


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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


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







def 							UpperCAmelCase				(     UpperCAmelCase      )->		Any:







     '''simple docstring'''



     SCREAMING_SNAKE_CASE_	      =       MobileNetVaConfig(layer_norm_eps=0.0_0_1      )

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

     SCREAMING_SNAKE_CASE_	      =       re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''				,UpperCAmelCase      )
     if matches:
          SCREAMING_SNAKE_CASE_	      =       float(matches[1]      )
          SCREAMING_SNAKE_CASE_	      =       int(matches[2]      )

     if model_name.startswith('''deeplabv3_'''      ):
          SCREAMING_SNAKE_CASE_	      =       8
          SCREAMING_SNAKE_CASE_	      =       21
          SCREAMING_SNAKE_CASE_	      =       '''pascal-voc-id2label.json'''
     else:
          # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
          # of the usual 1000. The first class (index 0) is "background".
          SCREAMING_SNAKE_CASE_	      =       1001
          SCREAMING_SNAKE_CASE_	      =       '''imagenet-1k-id2label.json'''

     SCREAMING_SNAKE_CASE_	      =       '''huggingface/label-files'''
     SCREAMING_SNAKE_CASE_	      =       json.load(open(hf_hub_download(UpperCAmelCase				,UpperCAmelCase				,repo_type='''dataset'''      )				,'''r'''      )      )

     if config.num_labels == 1001:
          SCREAMING_SNAKE_CASE_	      =       {int(UpperCAmelCase      ) + 1: v for k, v in idalabel.items()}
          SCREAMING_SNAKE_CASE_	      =       '''background'''
     else:
          SCREAMING_SNAKE_CASE_	      =       {int(UpperCAmelCase      ): v for k, v in idalabel.items()}

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

     return config







def 							UpperCAmelCase				(     )->		List[str]:







     '''simple docstring'''



     SCREAMING_SNAKE_CASE_	      =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     SCREAMING_SNAKE_CASE_	      =       Image.open(requests.get(UpperCAmelCase				,stream=UpperCAmelCase      ).raw      )
     return im







@torch.no_grad()
def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase=False      )->		Tuple:







     '''simple docstring'''



     SCREAMING_SNAKE_CASE_	      =       get_mobilenet_va_config(UpperCAmelCase      )

     # Load 🤗 model
     if model_name.startswith('''deeplabv3_'''      ):
          SCREAMING_SNAKE_CASE_	      =       MobileNetVaForSemanticSegmentation(UpperCAmelCase      ).eval()
     else:
          SCREAMING_SNAKE_CASE_	      =       MobileNetVaForImageClassification(UpperCAmelCase      ).eval()

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

     # Check outputs on an image, prepared by MobileNetV2ImageProcessor
     SCREAMING_SNAKE_CASE_	      =       MobileNetVaImageProcessor(
         crop_size={'''width''': config.image_size, '''height''': config.image_size}				,size={'''shortest_edge''': config.image_size + 32}				,)
     SCREAMING_SNAKE_CASE_	      =       image_processor(images=prepare_img()				,return_tensors='''pt'''      )
     SCREAMING_SNAKE_CASE_	      =       model(**UpperCAmelCase      )
     SCREAMING_SNAKE_CASE_	      =       outputs.logits

     if model_name.startswith('''deeplabv3_'''      ):
          assert logits.shape == (1, 21, 65, 65)

          if model_name == "deeplabv3_mobilenet_v2_1.0_513":
               SCREAMING_SNAKE_CASE_	      =       torch.tensor(
                   [
                       [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
                       [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
                       [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
                   ]      )

          else:
               raise ValueError(f'''Unknown model name: {model_name}'''      )

          assert torch.allclose(logits[0, :3, :3, :3]				,UpperCAmelCase				,atol=1E-4      )
     else:
          assert logits.shape == (1, 1001)

          if model_name == "mobilenet_v2_1.4_224":
               SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8]      )
          elif model_name == "mobilenet_v2_1.0_224":
               SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]      )
          elif model_name == "mobilenet_v2_0.75_160":
               SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9]      )
          elif model_name == "mobilenet_v2_0.35_96":
               SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]      )
          else:
               SCREAMING_SNAKE_CASE_	      =       None

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

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

     if push_to_hub:
          print('''Pushing to the hub...'''      )
          SCREAMING_SNAKE_CASE_	      =       '''google/''' + model_name
          image_processor.push_to_hub(UpperCAmelCase      )
          model.push_to_hub(UpperCAmelCase      )


if __name__ == "__main__":
  A_														=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="mobilenet_v2_1.0_224",
      type=str,
      help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<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_														=      parser.parse_args()
  convert_movilevit_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )






import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()

class 						snake_case			(       unittest.TestCase	):
			'''simple docstring'''

			UpperCAmelCase       :       Union[str, Any]       = """hf-internal-testing/tiny-stable-diffusion-pipe"""
			UpperCAmelCase       :       int       = 1
			UpperCAmelCase       :       Optional[Any]       = 77
			UpperCAmelCase       :       Any       = 32
			UpperCAmelCase       :       str       = 4
			UpperCAmelCase       :       Optional[int]       = 64
			UpperCAmelCase       :       Tuple       = torch.manual_seed(0	)

