


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, 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


enable_full_determinism()




class       lowercase_					(unittest.TestCase   ):
				snake_case	    =StableDiffusionLDMaDPipeline
				snake_case	    =TEXT_TO_IMAGE_PARAMS
				snake_case	    =TEXT_TO_IMAGE_BATCH_PARAMS
				snake_case	    =TEXT_TO_IMAGE_IMAGE_PARAMS





				def 						__UpperCamelCase    (				self)  -> Any:
											torch.manual_seed(0)
											a__      =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       ,  )
											a__      =DDIMScheduler(
											    beta_start=0.0_00_85       ,  beta_end=0.0_12       ,  beta_schedule='scaled_linear'       ,  clip_sample=lowercase_       ,  set_alpha_to_one=lowercase_       ,  )
											torch.manual_seed(0)
											a__      =AutoencoderKL(
											    block_out_channels=[32, 64]       ,  in_channels=6       ,  out_channels=6       ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,  latent_channels=4       ,  )
											torch.manual_seed(0)
											a__      =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=1000       ,  )
											a__      =CLIPTextModel(lowercase_)
											a__      =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

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





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_=0)  -> List[str]:
											if str(lowercase_).startswith('mps'):
																		a__      =torch.manual_seed(lowercase_)
											else:
																		a__      =torch.Generator(device=lowercase_).manual_seed(lowercase_)
											a__      ={
											    '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:
											a__      ='cpu'  # ensure determinism for the device-dependent torch.Generator

											a__      =self.get_dummy_components()
											a__      =StableDiffusionLDMaDPipeline(**lowercase_)
											a__      =ldmad_pipe.to(lowercase_)
											ldmad_pipe.set_progress_bar_config(disable=lowercase_)

											a__      =self.get_dummy_inputs(lowercase_)
											a__      =ldmad_pipe(**lowercase_)
											a__    ,     a__      =output.rgb, output.depth

											a__      =rgb[0, -3:, -3:, -1]
											a__      =depth[0, -3:, -1]

											assert rgb.shape == (1, 64, 64, 3)
											assert depth.shape == (1, 64, 64)

											a__      =np.array(
											    [0.37_33_81_76, 0.7_02_47, 0.74_20_31_93, 0.51_64_36_04, 0.58_25_67_93, 0.60_93_21_36, 0.4_18_10_95, 0.48_35_58_77, 0.46_53_52_62])
											a__      =np.array([1_03.4_67_27, 85.81_20_04, 87.84_92_36])

											assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb).max() < 1e-2
											assert np.abs(image_slice_depth.flatten() - expected_slice_depth).max() < 1e-2





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =self.get_dummy_components()
											a__      =StableDiffusionLDMaDPipeline(**lowercase_)
											a__      =ldmad_pipe.to(lowercase_)
											ldmad_pipe.set_progress_bar_config(disable=lowercase_)

											a__      =self.get_dummy_inputs(lowercase_)
											a__      =3 * [inputs['prompt']]

											# forward
											a__      =ldmad_pipe(**lowercase_)
											a__    ,     a__      =output.rgb, output.depth
											a__      =rgb_slice_a[0, -3:, -3:, -1]
											a__      =depth_slice_a[0, -3:, -1]

											a__      =self.get_dummy_inputs(lowercase_)
											a__      =3 * [inputs.pop('prompt')]

											a__      =ldmad_pipe.tokenizer(
											    lowercase_       ,  padding='max_length'       ,  max_length=ldmad_pipe.tokenizer.model_max_length       ,  truncation=lowercase_       ,  return_tensors='pt'       ,  )
											a__      =text_inputs['input_ids'].to(lowercase_)

											a__      =ldmad_pipe.text_encoder(lowercase_)[0]

											a__      =prompt_embeds

											# forward
											a__      =ldmad_pipe(**lowercase_)
											a__    ,     a__      =output.rgb, output.depth
											a__      =rgb_slice_a[0, -3:, -3:, -1]
											a__      =depth_slice_a[0, -3:, -1]

											assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()).max() < 1e-4
											assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()).max() < 1e-4





