



"""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				:				int   		=		logging.get_logger(__name__)






def   snake_case__     (	__lowerCamelCase       :					str			):
			"""simple docstring"""
			lowerCamelCase__	:	str				=SwinConfig.from_pretrained(
			    '''microsoft/swin-tiny-patch4-window7-224'''							,	out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']			)
			lowerCamelCase__	:	List[Any]				=MaskFormerConfig(backbone_config=__lowerCamelCase			)

			lowerCamelCase__	:	List[str]				='''huggingface/label-files'''
			if "ade20k-full" in model_name:
						# this should be ok
						lowerCamelCase__	:	Dict				=847
						lowerCamelCase__	:	str				='''maskformer-ade20k-full-id2label.json'''
			elif "ade" in model_name:
						# this should be ok
						lowerCamelCase__	:	int				=150
						lowerCamelCase__	:	Tuple				='''ade20k-id2label.json'''
			elif "coco-stuff" in model_name:
						# this should be ok
						lowerCamelCase__	:	str				=171
						lowerCamelCase__	:	str				='''maskformer-coco-stuff-id2label.json'''
			elif "coco" in model_name:
						# TODO
						lowerCamelCase__	:	Any				=133
						lowerCamelCase__	:	Any				='''coco-panoptic-id2label.json'''
			elif "cityscapes" in model_name:
						# this should be ok
						lowerCamelCase__	:	Union[str, Any]				=19
						lowerCamelCase__	:	Any				='''cityscapes-id2label.json'''
			elif "vistas" in model_name:
						# this should be ok
						lowerCamelCase__	:	Tuple				=65
						lowerCamelCase__	:	int				='''mapillary-vistas-id2label.json'''

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

			return config






def   snake_case__     (	__lowerCamelCase       :					Tuple			):
			"""simple docstring"""
			lowerCamelCase__	:	int				=[]
			# 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   snake_case__     (	__lowerCamelCase       :					Any							,	__lowerCamelCase       :					Optional[Any]							,	__lowerCamelCase       :					Optional[int]			):
			"""simple docstring"""
			lowerCamelCase__	:	Tuple				=dct.pop(__lowerCamelCase			)
			lowerCamelCase__	:	Any				=val






def   snake_case__     (	__lowerCamelCase       :					str							,	__lowerCamelCase       :					int			):
			"""simple docstring"""
			lowerCamelCase__	:	Any				=[int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
			for i in range(len(backbone_config.depths			)			):
						lowerCamelCase__	:	Any				=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__	:	Dict				=state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''			)
									lowerCamelCase__	:	Optional[int]				=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__	:	Optional[int]				=in_proj_weight[:dim, :]
									lowerCamelCase__	:	Optional[Any]				=in_proj_bias[: dim]
									lowerCamelCase__	:	Optional[Any]				=in_proj_weight[
									    dim : dim * 2, :
									]
									lowerCamelCase__	:	str				=in_proj_bias[
									    dim : dim * 2
									]
									lowerCamelCase__	:	Any				=in_proj_weight[
									    -dim :, :
									]
									lowerCamelCase__	:	Optional[Any]				=in_proj_bias[-dim :]
									# fmt: on






def   snake_case__     (	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Union[str, Any]			):
			"""simple docstring"""
			# fmt: off
			lowerCamelCase__	:	Any				=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__	:	str				=state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''			)
						lowerCamelCase__	:	Dict				=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__	:	Optional[Any]				=in_proj_weight[: hidden_size, :]
						lowerCamelCase__	:	int				=in_proj_bias[:config.hidden_size]
						lowerCamelCase__	:	Optional[Any]				=in_proj_weight[hidden_size : hidden_size * 2, :]
						lowerCamelCase__	:	Union[str, Any]				=in_proj_bias[hidden_size : hidden_size * 2]
						lowerCamelCase__	:	List[Any]				=in_proj_weight[-hidden_size :, :]
						lowerCamelCase__	:	int				=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__	:	Dict				=state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''			)
						lowerCamelCase__	:	Optional[Any]				=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__	:	str				=in_proj_weight[: hidden_size, :]
						lowerCamelCase__	:	Any				=in_proj_bias[:config.hidden_size]
						lowerCamelCase__	:	List[Any]				=in_proj_weight[hidden_size : hidden_size * 2, :]
						lowerCamelCase__	:	Optional[Any]				=in_proj_bias[hidden_size : hidden_size * 2]
						lowerCamelCase__	:	Tuple				=in_proj_weight[-hidden_size :, :]
						lowerCamelCase__	:	Any				=in_proj_bias[-hidden_size :]
			# fmt: on






def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	Union[str, Any]				='''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCamelCase__	:	str				=Image.open(requests.get(__lowerCamelCase							,	stream=__lowerCamelCase			).raw			)
			return im






