

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

def 			a__     (		UpperCAmelCase							:			Optional[Any]			)  ->						List[Any]:
  UpperCAmelCase       :							Dict  = MobileNetVaConfig(layer_norm_eps=0.001			)

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

  UpperCAmelCase       :							Dict  = re.match(r'''^mobilenet_v1_([^_]*)_([^_]*)$'''				, __a			)
  if matches:
    UpperCAmelCase       :							Optional[int]  = float(matches[1]			)
    UpperCAmelCase       :							str  = int(matches[2]			)

  # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
  # the usual 1000. The first class (index 0) is "background".
  UpperCAmelCase       :							List[str]  = 1_001
  UpperCAmelCase       :							str  = "imagenet-1k-id2label.json"
  UpperCAmelCase       :							List[Any]  = "huggingface/label-files"
  UpperCAmelCase       :							Optional[Any]  = json.load(open(hf_hub_download(__a				, __a				, repo_type='''dataset'''			)				, '''r'''			)			)
  UpperCAmelCase       :							List[str]  = {int(__a			) + 1: v for k, v in idalabel.items()}
  UpperCAmelCase       :							str  = "background"
  UpperCAmelCase       :							int  = idalabel
  UpperCAmelCase       :							List[Any]  = {v: k for k, v in idalabel.items()}

  return config

def 			a__     (		)  ->						Optional[Any]:
  UpperCAmelCase       :							Optional[Any]  = "http://images.cocodataset.org/val2017/000000039769.jpg"
  UpperCAmelCase       :							Tuple  = Image.open(requests.get(__a				, stream=__a			).raw			)
  return im

@torch.no_grad()
def 			a__     (		UpperCAmelCase							:			List[str]				, UpperCAmelCase							:			Dict				, UpperCAmelCase							:			List[str]				, UpperCAmelCase							:			Optional[Any]=False			)  ->						str:
  UpperCAmelCase       :							Optional[Any]  = get_mobilenet_va_config(__a			)

  # Load 🤗 model
  UpperCAmelCase       :							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
  UpperCAmelCase       :							Optional[int]  = MobileNetVaImageProcessor(
      crop_size={'''width''': config.image_size, '''height''': config.image_size}				, size={'''shortest_edge''': config.image_size + 32}				, )
  UpperCAmelCase       :							Union[str, Any]  = image_processor(images=prepare_img()				, return_tensors='''pt'''			)
  UpperCAmelCase       :							Dict  = model(**__a			)
  UpperCAmelCase       :							List[str]  = outputs.logits

  assert logits.shape == (1, 1_001)

  if model_name == "mobilenet_v1_1.0_224":
    UpperCAmelCase       :							Tuple  = torch.tensor([-4.1739, -1.1233, 3.1205]			)
  elif model_name == "mobilenet_v1_0.75_192":
    UpperCAmelCase       :							Tuple  = torch.tensor([-3.9440, -2.3141, -0.3333]			)
  else:
    UpperCAmelCase       :							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...'''			)
    UpperCAmelCase       :							Optional[Any]  = "google/" + model_name
    image_processor.push_to_hub(__a			)
    model.push_to_hub(__a			)


if __name__ == "__main__":
     _lowerCamelCase				:   List[str]					   =							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."
     )

     _lowerCamelCase				:   str					   =							parser.parse_args()
     convert_movilevit_checkpoint(
         args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )












import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			(DDPMParallelScheduler,)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , **UpperCAmelCase_				:   Any)       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Any			  ={
       "num_train_timesteps": 1_000,
       "beta_start": 0.0001,
       "beta_end": 0.02,
       "beta_schedule": "linear",
       "variance_type": "fixed_small",
       "clip_sample": True,
   }

   config.update(**UpperCAmelCase_)
   return config

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Dict:



   '''simple docstring'''



   for timesteps in [1, 5, 100, 1_000]:
     self.check_over_configs(num_train_timesteps=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Optional[int]:



   '''simple docstring'''



   for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       , [0.002, 0.02, 0.2, 2]):
     self.check_over_configs(beta_start=UpperCAmelCase_       , beta_end=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Any:



   '''simple docstring'''



   for schedule in ["linear", "squaredcos_cap_v2"]:
     self.check_over_configs(beta_schedule=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Optional[int]:



   '''simple docstring'''



   for variance in ["fixed_small", "fixed_large", "other"]:
     self.check_over_configs(variance_type=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int])       ->Optional[Any]:



   '''simple docstring'''



   for clip_sample in [True, False]:
     self.check_over_configs(clip_sample=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Tuple:



   '''simple docstring'''



   self.check_over_configs(thresholding=UpperCAmelCase_)
   for threshold in [0.5, 1.0, 2.0]:
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
       self.check_over_configs(
           thresholding=UpperCAmelCase_       , prediction_type=UpperCAmelCase_       , sample_max_value=UpperCAmelCase_       , )

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Optional[int]:



   '''simple docstring'''



   for prediction_type in ["epsilon", "sample", "v_prediction"]:
     self.check_over_configs(prediction_type=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->int:



   '''simple docstring'''



   for t in [0, 500, 999]:
     self.check_over_forward(time_step=UpperCAmelCase_)

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



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.scheduler_classes[0]
   lowerCamelCase__:      Tuple			  =self.get_scheduler_config()
   lowerCamelCase__:      Any			  =scheduler_class(**UpperCAmelCase_)

   assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_0979)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1E-5

