"""simple docstring"""




import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

__lowerCamelCase							 =     [
    # (stable-diffusion, HF Diffusers)
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
]

__lowerCamelCase							 =     [
    # (stable-diffusion, HF Diffusers)
    ("in_layers.0", "norm1"),
    ("in_layers.2", "conv1"),
    ("out_layers.0", "norm2"),
    ("out_layers.3", "conv2"),
    ("emb_layers.1", "time_emb_proj"),
    ("skip_connection", "conv_shortcut"),
]

__lowerCamelCase							 =     []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
				# loop over downblocks/upblocks

				for j in range(2):
								# loop over resnets/attentions for downblocks
								__lowerCamelCase							 =     F'''down_blocks.{i}.resnets.{j}.'''
								__lowerCamelCase							 =     F'''input_blocks.{3*i + j + 1}.0.'''
								unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

								if i < 3:
												# no attention layers in down_blocks.3
												__lowerCamelCase							 =     F'''down_blocks.{i}.attentions.{j}.'''
												__lowerCamelCase							 =     F'''input_blocks.{3*i + j + 1}.1.'''
												unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

				for j in range(3):
								# loop over resnets/attentions for upblocks
								__lowerCamelCase							 =     F'''up_blocks.{i}.resnets.{j}.'''
								__lowerCamelCase							 =     F'''output_blocks.{3*i + j}.0.'''
								unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

								if i > 0:
												# no attention layers in up_blocks.0
												__lowerCamelCase							 =     F'''up_blocks.{i}.attentions.{j}.'''
												__lowerCamelCase							 =     F'''output_blocks.{3*i + j}.1.'''
												unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

				if i < 3:
								# no downsample in down_blocks.3
								__lowerCamelCase							 =     F'''down_blocks.{i}.downsamplers.0.conv.'''
								__lowerCamelCase							 =     F'''input_blocks.{3*(i+1)}.0.op.'''
								unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

								# no upsample in up_blocks.3
								__lowerCamelCase							 =     F'''up_blocks.{i}.upsamplers.0.'''
								__lowerCamelCase							 =     F'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
								unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

__lowerCamelCase							 =     "mid_block.attentions.0."
__lowerCamelCase							 =     "middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
				__lowerCamelCase							 =     F'''mid_block.resnets.{j}.'''
				__lowerCamelCase							 =     F'''middle_block.{2*j}.'''
				unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							{k: k for k in unet_state_dict.keys()}
							for sd_name, hf_name in unet_conversion_map:
														A__		   =							sd_name
							for k, v in mapping.items():
														if "resnets" in k:
																					for sd_part, hf_part in unet_conversion_map_resnet:
																												A__		   =							v.replace(UpperCamelCase__				,			UpperCamelCase__ )
																					A__		   =							v
							for k, v in mapping.items():
														for sd_part, hf_part in unet_conversion_map_layer:
																					A__		   =							v.replace(UpperCamelCase__				,			UpperCamelCase__ )
														A__		   =							v
							A__		   =							{v: unet_state_dict[k] for k, v in mapping.items()}
							return new_state_dict


# ================#
# VAE Conversion #
# ================#

__lowerCamelCase							 =     [
    # (stable-diffusion, HF Diffusers)
    ("nin_shortcut", "conv_shortcut"),
    ("norm_out", "conv_norm_out"),
    ("mid.attn_1.", "mid_block.attentions.0."),
]

for i in range(4):
				# down_blocks have two resnets
				for j in range(2):
								__lowerCamelCase							 =     F'''encoder.down_blocks.{i}.resnets.{j}.'''
								__lowerCamelCase							 =     F'''encoder.down.{i}.block.{j}.'''
								vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

				if i < 3:
								__lowerCamelCase							 =     F'''down_blocks.{i}.downsamplers.0.'''
								__lowerCamelCase							 =     F'''down.{i}.downsample.'''
								vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

								__lowerCamelCase							 =     F'''up_blocks.{i}.upsamplers.0.'''
								__lowerCamelCase							 =     F'''up.{3-i}.upsample.'''
								vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

				# up_blocks have three resnets
				# also, up blocks in hf are numbered in reverse from sd
				for j in range(3):
								__lowerCamelCase							 =     F'''decoder.up_blocks.{i}.resnets.{j}.'''
								__lowerCamelCase							 =     F'''decoder.up.{3-i}.block.{j}.'''
								vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
				__lowerCamelCase							 =     F'''mid_block.resnets.{i}.'''
				__lowerCamelCase							 =     F'''mid.block_{i+1}.'''
				vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


