


"""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()
__snake_case :    Union[str, Any]             =   logging.get_logger()







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  str						,							lowercase  :  LevitConfig						,							lowercase  :  Path						,							lowercase  :  bool = True			)     ->						Any:


    """simple docstring"""





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

    with torch.no_grad():
        if hidden_sizes == 128:
            if name[-1] == "S":
                snake_case   :				str				 =   timm.create_model("levit_128s"						,							pretrained=lowercase			)
            else:
                snake_case   :				Tuple				 =   timm.create_model("levit_128"						,							pretrained=lowercase			)
        if hidden_sizes == 192:
            snake_case   :				str				 =   timm.create_model("levit_192"						,							pretrained=lowercase			)
        if hidden_sizes == 256:
            snake_case   :				Any				 =   timm.create_model("levit_256"						,							pretrained=lowercase			)
        if hidden_sizes == 384:
            snake_case   :				Optional[int]				 =   timm.create_model("levit_384"						,							pretrained=lowercase			)

        from_model.eval()
        snake_case   :				Any				 =   LevitForImageClassificationWithTeacher(lowercase			).eval()
        snake_case   :				Optional[Any]				 =   OrderedDict()

        snake_case   :				Dict				 =   from_model.state_dict()
        snake_case   :				List[Any]				 =   list(from_model.state_dict().keys()			)
        snake_case   :				Tuple				 =   list(our_model.state_dict().keys()			)
        print(len(lowercase			)						,							len(lowercase			)			)
        for i in range(len(lowercase			)			):
            snake_case   :				List[str]				 =   weights[og_keys[i]]
        our_model.load_state_dict(lowercase			)

        snake_case   :				str				 =   torch.randn((2, 3, 224, 224)			)
        snake_case   :				Optional[Any]				 =   from_model(lowercase			)
        snake_case   :				Dict				 =   our_model(lowercase			).logits

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

    snake_case   :				str				 =   name
    print(lowercase			)

    if push_to_hub:
        our_model.save_pretrained(save_directory / checkpoint_name			)
        snake_case   :				Optional[int]				 =   LevitImageProcessor()
        image_processor.save_pretrained(save_directory / checkpoint_name			)

        print(F'Pushed {checkpoint_name}'			)







def   __lowerCAmelCase						(						lowercase  :  Path						,							lowercase  :  str = None						,							lowercase  :  bool = True			)     ->						Tuple:


    """simple docstring"""





    snake_case   :				List[Any]				 =   "imagenet-1k-id2label.json"
    snake_case   :				Tuple				 =   1000
    snake_case   :				Tuple				 =   (1, num_labels)

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

    snake_case   :				Union[str, Any]				 =   idalabel
    snake_case   :				Tuple				 =   {v: k for k, v in idalabel.items()}

    snake_case   :				Union[str, Any]				 =   partial(lowercase						,							num_labels=lowercase						,							idalabel=lowercase						,							labelaid=lowercase			)

    snake_case   :				List[str]				 =   {
        "levit-128S": 128,
        "levit-128": 128,
        "levit-192": 192,
        "levit-256": 256,
        "levit-384": 384,
    }

    snake_case   :				Optional[Any]				 =   {
        "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]						,							lowercase						,							names_to_config[model_name]						,							lowercase						,							lowercase			)
    else:
        for model_name, config in names_to_config.items():
            convert_weight_and_push(names_to_hidden_sizes[model_name]						,							lowercase						,							lowercase						,							lowercase						,							lowercase			)
    return config, expected_shape


if __name__ == "__main__":
  __snake_case :    List[str]             =   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""",
  )

  __snake_case :    Union[str, Any]             =   parser.parse_args()
  __snake_case :    Optional[Any]             =   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"""



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)







@dataclass
class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Union[List[PIL.Image.Image], np.ndarray]
   __UpperCAmelCase			:	Optional[List[bool]]


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
  from .pipeline_cycle_diffusion import CycleDiffusionPipeline
  from .pipeline_stable_diffusion import StableDiffusionPipeline
  from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
  from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
  from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
  from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
  from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
  from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
  from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
  from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
  from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
  from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
  from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
  from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
  from .pipeline_stable_unclip import StableUnCLIPPipeline
  from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
  from .safety_checker import StableDiffusionSafetyChecker
  from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
  from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.26.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import (
      StableDiffusionDepthaImgPipeline,
      StableDiffusionDiffEditPipeline,
      StableDiffusionPixaPixZeroPipeline,
  )
else:
  from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
  from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
  from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
  if not (
      is_torch_available()
      and is_transformers_available()
      and is_k_diffusion_available()
      and is_k_diffusion_version(""">=""", """0.0.12""")
  ):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
  from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
  if not (is_transformers_available() and is_onnx_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_onnx_objects import *  # noqa F403
else:
  from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
  from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
  from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
  from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
  from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
  import flax







  @flax.struct.dataclass
  class _lowerCAmelCase    (				snake_case_     ):
     __UpperCAmelCase			:	np.ndarray
     __UpperCAmelCase			:	List[bool]



  from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
  from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
  from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
  from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
  from .safety_checker_flax import FlaxStableDiffusionSafetyChecker