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      int			  =self.scheduler_classes[0]
   lowerCamelCase__:      Tuple			  =self.get_scheduler_config()
   lowerCamelCase__:      Tuple			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      str			  =len(UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =self.dummy_model()
   lowerCamelCase__:      int			  =self.dummy_sample_deter
   lowerCamelCase__:      Union[str, Any]			  =self.dummy_sample_deter + 0.1
   lowerCamelCase__:      Optional[Any]			  =self.dummy_sample_deter - 0.1

   lowerCamelCase__:      Optional[Any]			  =samplea.shape[0]
   lowerCamelCase__:      List[Any]			  =torch.stack([samplea, samplea, samplea]       , dim=0)
   lowerCamelCase__:      Union[str, Any]			  =torch.arange(UpperCAmelCase_)[0:3, None].repeat(1       , UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =model(samples.flatten(0       , 1)       , timesteps.flatten(0       , 1))
   lowerCamelCase__:      Tuple			  =scheduler.batch_step_no_noise(UpperCAmelCase_       , timesteps.flatten(0       , 1)       , samples.flatten(0       , 1))

   lowerCamelCase__:      List[str]			  =torch.sum(torch.abs(UpperCAmelCase_))
   lowerCamelCase__:      Any			  =torch.mean(torch.abs(UpperCAmelCase_))

   assert abs(result_sum.item() - 1153.1833) < 1E-2
   assert abs(result_mean.item() - 0.5005) < 1E-3

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



   '''simple docstring'''



   lowerCamelCase__:      Any			  =self.scheduler_classes[0]
   lowerCamelCase__:      Optional[Any]			  =self.get_scheduler_config()
   lowerCamelCase__:      Optional[int]			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      Union[str, Any]			  =len(UpperCAmelCase_)

   lowerCamelCase__:      Union[str, Any]			  =self.dummy_model()
   lowerCamelCase__:      List[Any]			  =self.dummy_sample_deter
   lowerCamelCase__:      int			  =torch.manual_seed(0)

   for t in reversed(range(UpperCAmelCase_)):
     # 1. predict noise residual
     lowerCamelCase__:      Tuple			  =model(UpperCAmelCase_       , UpperCAmelCase_)

     # 2. predict previous mean of sample x_t-1
     lowerCamelCase__:      Optional[Any]			  =scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , generator=UpperCAmelCase_).prev_sample

     lowerCamelCase__:      Any			  =pred_prev_sample

   lowerCamelCase__:      Any			  =torch.sum(torch.abs(UpperCAmelCase_))
   lowerCamelCase__:      List[str]			  =torch.mean(torch.abs(UpperCAmelCase_))

   assert abs(result_sum.item() - 258.9606) < 1E-2
   assert abs(result_mean.item() - 0.3372) < 1E-3

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.scheduler_classes[0]
   lowerCamelCase__:      Any			  =self.get_scheduler_config(prediction_type="v_prediction")
   lowerCamelCase__:      Any			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      str			  =len(UpperCAmelCase_)

   lowerCamelCase__:      str			  =self.dummy_model()
   lowerCamelCase__:      str			  =self.dummy_sample_deter
   lowerCamelCase__:      Dict			  =torch.manual_seed(0)

   for t in reversed(range(UpperCAmelCase_)):
     # 1. predict noise residual
     lowerCamelCase__:      Union[str, Any]			  =model(UpperCAmelCase_       , UpperCAmelCase_)

     # 2. predict previous mean of sample x_t-1
     lowerCamelCase__:      Dict			  =scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , generator=UpperCAmelCase_).prev_sample

     lowerCamelCase__:      List[str]			  =pred_prev_sample

   lowerCamelCase__:      List[Any]			  =torch.sum(torch.abs(UpperCAmelCase_))
   lowerCamelCase__:      Tuple			  =torch.mean(torch.abs(UpperCAmelCase_))

   assert abs(result_sum.item() - 202.0296) < 1E-2
   assert abs(result_mean.item() - 0.2631) < 1E-3

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      str			  =self.scheduler_classes[0]
   lowerCamelCase__:      Union[str, Any]			  =self.get_scheduler_config()
   lowerCamelCase__:      Any			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      List[Any]			  =[100, 87, 50, 1, 0]

   scheduler.set_timesteps(timesteps=UpperCAmelCase_)

   lowerCamelCase__:      Union[str, Any]			  =scheduler.timesteps

   for i, timestep in enumerate(UpperCAmelCase_):
     if i == len(UpperCAmelCase_) - 1:
       lowerCamelCase__:      Dict			  =-1
     else:
       lowerCamelCase__:      Union[str, Any]			  =timesteps[i + 1]

     lowerCamelCase__:      Tuple			  =scheduler.previous_timestep(UpperCAmelCase_)
     lowerCamelCase__:      str			  =prev_t.item()

     self.assertEqual(UpperCAmelCase_       , UpperCAmelCase_)

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



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.scheduler_classes[0]
   lowerCamelCase__:      List[Any]			  =self.get_scheduler_config()
   lowerCamelCase__:      Dict			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      Optional[Any]			  =[100, 87, 50, 51, 0]

   with self.assertRaises(UpperCAmelCase_       , msg="`custom_timesteps` must be in descending order."):
     scheduler.set_timesteps(timesteps=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.scheduler_classes[0]
   lowerCamelCase__:      Any			  =self.get_scheduler_config()
   lowerCamelCase__:      int			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =[100, 87, 50, 1, 0]
   lowerCamelCase__:      int			  =len(UpperCAmelCase_)

   with self.assertRaises(UpperCAmelCase_       , msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."):
     scheduler.set_timesteps(num_inference_steps=UpperCAmelCase_       , timesteps=UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.scheduler_classes[0]
   lowerCamelCase__:      Optional[Any]			  =self.get_scheduler_config()
   lowerCamelCase__:      Optional[Any]			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      Dict			  =[scheduler.config.num_train_timesteps]

   with self.assertRaises(
       UpperCAmelCase_       , msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"       , ):
     scheduler.set_timesteps(timesteps=UpperCAmelCase_)

