

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()
lowerCamelCase      				=     logging.get_logger(__name__)


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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

					__lowerCAmelCase					=							'huggingface/label-files'
					if "ade20k-full" in model_name:
										# this should be ok
										__lowerCAmelCase					=							847
										__lowerCAmelCase					=							'maskformer-ade20k-full-id2label.json'
					elif "ade" in model_name:
										# this should be ok
										__lowerCAmelCase					=							150
										__lowerCAmelCase					=							'ade20k-id2label.json'
					elif "coco-stuff" in model_name:
										# this should be ok
										__lowerCAmelCase					=							171
										__lowerCAmelCase					=							'maskformer-coco-stuff-id2label.json'
					elif "coco" in model_name:
										# TODO
										__lowerCAmelCase					=							133
										__lowerCAmelCase					=							'coco-panoptic-id2label.json'
					elif "cityscapes" in model_name:
										# this should be ok
										__lowerCAmelCase					=							19
										__lowerCAmelCase					=							'cityscapes-id2label.json'
					elif "vistas" in model_name:
										# this should be ok
										__lowerCAmelCase					=							65
										__lowerCAmelCase					=							'mapillary-vistas-id2label.json'

					__lowerCAmelCase					=							json.load(open(hf_hub_download(UpperCAmelCase__		,					UpperCAmelCase__		,					repo_type='dataset'      )		,					'r'      )      )
					__lowerCAmelCase					=							{int(UpperCAmelCase__      ): v for k, v in idalabel.items()}

					return config


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							[]
					# 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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							dct.pop(UpperCAmelCase__      )
					__lowerCAmelCase					=							val


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__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.layers.{i}.blocks.{j}.attn.qkv.weight"""      )
															__lowerCAmelCase					=							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
															__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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							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)
										__lowerCAmelCase					=							state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""      )
										__lowerCAmelCase					=							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
										__lowerCAmelCase					=							in_proj_weight[: hidden_size, :]
										__lowerCAmelCase					=							in_proj_bias[:config.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 :]
										# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
										__lowerCAmelCase					=							state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""      )
										__lowerCAmelCase					=							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
										__lowerCAmelCase					=							in_proj_weight[: hidden_size, :]
										__lowerCAmelCase					=							in_proj_bias[:config.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 :]
					# fmt: on


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							'http://images.cocodataset.org/val2017/000000039769.jpg'
					__lowerCAmelCase					=							Image.open(requests.get(UpperCAmelCase__		,					stream=UpperCAmelCase__      ).raw      )
					return im


@torch.no_grad()
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__ = False      ):

					"""simple docstring"""






					__lowerCAmelCase					=							get_maskformer_config(UpperCAmelCase__      )

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

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

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

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

					# load 🤗 model
					__lowerCAmelCase					=							MaskFormerForInstanceSegmentation(UpperCAmelCase__      )
					model.eval()

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

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

					# verify results
					__lowerCAmelCase					=							prepare_img()
					if "vistas" in model_name:
										__lowerCAmelCase					=							65
					elif "cityscapes" in model_name:
										__lowerCAmelCase					=							65_535
					else:
										__lowerCAmelCase					=							255
					__lowerCAmelCase					=							True if 'ade' in model_name else False
					__lowerCAmelCase					=							MaskFormerImageProcessor(ignore_index=UpperCAmelCase__		,					reduce_labels=UpperCAmelCase__      )

					__lowerCAmelCase					=							image_processor(UpperCAmelCase__		,					return_tensors='pt'      )

					__lowerCAmelCase					=							model(**UpperCAmelCase__      )

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

					if model_name == "maskformer-swin-tiny-ade":
										__lowerCAmelCase					=							torch.tensor(
										    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]      )
					assert torch.allclose(outputs.class_queries_logits[0, :3, :3]		,					UpperCAmelCase__		,					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(UpperCAmelCase__      ).mkdir(exist_ok=UpperCAmelCase__      )
										model.save_pretrained(UpperCAmelCase__      )
										image_processor.save_pretrained(UpperCAmelCase__      )

					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__":
		lowerCamelCase      				=     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.'''
		)

		lowerCamelCase      				=     parser.parse_args()
		convert_maskformer_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)


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     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=None					,						_A=None					,						_A=None					,						_A="resnet50"					,						_A=3					,						_A=3_2					,						_A=3					,						_A=True					,						_A=True					,						):
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							out_indices if out_indices is not None else [4]
												__lowerCAmelCase					=							stage_names
												__lowerCAmelCase					=							out_features
												__lowerCAmelCase					=							backbone
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							image_size
												__lowerCAmelCase					=							num_channels
												__lowerCAmelCase					=							use_pretrained_backbone
												__lowerCAmelCase					=							is_training




							def        A__  ( self							):
												__lowerCAmelCase					=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
												__lowerCAmelCase					=							self.get_config()

												return config, pixel_values




							def        A__  ( self							):
												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					,						_A					,						_A							):
												__lowerCAmelCase					=							TimmBackbone(config=_A							)
												model.to(_A							)
												model.eval()
												with torch.no_grad():
																	__lowerCAmelCase					=							model(_A							)
												self.parent.assertEqual(
												    result.feature_map[-1].shape					,						(self.batch_size, model.channels[-1], 1_4, 1_4)					,						)




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





@require_torch
@require_timm
class     snake_case_			(				_a			, _a			, _a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =(TimmBackbone,) if is_torch_available() else ()
							__UpperCAmelCase       ={"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False




							def        A__  ( self							):
												__lowerCAmelCase					=							TimmBackboneModelTester(self							)
												__lowerCAmelCase					=							ConfigTester(self					,						config_class=_A					,						has_text_modality=_A							)




							def        A__  ( self							):
												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							):
												__lowerCAmelCase					=							'resnet18'
												__lowerCAmelCase					=							'microsoft/resnet-18'

												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						use_timm_backbone=_A							)
												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A							)

												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]							)

												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						use_timm_backbone=_A					,						out_indices=[1, 2, 3]							)
												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						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							):
												pass




							@unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone initialization is managed on the timm side'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('model weights aren\'t tied in TimmBackbone.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('model weights aren\'t tied in TimmBackbone.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t support output_attentions.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Safetensors is not supported by timm.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
							def        A__  ( self							):
												pass




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

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

																	__lowerCAmelCase					=							['pixel_values']
																	self.assertListEqual(arg_names[:1]					,						_A							)




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()
												__lowerCAmelCase					=							True
												__lowerCAmelCase					=							self.has_attentions

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

												__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A							)
												__lowerCAmelCase					=							model(**_A							)
												__lowerCAmelCase					=							outputs[0][-1]

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

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

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

												self.assertIsNotNone(hidden_states.grad							)

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




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)

																	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
																	__lowerCAmelCase					=							copy.deepcopy(_A							)
																	__lowerCAmelCase					=							None
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)

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

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