__lowerCamelCase							 =     [
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							return w.reshape(*w.shape				,			1				,			1 )






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							{k: k for k in vae_state_dict.keys()}
							for k, v in mapping.items():
														for sd_part, hf_part in vae_conversion_map:
																					A__		   =							v.replace(UpperCamelCase__				,			UpperCamelCase__ )
														A__		   =							v
							for k, v in mapping.items():
														if "attentions" in k:
																					for sd_part, hf_part in vae_conversion_map_attn:
																												A__		   =							v.replace(UpperCamelCase__				,			UpperCamelCase__ )
																					A__		   =							v
							A__		   =							{v: vae_state_dict[k] for k, v in mapping.items()}
							A__		   =							['q', 'k', 'v', 'proj_out']
							for k, v in new_state_dict.items():
														for weight_name in weights_to_convert:
																					if F'''mid.attn_1.{weight_name}.weight''' in k:
																												print(F'''Reshaping {k} for SD format''' )
																												A__		   =							reshape_weight_for_sd(UpperCamelCase__ )
							return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


__lowerCamelCase							 =     [
    # (stable-diffusion, HF Diffusers)
    ("resblocks.", "text_model.encoder.layers."),
    ("ln_1", "layer_norm1"),
    ("ln_2", "layer_norm2"),
    (".c_fc.", ".fc1."),
    (".c_proj.", ".fc2."),
    (".attn", ".self_attn"),
    ("ln_final.", "transformer.text_model.final_layer_norm."),
    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
__lowerCamelCase							 =     {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__lowerCamelCase							 =     re.compile("|".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
__lowerCamelCase							 =     {"q": 0, "k": 1, "v": 2}






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							{}
							A__		   =							{}
							A__		   =							{}
							for k, v in text_enc_dict.items():
														if (
														    k.endswith('.self_attn.q_proj.weight' )
														    or k.endswith('.self_attn.k_proj.weight' )
														    or k.endswith('.self_attn.v_proj.weight' )
														):
																					A__		   =							k[: -len('.q_proj.weight' )]
																					A__		   =							k[-len('q_proj.weight' )]
																					if k_pre not in capture_qkv_weight:
																												A__		   =							[None, None, None]
																					A__		   =							v
																					continue

														if (
														    k.endswith('.self_attn.q_proj.bias' )
														    or k.endswith('.self_attn.k_proj.bias' )
														    or k.endswith('.self_attn.v_proj.bias' )
														):
																					A__		   =							k[: -len('.q_proj.bias' )]
																					A__		   =							k[-len('q_proj.bias' )]
																					if k_pre not in capture_qkv_bias:
																												A__		   =							[None, None, None]
																					A__		   =							v
																					continue

														A__		   =							textenc_pattern.sub(lambda UpperCamelCase__ : protected[re.escape(m.group(0 ) )]				,			UpperCamelCase__ )
														A__		   =							v

							for k_pre, tensors in capture_qkv_weight.items():
														if None in tensors:
																					raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing' )
														A__		   =							textenc_pattern.sub(lambda UpperCamelCase__ : protected[re.escape(m.group(0 ) )]				,			UpperCamelCase__ )
														A__		   =							torch.cat(UpperCamelCase__ )

							for k_pre, tensors in capture_qkv_bias.items():
														if None in tensors:
																					raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing' )
														A__		   =							textenc_pattern.sub(lambda UpperCamelCase__ : protected[re.escape(m.group(0 ) )]				,			UpperCamelCase__ )
														A__		   =							torch.cat(UpperCamelCase__ )

							return new_state_dict






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							return text_enc_dict


if __name__ == "__main__":
				__lowerCamelCase							 =     argparse.ArgumentParser()

				parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
				parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
				parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
				parser.add_argument(
				    "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
				)

				__lowerCamelCase							 =     parser.parse_args()

				assert args.model_path is not None, "Must provide a model path!"

				assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

				# Path for safetensors
				__lowerCamelCase							 =     osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
				__lowerCamelCase							 =     osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
				__lowerCamelCase							 =     osp.join(args.model_path, "text_encoder", "model.safetensors")

				# Load models from safetensors if it exists, if it doesn't pytorch
				if osp.exists(unet_path):
								__lowerCamelCase							 =     load_file(unet_path, device="cpu")
				else:
								__lowerCamelCase							 =     osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
								__lowerCamelCase							 =     torch.load(unet_path, map_location="cpu")

				if osp.exists(vae_path):
								__lowerCamelCase							 =     load_file(vae_path, device="cpu")
				else:
								__lowerCamelCase							 =     osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
								__lowerCamelCase							 =     torch.load(vae_path, map_location="cpu")

				if osp.exists(text_enc_path):
								__lowerCamelCase							 =     load_file(text_enc_path, device="cpu")
				else:
								__lowerCamelCase							 =     osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
								__lowerCamelCase							 =     torch.load(text_enc_path, map_location="cpu")

