





"""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 			=  logging.get_logger(__name__)

def     __A      (			a_						:Tuple)		->		Optional[int]:
				__a :						Tuple			       =  MobileNetVaConfig(layer_norm_eps=0.0_0_1)

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

				__a :						Any			       =  re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''   ,       a_)
				if matches:
								__a :						List[Any]			       =  float(matches[1])
								__a :						Tuple			       =  int(matches[2])

				# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
				# the usual 1000. The first class (index 0) is "background".
				__a :						List[str]			       =  10_01
				__a :						Union[str, Any]			       =  '''imagenet-1k-id2label.json'''
				__a :						Optional[int]			       =  '''huggingface/label-files'''
				__a :						Tuple			       =  json.load(open(hf_hub_download(a_   ,       a_   ,       repo_type='''dataset''')   ,       '''r'''))
				__a :						Any			       =  {int(a_) + 1: v for k, v in idalabel.items()}
				__a :						int			       =  '''background'''
				__a :						str			       =  idalabel
				__a :						List[Any]			       =  {v: k for k, v in idalabel.items()}

				return config

def     __A      (			)		->		List[str]:
				__a :						Optional[Any]			       =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__a :						Optional[Any]			       =  Image.open(requests.get(a_   ,       stream=a_).raw)
				return im

@torch.no_grad()
def     __A      (			a_						:Optional[int]   ,       a_						:int   ,       a_						:str   ,       a_						:Tuple=False)		->		List[Any]:
				__a :						int			       =  get_mobilenet_va_config(a_)

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

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

				if model_name == "mobilenet_v1_1.0_224":
								__a :						Optional[int]			       =  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":
								__a :						str			       =  torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3])
				else:
								__a :						Optional[Any]			       =  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...''')
								__a :						Union[str, Any]			       =  '''google/''' + model_name
								image_processor.push_to_hub(a_)
								model.push_to_hub(a_)


if __name__ == "__main__":
		A 			=  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 			=  parser.parse_args()
		convert_movilevit_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)






"""simple docstring"""






import copy
import re




class 				__lowercase			:







					'''simple docstring'''




					__lowerCAmelCase  =  '''hp'''
					__lowerCAmelCase  =  {}
					__lowerCAmelCase  =  None







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						Optional[int]			       =  prefix
									__a :						List[str]			       =  defaults
									cls.build_naming_info()







					@staticmethod
					def  _lowerCamelCase		(						_UpperCAmelCase	,			_UpperCAmelCase     ):
									if len(_UpperCAmelCase     ) == 0:
													return ""
									__a :						Optional[int]			       =  None
									if any(char.isdigit() for char in word     ):
													raise Exception(f"""Parameters should not contain numbers: '{word}' contains a number"""     )
									if word in info["short_word"]:
													return info["short_word"][word]
									for prefix_len in range(1	,			len(_UpperCAmelCase     ) + 1     ):
													__a :						str			       =  word[:prefix_len]
													if prefix in info["reverse_short_word"]:
																	continue
													else:
																	__a :						Tuple			       =  prefix
																	break

									if short_word is None:
													# Paranoid fallback
													def int_to_alphabetic(_UpperCAmelCase     ):
																	__a :						List[str]			       =  ''''''
																	while integer != 0:
																					__a :						Union[str, Any]			       =  chr(ord('''A'''     ) + integer % 10     ) + s
																					integer //= 10
																	return s

													__a :						Optional[int]			       =  0
													while True:
																	__a :						List[str]			       =  word + '''#''' + int_to_alphabetic(_UpperCAmelCase     )
																	if sword in info["reverse_short_word"]:
																					continue
																	else:
																					__a :						Optional[Any]			       =  sword
																					break

									__a :						List[str]			       =  short_word
									__a :						Dict			       =  word
									return short_word







					@staticmethod
					def  _lowerCamelCase		(						_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						str			       =  param_name.split('''_'''     )

									__a :						str			       =  [TrialShortNamer.shortname_for_word(_UpperCAmelCase	,			_UpperCAmelCase     ) for word in words]

									# We try to create a separatorless short name, but if there is a collision we have to fallback
									# to a separated short name
									__a :						Dict			       =  ['''''', '''_''']

									for separator in separators:
													__a :						Union[str, Any]			       =  separator.join(_UpperCAmelCase     )
													if shortname not in info["reverse_short_param"]:
																	__a :						List[str]			       =  shortname
																	__a :						Union[str, Any]			       =  param_name
																	return shortname

									return param_name







					@staticmethod
					def  _lowerCamelCase		(						_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						int			       =  TrialShortNamer.shortname_for_key(_UpperCAmelCase	,			_UpperCAmelCase     )
									__a :						Optional[Any]			       =  short_name
									__a :						Optional[Any]			       =  param_name







					@classmethod
					def  _lowerCamelCase		(						cls     ):
									if cls.NAMING_INFO is not None:
													return

									__a :						Optional[Any]			       =  {
									    '''short_word''': {},
									    '''reverse_short_word''': {},
									    '''short_param''': {},
									    '''reverse_short_param''': {},
									}

									__a :						List[str]			       =  list(cls.DEFAULTS.keys()     )

									for k in field_keys:
													cls.add_new_param_name(_UpperCAmelCase	,			_UpperCAmelCase     )

									__a :						Optional[Any]			       =  info







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase     ):
									cls.build_naming_info()
									assert cls.PREFIX is not None
									__a :						str			       =  [copy.copy(cls.PREFIX     )]

									for k, v in params.items():
													if k not in cls.DEFAULTS:
																	raise Exception(f"""You should provide a default value for the param name {k} with value {v}"""     )
													if v == cls.DEFAULTS[k]:
																	# The default value is not added to the name
																	continue

													__a :						Union[str, Any]			       =  cls.NAMING_INFO['''short_param'''][k]

													if isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ):
																	__a :						str			       =  1 if v else 0

													__a :						Tuple			       =  '''''' if isinstance(_UpperCAmelCase	,			(int, float)     ) else '''-'''
													__a :						Dict			       =  f"""{key}{sep}{v}"""
													name.append(_UpperCAmelCase     )

									return "_".join(_UpperCAmelCase     )







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase     ):
									__a :						List[Any]			       =  repr[len(cls.PREFIX     ) + 1 :]
									if repr == "":
													__a :						Tuple			       =  []
									else:
													__a :						str			       =  repr.split('''_'''     )

									__a :						Optional[Any]			       =  {}

									for value in values:
													if "-" in value:
																	__a       ,						__a :						List[Any]			       =  value.split('''-'''     )
													else:
																	__a :						int			       =  re.sub('''[0-9.]'''	,			''''''	,			_UpperCAmelCase     )
																	__a :						Union[str, Any]			       =  float(re.sub('''[^0-9.]'''	,			''''''	,			_UpperCAmelCase     )     )

													__a :						Dict			       =  cls.NAMING_INFO['''reverse_short_param'''][p_k]

													__a :						Union[str, Any]			       =  p_v

									for k in cls.DEFAULTS:
													if k not in parameters:
																	__a :						Optional[int]			       =  cls.DEFAULTS[k]

									return parameters