
import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Tuple       )  ->     int:
						_lowercase					       =					SwinvaConfig()
						_lowercase					       =					swinva_name.split("""_"""       )

						_lowercase					       =					name_split[1]
						if "to" in name_split[3]:
												_lowercase					       =					int(name_split[3][-3:]       )
						else:
												_lowercase					       =					int(name_split[3]       )
						if "to" in name_split[2]:
												_lowercase					       =					int(name_split[2][-2:]       )
						else:
												_lowercase					       =					int(name_split[2][6:]       )

						if model_size == "tiny":
												_lowercase					       =					96
												_lowercase					       =					(2, 2, 6, 2)
												_lowercase					       =					(3, 6, 12, 24)
						elif model_size == "small":
												_lowercase					       =					96
												_lowercase					       =					(2, 2, 18, 2)
												_lowercase					       =					(3, 6, 12, 24)
						elif model_size == "base":
												_lowercase					       =					1_28
												_lowercase					       =					(2, 2, 18, 2)
												_lowercase					       =					(4, 8, 16, 32)
						else:
												_lowercase					       =					1_92
												_lowercase					       =					(2, 2, 18, 2)
												_lowercase					       =					(6, 12, 24, 48)

						if "to" in swinva_name:
												_lowercase					       =					(12, 12, 12, 6)

						if ("22k" in swinva_name) and ("to" not in swinva_name):
												_lowercase					       =					2_18_41
												_lowercase					       =					"""huggingface/label-files"""
												_lowercase					       =					"""imagenet-22k-id2label.json"""
												_lowercase					       =					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   repo_type="""dataset"""       )		,   """r"""       )       )
												_lowercase					       =					{int(SCREAMING_SNAKE_CASE_       ): v for k, v in idalabel.items()}
												_lowercase					       =					idalabel
												_lowercase					       =					{v: k for k, v in idalabel.items()}

						else:
												_lowercase					       =					10_00
												_lowercase					       =					"""huggingface/label-files"""
												_lowercase					       =					"""imagenet-1k-id2label.json"""
												_lowercase					       =					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   repo_type="""dataset"""       )		,   """r"""       )       )
												_lowercase					       =					{int(SCREAMING_SNAKE_CASE_       ): v for k, v in idalabel.items()}
												_lowercase					       =					idalabel
												_lowercase					       =					{v: k for k, v in idalabel.items()}

						_lowercase					       =					img_size
						_lowercase					       =					num_classes
						_lowercase					       =					embed_dim
						_lowercase					       =					depths
						_lowercase					       =					num_heads
						_lowercase					       =					window_size

						return config




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     Tuple:
						if "patch_embed.proj" in name:
												_lowercase					       =					name.replace("""patch_embed.proj"""		,   """embeddings.patch_embeddings.projection"""       )
						if "patch_embed.norm" in name:
												_lowercase					       =					name.replace("""patch_embed.norm"""		,   """embeddings.norm"""       )
						if "layers" in name:
												_lowercase					       =					"""encoder.""" + name
						if "attn.proj" in name:
												_lowercase					       =					name.replace("""attn.proj"""		,   """attention.output.dense"""       )
						if "attn" in name:
												_lowercase					       =					name.replace("""attn"""		,   """attention.self"""       )
						if "norm1" in name:
												_lowercase					       =					name.replace("""norm1"""		,   """layernorm_before"""       )
						if "norm2" in name:
												_lowercase					       =					name.replace("""norm2"""		,   """layernorm_after"""       )
						if "mlp.fc1" in name:
												_lowercase					       =					name.replace("""mlp.fc1"""		,   """intermediate.dense"""       )
						if "mlp.fc2" in name:
												_lowercase					       =					name.replace("""mlp.fc2"""		,   """output.dense"""       )
						if "q_bias" in name:
												_lowercase					       =					name.replace("""q_bias"""		,   """query.bias"""       )
						if "k_bias" in name:
												_lowercase					       =					name.replace("""k_bias"""		,   """key.bias"""       )
						if "v_bias" in name:
												_lowercase					       =					name.replace("""v_bias"""		,   """value.bias"""       )
						if "cpb_mlp" in name:
												_lowercase					       =					name.replace("""cpb_mlp"""		,   """continuous_position_bias_mlp"""       )
						if name == "norm.weight":
												_lowercase					       =					"""layernorm.weight"""
						if name == "norm.bias":
												_lowercase					       =					"""layernorm.bias"""

						if "head" in name:
												_lowercase					       =					name.replace("""head"""		,   """classifier"""       )
						else:
												_lowercase					       =					"""swinv2.""" + name

						return name




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]		,   SCREAMING_SNAKE_CASE_	:	Tuple       )  ->     Any:
						for key in orig_state_dict.copy().keys():
												_lowercase					       =					orig_state_dict.pop(SCREAMING_SNAKE_CASE_       )

