




"""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      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  Dict								=       MobileNetVaConfig(layer_norm_eps=0.001	)

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

			__lowercase			:  str								=       re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''	,				__UpperCamelCase	)
			if matches:
						__lowercase			:  Any								=       float(matches[1]	)
						__lowercase			:  Any								=       int(matches[2]	)

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

			return config







def      __UpperCAmelCase     ( ):
			__lowercase			:  Optional[int]								=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			__lowercase			:  Tuple								=       Image.open(requests.get(__UpperCamelCase	,				stream=__UpperCamelCase	).raw	)
			return im







@torch.no_grad()
def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=False	):
			__lowercase			:  List[Any]								=       get_mobilenet_va_config(__UpperCamelCase	)

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

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

			if model_name == "mobilenet_v1_1.0_224":
						__lowercase			:  str								=       torch.tensor([-4.1_739, -1.1_233, 3.1_205]	)
			elif model_name == "mobilenet_v1_0.75_192":
						__lowercase			:  List[Any]								=       torch.tensor([-3.9_440, -2.3_141, -0.3_333]	)
			else:
						__lowercase			:  Optional[int]								=       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...'''	)
						__lowercase			:  Optional[int]								=       '''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_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 argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def  __snake_case							(	__A      :						Union[str, Any]							,		__A      :						Any							,		__A      :						Dict     )     ->       Tuple:






				'''simple docstring'''
				# Initialise PyTorch model
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     BertConfig.from_json_file(__A     )
				print(F"""Building PyTorch model from configuration: {config}"""     )
				SCREAMING_SNAKE_CASE						:							List[Any]							=     BertForPreTraining(__A     )

				# Load weights from tf checkpoint
				load_tf_weights_in_bert(__A							,		__A							,		__A     )

				# Save pytorch-model
				print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
				torch.save(model.state_dict()							,		__A     )


if __name__ == "__main__":
							A_						:    int				=      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
							)
							parser.add_argument(
							    '--bert_config_file',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        'The config json file corresponding to the pre-trained BERT model. \n'
							        'This specifies the model architecture.'
							    ),
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							A_						:    str				=      parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





