
"""simple docstring"""






import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase       =  logging.get_logger()




@dataclass
class 			_SCREAMING_SNAKE_CASE					:
			UpperCAmelCase_  :nn.Module
			UpperCAmelCase_  :List[nn.Module]				 =						field(default_factory=A__							)
			UpperCAmelCase_  :list				 =						field(default_factory=A__							)






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       )			->     List[Any]:
						lowerCAmelCase_    :Optional[int]			     =  len(list(m.modules()       )       ) == 1 or isinstance(__A       ,   nn.Convad       ) or isinstance(__A       ,   nn.BatchNormad       )
						if has_not_submodules:
									self.traced.append(__A       )






			def __call__(					self       ,   __A       )			->     Union[str, Any]:
						for m in self.module.modules():
									self.handles.append(m.register_forward_hook(self._forward_hook       )       )
						self.module(__A       )
						[x.remove() for x in self.handles]
						return self






			@property
			def  __lowerCAmelCase (					self       )			->     List[str]:
						# check the len of the state_dict keys to see if we have learnable params
						return list(filter(lambda __A       : len(list(x.state_dict().keys()       )       ) > 0       ,   self.traced       )       )




@dataclass
class 			_SCREAMING_SNAKE_CASE					:
			UpperCAmelCase_  :nn.Module
			UpperCAmelCase_  :nn.Module
			UpperCAmelCase_  :int				 =						0
			UpperCAmelCase_  :List				 =						field(default_factory=A__							)
			UpperCAmelCase_  :List				 =						field(default_factory=A__							)






			def __call__(					self       ,   __A       )			->     List[str]:
						lowerCAmelCase_    :Tuple			     =  Tracker(self.dest       )(__A       ).parametrized
						lowerCAmelCase_    :Optional[Any]			     =  Tracker(self.src       )(__A       ).parametrized

						lowerCAmelCase_    :Optional[Any]			     =  list(filter(lambda __A       : type(__A       ) not in self.src_skip       ,   __A       )       )
						lowerCAmelCase_    :Dict			     =  list(filter(lambda __A       : type(__A       ) not in self.dest_skip       ,   __A       )       )

						if len(__A       ) != len(__A       ):
									raise Exception(
									    f"""Numbers of operations are different. Source module has {len(__A       )} operations while"""
									    f""" destination module has {len(__A       )}."""       )

						for dest_m, src_m in zip(__A       ,   __A       ):
									dest_m.load_state_dict(src_m.state_dict()       )
									if self.verbose == 1:
												print(f"""Transfered from={src_m} to={dest_m}"""       )






def 						_snake_case       (	lowercase__		:  str      ,     lowercase__		:  ResNetConfig      ,     lowercase__		:  Path      ,     lowercase__		:  bool = True		)  ->				Union[str, Any]:


			'''simple docstring'''





			print(f"""Converting {name}..."""		)
			with torch.no_grad():
						lowerCAmelCase_    :int			     =  timm.create_model(lowercase__      ,     pretrained=lowercase__		).eval()
						lowerCAmelCase_    :Tuple			     =  ResNetForImageClassification(lowercase__		).eval()
						lowerCAmelCase_    :List[Any]			     =  ModuleTransfer(src=lowercase__      ,     dest=lowercase__		)
						lowerCAmelCase_    :str			     =  torch.randn((1, 3, 2_2_4, 2_2_4)		)
						module_transfer(lowercase__		)

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

			lowerCAmelCase_    :Optional[Any]			     =  f"""resnet{"-".join(name.split("resnet"		)		)}"""
			print(lowercase__		)

			if push_to_hub:
						our_model.push_to_hub(
						    repo_path_or_name=save_directory / checkpoint_name      ,     commit_message="""Add model"""      ,     use_temp_dir=lowercase__      ,     )

						# we can use the convnext one
						lowerCAmelCase_    :Optional[int]			     =  AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""		)
						image_processor.push_to_hub(
						    repo_path_or_name=save_directory / checkpoint_name      ,     commit_message="""Add image processor"""      ,     use_temp_dir=lowercase__      ,     )

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






def 						_snake_case       (	lowercase__		:  Path      ,     lowercase__		:  str = None      ,     lowercase__		:  bool = True		)  ->				List[Any]:


			'''simple docstring'''





			lowerCAmelCase_    :Union[str, Any]			     =  """imagenet-1k-id2label.json"""
			lowerCAmelCase_    :Optional[Any]			     =  1_0_0_0
			lowerCAmelCase_    :Union[str, Any]			     =  (1, num_labels)

