



"""simple docstring"""







import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class        __A       (	A_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					Any   	=				LDMTextToImagePipeline
		lowerCAmelCase			:					str   	=				TEXT_TO_IMAGE_PARAMS - {
		    "negative_prompt",
		    "negative_prompt_embeds",
		    "cross_attention_kwargs",
		    "prompt_embeds",
		}
		lowerCAmelCase			:					List[Any]   	=				PipelineTesterMixin.required_optional_params - {
		    "num_images_per_prompt",
		    "callback",
		    "callback_steps",
		}
		lowerCAmelCase			:					List[Any]   	=				TEXT_TO_IMAGE_BATCH_PARAMS
		lowerCAmelCase			:					Optional[int]   	=				False







		def 					UpperCAmelCase    (	self						:  Any						)					->		int:






				"""simple docstring"""



				torch.manual_seed(0						)
				lowercase__   :    Any				=			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__   :    Optional[int]				=			DDIMScheduler(
				    beta_start=0.0_0085			,beta_end=0.012			,beta_schedule='''scaled_linear'''			,clip_sample=_snake_case			,set_alpha_to_one=_snake_case			,)
				torch.manual_seed(0						)
				lowercase__   :    str				=			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			,)
				torch.manual_seed(0						)
				lowercase__   :    int				=			CLIPTextConfig(
				    bos_token_id=0			,eos_token_id=2			,hidden_size=32			,intermediate_size=37			,layer_norm_eps=1e-05			,num_attention_heads=4			,num_hidden_layers=5			,pad_token_id=1			,vocab_size=1_000			,)
				lowercase__   :    int				=			CLIPTextModel(_snake_case						)
				lowercase__   :    Tuple				=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

				lowercase__   :    Dict				=			{
				    '''unet''': unet,
				    '''scheduler''': scheduler,
				    '''vqvae''': vae,
				    '''bert''': text_encoder,
				    '''tokenizer''': tokenizer,
				}
				return components







		def 					UpperCAmelCase    (	self						:  Union[str, Any]			,_snake_case						:  Tuple			,_snake_case						:  Union[str, Any]=0						)					->		List[str]:






				"""simple docstring"""



				if str(_snake_case						).startswith('''mps'''						):
						lowercase__   :    List[str]				=			torch.manual_seed(_snake_case						)
				else:
						lowercase__   :    Optional[int]				=			torch.Generator(device=_snake_case						).manual_seed(_snake_case						)
				lowercase__   :    Dict				=			{
				    '''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 					UpperCAmelCase    (	self						:  Any						)					->		int:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator

				lowercase__   :    Optional[int]				=			self.get_dummy_components()
				lowercase__   :    Tuple				=			LDMTextToImagePipeline(**_snake_case						)
				pipe.to(_snake_case						)
				pipe.set_progress_bar_config(disable=_snake_case						)

				lowercase__   :    Union[str, Any]				=			self.get_dummy_inputs(_snake_case						)
				lowercase__   :    List[str]				=			pipe(**_snake_case						).images
				lowercase__   :    str				=			image[0, -3:, -3:, -1]

				assert image.shape == (1, 16, 16, 3)
				lowercase__   :    Optional[int]				=			np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3





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







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		Optional[Any]:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  int			,_snake_case						:  List[Any]=torch.floataa			,_snake_case						:  int=0						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			torch.manual_seed(_snake_case						)
				lowercase__   :    List[str]				=			np.random.RandomState(_snake_case						).standard_normal((1, 4, 32, 32)						)
				lowercase__   :    Any				=			torch.from_numpy(_snake_case						).to(device=_snake_case			,dtype=_snake_case						)
				lowercase__   :    Any				=			{
				    '''prompt''': '''A painting of a squirrel eating a burger''',
				    '''latents''': latents,
				    '''generator''': generator,
				    '''num_inference_steps''': 3,
				    '''guidance_scale''': 6.0,
				    '''output_type''': '''numpy''',
				}
				return inputs







