
'''simple docstring'''







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 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Dict:
	_snake_case   						=		SwinvaConfig()
	_snake_case   						=		swinva_name.split('_'	)

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

	if model_size == "tiny":
		_snake_case   						=		96
		_snake_case   						=		(2, 2, 6, 2)
		_snake_case   						=		(3, 6, 12, 24)
	elif model_size == "small":
		_snake_case   						=		96
		_snake_case   						=		(2, 2, 18, 2)
		_snake_case   						=		(3, 6, 12, 24)
	elif model_size == "base":
		_snake_case   						=		128
		_snake_case   						=		(2, 2, 18, 2)
		_snake_case   						=		(4, 8, 16, 32)
	else:
		_snake_case   						=		192
		_snake_case   						=		(2, 2, 18, 2)
		_snake_case   						=		(6, 12, 24, 48)

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

	if ("22k" in swinva_name) and ("to" not in swinva_name):
		_snake_case   						=		21_841
		_snake_case   						=		'huggingface/label-files'
		_snake_case   						=		'imagenet-22k-id2label.json'
		_snake_case   						=		json.load(open(hf_hub_download(__A			,				__A			,				repo_type='dataset'	)			,				'r'	)	)
		_snake_case   						=		{int(__A	): v for k, v in idalabel.items()}
		_snake_case   						=		idalabel
		_snake_case   						=		{v: k for k, v in idalabel.items()}

	else:
		_snake_case   						=		1_000
		_snake_case   						=		'huggingface/label-files'
		_snake_case   						=		'imagenet-1k-id2label.json'
		_snake_case   						=		json.load(open(hf_hub_download(__A			,				__A			,				repo_type='dataset'	)			,				'r'	)	)
		_snake_case   						=		{int(__A	): v for k, v in idalabel.items()}
		_snake_case   						=		idalabel
		_snake_case   						=		{v: k for k, v in idalabel.items()}

	_snake_case   						=		img_size
	_snake_case   						=		num_classes
	_snake_case   						=		embed_dim
	_snake_case   						=		depths
	_snake_case   						=		num_heads
	_snake_case   						=		window_size

	return config







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

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

	return name







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	Optional[int]:
	for key in orig_state_dict.copy().keys():
		_snake_case   						=		orig_state_dict.pop(__A	)

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

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

	return orig_state_dict







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	Optional[Any]:
	_snake_case   						=		timm.create_model(__A			,				pretrained=__A	)
	timm_model.eval()

	_snake_case   						=		get_swinva_config(__A	)
	_snake_case   						=		SwinvaForImageClassification(__A	)
	model.eval()

	_snake_case   						=		convert_state_dict(timm_model.state_dict()			,				__A	)
	model.load_state_dict(__A	)

	_snake_case   						=		'http://images.cocodataset.org/val2017/000000039769.jpg'

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

	_snake_case   						=		timm_model(inputs['pixel_values']	)
	_snake_case   						=		model(**__A	).logits

	assert torch.allclose(__A			,				__A			,				atol=1e-3	)

	print(F'Saving model {swinva_name} to {pytorch_dump_folder_path}'	)
	model.save_pretrained(__A	)

	print(F'Saving image processor to {pytorch_dump_folder_path}'	)
	image_processor.save_pretrained(__A	)

	model.push_to_hub(
	    repo_path_or_name=Path(__A			,				__A	)			,				organization='nandwalritik'			,				commit_message='Add model'			,				)


if __name__ == "__main__":
					lowercase    :							int      =					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."
					)

					lowercase    :							Dict      =					parser.parse_args()
					convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)







import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   (UnCLIPScheduler,)



		def        SCREAMING_SNAKE_CASE__			(     self,	**UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  {
								    '''num_train_timesteps''': 1000,
								    '''variance_type''': '''fixed_small_log''',
								    '''clip_sample''': True,
								    '''clip_sample_range''': 1.0,
								    '''prediction_type''': '''epsilon''',
								}

								config.update(**UpperCamelCase__   )
								return config



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for timesteps in [1, 5, 100, 1000]:
														self.check_over_configs(num_train_timesteps=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for variance in ["fixed_small_log", "learned_range"]:
														self.check_over_configs(variance_type=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for clip_sample_range in [1, 5, 10, 20]:
														self.check_over_configs(clip_sample_range=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for prediction_type in ["epsilon", "sample"]:
														self.check_over_configs(prediction_type=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for time_step in [0, 500, 999]:
														for prev_timestep in [None, 5, 100, 250, 500, 750]:
																				if prev_timestep is not None and prev_timestep >= time_step:
																										continue

																				self.check_over_forward(time_step=UpperCamelCase__,	prev_timestep=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config(variance_type='''fixed_small_log'''   )
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0000E-10   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_549_625   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.9_994_987   )   ) < 1E-5



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config(variance_type='''learned_range'''   )
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								lowerCAmelCase_      			=  0.5

								assert scheduler._get_variance(1,	predicted_variance=UpperCamelCase__   ) - -10.1_712_790 < 1E-5
								assert scheduler._get_variance(487,	predicted_variance=UpperCamelCase__   ) - -5.7_998_052 < 1E-5
								assert scheduler._get_variance(999,	predicted_variance=UpperCamelCase__   ) - -0.0_010_011 < 1E-5



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config()
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								lowerCAmelCase_      			=  scheduler.timesteps

								lowerCAmelCase_      			=  self.dummy_model()
								lowerCAmelCase_      			=  self.dummy_sample_deter
								lowerCAmelCase_      			=  torch.manual_seed(0   )

								for i, t in enumerate(UpperCamelCase__   ):
														# 1. predict noise residual
														lowerCAmelCase_      			=  model(UpperCamelCase__,	UpperCamelCase__   )

														# 2. predict previous mean of sample x_t-1
														lowerCAmelCase_      			=  scheduler.step(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	generator=UpperCamelCase__   ).prev_sample

														lowerCAmelCase_      			=  pred_prev_sample

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 252.2_682_495   ) < 1E-2
								assert abs(result_mean.item() - 0.3_284_743   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config()
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								scheduler.set_timesteps(25   )

								lowerCAmelCase_      			=  scheduler.timesteps

								lowerCAmelCase_      			=  self.dummy_model()
								lowerCAmelCase_      			=  self.dummy_sample_deter
								lowerCAmelCase_      			=  torch.manual_seed(0   )

								for i, t in enumerate(UpperCamelCase__   ):
														# 1. predict noise residual
														lowerCAmelCase_      			=  model(UpperCamelCase__,	UpperCamelCase__   )

														if i + 1 == timesteps.shape[0]:
																				lowerCAmelCase_      			=  None
														else:
																				lowerCAmelCase_      			=  timesteps[i + 1]

														# 2. predict previous mean of sample x_t-1
														lowerCAmelCase_      			=  scheduler.step(
														    UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	prev_timestep=UpperCamelCase__,	generator=UpperCamelCase__   ).prev_sample

														lowerCAmelCase_      			=  pred_prev_sample

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 258.2_044_983   ) < 1E-2
								assert abs(result_mean.item() - 0.3_362_038   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass




