


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       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		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(snake_case__						,    snake_case__						,    repo_type='''dataset'''					)						,    '''r'''					)					)
				lowercase__						=				{int(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(snake_case__						,    snake_case__						,    repo_type='''dataset'''					)						,    '''r'''					)					)
				lowercase__						=				{int(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       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		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       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		for key in orig_state_dict.copy().keys():
				lowercase__						=				orig_state_dict.pop(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       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				timm.create_model(snake_case__						,    pretrained=snake_case__					)
		timm_model.eval()

		lowercase__						=				get_swinva_config(snake_case__					)
		lowercase__						=				SwinvaForImageClassification(snake_case__					)
		model.eval()

		lowercase__						=				convert_state_dict(timm_model.state_dict()						,    snake_case__					)
		model.load_state_dict(snake_case__					)

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

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

		lowercase__						=				timm_model(inputs['''pixel_values''']					)
		lowercase__						=				model(**snake_case__					).logits

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

		print(f'Saving model {swinva_name} to {pytorch_dump_folder_path}'					)
		model.save_pretrained(snake_case__					)

		print(f'Saving image processor to {pytorch_dump_folder_path}'					)
		image_processor.save_pretrained(snake_case__					)

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


if __name__ == "__main__":
			lowerCAmelCase	=							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.'
			)

			lowerCAmelCase	=							parser.parse_args()
			convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 	_a							(      UpperCamelCase__					,     UpperCamelCase__					,     UpperCamelCase__					,     unittest.TestCase      ):
							_lowercase     :      int  				=			AltDiffusionPipeline
							_lowercase     :      Tuple  				=			TEXT_TO_IMAGE_PARAMS
							_lowercase     :      List[str]  				=			TEXT_TO_IMAGE_BATCH_PARAMS
							_lowercase     :      Union[str, Any]  				=			TEXT_TO_IMAGE_IMAGE_PARAMS
							_lowercase     :      int  				=			TEXT_TO_IMAGE_IMAGE_PARAMS

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Union[str, Any]:





									"""simple docstring"""







									torch.manual_seed(0							)
									lowercase__						=				UNetaDConditionModel(
									    block_out_channels=(32, 64)   ,						layers_per_block=2   ,						sample_size=32   ,						in_channels=4   ,						out_channels=4   ,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,						up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,						cross_attention_dim=32   ,						)
									lowercase__						=				DDIMScheduler(
									    beta_start=0.00085   ,						beta_end=0.012   ,						beta_schedule='''scaled_linear'''   ,						clip_sample=UpperCamelCase_   ,						set_alpha_to_one=UpperCamelCase_   ,						)
									torch.manual_seed(0							)
									lowercase__						=				AutoencoderKL(
									    block_out_channels=[32, 64]   ,						in_channels=3   ,						out_channels=3   ,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,						latent_channels=4   ,						)

									# TODO: address the non-deterministic text encoder (fails for save-load tests)
									# torch.manual_seed(0)
									# text_encoder_config = RobertaSeriesConfig(
									#     hidden_size=32,
									#     project_dim=32,
									#     intermediate_size=37,
									#     layer_norm_eps=1e-05,
									#     num_attention_heads=4,
									#     num_hidden_layers=5,
									#     vocab_size=5002,
									# )
									# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

									torch.manual_seed(0							)
									lowercase__						=				CLIPTextConfig(
									    bos_token_id=0   ,						eos_token_id=2   ,						hidden_size=32   ,						projection_dim=32   ,						intermediate_size=37   ,						layer_norm_eps=1E-0_5   ,						num_attention_heads=4   ,						num_hidden_layers=5   ,						pad_token_id=1   ,						vocab_size=5_002   ,						)
									lowercase__						=				CLIPTextModel(UpperCamelCase_							)

									lowercase__						=				XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''							)
									lowercase__						=				77

									lowercase__						=				{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''safety_checker''': None,
									    '''feature_extractor''': None,
									}
									return components

							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     Optional[int]   ,						UpperCamelCase_:     str=0							)      ->       Union[str, Any]:





									"""simple docstring"""







									if str(UpperCamelCase_							).startswith('''mps'''							):
											lowercase__						=				torch.manual_seed(UpperCamelCase_							)
									else:
											lowercase__						=				torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
									lowercase__						=				{
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 6.0,
									    '''output_type''': '''numpy''',
									}
									return inputs

							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       Dict:





									"""simple docstring"""







									super().test_attention_slicing_forward_pass(expected_max_diff=3E-3							)

							def 				lowerCamelCase_			(							self:     Dict							)      ->       str:





									"""simple docstring"""







									super().test_inference_batch_single_identical(expected_max_diff=3E-3							)

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator

									lowercase__						=				self.get_dummy_components()
									torch.manual_seed(0							)
									lowercase__						=				RobertaSeriesConfig(
									    hidden_size=32   ,						project_dim=32   ,						intermediate_size=37   ,						layer_norm_eps=1E-0_5   ,						num_attention_heads=4   ,						num_hidden_layers=5   ,						vocab_size=5_002   ,						)
									# TODO: remove after fixing the non-deterministic text encoder
									lowercase__						=				RobertaSeriesModelWithTransformation(UpperCamelCase_							)
									lowercase__						=				text_encoder

									lowercase__						=				AltDiffusionPipeline(**UpperCamelCase_							)
									lowercase__						=				alt_pipe.to(UpperCamelCase_							)
									alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				self.get_dummy_inputs(UpperCamelCase_							)
									lowercase__						=				'''A photo of an astronaut'''
									lowercase__						=				alt_pipe(**UpperCamelCase_							)
									lowercase__						=				output.images
									lowercase__						=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									lowercase__						=				np.array(
									    [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]							)

									assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2


							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator

									lowercase__						=				self.get_dummy_components()
									lowercase__						=				PNDMScheduler(skip_prk_steps=UpperCamelCase_							)
									torch.manual_seed(0							)
									lowercase__						=				RobertaSeriesConfig(
									    hidden_size=32   ,						project_dim=32   ,						intermediate_size=37   ,						layer_norm_eps=1E-0_5   ,						num_attention_heads=4   ,						num_hidden_layers=5   ,						vocab_size=5_002   ,						)
									# TODO: remove after fixing the non-deterministic text encoder
									lowercase__						=				RobertaSeriesModelWithTransformation(UpperCamelCase_							)
									lowercase__						=				text_encoder
									lowercase__						=				AltDiffusionPipeline(**UpperCamelCase_							)
									lowercase__						=				alt_pipe.to(UpperCamelCase_							)
									alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				self.get_dummy_inputs(UpperCamelCase_							)
									lowercase__						=				alt_pipe(**UpperCamelCase_							)
									lowercase__						=				output.images
									lowercase__						=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									lowercase__						=				np.array(
									    [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]							)

									assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2


@slow
@require_torch_gpu
class 	_a							(      unittest.TestCase      ):

							def 				lowerCamelCase_			(							self:     Dict							)      ->       Optional[int]:





									"""simple docstring"""







									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       Union[str, Any]:





									"""simple docstring"""







									lowercase__						=				AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,						safety_checker=UpperCamelCase_							)
									lowercase__						=				alt_pipe.to(UpperCamelCase_							)
									alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				'''A painting of a squirrel eating a burger'''
									lowercase__						=				torch.manual_seed(0							)
									lowercase__						=				alt_pipe([prompt]   ,						generator=UpperCamelCase_   ,						guidance_scale=6.0   ,						num_inference_steps=20   ,						output_type='''np'''							)

									lowercase__						=				output.images

									lowercase__						=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									lowercase__						=				np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]							)

									assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2


							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''   ,						subfolder='''scheduler'''							)

									lowercase__						=				AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,						scheduler=UpperCamelCase_   ,						safety_checker=UpperCamelCase_							)
									lowercase__						=				alt_pipe.to(UpperCamelCase_							)
									alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				'''A painting of a squirrel eating a burger'''
									lowercase__						=				torch.manual_seed(0							)

									lowercase__						=				alt_pipe([prompt]   ,						generator=UpperCamelCase_   ,						num_inference_steps=2   ,						output_type='''numpy'''							)
									lowercase__						=				output.images

									lowercase__						=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									lowercase__						=				np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]							)

									assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2