@torch.no_grad()
def   snake_case__     (	__lowerCamelCase       :					str							,	__lowerCamelCase       :					str							,	__lowerCamelCase       :					str							,	__lowerCamelCase       :					bool = False			):
			"""simple docstring"""
			lowerCamelCase__	:	int				=get_maskformer_config(__lowerCamelCase			)

			# load original state_dict
			with open(__lowerCamelCase							,	'''rb'''			) as f:
						lowerCamelCase__	:	Dict				=pickle.load(__lowerCamelCase			)
			lowerCamelCase__	:	List[str]				=data['''model''']

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

			# rename keys
			lowerCamelCase__	:	Optional[Any]				=create_rename_keys(__lowerCamelCase			)
			for src, dest in rename_keys:
						rename_key(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)
			read_in_swin_q_k_v(__lowerCamelCase							,	config.backbone_config			)
			read_in_decoder_q_k_v(__lowerCamelCase							,	__lowerCamelCase			)

			# update to torch tensors
			for key, value in state_dict.items():
						lowerCamelCase__	:	int				=torch.from_numpy(__lowerCamelCase			)

			# load 🤗 model
			lowerCamelCase__	:	List[str]				=MaskFormerForInstanceSegmentation(__lowerCamelCase			)
			model.eval()

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

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

			# verify results
			lowerCamelCase__	:	Optional[Any]				=prepare_img()
			if "vistas" in model_name:
						lowerCamelCase__	:	List[Any]				=65
			elif "cityscapes" in model_name:
						lowerCamelCase__	:	str				=65535
			else:
						lowerCamelCase__	:	Any				=255
			lowerCamelCase__	:	List[str]				=True if '''ade''' in model_name else False
			lowerCamelCase__	:	Dict				=MaskFormerImageProcessor(ignore_index=__lowerCamelCase							,	reduce_labels=__lowerCamelCase			)

			lowerCamelCase__	:	Tuple				=image_processor(__lowerCamelCase							,	return_tensors='''pt'''			)

			lowerCamelCase__	:	str				=model(**__lowerCamelCase			)

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

			if model_name == "maskformer-swin-tiny-ade":
						lowerCamelCase__	:	Optional[Any]				=torch.tensor(
						    [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]			)
			assert torch.allclose(outputs.class_queries_logits[0, :3, :3]							,	__lowerCamelCase							,	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(__lowerCamelCase			).mkdir(exist_ok=__lowerCamelCase			)
						model.save_pretrained(__lowerCamelCase			)
						image_processor.save_pretrained(__lowerCamelCase			)

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









"""simple docstring"""






import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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


if is_torch_available():
		import torch

		from transformers import UperNetForSemanticSegmentation
		from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor

class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					def __init__( self							:						Optional[Any],						lowerCamelCase							:						Tuple,						lowerCamelCase							:						List[str]=13,						lowerCamelCase							:						List[Any]=32,						lowerCamelCase							:						Dict=3,						lowerCamelCase							:						int=4,						lowerCamelCase							:						str=[10, 20, 30, 40],						lowerCamelCase							:						Any=[2, 2, 3, 2],						lowerCamelCase							:						int=True,						lowerCamelCase							:						int=True,						lowerCamelCase							:						str=37,						lowerCamelCase							:						Optional[int]="gelu",						lowerCamelCase							:						Optional[int]=10,						lowerCamelCase							:						Any=0.02,						lowerCamelCase							:						Union[str, Any]=["stage2", "stage3", "stage4"],						lowerCamelCase							:						Optional[int]=3,						lowerCamelCase							:						Tuple=None,						)->      List[str]:
								lowerCamelCase__	:	List[str]				=parent
								lowerCamelCase__	:	Tuple				=batch_size
								lowerCamelCase__	:	str				=image_size
								lowerCamelCase__	:	Any				=num_channels
								lowerCamelCase__	:	Tuple				=num_stages
								lowerCamelCase__	:	List[str]				=hidden_sizes
								lowerCamelCase__	:	Any				=depths
								lowerCamelCase__	:	Union[str, Any]				=is_training
								lowerCamelCase__	:	Tuple				=use_labels
								lowerCamelCase__	:	int				=intermediate_size
								lowerCamelCase__	:	Optional[int]				=hidden_act
								lowerCamelCase__	:	Dict				=type_sequence_label_size
								lowerCamelCase__	:	Tuple				=initializer_range
								lowerCamelCase__	:	Any				=out_features
								lowerCamelCase__	:	Tuple				=num_labels
								lowerCamelCase__	:	Optional[int]				=scope
								lowerCamelCase__	:	Optional[int]				=num_stages