				# Convert the UNet model
				__lowerCamelCase							 =     convert_unet_state_dict(unet_state_dict)
				__lowerCamelCase							 =     {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

				# Convert the VAE model
				__lowerCamelCase							 =     convert_vae_state_dict(vae_state_dict)
				__lowerCamelCase							 =     {"first_stage_model." + k: v for k, v in vae_state_dict.items()}

				# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
				__lowerCamelCase							 =     "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict

				if is_vaa_model:
								# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
								__lowerCamelCase							 =     {"transformer." + k: v for k, v in text_enc_dict.items()}
								__lowerCamelCase							 =     convert_text_enc_state_dict_vaa(text_enc_dict)
								__lowerCamelCase							 =     {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
				else:
								__lowerCamelCase							 =     convert_text_enc_state_dict(text_enc_dict)
								__lowerCamelCase							 =     {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

				# Put together new checkpoint
				__lowerCamelCase							 =     {**unet_state_dict, **vae_state_dict, **text_enc_dict}
				if args.half:
								__lowerCamelCase							 =     {k: v.half() for k, v in state_dict.items()}

				if args.use_safetensors:
								save_file(state_dict, args.checkpoint_path)
				else:
								__lowerCamelCase							 =     {"state_dict": state_dict}
								torch.save(state_dict, args.checkpoint_path)






"""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 			UpperCamelCase__:





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase=13						,__UpperCAmelCase=32						,__UpperCAmelCase=3						,__UpperCAmelCase=4						,__UpperCAmelCase=[10, 20, 30, 40]						,__UpperCAmelCase=[2, 2, 3, 2]						,__UpperCAmelCase=True						,__UpperCAmelCase=True						,__UpperCAmelCase=37						,__UpperCAmelCase="gelu"						,__UpperCAmelCase=10						,__UpperCAmelCase=0.0_2						,__UpperCAmelCase=["stage2", "stage3", "stage4"]						,__UpperCAmelCase=3						,__UpperCAmelCase=None						,)    -> Optional[int]:
													A__		   =							parent
													A__		   =							batch_size
													A__		   =							image_size
													A__		   =							num_channels
													A__		   =							num_stages
													A__		   =							hidden_sizes
													A__		   =							depths
													A__		   =							is_training
													A__		   =							use_labels
													A__		   =							intermediate_size
													A__		   =							hidden_act
													A__		   =							type_sequence_label_size
													A__		   =							initializer_range
													A__		   =							out_features
													A__		   =							num_labels
													A__		   =							scope
													A__		   =							num_stages





						def  snake_case__		(				self   )    -> List[Any]:
													A__		   =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

													A__		   =							self.get_config()

													return config, pixel_values, labels





						def  snake_case__		(				self   )    -> str:
													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   )    -> Tuple:
													return UperNetConfig(
													    backbone_config=self.get_backbone_config()						,hidden_size=5_12						,pool_scales=[1, 2, 3, 6]						,use_auxiliary_head=__UpperCAmelCase						,auxiliary_loss_weight=0.4						,auxiliary_in_channels=40						,auxiliary_channels=2_56						,auxiliary_num_convs=1						,auxiliary_concat_input=__UpperCAmelCase						,loss_ignore_index=2_55						,num_labels=self.num_labels						,)





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Dict:
													A__		   =							UperNetForSemanticSegmentation(config=__UpperCAmelCase   )
													model.to(__UpperCAmelCase   )
													model.eval()
													A__		   =							model(__UpperCAmelCase   )
													self.parent.assertEqual(
													    result.logits.shape						,(self.batch_size, self.num_labels, self.image_size, self.image_size)   )





						def  snake_case__		(				self   )    -> str:
													A__		   =							self.prepare_config_and_inputs()
													(
													    (
													    A__
													)   ,     (
													    A__
													)   ,     (
													    A__
													)   ,     
													)		   =							config_and_inputs
													A__		   =							{'pixel_values': pixel_values}
													return config, inputs_dict






