



'''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()
__lowerCAmelCase									=   logging.get_logger(__name__)



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Union[str, Any]:
		_a     :      str        =							MobileNetVaConfig(layer_norm_eps=0.001  )

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

		_a     :      int        =							re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'						,			lowerCAmelCase_  )
		if matches:
				_a     :      str        =							float(matches[1]  )
				_a     :      List[Any]        =							int(matches[2]  )

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

		return config



def      __lowerCamelCase     (      )			->      Dict:
		_a     :      Any        =							'http://images.cocodataset.org/val2017/000000039769.jpg'
		_a     :      Tuple        =							Image.open(requests.get(lowerCAmelCase_						,			stream=lowerCAmelCase_  ).raw  )
		return im



@torch.no_grad()
def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      Optional[int]:
		_a     :      str        =							get_mobilenet_va_config(lowerCAmelCase_  )

		# Load 🤗 model
		_a     :      int        =							MobileNetVaForImageClassification(lowerCAmelCase_  ).eval()

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

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

		assert logits.shape == (1, 1001)

		if model_name == "mobilenet_v1_1.0_224":
				_a     :      List[str]        =							torch.tensor([-4.1_739, -1.1_233, 3.1_205]  )
		elif model_name == "mobilenet_v1_0.75_192":
				_a     :      Any        =							torch.tensor([-3.9_440, -2.3_141, -0.3_333]  )
		else:
				_a     :      str        =							None

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

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

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


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









'''simple docstring'''

import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__lowerCAmelCase									=   logging.getLogger()

@unittest.skip('Temporarily disable the doc tests.'   )
@require_torch
@require_tf
@slow
class 	__magic_name__						(    unittest.TestCase   ):







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       Path	,_UpperCAmelCase					:       Union[str, None] = None	,_UpperCAmelCase					:       Union[List[str], None] = None	,_UpperCAmelCase					:       Union[str, List[str], None] = None	,_UpperCAmelCase					:       bool = True	,):
									_a     :      Dict        =							[file for file in os.listdir(_UpperCAmelCase		) if os.path.isfile(os.path.join(_UpperCAmelCase	,_UpperCAmelCase		)		)]

									if identifier is not None:
											_a     :      str        =							[file for file in files if identifier in file]

									if n_identifier is not None:
											if isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
													for n_ in n_identifier:
															_a     :      int        =							[file for file in files if n_ not in file]
											else:
													_a     :      Optional[Any]        =							[file for file in files if n_identifier not in file]

									_a     :      Dict        =							ignore_files or []
									ignore_files.append('__init__.py'		)
									_a     :      List[str]        =							[file for file in files if file not in ignore_files]

									for file in files:
											# Open all files
											print('Testing'	,_UpperCAmelCase		)

											if only_modules:
													_a     :      Any        =							file.split('.'		)[0]
													try:
															_a     :      Optional[int]        =							getattr(_UpperCAmelCase	,_UpperCAmelCase		)
															_a     :      Dict        =							doctest.DocTestSuite(_UpperCAmelCase		)
															_a     :      Optional[int]        =							unittest.TextTestRunner().run(_UpperCAmelCase		)
															self.assertIs(len(result.failures		)	,0		)
													except AttributeError:
															logger.info(F"""{module_identifier} is not a module."""		)
											else:
													_a     :      str        =							doctest.testfile(str('..' / directory / file		)	,optionflags=doctest.ELLIPSIS		)
													self.assertIs(result.failed	,0		)







							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      Optional[Any]        =							Path('src/transformers'		)
									_a     :      Optional[Any]        =							'modeling'
									_a     :      Union[str, Any]        =							[
									    'modeling_ctrl.py',
									    'modeling_tf_ctrl.py',
									]
									self.analyze_directory(_UpperCAmelCase	,identifier=_UpperCAmelCase	,ignore_files=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       int		):
									_a     :      str        =							Path('src/transformers'		)
									_a     :      List[str]        =							'tokenization'
									self.analyze_directory(_UpperCAmelCase	,identifier=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       int		):
									_a     :      Any        =							Path('src/transformers'		)
									_a     :      str        =							'configuration'
									self.analyze_directory(_UpperCAmelCase	,identifier=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      Tuple        =							Path('src/transformers'		)
									_a     :      Optional[int]        =							['configuration', 'modeling', 'tokenization']
									self.analyze_directory(_UpperCAmelCase	,n_identifier=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      Union[str, Any]        =							Path('docs/source'		)
									_a     :      List[str]        =							['favicon.ico']
									self.analyze_directory(_UpperCAmelCase	,ignore_files=_UpperCAmelCase	,only_modules=_UpperCAmelCase		)