												if "mask" in key:
																		continue
												elif "qkv" in key:
																		_lowercase					       =					key.split("""."""       )
																		_lowercase					       =					int(key_split[1]       )
																		_lowercase					       =					int(key_split[3]       )
																		_lowercase					       =					model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																		if "weight" in key:
																								_lowercase					       =					val[:dim, :]
																								_lowercase					       =					val[dim : dim * 2, :]
																								_lowercase					       =					val[-dim:, :]
																		else:
																								_lowercase					       =					val[:dim]
																								_lowercase					       =					val[
																								    dim : dim * 2
																								]
																								_lowercase					       =					val[-dim:]
												else:
																		_lowercase					       =					val

						return orig_state_dict




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	Tuple       )  ->     Dict:
						_lowercase					       =					timm.create_model(SCREAMING_SNAKE_CASE_		,   pretrained=SCREAMING_SNAKE_CASE_       )
						timm_model.eval()

						_lowercase					       =					get_swinva_config(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					SwinvaForImageClassification(SCREAMING_SNAKE_CASE_       )
						model.eval()

						_lowercase					       =					convert_state_dict(timm_model.state_dict()		,   SCREAMING_SNAKE_CASE_       )
						model.load_state_dict(SCREAMING_SNAKE_CASE_       )

						_lowercase					       =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""

						_lowercase					       =					AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""		,   """-"""       )       )       )
						_lowercase					       =					Image.open(requests.get(SCREAMING_SNAKE_CASE_		,   stream=SCREAMING_SNAKE_CASE_       ).raw       )
						_lowercase					       =					image_processor(images=SCREAMING_SNAKE_CASE_		,   return_tensors="""pt"""       )

						_lowercase					       =					timm_model(inputs["""pixel_values"""]       )
						_lowercase					       =					model(**SCREAMING_SNAKE_CASE_       ).logits

						assert torch.allclose(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   atol=1E-3       )

						print(f"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""       )
						model.save_pretrained(SCREAMING_SNAKE_CASE_       )

						print(f"""Saving image processor to {pytorch_dump_folder_path}"""       )
						image_processor.save_pretrained(SCREAMING_SNAKE_CASE_       )

						model.push_to_hub(
						    repo_path_or_name=Path(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )		,   organization="""nandwalritik"""		,   commit_message="""Add model"""		,   )


if __name__ == "__main__":
		A		: str								=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--swinv2_name''',
		    default='''swinv2_tiny_patch4_window8_256''',
		    type=str,
		    help='''Name of the Swinv2 timm model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)

		A		: Optional[int]								=     parser.parse_args()
		convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)

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    a_ :






			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=0.02				,			__UpperCamelCase=["stage2", "stage3", "stage4"]				,			__UpperCamelCase=3				,			__UpperCamelCase=None				,			):
									_lowercase					       =					parent
									_lowercase					       =					batch_size
									_lowercase					       =					image_size
									_lowercase					       =					num_channels
									_lowercase					       =					num_stages
									_lowercase					       =					hidden_sizes
									_lowercase					       =					depths
									_lowercase					       =					is_training
									_lowercase					       =					use_labels
									_lowercase					       =					intermediate_size
									_lowercase					       =					hidden_act
									_lowercase					       =					type_sequence_label_size
									_lowercase					       =					initializer_range
									_lowercase					       =					out_features
									_lowercase					       =					num_labels
									_lowercase					       =					scope
									_lowercase					       =					num_stages






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

									_lowercase					       =					self.get_config()

									return config, pixel_values, labels






			def    UpperCamelCase_				(      self  ):
									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    UpperCamelCase_				(      self  ):
									return UperNetConfig(
									    backbone_config=self.get_backbone_config()				,			hidden_size=512				,			pool_scales=[1, 2, 3, 6]				,			use_auxiliary_head=__UpperCamelCase				,			auxiliary_loss_weight=0.4				,			auxiliary_in_channels=40				,			auxiliary_channels=256				,			auxiliary_num_convs=1				,			auxiliary_concat_input=__UpperCamelCase				,			loss_ignore_index=255				,			num_labels=self.num_labels				,			)






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					UperNetForSemanticSegmentation(config=__UpperCamelCase  )
									model.to(__UpperCamelCase  )
									model.eval()
									_lowercase					       =					model(__UpperCamelCase  )
									self.parent.assertEqual(
									    result.logits.shape				,			(self.batch_size, self.num_labels, self.image_size, self.image_size)  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.prepare_config_and_inputs()
									(
									    (
									    _lowercase
									)       ,						(
									    _lowercase
									)       ,						(
									    _lowercase
									)       ,						
									)					       =					config_and_inputs
									_lowercase					       =					{"""pixel_values""": pixel_values}
									return config, inputs_dict