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      ='cpu'  # ensure determinism for the device-dependent torch.Generator
											a__      =self.get_dummy_components()
											a__      =PNDMScheduler(skip_prk_steps=lowercase_)
											a__      =StableDiffusionLDMaDPipeline(**lowercase_)
											a__      =ldmad_pipe.to(lowercase_)
											ldmad_pipe.set_progress_bar_config(disable=lowercase_)

											a__      =self.get_dummy_inputs(lowercase_)
											a__      ='french fries'
											a__      =ldmad_pipe(**lowercase_       ,  negative_prompt=lowercase_)

											a__    ,     a__      =output.rgb, output.depth
											a__      =rgb[0, -3:, -3:, -1]
											a__      =depth[0, -3:, -1]

											assert rgb.shape == (1, 64, 64, 3)
											assert depth.shape == (1, 64, 64)

											a__      =np.array(
											    [0.3_70_44, 0.71_81_15_03, 0.7_22_32_51, 0.48_60_36_75, 0.5_63_83_91, 0.6_36_49_48, 0.42_83_37_04, 0.4_90_13_15, 0.47_92_62_17])
											a__      =np.array([1_07.8_47_38, 84.6_28_02, 89.96_21_35])
											assert np.abs(rgb_slice.flatten() - expected_slice_rgb).max() < 1e-2
											assert np.abs(depth_slice.flatten() - expected_slice_depth).max() < 1e-2







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





				def 						__UpperCamelCase    (				self)  -> Tuple:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_="cpu"       ,  lowercase_=torch.floataa       ,  lowercase_=0)  -> Optional[Any]:
											a__      =torch.Generator(device=lowercase_).manual_seed(lowercase_)
											a__      =np.random.RandomState(lowercase_).standard_normal((1, 4, 64, 64))
											a__      =torch.from_numpy(lowercase_).to(device=lowercase_       ,  dtype=lowercase_)
											a__      ={
											    'prompt': 'a photograph of an astronaut riding a horse',
											    'latents': latents,
											    'generator': generator,
											    'num_inference_steps': 3,
											    'guidance_scale': 7.5,
											    'output_type': 'numpy',
											}
											return inputs





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d')
											a__      =ldmad_pipe.to(lowercase_)
											ldmad_pipe.set_progress_bar_config(disable=lowercase_)

											a__      =self.get_inputs(lowercase_)
											a__      =ldmad_pipe(**lowercase_)
											a__    ,     a__      =output.rgb, output.depth
											a__      =rgb[0, -3:, -3:, -1].flatten()
											a__      =rgb[0, -3:, -1].flatten()

											assert rgb.shape == (1, 512, 512, 3)
											assert depth.shape == (1, 512, 512)

											a__      =np.array(
											    [0.53_80_54_65, 0.56_70_73_05, 0.5_48_65_15, 0.57_01_22_36, 0.5_81_45_11, 0.56_25_34_87, 0.54_84_30_14, 0.55_09_22_63, 0.6_45_97_06])
											a__      =np.array(
											    [0.9_26_37_81, 0.6_67_86_72, 0.5_48_65_15, 0.92_20_21_45, 0.67_83_11_35, 0.56_25_34_87, 0.9_24_16_94, 0.7_55_14_78, 0.6_45_97_06])
											assert np.abs(rgb_slice - expected_slice_rgb).max() < 3e-3
											assert np.abs(depth_slice - expected_slice_depth).max() < 3e-3







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





				def 						__UpperCamelCase    (				self)  -> Any:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_="cpu"       ,  lowercase_=torch.floataa       ,  lowercase_=0)  -> List[str]:
											a__      =torch.Generator(device=lowercase_).manual_seed(lowercase_)
											a__      =np.random.RandomState(lowercase_).standard_normal((1, 4, 64, 64))
											a__      =torch.from_numpy(lowercase_).to(device=lowercase_       ,  dtype=lowercase_)
											a__      ={
											    'prompt': 'a photograph of an astronaut riding a horse',
											    'latents': latents,
											    'generator': generator,
											    'num_inference_steps': 50,
											    'guidance_scale': 7.5,
											    'output_type': 'numpy',
											}
											return inputs





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d').to(lowercase_)
											ldmad_pipe.set_progress_bar_config(disable=lowercase_)

											a__      =self.get_inputs(lowercase_)
											a__      =ldmad_pipe(**lowercase_)
											a__    ,     a__      =output.rgb, output.depth

											a__      =0.49_55_86
											a__      =0.33_79_55_15
											a__      =1_12.4_85_18
											a__      =98.48_97_46
											assert np.abs(expected_rgb_mean - rgb.mean()) < 1e-3
											assert np.abs(expected_rgb_std - rgb.std()) < 1e-3
											assert np.abs(expected_depth_mean - depth.mean()) < 1e-3
											assert np.abs(expected_depth_std - depth.std()) < 1e-3





