality
										UpperCAmelCase					    =     self.all_model_classes[0]
										UpperCAmelCase					    =     model_class(lowerCAmelCase				)
										model.to(lowerCAmelCase				)

										UpperCAmelCase					    =     self._prepare_for_class(lowerCAmelCase				,   lowerCAmelCase				)
										UpperCAmelCase					    =     model(**lowerCAmelCase				)
										UpperCAmelCase					    =     outputs[0][-1]

										# Encoder-/Decoder-only models
										UpperCAmelCase					    =     outputs.hidden_states[0]
										hidden_states.retain_grad()

										if self.has_attentions:
																UpperCAmelCase					    =     outputs.attentions[0]
																attentions.retain_grad()

										output.flatten()[0].backward(retain_graph=lowerCAmelCase				)

										self.assertIsNotNone(hidden_states.grad				)

										if self.has_attentions:
																self.assertIsNotNone(attentions.grad				)





				def 					a__(		self      :			Optional[int]				)->					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				)
																model.to(lowerCAmelCase				)
																model.eval()
																UpperCAmelCase					    =     model(**lowerCAmelCase				)

																self.assertEqual(len(result.feature_maps				)				,   len(config.out_indices				)				)
																self.assertEqual(len(model.channels				)				,   len(config.out_indices				)				)

																# Check output of last stage is taken if out_features=None, out_indices=None
																UpperCAmelCase					    =     copy.deepcopy(lowerCAmelCase				)
																UpperCAmelCase					    =     None
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)
																model.to(lowerCAmelCase				)
																model.eval()
																UpperCAmelCase					    =     model(**lowerCAmelCase				)

																self.assertEqual(len(result.feature_maps				)				,   1				)
																self.assertEqual(len(model.channels				)				,   1				)

																# Check backbone can be initialized with fresh weights
																UpperCAmelCase					    =     copy.deepcopy(lowerCAmelCase				)
																UpperCAmelCase					    =     False
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)
																model.to(lowerCAmelCase				)
																model.eval()
																UpperCAmelCase					    =     model(**lowerCAmelCase				)