					def 	snake_case ( self							:						str  )->      Optional[int]:
								lowerCamelCase__	:	str				=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

								lowerCamelCase__	:	Tuple				=None
								if self.use_labels:
											lowerCamelCase__	:	Union[str, Any]				=ids_tensor([self.batch_size],						self.type_sequence_label_size  )

								lowerCamelCase__	:	int				=self.get_config()

								return config, pixel_values, labels



					def 	snake_case ( self							:						Union[str, Any]  )->      Any:
								return ConvNextConfig(
								    num_channels=self.num_channels,						num_stages=self.num_stages,						hidden_sizes=self.hidden_sizes,						depths=self.depths,						is_training=self.is_training,						intermediate_size=self.intermediate_size,						hidden_act=self.hidden_act,						out_features=self.out_features,						)



					def 	snake_case ( self							:						Union[str, Any]  )->      Any:
								return UperNetConfig(
								    backbone_config=self.get_backbone_config(),						hidden_size=512,						pool_scales=[1, 2, 3, 6],						use_auxiliary_head=lowerCamelCase,						auxiliary_loss_weight=0.4,						auxiliary_in_channels=40,						auxiliary_channels=256,						auxiliary_num_convs=1,						auxiliary_concat_input=lowerCamelCase,						loss_ignore_index=255,						num_labels=self.num_labels,						)



					def 	snake_case ( self							:						int,						lowerCamelCase							:						str,						lowerCamelCase							:						List[str],						lowerCamelCase							:						List[Any]  )->      Tuple:
								lowerCamelCase__	:	List[str]				=UperNetForSemanticSegmentation(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowerCamelCase__	:	int				=model(lowerCamelCase  )
								self.parent.assertEqual(
								    result.logits.shape,						(self.batch_size, self.num_labels, self.image_size, self.image_size)  )




					def 	snake_case ( self							:						Any  )->      Tuple:
								lowerCamelCase__	:	Dict				=self.prepare_config_and_inputs()
								(
								    (
								    lowerCamelCase__
								)			,     (
								    lowerCamelCase__
								)			,     (
								    lowerCamelCase__
								)			,     
								)	:	Any				=config_and_inputs
								lowerCamelCase__	:	Optional[int]				={'''pixel_values''': pixel_values}
								return config, inputs_dict





@require_torch
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							(UperNetForSemanticSegmentation,) if is_torch_available() else ()
					_a         =							{'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
					_a         =							False
					_a         =							False
					_a         =							False
					_a         =							False
					_a         =							False
					_a         =							False



					def 	snake_case ( self							:						Optional[int]  )->      Optional[int]:
								lowerCamelCase__	:	Optional[Any]				=UperNetModelTester(self  )
								lowerCamelCase__	:	Union[str, Any]				=ConfigTester(self,						config_class=lowerCamelCase,						has_text_modality=lowerCamelCase,						hidden_size=37  )



					def 	snake_case ( self							:						Optional[int]  )->      Optional[int]:
								self.create_and_test_config_common_properties()
								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 	snake_case ( self							:						List[str]  )->      Dict:
								return



					def 	snake_case ( self							:						Optional[int]  )->      List[str]:
								lowerCamelCase__			,     lowerCamelCase__	:	str				=self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											lowerCamelCase__	:	Union[str, Any]				=model_class(lowerCamelCase  )
											lowerCamelCase__	:	Tuple				=inspect.signature(model.forward  )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											lowerCamelCase__	:	Tuple				=[*signature.parameters.keys()]

											lowerCamelCase__	:	List[Any]				=['''pixel_values''']
											self.assertListEqual(arg_names[:1],						lowerCamelCase  )



					def 	snake_case ( self							:						Any  )->      Union[str, Any]:
								lowerCamelCase__	:	Dict				=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase  )



					@unittest.skip(reason='''UperNet does not use inputs_embeds'''  )
					def 	snake_case ( self							:						Optional[Any]  )->      List[Any]:
								pass



					@unittest.skip(reason='''UperNet does not support input and output embeddings'''  )
					def 	snake_case ( self							:						Any  )->      List[str]:
								pass



					@unittest.skip(reason='''UperNet does not have a base model'''  )
					def 	snake_case ( self							:						int  )->      Any:
								pass



					@unittest.skip(reason='''UperNet does not have a base model'''  )
					def 	snake_case ( self							:						Dict  )->      str:
								pass



					@require_torch_multi_gpu
					@unittest.skip(reason='''UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''  )
					def 	snake_case ( self							:						List[Any]  )->      List[str]:
								pass