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c').to(lowercase_)
											ldmad_pipe.set_progress_bar_config(disable=lowercase_)

											a__      =self.get_inputs(lowercase_)
											a__      =ldmad_pipe(**lowercase_)
											a__    ,     a__      =output.rgb, output.depth

											a__      =0.4_19_41_27
											a__      =0.35_37_55_86
											a__      =0.5_63_85_02
											a__      =0.34_68_61_03

											assert rgb.shape == (1, 512, 512, 3)
											assert depth.shape == (1, 512, 512, 1)
											assert np.abs(expected_rgb_mean - rgb.mean()) < 1e-3
											assert np.abs(expected_rgb_std - rgb.std()) < 1e-3
											assert np.abs(expected_depth_mean - depth.mean()) < 1e-3
											assert np.abs(expected_depth_std - depth.std()) < 1e-3





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:						List[str]   				=       logging.get_logger()






def   _lowercase(					__a				:						int    ,     __a				:						str    ,     __a				:						LevitConfig    ,     __a				:						Path    ,     __a				:						bool = True     ):
							print(f"""Converting {name}..."""     )

							with torch.no_grad():
														if hidden_sizes == 128:
																					if name[-1] == "S":
																												a__      =timm.create_model('levit_128s'    ,     pretrained=__a     )
																					else:
																												a__      =timm.create_model('levit_128'    ,     pretrained=__a     )
														if hidden_sizes == 192:
																					a__      =timm.create_model('levit_192'    ,     pretrained=__a     )
														if hidden_sizes == 256:
																					a__      =timm.create_model('levit_256'    ,     pretrained=__a     )
														if hidden_sizes == 384:
																					a__      =timm.create_model('levit_384'    ,     pretrained=__a     )

														from_model.eval()
														a__      =LevitForImageClassificationWithTeacher(__a     ).eval()
														a__      =OrderedDict()

														a__      =from_model.state_dict()
														a__      =list(from_model.state_dict().keys()     )
														a__      =list(our_model.state_dict().keys()     )
														print(len(__a     )    ,     len(__a     )     )
														for i in range(len(__a     )     ):
																					a__      =weights[og_keys[i]]
														our_model.load_state_dict(__a     )

														a__      =torch.randn((2, 3, 224, 224)     )
														a__      =from_model(__a     )
														a__      =our_model(__a     ).logits

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

							a__      =name
							print(__a     )

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

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






def   _lowercase(					__a				:						Path    ,     __a				:						str = None    ,     __a				:						bool = True     ):
							a__      ='imagenet-1k-id2label.json'
							a__      =1000
							a__      =(1, num_labels)

							a__      ='huggingface/label-files'
							a__      =num_labels
							a__      =json.load(open(hf_hub_download(__a    ,     __a    ,     repo_type='dataset'     )    ,     'r'     )     )
							a__      ={int(__a     ): v for k, v in idalabel.items()}

							a__      =idalabel
							a__      ={v: k for k, v in idalabel.items()}

							a__      =partial(__a    ,     num_labels=__a    ,     idalabel=__a    ,     labelaid=__a     )

							a__      ={
							    'levit-128S': 128,
							    'levit-128': 128,
							    'levit-192': 192,
							    'levit-256': 256,
							    'levit-384': 384,
							}

							a__      ={
							    'levit-128S': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]    ,     num_attention_heads=[4, 6, 8]    ,     depths=[2, 3, 4]    ,     key_dim=[16, 16, 16]    ,     drop_path_rate=0    ,     ),
							    'levit-128': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]    ,     num_attention_heads=[4, 8, 12]    ,     depths=[4, 4, 4]    ,     key_dim=[16, 16, 16]    ,     drop_path_rate=0    ,     ),
							    'levit-192': ImageNetPreTrainedConfig(
							        hidden_sizes=[192, 288, 384]    ,     num_attention_heads=[3, 5, 6]    ,     depths=[4, 4, 4]    ,     key_dim=[32, 32, 32]    ,     drop_path_rate=0    ,     ),
							    'levit-256': ImageNetPreTrainedConfig(
							        hidden_sizes=[256, 384, 512]    ,     num_attention_heads=[4, 6, 8]    ,     depths=[4, 4, 4]    ,     key_dim=[32, 32, 32]    ,     drop_path_rate=0    ,     ),
							    'levit-384': ImageNetPreTrainedConfig(
							        hidden_sizes=[384, 512, 768]    ,     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]    ,     __a    ,     names_to_config[model_name]    ,     __a    ,     __a     )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(names_to_hidden_sizes[model_name]    ,     __a    ,     __a    ,     __a    ,     __a     )
							return config, expected_shape


if __name__ == "__main__":
	_lowerCAmelCase:						Any   				=       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:						Union[str, Any]   				=       parser.parse_args()
	_lowerCAmelCase:						Path   				=       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)