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''						).to(_snake_case						)
				pipe.set_progress_bar_config(disable=_snake_case						)

				lowercase__   :    Dict				=			self.get_inputs(_snake_case						)
				lowercase__   :    int				=			pipe(**_snake_case						).images
				lowercase__   :    Optional[Any]				=			image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 256, 256, 3)
				lowercase__   :    List[Any]				=			np.array([0.5_1825, 0.5_2850, 0.5_2543, 0.5_4258, 0.5_2304, 0.5_2569, 0.5_4363, 0.5_5276, 0.5_6878]						)
				lowercase__   :    Optional[Any]				=			np.abs(expected_slice - image_slice						).max()
				assert max_diff < 1e-3





@nightly
@require_torch_gpu
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





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






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  str			,_snake_case						:  Union[str, Any]			,_snake_case						:  int=torch.floataa			,_snake_case						:  Any=0						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			torch.manual_seed(_snake_case						)
				lowercase__   :    List[Any]				=			np.random.RandomState(_snake_case						).standard_normal((1, 4, 32, 32)						)
				lowercase__   :    Dict				=			torch.from_numpy(_snake_case						).to(device=_snake_case			,dtype=_snake_case						)
				lowercase__   :    int				=			{
				    '''prompt''': '''A painting of a squirrel eating a burger''',
				    '''latents''': latents,
				    '''generator''': generator,
				    '''num_inference_steps''': 50,
				    '''guidance_scale''': 6.0,
				    '''output_type''': '''numpy''',
				}
				return inputs







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		List[str]:






				"""simple docstring"""



				lowercase__   :    str				=			LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''						).to(_snake_case						)
				pipe.set_progress_bar_config(disable=_snake_case						)

				lowercase__   :    int				=			self.get_inputs(_snake_case						)
				lowercase__   :    str				=			pipe(**_snake_case						).images[0]

				lowercase__   :    List[Any]				=			load_numpy(
				    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'''						)
				lowercase__   :    List[Any]				=			np.abs(expected_image - image						).max()
				assert max_diff < 1e-3





"""simple docstring"""







import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_   						=   logging.get_logger()



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase = True	)							->      Union[str, Any]:
		print(f"""Converting {name}..."""	)

		with torch.no_grad():
				if hidden_sizes == 1_28:
						if name[-1] == "S":
								lowercase__   :    str				=			timm.create_model('''levit_128s'''						,     pretrained=__lowerCamelCase	)
						else:
								lowercase__   :    Tuple				=			timm.create_model('''levit_128'''						,     pretrained=__lowerCamelCase	)
				if hidden_sizes == 1_92:
						lowercase__   :    Union[str, Any]				=			timm.create_model('''levit_192'''						,     pretrained=__lowerCamelCase	)
				if hidden_sizes == 2_56:
						lowercase__   :    str				=			timm.create_model('''levit_256'''						,     pretrained=__lowerCamelCase	)
				if hidden_sizes == 3_84:
						lowercase__   :    str				=			timm.create_model('''levit_384'''						,     pretrained=__lowerCamelCase	)

				from_model.eval()
				lowercase__   :    Optional[int]				=			LevitForImageClassificationWithTeacher(__lowerCamelCase	).eval()
				lowercase__   :    str				=			OrderedDict()

				lowercase__   :    int				=			from_model.state_dict()
				lowercase__   :    Dict				=			list(from_model.state_dict().keys()	)
				lowercase__   :    Any				=			list(our_model.state_dict().keys()	)
				print(len(__lowerCamelCase	)						,     len(__lowerCamelCase	)	)
				for i in range(len(__lowerCamelCase	)	):
						lowercase__   :    str				=			weights[og_keys[i]]
				our_model.load_state_dict(__lowerCamelCase	)

				lowercase__   :    Optional[int]				=			torch.randn((2, 3, 2_24, 2_24)	)
				lowercase__   :    Optional[int]				=			from_model(__lowerCamelCase	)
				lowercase__   :    List[Any]				=			our_model(__lowerCamelCase	).logits

		assert torch.allclose(__lowerCamelCase						,     __lowerCamelCase	), "The model logits don't match the original one."

