

from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)







class     __lowercase (A__					):
 def __init__(				self ,				A_ ,				A_=None ,				A_=None ,				A_=0   )   ->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase   :		str   =   1.0 if scale is None else scale
       __lowerCAmelCase   :		int   =   0.0 if loc is None else loc

       super().__init__(a__ ,				[AffineTransform(loc=self.loc ,				scale=self.scale ,				event_dim=a__   )]   )
 @property
 def       UpperCamelCase__    (				self   )   ->Optional[Any]:



       '''simple docstring'''





       return self.base_dist.mean * self.scale + self.loc
 @property
 def       UpperCamelCase__    (				self   )   ->Tuple:



       '''simple docstring'''





       return self.base_dist.variance * self.scale**2





 @property
 def       UpperCamelCase__    (				self   )   ->Optional[int]:



       '''simple docstring'''





       return self.variance.sqrt()







class     __lowercase (nn.Module					):
 def __init__(				self ,				A_ ,				A_ ,				A_ ,				**A_   )   ->Any:



       '''simple docstring'''





       super().__init__(**a__   )
       __lowerCAmelCase   :		Optional[Any]   =   args_dim
       __lowerCAmelCase   :		Dict   =   nn.ModuleList([nn.Linear(a__ ,				a__   ) for dim in args_dim.values()]   )
       __lowerCAmelCase   :		Union[str, Any]   =   domain_map





 def       UpperCamelCase__    (				self ,				A_   )   ->List[str]:



       '''simple docstring'''





       __lowerCAmelCase   :		Dict   =   [proj(a__   ) for proj in self.proj]

       return self.domain_map(*a__   )







class     __lowercase (nn.Module					):
 def __init__(				self ,				A_   )   ->Optional[int]:



       '''simple docstring'''





       super().__init__()
       __lowerCAmelCase   :		Dict   =   function





 def       UpperCamelCase__    (				self ,				A_ ,				*A_   )   ->List[str]:



       '''simple docstring'''





       return self.function(a__ ,				*a__   )







class     __lowercase :
 _UpperCamelCase         =    42
 _UpperCamelCase         =    42
 _UpperCamelCase         =    42
 def __init__(				self ,				A_ = 1   )   ->List[str]:



       '''simple docstring'''





       __lowerCAmelCase   :		Any   =   dim
       __lowerCAmelCase   :		Dict   =   {k: dim * self.args_dim[k] for k in self.args_dim}
 def       UpperCamelCase__    (				self ,				A_   )   ->Any:



       '''simple docstring'''





       if self.dim == 1:
             return self.distribution_class(*a__   )
       else:
             return Independent(self.distribution_class(*a__   ) ,				1   )
 def       UpperCamelCase__    (				self ,				A_ ,				A_ = None ,				A_ = None ,				)   ->str:



       '''simple docstring'''





       __lowerCAmelCase   :		List[Any]   =   self._base_distribution(a__   )
       if loc is None and scale is None:
             return distr
       else:
             return AffineTransformed(a__ ,				loc=a__ ,				scale=a__ ,				event_dim=self.event_dim   )
 @property
 def       UpperCamelCase__    (				self   )   ->int:



       '''simple docstring'''





       return () if self.dim == 1 else (self.dim,)
 @property
 def       UpperCamelCase__    (				self   )   ->List[str]:



       '''simple docstring'''





       return len(self.event_shape   )
 @property
 def       UpperCamelCase__    (				self   )   ->List[Any]:



       '''simple docstring'''





       return 0.0
 def       UpperCamelCase__    (				self ,				A_   )   ->List[str]:



       '''simple docstring'''





       return ParameterProjection(
           in_features=a__ ,				args_dim=self.args_dim ,				domain_map=LambdaLayer(self.domain_map   ) ,				)
 def       UpperCamelCase__    (				self ,				*A_   )   ->List[Any]:



       '''simple docstring'''





       raise NotImplementedError()





