

import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase 			=		logging.get_logger(__name__)







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Optional[int]					=					MobileNetVaConfig(layer_norm_eps=0.0_0_1						)

      if "_quant" in model_name:
            raise ValueError('''Quantized models are not supported.'''						)

      __lowerCAmelCase					:						List[Any]					=					re.match(r'''^mobilenet_v1_([^_]*)_([^_]*)$'''   ,	__lowercase						)
      if matches:
            __lowerCAmelCase					:						Union[str, Any]					=					float(matches[1]						)
            __lowerCAmelCase					:						Dict					=					int(matches[2]						)

      # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
      # the usual 1000. The first class (index 0) is "background".
      __lowerCAmelCase					:						int					=					1_0_0_1
      __lowerCAmelCase					:						Dict					=					'''imagenet-1k-id2label.json'''
      __lowerCAmelCase					:						List[str]					=					'''huggingface/label-files'''
      __lowerCAmelCase					:						Any					=					json.load(open(hf_hub_download(__lowercase   ,	__lowercase   ,	repo_type='''dataset'''						)   ,	'''r'''						)						)
      __lowerCAmelCase					:						Optional[int]					=					{int(__lowercase						) + 1: v for k, v in idalabel.items()}
      __lowerCAmelCase					:						Optional[int]					=					'''background'''
      __lowerCAmelCase					:						List[Any]					=					idalabel
      __lowerCAmelCase					:						Optional[Any]					=					{v: k for k, v in idalabel.items()}

      return config







def 		_lowercase (     ):
      __lowerCAmelCase					:						Optional[Any]					=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __lowerCAmelCase					:						Tuple					=					Image.open(requests.get(__lowercase   ,	stream=__lowercase						).raw						)
      return im







@torch.no_grad()
def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__=False						):
      __lowerCAmelCase					:						Union[str, Any]					=					get_mobilenet_va_config(__lowercase						)

      # Load 🤗 model
      __lowerCAmelCase					:						int					=					MobileNetVaForImageClassification(__lowercase						).eval()

      # Load weights from TensorFlow checkpoint
      load_tf_weights_in_mobilenet_va(__lowercase   ,	__lowercase   ,	__lowercase						)

      # Check outputs on an image, prepared by MobileNetV1ImageProcessor
      __lowerCAmelCase					:						Tuple					=					MobileNetVaImageProcessor(
          crop_size={'''width''': config.image_size, '''height''': config.image_size}   ,	size={'''shortest_edge''': config.image_size + 3_2}   ,	)
      __lowerCAmelCase					:						Any					=					image_processor(images=prepare_img()   ,	return_tensors='''pt'''						)
      __lowerCAmelCase					:						Optional[Any]					=					model(**__lowercase						)
      __lowerCAmelCase					:						Union[str, Any]					=					outputs.logits

      assert logits.shape == (1, 1_0_0_1)

      if model_name == "mobilenet_v1_1.0_224":
            __lowerCAmelCase					:						List[Any]					=					torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]						)
      elif model_name == "mobilenet_v1_0.75_192":
            __lowerCAmelCase					:						Optional[int]					=					torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]						)
      else:
            __lowerCAmelCase					:						Union[str, Any]					=					None

      if expected_logits is not None:
            assert torch.allclose(logits[0, :3]   ,	__lowercase   ,	atol=1E-4						)

      Path(__lowercase						).mkdir(exist_ok=__lowercase						)
      print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""						)
      model.save_pretrained(__lowercase						)
      print(f"""Saving image processor to {pytorch_dump_folder_path}"""						)
      image_processor.save_pretrained(__lowercase						)

      if push_to_hub:
            print('''Pushing to the hub...'''						)
            __lowerCAmelCase					:						int					=					'''google/''' + model_name
            image_processor.push_to_hub(__lowercase						)
            model.push_to_hub(__lowercase						)


if __name__ == "__main__":
 _UpperCamelCase 			=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="mobilenet_v1_1.0_224",
     type=str,
     help="Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.",
 )
 parser.add_argument(
     "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
 )
 parser.add_argument(
     "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
 )

 _UpperCamelCase 			=		parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )









'''simple docstring'''




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ..utils.dummy_pt_objects import *  # noqa F403
else:
  from .scheduling_consistency_models import CMStochasticIterativeScheduler
  from .scheduling_ddim import DDIMScheduler
  from .scheduling_ddim_inverse import DDIMInverseScheduler
  from .scheduling_ddim_parallel import DDIMParallelScheduler
  from .scheduling_ddpm import DDPMScheduler
  from .scheduling_ddpm_parallel import DDPMParallelScheduler
  from .scheduling_deis_multistep import DEISMultistepScheduler
  from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
  from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
  from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
  from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
  from .scheduling_euler_discrete import EulerDiscreteScheduler
  from .scheduling_heun_discrete import HeunDiscreteScheduler
  from .scheduling_ipndm import IPNDMScheduler
  from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
  from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
  from .scheduling_karras_ve import KarrasVeScheduler
  from .scheduling_pndm import PNDMScheduler
  from .scheduling_repaint import RePaintScheduler
  from .scheduling_sde_ve import ScoreSdeVeScheduler
  from .scheduling_sde_vp import ScoreSdeVpScheduler
  from .scheduling_unclip import UnCLIPScheduler
  from .scheduling_unipc_multistep import UniPCMultistepScheduler
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
  from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ..utils.dummy_flax_objects import *  # noqa F403
else:
  from .scheduling_ddim_flax import FlaxDDIMScheduler
  from .scheduling_ddpm_flax import FlaxDDPMScheduler
  from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
  from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
  from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
  from .scheduling_pndm_flax import FlaxPNDMScheduler
  from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
  from .scheduling_utils_flax import (
      FlaxKarrasDiffusionSchedulers,
      FlaxSchedulerMixin,
      FlaxSchedulerOutput,
      broadcast_to_shape_from_left,
  )


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 .scheduling_lms_discrete 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 .scheduling_dpmsolver_sde import DPMSolverSDEScheduler