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

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

			lowerCAmelCase_    :Any			     =  partial(lowercase__      ,     num_labels=lowercase__      ,     idalabel=lowercase__      ,     labelaid=lowercase__		)

			lowerCAmelCase_    :str			     =  {
			    """resnet18""": ImageNetPreTrainedConfig(
			        depths=[2, 2, 2, 2]      ,     hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]      ,     layer_type="""basic"""		),
			    """resnet26""": ImageNetPreTrainedConfig(
			        depths=[2, 2, 2, 2]      ,     hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]      ,     layer_type="""bottleneck"""		),
			    """resnet34""": ImageNetPreTrainedConfig(
			        depths=[3, 4, 6, 3]      ,     hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]      ,     layer_type="""basic"""		),
			    """resnet50""": ImageNetPreTrainedConfig(
			        depths=[3, 4, 6, 3]      ,     hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]      ,     layer_type="""bottleneck"""		),
			    """resnet101""": ImageNetPreTrainedConfig(
			        depths=[3, 4, 2_3, 3]      ,     hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]      ,     layer_type="""bottleneck"""		),
			    """resnet152""": ImageNetPreTrainedConfig(
			        depths=[3, 8, 3_6, 3]      ,     hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]      ,     layer_type="""bottleneck"""		),
			}

			if model_name:
						convert_weight_and_push(lowercase__      ,     names_to_config[model_name]      ,     lowercase__      ,     lowercase__		)
			else:
						for model_name, config in names_to_config.items():
									convert_weight_and_push(lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__		)
			return config, expected_shape


if __name__ == "__main__":
				__UpperCAmelCase       =  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 resnet* architecture,'
				        ' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
				    ),
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default=None,
				    type=Path,
				    required=True,
				    help='Path to the output PyTorch model directory.',
				)
				parser.add_argument(
				    '--push_to_hub',
				    default=True,
				    type=bool,
				    required=False,
				    help='If True, push model and image processor to the hub.',
				)

				__UpperCAmelCase       =  parser.parse_args()
				__UpperCAmelCase       =  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'''




__SCREAMING_SNAKE_CASE     :List[str]       =				'''0.18.2'''

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
  if not is_onnx_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_onnx_objects import *  # noqa F403
else:
  from .pipelines import OnnxRuntimeModel

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_pt_objects import *  # noqa F403
else:
  from .models import (
      AutoencoderKL,
      ControlNetModel,
      ModelMixin,
      PriorTransformer,
      TaFilmDecoder,
      TransformeraDModel,
      UNetaDModel,
      UNetaDConditionModel,
      UNetaDModel,
      UNetaDConditionModel,
      VQModel,
  )
  from .optimization import (
      get_constant_schedule,
      get_constant_schedule_with_warmup,
      get_cosine_schedule_with_warmup,
      get_cosine_with_hard_restarts_schedule_with_warmup,
      get_linear_schedule_with_warmup,
      get_polynomial_decay_schedule_with_warmup,
      get_scheduler,
  )
  from .pipelines import (
      AudioPipelineOutput,
      ConsistencyModelPipeline,
      DanceDiffusionPipeline,
      DDIMPipeline,
      DDPMPipeline,
      DiffusionPipeline,
      DiTPipeline,
      ImagePipelineOutput,
      KarrasVePipeline,
      LDMPipeline,
      LDMSuperResolutionPipeline,
      PNDMPipeline,
      RePaintPipeline,
      ScoreSdeVePipeline,
  )
  from .schedulers import (
      CMStochasticIterativeScheduler,
      DDIMInverseScheduler,
      DDIMParallelScheduler,
      DDIMScheduler,
      DDPMParallelScheduler,
      DDPMScheduler,
      DEISMultistepScheduler,
      DPMSolverMultistepInverseScheduler,
      DPMSolverMultistepScheduler,
      DPMSolverSinglestepScheduler,
      EulerAncestralDiscreteScheduler,
      EulerDiscreteScheduler,
      HeunDiscreteScheduler,
      IPNDMScheduler,
      KarrasVeScheduler,
      KDPMaAncestralDiscreteScheduler,
      KDPMaDiscreteScheduler,
      PNDMScheduler,
      RePaintScheduler,
      SchedulerMixin,
      ScoreSdeVeScheduler,
      UnCLIPScheduler,
      UniPCMultistepScheduler,
      VQDiffusionScheduler,
  )
  from .training_utils import EMAModel

try:
  if not (is_torch_available() and is_scipy_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
  from .schedulers import LMSDiscreteScheduler

try:
  if not (is_torch_available() and is_torchsde_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
  from .schedulers import DPMSolverSDEScheduler