@require_torch
class 			UpperCamelCase__(	__A						,  __A						,  unittest.TestCase       ):
						lowerCAmelCase__     :  int  							=						(UperNetForSemanticSegmentation,) if is_torch_available() else ()
						lowerCAmelCase__     :  int  							=						{'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
						lowerCAmelCase__     :  Optional[int]  							=						False
						lowerCAmelCase__     :  List[Any]  							=						False
						lowerCAmelCase__     :  Tuple  							=						False
						lowerCAmelCase__     :  Optional[Any]  							=						False
						lowerCAmelCase__     :  Union[str, Any]  							=						False
						lowerCAmelCase__     :  Dict  							=						False





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							UperNetModelTester(self   )
													A__		   =							ConfigTester(self						,config_class=__UpperCAmelCase						,has_text_modality=__UpperCAmelCase						,hidden_size=37   )





						def  snake_case__		(				self   )    -> List[Any]:
													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   )    -> int:
													return





						def  snake_case__		(				self   )    -> List[Any]:
													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()

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

																				A__		   =							['pixel_values']
																				self.assertListEqual(arg_names[:1]						,__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Tuple:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCAmelCase   )





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





						@unittest.skip(reason='UperNet does not support input and output embeddings'   )
						def  snake_case__		(				self   )    -> Tuple:
													pass





						@unittest.skip(reason='UperNet does not have a base model'   )
						def  snake_case__		(				self   )    -> List[Any]:
													pass





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





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





						def  snake_case__		(				self   )    -> Optional[int]:
													def check_hidden_states_output(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   ):
																				A__		   =							model_class(__UpperCAmelCase   )
																				model.to(__UpperCAmelCase   )
																				model.eval()

																				with torch.no_grad():
																											A__		   =							model(**self._prepare_for_class(__UpperCAmelCase						,__UpperCAmelCase   )   )

																				A__		   =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																				A__		   =							self.model_tester.num_stages
																				self.assertEqual(len(__UpperCAmelCase   )						,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]						,)

													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__		   =							True
																				check_hidden_states_output(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				A__		   =							True

																				check_hidden_states_output(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Optional[Any]:
													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()

													A__		   =							_config_zero_init(__UpperCAmelCase   )
													A__		   =							_config_zero_init(configs_no_init.backbone_config   )
													for model_class in self.all_model_classes:
																				A__		   =							model_class(config=__UpperCAmelCase   )
																				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   )    -> str:
													pass





						@slow
						def  snake_case__		(				self   )    -> List[str]:
													for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				A__		   =							UperNetForSemanticSegmentation.from_pretrained(__UpperCAmelCase   )
																				self.assertIsNotNone(__UpperCAmelCase   )






def      UpperCAmelCase    (		):






							"""simple docstring"""





							A__		   =							hf_hub_download(
							    repo_id='hf-internal-testing/fixtures_ade20k'				,			repo_type='dataset'				,			filename='ADE_val_00000001.jpg' )
							A__		   =							Image.open(UpperCamelCase__ ).convert('RGB' )
							return image


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





						def  snake_case__		(				self   )    -> Dict:
													A__		   =							AutoImageProcessor.from_pretrained('openmmlab/upernet-swin-tiny'   )
													A__		   =							UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-swin-tiny'   ).to(__UpperCAmelCase   )

													A__		   =							prepare_img()
													A__		   =							processor(images=__UpperCAmelCase						,return_tensors='pt'   ).to(__UpperCAmelCase   )

													with torch.no_grad():
																				A__		   =							model(**__UpperCAmelCase   )

													A__		   =							torch.Size((1, model.config.num_labels, 5_12, 5_12)   )
													self.assertEqual(outputs.logits.shape						,__UpperCAmelCase   )

													A__		   =							torch.tensor(
													    [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]]   ).to(__UpperCAmelCase   )
													self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]						,__UpperCAmelCase						,atol=1e-4   )   )





						def  snake_case__		(				self   )    -> str:
													A__		   =							AutoImageProcessor.from_pretrained('openmmlab/upernet-convnext-tiny'   )
													A__		   =							UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-convnext-tiny'   ).to(__UpperCAmelCase   )

													A__		   =							prepare_img()
													A__		   =							processor(images=__UpperCAmelCase						,return_tensors='pt'   ).to(__UpperCAmelCase   )

													with torch.no_grad():
																				A__		   =							model(**__UpperCAmelCase   )

													A__		   =							torch.Size((1, model.config.num_labels, 5_12, 5_12)   )
													self.assertEqual(outputs.logits.shape						,__UpperCAmelCase   )

													A__		   =							torch.tensor(
													    [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]   ).to(__UpperCAmelCase   )
													self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]						,__UpperCAmelCase						,atol=1e-4   )   )