		lowercase__   :    Any				=			name
		print(__lowerCamelCase	)

		if push_to_hub:
				our_model.save_pretrained(save_directory / checkpoint_name	)
				lowercase__   :    int				=			LevitImageProcessor()
				image_processor.save_pretrained(save_directory / checkpoint_name	)

				print(f"""Pushed {checkpoint_name}"""	)



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase = None						,     __lowerCamelCase = True	)							->      List[Any]:
		lowercase__   :    Any				=			'''imagenet-1k-id2label.json'''
		lowercase__   :    Tuple				=			10_00
		lowercase__   :    Dict				=			(1, num_labels)

		lowercase__   :    List[str]				=			'''huggingface/label-files'''
		lowercase__   :    str				=			num_labels
		lowercase__   :    List[Any]				=			json.load(open(hf_hub_download(__lowerCamelCase						,     __lowerCamelCase						,     repo_type='''dataset'''	)						,     '''r'''	)	)
		lowercase__   :    Union[str, Any]				=			{int(__lowerCamelCase	): v for k, v in idalabel.items()}

		lowercase__   :    Union[str, Any]				=			idalabel
		lowercase__   :    Optional[int]				=			{v: k for k, v in idalabel.items()}

		lowercase__   :    List[Any]				=			partial(__lowerCamelCase						,     num_labels=__lowerCamelCase						,     idalabel=__lowerCamelCase						,     labelaid=__lowerCamelCase	)

		lowercase__   :    Tuple				=			{
		    '''levit-128S''': 1_28,
		    '''levit-128''': 1_28,
		    '''levit-192''': 1_92,
		    '''levit-256''': 2_56,
		    '''levit-384''': 3_84,
		}

		lowercase__   :    Any				=			{
		    '''levit-128S''': ImageNetPreTrainedConfig(
		        hidden_sizes=[1_28, 2_56, 3_84]						,     num_attention_heads=[4, 6, 8]						,     depths=[2, 3, 4]						,     key_dim=[16, 16, 16]						,     drop_path_rate=0						,     ),
		    '''levit-128''': ImageNetPreTrainedConfig(
		        hidden_sizes=[1_28, 2_56, 3_84]						,     num_attention_heads=[4, 8, 12]						,     depths=[4, 4, 4]						,     key_dim=[16, 16, 16]						,     drop_path_rate=0						,     ),
		    '''levit-192''': ImageNetPreTrainedConfig(
		        hidden_sizes=[1_92, 2_88, 3_84]						,     num_attention_heads=[3, 5, 6]						,     depths=[4, 4, 4]						,     key_dim=[32, 32, 32]						,     drop_path_rate=0						,     ),
		    '''levit-256''': ImageNetPreTrainedConfig(
		        hidden_sizes=[2_56, 3_84, 5_12]						,     num_attention_heads=[4, 6, 8]						,     depths=[4, 4, 4]						,     key_dim=[32, 32, 32]						,     drop_path_rate=0						,     ),
		    '''levit-384''': ImageNetPreTrainedConfig(
		        hidden_sizes=[3_84, 5_12, 7_68]						,     num_attention_heads=[6, 9, 12]						,     depths=[4, 4, 4]						,     key_dim=[32, 32, 32]						,     drop_path_rate=0.1						,     ),
		}

		if model_name:
				convert_weight_and_push(
				    names_to_hidden_sizes[model_name]						,     __lowerCamelCase						,     names_to_config[model_name]						,     __lowerCamelCase						,     __lowerCamelCase	)
		else:
				for model_name, config in names_to_config.items():
						convert_weight_and_push(names_to_hidden_sizes[model_name]						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
		return config, expected_shape


if __name__ == "__main__":
							lowerCAmelCase_   						=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default=None,
							    type=str,
							    help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default='levit-dump-folder/',
							    type=Path,
							    required=False,
							    help='Path to the output PyTorch model directory.',
							)
							parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
							parser.add_argument(
							    '--no-push_to_hub',
							    dest='push_to_hub',
							    action='store_false',
							    help='Do not push model and image processor to the hub',
							)

							lowerCAmelCase_   						=   parser.parse_args()
							lowerCAmelCase_   						=   args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