 @staticmethod
 def       UpperCamelCase__    (				A_   )   ->Any:



       '''simple docstring'''





       return (x + torch.sqrt(torch.square(a__   ) + 4.0   )) / 2.0







class     __lowercase (A__					):
 _UpperCamelCase         =    {"""df""": 1, """loc""": 1, """scale""": 1}
 _UpperCamelCase         =    StudentT
 @classmethod
 def       UpperCamelCase__    (				cls ,				A_ ,				A_ ,				A_   )   ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase   :		Optional[int]   =   cls.squareplus(a__   ).clamp_min(torch.finfo(scale.dtype   ).eps   )
       __lowerCAmelCase   :		Any   =   2.0 + cls.squareplus(a__   )
       return df.squeeze(-1   ), loc.squeeze(-1   ), scale.squeeze(-1   )







class     __lowercase (A__					):
 _UpperCamelCase         =    {"""loc""": 1, """scale""": 1}
 _UpperCamelCase         =    Normal
 @classmethod
 def       UpperCamelCase__    (				cls ,				A_ ,				A_   )   ->int:



       '''simple docstring'''





       __lowerCAmelCase   :		List[Any]   =   cls.squareplus(a__   ).clamp_min(torch.finfo(scale.dtype   ).eps   )
       return loc.squeeze(-1   ), scale.squeeze(-1   )







class     __lowercase (A__					):
 _UpperCamelCase         =    {"""total_count""": 1, """logits""": 1}
 _UpperCamelCase         =    NegativeBinomial
 @classmethod
 def       UpperCamelCase__    (				cls ,				A_ ,				A_   )   ->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase   :		str   =   cls.squareplus(a__   )
       return total_count.squeeze(-1   ), logits.squeeze(-1   )
 def       UpperCamelCase__    (				self ,				A_   )   ->Any:



       '''simple docstring'''





       __lowerCAmelCase,				__lowerCAmelCase   :		str   =   distr_args
       if self.dim == 1:
             return self.distribution_class(total_count=a__ ,				logits=a__   )
       else:
             return Independent(self.distribution_class(total_count=a__ ,				logits=a__   ) ,				1   )





 def       UpperCamelCase__    (				self ,				A_ ,				A_ = None ,				A_ = None   )   ->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase,				__lowerCAmelCase   :		List[Any]   =   distr_args

       if scale is not None:
             # See scaling property of Gamma.
             logits += scale.log()

       return self._base_distribution((total_count, logits)   )







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()
_lowercase    	=		logging.get_logger(__name__)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Optional[Any]						)->	Union[str, Any]:
    A__					=       MobileNetVaConfig(layer_norm_eps=0.001						)

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

    A__					=       re.match(r'''^mobilenet_v1_([^_]*)_([^_]*)$'''			,							UpperCamelCase_						)
    if matches:
        A__					=       float(matches[1]						)
        A__					=       int(matches[2]						)

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

    return config



def 		lowerCAmelCase__     (       )->	Tuple:
    A__					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A__					=       Image.open(requests.get(UpperCamelCase_			,							stream=UpperCamelCase_						).raw						)
    return im



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]			,							UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							int			,							UpperCamelCase_					:							List[str]=False						)->	Tuple:
    A__					=       get_mobilenet_va_config(UpperCamelCase_						)

    # Load 🤗 model
    A__					=       MobileNetVaForImageClassification(UpperCamelCase_						).eval()

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

    # Check outputs on an image, prepared by MobileNetV1ImageProcessor
    A__					=       MobileNetVaImageProcessor(
        crop_size={'''width''': config.image_size, '''height''': config.image_size}			,							size={'''shortest_edge''': config.image_size + 3_2}			,							)
    A__					=       image_processor(images=prepare_img()			,							return_tensors='''pt'''						)
    A__					=       model(**UpperCamelCase_						)
    A__					=       outputs.logits

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

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


if __name__ == "__main__":
   _lowercase    	=		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."
   )

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