
"""simple docstring"""




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 			__magic_name__       (       __a   ,					__a   ,					unittest.TestCase ):
       UpperCamelCase_   =		StableDiffusionSAGPipeline
       UpperCamelCase_   =		TEXT_TO_IMAGE_PARAMS
       UpperCamelCase_   =		TEXT_TO_IMAGE_BATCH_PARAMS
       UpperCamelCase_   =		TEXT_TO_IMAGE_IMAGE_PARAMS
       UpperCamelCase_   =		TEXT_TO_IMAGE_IMAGE_PARAMS
       UpperCamelCase_   =		False
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         torch.manual_seed(0       )
         _lowercase:			Optional[int]       =     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:			int       =     DDIMScheduler(
             beta_start=0.0_00_85 ,				beta_end=0.0_12 ,				beta_schedule='''scaled_linear''' ,				clip_sample=A__ ,				set_alpha_to_one=A__ ,				)
         torch.manual_seed(0       )
         _lowercase:			Union[str, Any]       =     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:			Dict       =     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 ,				)
         _lowercase:			Any       =     CLIPTextModel(A__       )
         _lowercase:			Any       =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

         _lowercase:			List[Any]       =     {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }
         return components
       def 							lowercase_							(     self ,				A_ ,				A_=0       )     ->	Any:





         """simple docstring"""




         if str(A__       ).startswith('''mps'''       ):
           _lowercase:			Optional[Any]       =     torch.manual_seed(A__       )
         else:
           _lowercase:			str       =     torch.Generator(device=A__       ).manual_seed(A__       )
         _lowercase:			Optional[Any]       =     {
             '''prompt''': '''.''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 1.0,
             '''sag_scale''': 1.0,
             '''output_type''': '''numpy''',
         }
         return inputs



       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




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


@slow
@require_torch_gpu
class 			__magic_name__       (       unittest.TestCase ):
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			List[Any]       =     StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''       )
         _lowercase:			Optional[int]       =     sag_pipe.to(A__       )
         sag_pipe.set_progress_bar_config(disable=A__       )

         _lowercase:			Any       =     '''.'''
         _lowercase:			List[Any]       =     torch.manual_seed(0       )
         _lowercase:			Optional[Any]       =     sag_pipe(
             [prompt] ,				generator=A__ ,				guidance_scale=7.5 ,				sag_scale=1.0 ,				num_inference_steps=20 ,				output_type='''np'''       )

         _lowercase:			Any       =     output.images

         _lowercase:			Optional[Any]       =     image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         _lowercase:			Dict       =     np.array([0.15_68, 0.17_38, 0.16_95, 0.16_93, 0.15_07, 0.17_05, 0.15_47, 0.17_51, 0.19_49]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5E-2
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''       )
         _lowercase:			Tuple       =     sag_pipe.to(A__       )
         sag_pipe.set_progress_bar_config(disable=A__       )

         _lowercase:			Tuple       =     '''.'''
         _lowercase:			Union[str, Any]       =     torch.manual_seed(0       )
         _lowercase:			str       =     sag_pipe(
             [prompt] ,				generator=A__ ,				guidance_scale=7.5 ,				sag_scale=1.0 ,				num_inference_steps=20 ,				output_type='''np'''       )

         _lowercase:			List[Any]       =     output.images

         _lowercase:			int       =     image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         _lowercase:			Dict       =     np.array([0.34_59, 0.28_76, 0.25_37, 0.30_02, 0.26_71, 0.21_60, 0.30_26, 0.22_62, 0.23_71]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5E-2



       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         _lowercase:			int       =     StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''       )
         _lowercase:			int       =     sag_pipe.to(A__       )
         sag_pipe.set_progress_bar_config(disable=A__       )

         _lowercase:			Any       =     '''.'''
         _lowercase:			Optional[Any]       =     torch.manual_seed(0       )
         _lowercase:			Optional[Any]       =     sag_pipe(
             [prompt] ,				width=768 ,				height=512 ,				generator=A__ ,				guidance_scale=7.5 ,				sag_scale=1.0 ,				num_inference_steps=20 ,				output_type='''np''' ,				)

         _lowercase:			Any       =     output.images

         assert image.shape == (1, 512, 768, 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()
A__   :					List[Any]							=   logging.get_logger()




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase = True						):






  """simple docstring"""



  print(f'''Converting {name}...'''						)

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

    from_model.eval()
    _lowercase:			Any       =     LevitForImageClassificationWithTeacher(_UpperCamelCase						).eval()
    _lowercase:			Union[str, Any]       =     OrderedDict()

    _lowercase:			Optional[Any]       =     from_model.state_dict()
    _lowercase:			List[Any]       =     list(from_model.state_dict().keys()						)
    _lowercase:			int       =     list(our_model.state_dict().keys()						)
    print(len(_UpperCamelCase						) ,				len(_UpperCamelCase						)						)
    for i in range(len(_UpperCamelCase						)						):
      _lowercase:			Any       =     weights[og_keys[i]]
    our_model.load_state_dict(_UpperCamelCase						)

    _lowercase:			int       =     torch.randn((2, 3, 224, 224)						)
    _lowercase:			List[str]       =     from_model(_UpperCamelCase						)
    _lowercase:			Optional[int]       =     our_model(_UpperCamelCase						).logits

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

  _lowercase:			Union[str, Any]       =     name
  print(_UpperCamelCase						)

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

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




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase = None ,				_UpperCamelCase = True						):






  """simple docstring"""



  _lowercase:			int       =     '''imagenet-1k-id2label.json'''
  _lowercase:			int       =     1_000
  _lowercase:			Tuple       =     (1, num_labels)

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

  _lowercase:			int       =     idalabel
  _lowercase:			Tuple       =     {v: k for k, v in idalabel.items()}

  _lowercase:			Tuple       =     partial(_UpperCamelCase ,				num_labels=_UpperCamelCase ,				idalabel=_UpperCamelCase ,				labelaid=_UpperCamelCase						)

  _lowercase:			Union[str, Any]       =     {
      '''levit-128S''': 128,
      '''levit-128''': 128,
      '''levit-192''': 192,
      '''levit-256''': 256,
      '''levit-384''': 384,
  }

  _lowercase:			int       =     {
      '''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] ,				_UpperCamelCase ,				names_to_config[model_name] ,				_UpperCamelCase ,				_UpperCamelCase						)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(names_to_hidden_sizes[model_name] ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						)
  return config, expected_shape


if __name__ == "__main__":
  A__   :					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',
  )

  A__   :					Optional[Any]							=   parser.parse_args()
  A__   :					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)
