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()
_SCREAMING_SNAKE_CASE      						=	logging.get_logger()





def        lowercase(     UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_ = True    )						->     Dict:







 '''simple docstring'''
 print(f"""Converting {name}..."""    )

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

  from_model.eval()
  UpperCamelCase          =	LevitForImageClassificationWithTeacher(UpperCamelCase_    ).eval()
  UpperCamelCase          =	OrderedDict()

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

  UpperCamelCase          =	torch.randn((2, 3, 224, 224)    )
  UpperCamelCase          =	from_model(UpperCamelCase_    )
  UpperCamelCase          =	our_model(UpperCamelCase_    ).logits

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

 UpperCamelCase          =	name
 print(UpperCamelCase_    )

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

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





def        lowercase(     UpperCamelCase_				, UpperCamelCase_ = None				, UpperCamelCase_ = True    )						->     Optional[Any]:







 '''simple docstring'''
 UpperCamelCase          =	"imagenet-1k-id2label.json"
 UpperCamelCase          =	1000
 UpperCamelCase          =	(1, num_labels)

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

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

 UpperCamelCase          =	partial(UpperCamelCase_				, num_labels=UpperCamelCase_				, idalabel=UpperCamelCase_				, labelaid=UpperCamelCase_    )

 UpperCamelCase          =	{
     "levit-128S": 128,
     "levit-128": 128,
     "levit-192": 192,
     "levit-256": 256,
     "levit-384": 384,
 }

 UpperCamelCase          =	{
     "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__":
     _SCREAMING_SNAKE_CASE      						=	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""",
     )

     _SCREAMING_SNAKE_CASE      						=	parser.parse_args()
     _SCREAMING_SNAKE_CASE      						=	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)






import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class       __lowerCAmelCase				(							unittest.TestCase):






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    int ):
         torch.manual_seed(0 )
         lowercase			:str							=  UNetaDModel(
             sample_size=(32, 64)				,    in_channels=1				,    out_channels=1				,    layers_per_block=2				,    block_out_channels=(1_28, 1_28)				,    down_block_types=("AttnDownBlock2D", "DownBlock2D")				,    up_block_types=("UpBlock2D", "AttnUpBlock2D")				,    )
         return model






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         torch.manual_seed(0 )
         lowercase			:Dict							=  UNetaDConditionModel(
             sample_size=(64, 32)				,    in_channels=1				,    out_channels=1				,    layers_per_block=2				,    block_out_channels=(1_28, 1_28)				,    down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")				,    up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")				,    cross_attention_dim=10				,    )
         return model






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         torch.manual_seed(0 )
         lowercase			:List[str]							=  AutoencoderKL(
             sample_size=(1_28, 64)				,    in_channels=1				,    out_channels=1				,    latent_channels=1				,    layers_per_block=2				,    block_out_channels=(1_28, 1_28)				,    down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D")				,    up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D")				,    )
         lowercase			:List[str]							=  UNetaDModel(
             sample_size=(64, 32)				,    in_channels=1				,    out_channels=1				,    layers_per_block=2				,    block_out_channels=(1_28, 1_28)				,    down_block_types=("AttnDownBlock2D", "DownBlock2D")				,    up_block_types=("UpBlock2D", "AttnUpBlock2D")				,    )
         return vqvae, unet






       @slow
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:Optional[Any]							=  "cpu"  # ensure determinism for the device-dependent torch.Generator
         lowercase			:Optional[int]							=  Mel(
             x_res=self.dummy_unet.config.sample_size[1]				,    y_res=self.dummy_unet.config.sample_size[0]				,    )

         lowercase			:List[str]							=  DDPMScheduler()
         lowercase			:Tuple							=  AudioDiffusionPipeline(vqvae=_lowerCAmelCase				,    unet=self.dummy_unet				,    mel=_lowerCAmelCase				,    scheduler=_lowerCAmelCase )
         lowercase			:Dict							=  pipe.to(_lowerCAmelCase )
         pipe.set_progress_bar_config(disable=_lowerCAmelCase )

         lowercase			:Tuple							=  torch.Generator(device=_lowerCAmelCase ).manual_seed(42 )
         lowercase			:Optional[int]							=  pipe(generator=_lowerCAmelCase				,    steps=4 )
         lowercase			:List[str]							=  output.audios[0]
         lowercase			:List[str]							=  output.images[0]

