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 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__    ,     a__ = True )      ->Union[str, Any]:




  '''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=__a )
      else:
        _UpperCamelCase           =    timm.create_model("levit_128"    ,     pretrained=__a )
    if hidden_sizes == 192:
      _UpperCamelCase           =    timm.create_model("levit_192"    ,     pretrained=__a )
    if hidden_sizes == 256:
      _UpperCamelCase           =    timm.create_model("levit_256"    ,     pretrained=__a )
    if hidden_sizes == 384:
      _UpperCamelCase           =    timm.create_model("levit_384"    ,     pretrained=__a )

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

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

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

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

  _UpperCamelCase           =    name
  print(__a )

  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 				lowerCAmelCase__       (			a__    ,     a__ = None    ,     a__ = True )      ->Optional[int]:




  '''simple docstring'''

  _UpperCamelCase           =    "imagenet-1k-id2label.json"
  _UpperCamelCase           =    1_000
  _UpperCamelCase           =    (1, num_labels)

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

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

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

  _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]    ,     __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__  =					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)






"""simple docstring"""





import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort



class __lowerCAmelCase					(       _lowercase  ,					unittest.TestCase   ):




			"""simple docstring"""







			pass





@nightly
@require_onnxruntime
@require_torch_gpu
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			@property
			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Dict:







								"""simple docstring"""
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)




			@property
			def   lowerCamelCase__  (					self    :       List[str]						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    ort.SessionOptions()
								A_								=    False
								return options




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
								A_								=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, revision="onnx"							, safety_checker=_snake_case							, feature_extractor=_snake_case							, provider=self.gpu_provider							, sess_options=self.gpu_options							, )
								pipe.set_progress_bar_config(disable=_snake_case						)

								A_								=    "A red cat sitting on a park bench"

								A_								=    np.random.RandomState(0						)
								A_								=    pipe(
								    prompt=_snake_case							, image=_snake_case							, mask_image=_snake_case							, guidance_scale=7.5							, num_inference_steps=10							, generator=_snake_case							, output_type="np"							, )
								A_								=    output.images
								A_								=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								A_								=    np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]						)

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






			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Tuple:







								"""simple docstring"""
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
								A_								=    LMSDiscreteScheduler.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, subfolder="scheduler"							, revision="onnx"						)
								A_								=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, revision="onnx"							, scheduler=_snake_case							, safety_checker=_snake_case							, feature_extractor=_snake_case							, provider=self.gpu_provider							, sess_options=self.gpu_options							, )
								pipe.set_progress_bar_config(disable=_snake_case						)

								A_								=    "A red cat sitting on a park bench"

								A_								=    np.random.RandomState(0						)
								A_								=    pipe(
								    prompt=_snake_case							, image=_snake_case							, mask_image=_snake_case							, guidance_scale=7.5							, num_inference_steps=20							, generator=_snake_case							, output_type="np"							, )
								A_								=    output.images
								A_								=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								A_								=    np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]						)

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




