





import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[Any]			,							snake_case__  :List[Any]     )      ->						Optional[int]:
  _lowercase							= old_name

  if "patch_embed" in old_name:
    _lowercase						,		_lowercase						,		_lowercase							= old_name.split('.'     )

    if layer == "0":
      _lowercase							= old_name.replace('0'			,							'convolution1'     )
    elif layer == "1":
      _lowercase							= old_name.replace('1'			,							'batchnorm_before'     )
    elif layer == "3":
      _lowercase							= old_name.replace('3'			,							'convolution2'     )
    else:
      _lowercase							= old_name.replace('4'			,							'batchnorm_after'     )

  if "network" in old_name and re.search(R'\d\.\d'			,							_SCREAMING_SNAKE_CASE     ):
    _lowercase							= R'\b\d{2}\b'
    if bool(re.search(_SCREAMING_SNAKE_CASE			,							_SCREAMING_SNAKE_CASE     )     ):
      _lowercase							= re.search(R'\d\.\d\d.'			,							_SCREAMING_SNAKE_CASE     ).group()
    else:
      _lowercase							= re.search(R'\d\.\d.'			,							_SCREAMING_SNAKE_CASE     ).group()
    if int(match[0]     ) < 6:
      _lowercase							= old_name.replace(_SCREAMING_SNAKE_CASE			,							''     )
      _lowercase							= trimmed_name.replace('network'			,							match[0] + '.meta4D_layers.blocks.' + match[2:-1]     )
      _lowercase							= 'intermediate_stages.' + trimmed_name
    else:
      _lowercase							= old_name.replace(_SCREAMING_SNAKE_CASE			,							''     )
      if int(match[2]     ) < num_meta4D_last_stage:
        _lowercase							= trimmed_name.replace('network'			,							'meta4D_layers.blocks.' + match[2]     )
      else:
        _lowercase							= str(int(match[2]     ) - num_meta4D_last_stage     )
        _lowercase							= trimmed_name.replace('network'			,							'meta3D_layers.blocks.' + layer_index     )
        if "norm1" in old_name:
          _lowercase							= trimmed_name.replace('norm1'			,							'layernorm1'     )
        elif "norm2" in old_name:
          _lowercase							= trimmed_name.replace('norm2'			,							'layernorm2'     )
        elif "fc1" in old_name:
          _lowercase							= trimmed_name.replace('fc1'			,							'linear_in'     )
        elif "fc2" in old_name:
          _lowercase							= trimmed_name.replace('fc2'			,							'linear_out'     )

      _lowercase							= 'last_stage.' + trimmed_name

  elif "network" in old_name and re.search(R'.\d.'			,							_SCREAMING_SNAKE_CASE     ):
    _lowercase							= old_name.replace('network'			,							'intermediate_stages'     )

  if "fc" in new_name:
    _lowercase							= new_name.replace('fc'			,							'convolution'     )
  elif ("norm1" in new_name) and ("layernorm1" not in new_name):
    _lowercase							= new_name.replace('norm1'			,							'batchnorm_before'     )
  elif ("norm2" in new_name) and ("layernorm2" not in new_name):
    _lowercase							= new_name.replace('norm2'			,							'batchnorm_after'     )
  if "proj" in new_name:
    _lowercase							= new_name.replace('proj'			,							'projection'     )
  if "dist_head" in new_name:
    _lowercase							= new_name.replace('dist_head'			,							'distillation_classifier'     )
  elif "head" in new_name:
    _lowercase							= new_name.replace('head'			,							'classifier'     )
  elif "patch_embed" in new_name:
    _lowercase							= 'efficientformer.' + new_name
  elif new_name == "norm.weight" or new_name == "norm.bias":
    _lowercase							= new_name.replace('norm'			,							'layernorm'     )
    _lowercase							= 'efficientformer.' + new_name
  else:
    _lowercase							= 'efficientformer.encoder.' + new_name

  return new_name

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[Any]			,							snake_case__  :Union[str, Any]     )      ->						List[str]:



  for key in checkpoint.copy().keys():
    _lowercase							= checkpoint.pop(_SCREAMING_SNAKE_CASE     )
    _lowercase							= val

  return checkpoint

def 							SCREAMING_SNAKE_CASE__       (  )      ->						str:
  _lowercase							= 'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase							= Image.open(requests.get(_SCREAMING_SNAKE_CASE			,							stream=_SCREAMING_SNAKE_CASE     ).raw     )

  return image

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Path			,							snake_case__  :Path			,							snake_case__  :Path			,							snake_case__  :bool     )      ->						Optional[int]:
  _lowercase							= torch.load(_SCREAMING_SNAKE_CASE			,							map_location='cpu'     )['model']
  _lowercase							= EfficientFormerConfig.from_json_file(_SCREAMING_SNAKE_CASE     )
  _lowercase							= EfficientFormerForImageClassificationWithTeacher(_SCREAMING_SNAKE_CASE     )
  _lowercase							= '_'.join(checkpoint_path.split('/'     )[-1].split('.'     )[0].split('_'     )[:-1]     )