         lowercase			:List[str]							=  torch.Generator(device=_lowerCAmelCase ).manual_seed(42 )
         lowercase			:Optional[int]							=  pipe(generator=_lowerCAmelCase				,    steps=4				,    return_dict=_lowerCAmelCase )
         lowercase			:int							=  output[0][0]

         assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
         assert (
             image.height == self.dummy_unet.config.sample_size[0]
             and image.width == self.dummy_unet.config.sample_size[1]
         )
         lowercase			:Any							=  np.frombuffer(image.tobytes()				,    dtype="uint8" )[:10]
         lowercase			:Dict							=  np.frombuffer(image_from_tuple.tobytes()				,    dtype="uint8" )[:10]
         lowercase			:List[Any]							=  np.array([69, 2_55, 2_55, 2_55, 0, 0, 77, 1_81, 12, 1_27] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0

         lowercase			:Optional[Any]							=  Mel(
             x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]				,    y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]				,    )

         lowercase			:List[str]							=  DDIMScheduler()
         lowercase			:Tuple							=  self.dummy_vqvae_and_unet
         lowercase			:str							=  AudioDiffusionPipeline(
             vqvae=self.dummy_vqvae_and_unet[0]				,    unet=dummy_vqvae_and_unet[1]				,    mel=_lowerCAmelCase				,    scheduler=_lowerCAmelCase )
         lowercase			:Union[str, Any]							=  pipe.to(_lowerCAmelCase )
         pipe.set_progress_bar_config(disable=_lowerCAmelCase )

         np.random.seed(0 )
         lowercase			:Dict							=  np.random.uniform(-1				,    1				,    ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
         lowercase			:List[Any]							=  torch.Generator(device=_lowerCAmelCase ).manual_seed(42 )
         lowercase			:Dict							=  pipe(raw_audio=_lowerCAmelCase				,    generator=_lowerCAmelCase				,    start_step=5				,    steps=10 )
         lowercase			:str							=  output.images[0]

         assert (
             image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
             and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
         )
         lowercase			:Optional[Any]							=  np.frombuffer(image.tobytes()				,    dtype="uint8" )[:10]
         lowercase			:List[str]							=  np.array([1_20, 1_17, 1_10, 1_09, 1_38, 1_67, 1_38, 1_48, 1_32, 1_21] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() == 0

         lowercase			:Optional[Any]							=  self.dummy_unet_condition
         lowercase			:Optional[int]							=  AudioDiffusionPipeline(
             vqvae=self.dummy_vqvae_and_unet[0]				,    unet=_lowerCAmelCase				,    mel=_lowerCAmelCase				,    scheduler=_lowerCAmelCase )
         lowercase			:int							=  pipe.to(_lowerCAmelCase )
         pipe.set_progress_bar_config(disable=_lowerCAmelCase )

         np.random.seed(0 )
         lowercase			:List[str]							=  torch.rand((1, 1, 10) )
         lowercase			:Union[str, Any]							=  pipe(generator=_lowerCAmelCase				,    encoding=_lowerCAmelCase )
         lowercase			:Tuple							=  output.images[0]
         lowercase			:Optional[Any]							=  np.frombuffer(image.tobytes()				,    dtype="uint8" )[:10]
         lowercase			:Any							=  np.array([1_07, 1_03, 1_20, 1_27, 1_42, 1_22, 1_13, 1_22, 97, 1_11] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() == 0







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






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:Tuple							=  torch_device

         lowercase			:List[Any]							=  DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256" )
         lowercase			:Dict							=  pipe.to(_lowerCAmelCase )
         pipe.set_progress_bar_config(disable=_lowerCAmelCase )

         lowercase			:Any							=  torch.Generator(device=_lowerCAmelCase ).manual_seed(42 )
         lowercase			:Optional[int]							=  pipe(generator=_lowerCAmelCase )
         lowercase			:List[str]							=  output.audios[0]
         lowercase			:Any							=  output.images[0]

         assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
         assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
         lowercase			:Dict							=  np.frombuffer(image.tobytes()				,    dtype="uint8" )[:10]
         lowercase			:List[str]							=  np.array([1_51, 1_67, 1_54, 1_44, 1_22, 1_34, 1_21, 1_05, 70, 26] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() == 0