try:
  if not (is_torch_available() and is_transformers_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
  from .pipelines import (
      AltDiffusionImgaImgPipeline,
      AltDiffusionPipeline,
      AudioLDMPipeline,
      CycleDiffusionPipeline,
      IFImgaImgPipeline,
      IFImgaImgSuperResolutionPipeline,
      IFInpaintingPipeline,
      IFInpaintingSuperResolutionPipeline,
      IFPipeline,
      IFSuperResolutionPipeline,
      ImageTextPipelineOutput,
      KandinskyImgaImgPipeline,
      KandinskyInpaintPipeline,
      KandinskyPipeline,
      KandinskyPriorPipeline,
      KandinskyVaaControlnetImgaImgPipeline,
      KandinskyVaaControlnetPipeline,
      KandinskyVaaImgaImgPipeline,
      KandinskyVaaInpaintPipeline,
      KandinskyVaaPipeline,
      KandinskyVaaPriorEmbaEmbPipeline,
      KandinskyVaaPriorPipeline,
      LDMTextToImagePipeline,
      PaintByExamplePipeline,
      SemanticStableDiffusionPipeline,
      ShapEImgaImgPipeline,
      ShapEPipeline,
      StableDiffusionAttendAndExcitePipeline,
      StableDiffusionControlNetImgaImgPipeline,
      StableDiffusionControlNetInpaintPipeline,
      StableDiffusionControlNetPipeline,
      StableDiffusionDepthaImgPipeline,
      StableDiffusionDiffEditPipeline,
      StableDiffusionImageVariationPipeline,
      StableDiffusionImgaImgPipeline,
      StableDiffusionInpaintPipeline,
      StableDiffusionInpaintPipelineLegacy,
      StableDiffusionInstructPixaPixPipeline,
      StableDiffusionLatentUpscalePipeline,
      StableDiffusionLDMaDPipeline,
      StableDiffusionModelEditingPipeline,
      StableDiffusionPanoramaPipeline,
      StableDiffusionParadigmsPipeline,
      StableDiffusionPipeline,
      StableDiffusionPipelineSafe,
      StableDiffusionPixaPixZeroPipeline,
      StableDiffusionSAGPipeline,
      StableDiffusionUpscalePipeline,
      StableUnCLIPImgaImgPipeline,
      StableUnCLIPPipeline,
      TextToVideoSDPipeline,
      TextToVideoZeroPipeline,
      UnCLIPImageVariationPipeline,
      UnCLIPPipeline,
      UniDiffuserModel,
      UniDiffuserPipeline,
      UniDiffuserTextDecoder,
      VersatileDiffusionDualGuidedPipeline,
      VersatileDiffusionImageVariationPipeline,
      VersatileDiffusionPipeline,
      VersatileDiffusionTextToImagePipeline,
      VideoToVideoSDPipeline,
      VQDiffusionPipeline,
  )

try:
  if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
  from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
  if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
  from .pipelines import StableDiffusionKDiffusionPipeline

try:
  if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
  from .pipelines import (
      OnnxStableDiffusionImgaImgPipeline,
      OnnxStableDiffusionInpaintPipeline,
      OnnxStableDiffusionInpaintPipelineLegacy,
      OnnxStableDiffusionPipeline,
      OnnxStableDiffusionUpscalePipeline,
      StableDiffusionOnnxPipeline,
  )

try:
  if not (is_torch_available() and is_librosa_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
  from .pipelines import AudioDiffusionPipeline, Mel

try:
  if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
  from .pipelines import SpectrogramDiffusionPipeline

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_flax_objects import *  # noqa F403
else:
  from .models.controlnet_flax import FlaxControlNetModel
  from .models.modeling_flax_utils import FlaxModelMixin
  from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
  from .models.vae_flax import FlaxAutoencoderKL
  from .pipelines import FlaxDiffusionPipeline
  from .schedulers import (
      FlaxDDIMScheduler,
      FlaxDDPMScheduler,
      FlaxDPMSolverMultistepScheduler,
      FlaxKarrasVeScheduler,
      FlaxLMSDiscreteScheduler,
      FlaxPNDMScheduler,
      FlaxSchedulerMixin,
      FlaxScoreSdeVeScheduler,
  )


try:
  if not (is_flax_available() and is_transformers_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
  from .pipelines import (
      FlaxStableDiffusionControlNetPipeline,
      FlaxStableDiffusionImgaImgPipeline,
      FlaxStableDiffusionInpaintPipeline,
      FlaxStableDiffusionPipeline,
  )

try:
  if not (is_note_seq_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_note_seq_objects import *  # noqa F403
else:
  from .pipelines import MidiProcessor