  _lowercase							= config.depths[-1] - config.num_metaad_blocks + 1
  _lowercase							= convert_torch_checkpoint(_SCREAMING_SNAKE_CASE			,							_SCREAMING_SNAKE_CASE     )

  model.load_state_dict(_SCREAMING_SNAKE_CASE     )
  model.eval()

  _lowercase							= {
      'bilinear': PILImageResampling.BILINEAR,
      'bicubic': PILImageResampling.BICUBIC,
      'nearest': PILImageResampling.NEAREST,
  }

  # prepare image
  _lowercase							= prepare_img()
  _lowercase							= 256
  _lowercase							= 224
  _lowercase							= EfficientFormerImageProcessor(
      size={'shortest_edge': image_size}			,							crop_size={'height': crop_size, 'width': crop_size}			,							resample=pillow_resamplings['bicubic']			,							)
  _lowercase							= processor(images=_SCREAMING_SNAKE_CASE			,							return_tensors='pt'     ).pixel_values

  # original processing pipeline
  _lowercase							= Compose(
      [
          Resize(_SCREAMING_SNAKE_CASE			,							interpolation=pillow_resamplings['bicubic']     ),
          CenterCrop(_SCREAMING_SNAKE_CASE     ),
          ToTensor(),
          Normalize(_SCREAMING_SNAKE_CASE			,							_SCREAMING_SNAKE_CASE     ),
      ]     )
  _lowercase							= image_transforms(_SCREAMING_SNAKE_CASE     ).unsqueeze(0     )

  assert torch.allclose(_SCREAMING_SNAKE_CASE			,							_SCREAMING_SNAKE_CASE     )

  _lowercase							= model(_SCREAMING_SNAKE_CASE     )
  _lowercase							= outputs.logits

  _lowercase							= (1, 1000)

  if "l1" in model_name:
    _lowercase							= torch.Tensor(
        [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]     )
    assert torch.allclose(logits[0, :10]			,							_SCREAMING_SNAKE_CASE			,							atol=1E-3     )
    assert logits.shape == expected_shape
  elif "l3" in model_name:
    _lowercase							= torch.Tensor(
        [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]     )
    assert torch.allclose(logits[0, :10]			,							_SCREAMING_SNAKE_CASE			,							atol=1E-3     )
    assert logits.shape == expected_shape
  elif "l7" in model_name:
    _lowercase							= torch.Tensor(
        [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]     )
    assert logits.shape == expected_shape
  else:
    raise ValueError(
        F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""     )

  # Save Checkpoints
  Path(_SCREAMING_SNAKE_CASE     ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE     )
  model.save_pretrained(_SCREAMING_SNAKE_CASE     )
  print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""     )
  processor.save_pretrained(_SCREAMING_SNAKE_CASE     )
  print(F"""Processor successfuly saved at {pytorch_dump_path}"""     )

  if push_to_hub:
    print('Pushing model to the hub...'     )

    model.push_to_hub(
        repo_id=F"""Bearnardd/{pytorch_dump_path}"""			,							commit_message='Add model'			,							use_temp_dir=_SCREAMING_SNAKE_CASE			,							)
    processor.push_to_hub(
        repo_id=F"""Bearnardd/{pytorch_dump_path}"""			,							commit_message='Add image processor'			,							use_temp_dir=_SCREAMING_SNAKE_CASE			,							)


if __name__ == "__main__":
      snake_case 				=       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--pytorch_model_path""",
          default=None,
          type=str,
          required=True,
          help="""Path to EfficientFormer pytorch checkpoint.""",
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help="""The json file for EfficientFormer model config.""",
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )

      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""",
      )
      parser.set_defaults(push_to_hub=True)

      snake_case 				=       parser.parse_args()
      convert_efficientformer_checkpoint(
          checkpoint_path=args.pytorch_model_path,
          efficientformer_config_file=args.config_file,
          pytorch_dump_path=args.pytorch_dump_path,
          push_to_hub=args.push_to_hub,
      )








snake_case 				=       """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