


'''simple docstring'''






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()
__A :  Any    							=				logging.get_logger(__name__)







def     UpperCamelCase_      (   A__ :       Any ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[int]      =       MobileNetVaConfig(layer_norm_eps=0.001 )

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

  lowerCAmelCase_      :				List[Any]      =       re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""							,   A__ )
  if matches:
    lowerCAmelCase_      :				List[str]      =       float(matches[1] )
    lowerCAmelCase_      :				List[str]      =       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      =       10_01
  lowerCAmelCase_      :				Optional[Any]      =       """imagenet-1k-id2label.json"""
  lowerCAmelCase_      :				Optional[int]      =       """huggingface/label-files"""
  lowerCAmelCase_      :				Tuple      =       json.load(open(hf_hub_download(A__							,   A__							,   repo_type="""dataset""" )							,   """r""" ) )
  lowerCAmelCase_      :				int      =       {int(A__ ) + 1: v for k, v in idalabel.items()}
  lowerCAmelCase_      :				str      =       """background"""
  lowerCAmelCase_      :				Optional[int]      =       idalabel
  lowerCAmelCase_      :				int      =       {v: k for k, v in idalabel.items()}

  return config







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				str      =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowerCAmelCase_      :				str      =       Image.open(requests.get(A__							,   stream=A__ ).raw )
  return im







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Optional[Any]							,   A__ :       List[str]							,   A__ :       Optional[Any]							,   A__ :       Dict=False ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[int]      =       get_mobilenet_va_config(A__ )

  # Load 🤗 model
  lowerCAmelCase_      :				Optional[int]      =       MobileNetVaForImageClassification(A__ ).eval()

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

  # Check outputs on an image, prepared by MobileNetV1ImageProcessor
  lowerCAmelCase_      :				Dict      =       MobileNetVaImageProcessor(
      crop_size={"""width""": config.image_size, """height""": config.image_size}							,   size={"""shortest_edge""": config.image_size + 32}							,   )
  lowerCAmelCase_      :				List[str]      =       image_processor(images=prepare_img()							,   return_tensors="""pt""" )
  lowerCAmelCase_      :				Tuple      =       model(**A__ )
  lowerCAmelCase_      :				Union[str, Any]      =       outputs.logits

  assert logits.shape == (1, 10_01)

  if model_name == "mobilenet_v1_1.0_224":
    lowerCAmelCase_      :				Any      =       torch.tensor([-4.1739, -1.1233, 3.1205] )
  elif model_name == "mobilenet_v1_0.75_192":
    lowerCAmelCase_      :				str      =       torch.tensor([-3.9440, -2.3141, -0.3333] )
  else:
    lowerCAmelCase_      :				Optional[int]      =       None

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

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

  if push_to_hub:
    print("""Pushing to the hub...""" )
    lowerCAmelCase_      :				List[str]      =       """google/""" + model_name
    image_processor.push_to_hub(A__ )
    model.push_to_hub(A__ )


if __name__ == "__main__":
  __A :  Any    							=				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."
  )

  __A :  Tuple    							=				parser.parse_args()
  convert_movilevit_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )







'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    @property
    def __lowercase       (       self   :  str		)       ->  List[Any]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Optional[Any]      =       UNetaDModel(
          block_out_channels=(32, 64)   ,						layers_per_block=2   ,						sample_size=32   ,						in_channels=3   ,						out_channels=3   ,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")   ,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")   ,						)
      return model





    def __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Optional[int]      =       self.dummy_uncond_unet
      lowerCAmelCase_      :				Tuple      =       PNDMScheduler()

      lowerCAmelCase_      :				List[Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Dict      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				List[Any]      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				str      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				int      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""   ,						return_dict=lowerCamelCase		)[0]

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]
      lowerCAmelCase_      :				Optional[Any]      =       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				int      =       np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2







@slow
@require_torch
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  str		)       ->  Tuple:
      lowerCAmelCase_      :				str      =       """google/ddpm-cifar10-32"""

      lowerCAmelCase_      :				Dict      =       UNetaDModel.from_pretrained(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       PNDMScheduler()

      lowerCAmelCase_      :				Union[str, Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				Union[str, Any]      =       pndm(generator=lowerCamelCase   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				List[Any]      =       np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2