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



					def 	snake_case ( self							:						Optional[int]  )->      List[str]:
								def check_hidden_states_output(lowerCamelCase							:						Dict,						lowerCamelCase							:						int,						lowerCamelCase							:						List[str]  ):
											lowerCamelCase__	:	Union[str, Any]				=model_class(lowerCamelCase  )
											model.to(lowerCamelCase  )
											model.eval()

											with torch.no_grad():
														lowerCamelCase__	:	Optional[Any]				=model(**self._prepare_for_class(lowerCamelCase,						lowerCamelCase  )  )

											lowerCamelCase__	:	Optional[Any]				=outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											lowerCamelCase__	:	List[str]				=self.model_tester.num_stages
											self.assertEqual(len(lowerCamelCase  ),						expected_num_stages + 1  )

											# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]  ),						[self.model_tester.image_size // 4, self.model_tester.image_size // 4],						)

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

								for model_class in self.all_model_classes:
											lowerCamelCase__	:	Optional[int]				=True
											check_hidden_states_output(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											lowerCamelCase__	:	Optional[Any]				=True

											check_hidden_states_output(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )



					def 	snake_case ( self							:						Any  )->      List[Any]:
								lowerCamelCase__			,     lowerCamelCase__	:	Optional[int]				=self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase__	:	str				=_config_zero_init(lowerCamelCase  )
								lowerCamelCase__	:	Union[str, Any]				=_config_zero_init(configs_no_init.backbone_config  )
								for model_class in self.all_model_classes:
											lowerCamelCase__	:	Optional[int]				=model_class(config=lowerCamelCase  )
											for name, param in model.named_parameters():
														if param.requires_grad:
																	self.assertIn(
																	    ((param.data.mean() * 1E9).round() / 1E9).item(),						[0.0, 1.0],						msg=F'''Parameter {name} of model {model_class} seems not properly initialized''',						)



					@unittest.skip(reason='''UperNet does not have tied weights'''  )
					def 	snake_case ( self							:						Any  )->      str:
								pass




					@slow
					def 	snake_case ( self							:						int  )->      Union[str, Any]:
								for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowerCamelCase__	:	str				=UperNetForSemanticSegmentation.from_pretrained(lowerCamelCase  )
											self.assertIsNotNone(lowerCamelCase  )






def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[int]				=hf_hub_download(
			    repo_id='''hf-internal-testing/fixtures_ade20k'''							,	repo_type='''dataset'''							,	filename='''ADE_val_00000001.jpg'''			)
			lowerCamelCase__	:	List[str]				=Image.open(__lowerCamelCase			).convert('''RGB'''			)
			return image

@require_torch
@require_vision
@slow
class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					def 	snake_case ( self							:						str  )->      Union[str, Any]:
								lowerCamelCase__	:	List[Any]				=AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny'''  )
								lowerCamelCase__	:	List[Any]				=UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny'''  ).to(lowerCamelCase  )

								lowerCamelCase__	:	List[Any]				=prepare_img()
								lowerCamelCase__	:	List[Any]				=processor(images=lowerCamelCase,						return_tensors='''pt'''  ).to(lowerCamelCase  )

								with torch.no_grad():
											lowerCamelCase__	:	List[Any]				=model(**lowerCamelCase  )

								lowerCamelCase__	:	Optional[int]				=torch.Size((1, model.config.num_labels, 512, 512)  )
								self.assertEqual(outputs.logits.shape,						lowerCamelCase  )

								lowerCamelCase__	:	Dict				=torch.tensor(
								    [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]  ).to(lowerCamelCase  )
								self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3],						lowerCamelCase,						atol=1E-4  )  )




					def 	snake_case ( self							:						Optional[int]  )->      Optional[Any]:
								lowerCamelCase__	:	str				=AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny'''  )
								lowerCamelCase__	:	Tuple				=UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny'''  ).to(lowerCamelCase  )

								lowerCamelCase__	:	Dict				=prepare_img()
								lowerCamelCase__	:	Any				=processor(images=lowerCamelCase,						return_tensors='''pt'''  ).to(lowerCamelCase  )

								with torch.no_grad():
											lowerCamelCase__	:	Any				=model(**lowerCamelCase  )

								lowerCamelCase__	:	Dict				=torch.Size((1, model.config.num_labels, 512, 512)  )
								self.assertEqual(outputs.logits.shape,						lowerCamelCase  )

								lowerCamelCase__	:	List[str]				=torch.tensor(
								    [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]  ).to(lowerCamelCase  )
								self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3],						lowerCamelCase,						atol=1E-4  )  )