@require_torch
class    a_ (			_a					,			_a					,			unittest.TestCase    ):
			a	:			Optional[int]					   =							(UperNetForSemanticSegmentation,) if is_torch_available() else ()
			a	:			Optional[Any]					   =							{'''image-segmentation''': UperNetForSemanticSegmentation} if is_torch_available() else {}
			a	:			Any					   =							False
			a	:			Union[str, Any]					   =							False
			a	:			Optional[Any]					   =							False
			a	:			int					   =							False
			a	:			Optional[int]					   =							False
			a	:			List[str]					   =							False






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					UperNetModelTester(self  )
									_lowercase					       =					ConfigTester(self				,			config_class=__UpperCamelCase				,			has_text_modality=__UpperCamelCase				,			hidden_size=37  )






			def    UpperCamelCase_				(      self  ):
									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    UpperCamelCase_				(      self  ):
									return






			def    UpperCamelCase_				(      self  ):
									_lowercase       ,						_lowercase					       =					self.model_tester.prepare_config_and_inputs_for_common()

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

															_lowercase					       =					["""pixel_values"""]
															self.assertListEqual(arg_names[:1]				,			__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					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    UpperCamelCase_				(      self  ):
									pass






			@unittest.skip(reason="""UperNet does not support input and output embeddings"""  )
			def    UpperCamelCase_				(      self  ):
									pass






			@unittest.skip(reason="""UperNet does not have a base model"""  )
			def    UpperCamelCase_				(      self  ):
									pass






			@unittest.skip(reason="""UperNet does not have a base model"""  )
			def    UpperCamelCase_				(      self  ):
									pass






			@require_torch_multi_gpu
			@unittest.skip(reason="""UperNet has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""  )
			def    UpperCamelCase_				(      self  ):
									pass






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






			def    UpperCamelCase_				(      self  ):
									def check_hidden_states_output(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					model_class(__UpperCamelCase  )
															model.to(__UpperCamelCase  )
															model.eval()

															with torch.no_grad():
																					_lowercase					       =					model(**self._prepare_for_class(__UpperCamelCase				,			__UpperCamelCase  )  )

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

															_lowercase					       =					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]				,			)

									_lowercase       ,						_lowercase					       =					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															_lowercase					       =					True
															check_hidden_states_output(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )

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

															check_hidden_states_output(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase       ,						_lowercase					       =					self.model_tester.prepare_config_and_inputs_for_common()

									_lowercase					       =					_config_zero_init(__UpperCamelCase  )
									_lowercase					       =					_config_zero_init(configs_no_init.backbone_config  )
									for model_class in self.all_model_classes:
															_lowercase					       =					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    UpperCamelCase_				(      self  ):
									pass






			@slow
			def    UpperCamelCase_				(      self  ):
									for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_lowercase					       =					UperNetForSemanticSegmentation.from_pretrained(__UpperCamelCase  )
															self.assertIsNotNone(__UpperCamelCase  )







def 	UpperCamelCase__  (     )  ->     str:
						_lowercase					       =					hf_hub_download(
						    repo_id="""hf-internal-testing/fixtures_ade20k"""		,   repo_type="""dataset"""		,   filename="""ADE_val_00000001.jpg"""       )
						_lowercase					       =					Image.open(SCREAMING_SNAKE_CASE_       ).convert("""RGB"""       )
						return image



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






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					AutoImageProcessor.from_pretrained("""openmmlab/upernet-swin-tiny"""  )
									_lowercase					       =					UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-swin-tiny"""  ).to(__UpperCamelCase  )

									_lowercase					       =					prepare_img()
									_lowercase					       =					processor(images=__UpperCamelCase				,			return_tensors="""pt"""  ).to(__UpperCamelCase  )

									with torch.no_grad():
															_lowercase					       =					model(**__UpperCamelCase  )

									_lowercase					       =					torch.Size((1, model.config.num_labels, 512, 512)  )
									self.assertEqual(outputs.logits.shape				,			__UpperCamelCase  )

									_lowercase					       =					torch.tensor(
									    [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]  ).to(__UpperCamelCase  )
									self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]				,			__UpperCamelCase				,			atol=1E-4  )  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					AutoImageProcessor.from_pretrained("""openmmlab/upernet-convnext-tiny"""  )
									_lowercase					       =					UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-convnext-tiny"""  ).to(__UpperCamelCase  )

									_lowercase					       =					prepare_img()
									_lowercase					       =					processor(images=__UpperCamelCase				,			return_tensors="""pt"""  ).to(__UpperCamelCase  )

									with torch.no_grad():
															_lowercase					       =					model(**__UpperCamelCase  )

									_lowercase					       =					torch.Size((1, model.config.num_labels, 512, 512)  )
									self.assertEqual(outputs.logits.shape				,			__UpperCamelCase  )

									_lowercase					       =					torch.tensor(
									    [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]  ).to(__UpperCamelCase  )
									self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]				,			__UpperCamelCase				,			atol=1E-4  )  )