			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     str=0.9_999					)					->					int:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       UNetaDConditionModel.from_pretrained(self.model_id							,							subfolder='''unet'''					)
								SCREAMING_SNAKE_CASE_	      =       unet.to(lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       EMAModel(unet.parameters()							,							decay=lowerCAmelCase_							,							model_cls=lowerCAmelCase_							,							model_config=unet.config					)
								return unet, ema_unet

			def _lowercase (	self     :     str					)					->					List[str]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       torch.randn(
								    self.batch_size							,							self.num_in_channels							,							self.latent_height							,							self.latent_width							,							generator=self.generator					).to(lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       torch.randint(0							,							1_000							,							size=(self.batch_size,)							,							generator=self.generator					).to(lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       torch.randn(
								    self.batch_size							,							self.prompt_length							,							self.text_encoder_hidden_dim							,							generator=self.generator					).to(lowerCAmelCase_					)
								return noisy_latents, timesteps, encoder_hidden_states

			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     Tuple					)					->					str:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       {}
								for k, param in unet.state_dict().items():
													SCREAMING_SNAKE_CASE_	      =       torch.randn_like(lowerCAmelCase_					) + (param * torch.randn_like(lowerCAmelCase_					))
													updated_state_dict.update({k: updated_param}					)
								unet.load_state_dict(lowerCAmelCase_					)
								return unet

			def _lowercase (	self     :     Dict					)					->					str:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models()
								# Take the first (hypothetical) EMA step.
								ema_unet.step(unet.parameters()					)
								assert ema_unet.optimization_step == 1

								# Take two more.
								for _ in range(2					):
													ema_unet.step(unet.parameters()					)
								assert ema_unet.optimization_step == 3

			def _lowercase (	self     :     Union[str, Any]					)					->					List[str]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models()
								# Since the `unet` is not being updated (i.e., backprop'd)
								# there won't be any difference between the `params` of `unet`
								# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
								ema_unet.step(unet.parameters()					)
								SCREAMING_SNAKE_CASE_	      =       list(unet.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params							,							lowerCAmelCase_					):
													assert torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)

								# The above holds true even if we call `ema.step()` multiple times since
								# `unet` params are still not being updated.
								for _ in range(4					):
													ema_unet.step(unet.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params							,							lowerCAmelCase_					):
													assert torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)

			def _lowercase (	self     :     Any					)					->					Union[str, Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models()
								# Here we simulate the parameter updates for `unet`. Since there might
								# be some parameters which are initialized to zero we take extra care to
								# initialize their values to something non-zero before the multiplication.
								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)

								# Take the EMA step.
								ema_unet.step(unet_pseudo_updated_step_one.parameters()					)

								# Now the EMA'd parameters won't be equal to the original model parameters.
								SCREAMING_SNAKE_CASE_	      =       list(unet_pseudo_updated_step_one.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params							,							lowerCAmelCase_					):
													assert ~torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)

								# Ensure this is the case when we take multiple EMA steps.
								for _ in range(4					):
													ema_unet.step(unet.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params							,							lowerCAmelCase_					):
													assert ~torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)

			def _lowercase (	self     :     Any					)					->					Tuple:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models()

								# First backprop + EMA
								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
								ema_unet.step(unet_step_one.parameters()					)
								SCREAMING_SNAKE_CASE_	      =       ema_unet.shadow_params

								# Second backprop + EMA
								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
								ema_unet.step(unet_step_two.parameters()					)
								SCREAMING_SNAKE_CASE_	      =       ema_unet.shadow_params

								for step_one, step_two in zip(lowerCAmelCase_							,							lowerCAmelCase_					):
													assert ~torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)

			def _lowercase (	self     :     str					)					->					Optional[Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models(decay=0.0					)
								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
								ema_unet.step(unet_step_one.parameters()					)
								SCREAMING_SNAKE_CASE_	      =       ema_unet.shadow_params

								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
								ema_unet.step(unet_step_two.parameters()					)
								SCREAMING_SNAKE_CASE_	      =       ema_unet.shadow_params

								for step_one, step_two in zip(lowerCAmelCase_							,							lowerCAmelCase_					):
													assert torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)




			@skip_mps
			def _lowercase (	self     :     str					)					->					List[Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models()
								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_dummy_inputs()

								with tempfile.TemporaryDirectory() as tmpdir:
													ema_unet.save_pretrained(lowerCAmelCase_					)
													SCREAMING_SNAKE_CASE_	      =       UNetaDConditionModel.from_pretrained(lowerCAmelCase_							,							model_cls=lowerCAmelCase_					)
													SCREAMING_SNAKE_CASE_	      =       loaded_unet.to(unet.device					)

								# Since no EMA step has been performed the outputs should match.
								SCREAMING_SNAKE_CASE_	      =       unet(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					).sample
								SCREAMING_SNAKE_CASE_	      =       loaded_unet(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					).sample

								assert torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_							,							atol=1e-4					)


