"""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__						:						Dict		  =			logging.get_logger(__name__)



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							MobileNetVaConfig(layer_norm_eps=0.001 )

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

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

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

				return config



def 						a_		(      ):
				UpperCAmelCase__    =							'http://images.cocodataset.org/val2017/000000039769.jpg'
				UpperCAmelCase__    =							Image.open(requests.get(lowerCamelCase      ,    stream=lowerCamelCase ).raw )
				return im



@torch.no_grad()
def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase=False ):
				UpperCAmelCase__    =							get_mobilenet_va_config(lowerCamelCase )

				# Load 🤗 model
				UpperCAmelCase__    =							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
				UpperCAmelCase__    =							MobileNetVaImageProcessor(
				    crop_size={'width': config.image_size, 'height': config.image_size}      ,    size={'shortest_edge': config.image_size + 3_2}      ,    )
				UpperCAmelCase__    =							image_processor(images=prepare_img()      ,    return_tensors='pt' )
				UpperCAmelCase__    =							model(**lowerCamelCase )
				UpperCAmelCase__    =							outputs.logits

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

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


if __name__ == "__main__":
						lowerCAmelCase__						:						int		  =			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__						:						Optional[int]		  =			parser.parse_args()
						convert_movilevit_checkpoint(
						    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)




"""simple docstring"""






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


lowerCAmelCase__						:						Dict		  =			'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'



def 						a_		(      ):
				UpperCAmelCase__    =							_ask_options(
				    'In which compute environment are you running?'      ,    ['This machine', 'AWS (Amazon SageMaker)']      ,    _convert_compute_environment      ,    )
				if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
								UpperCAmelCase__    =							get_sagemaker_input()
				else:
								UpperCAmelCase__    =							get_cluster_input()
				return config



def 						a_		(      lowerCamelCase=None ):
				if subparsers is not None:
								UpperCAmelCase__    =							subparsers.add_parser('config'      ,    description=lowerCamelCase )
				else:
								UpperCAmelCase__    =							argparse.ArgumentParser('Accelerate config command'      ,    description=lowerCamelCase )

				parser.add_argument(
				    '--config_file'      ,    default=lowerCamelCase      ,    help=(
				        'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
				        'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
				        'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
				        'with \'huggingface\'.'
				    )      ,    )

				if subparsers is not None:
								parser.set_defaults(func=lowerCamelCase )
				return parser



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							get_user_input()
				if args.config_file is not None:
								UpperCAmelCase__    =							args.config_file
				else:
								if not os.path.isdir(lowerCamelCase ):
												os.makedirs(lowerCamelCase )
								UpperCAmelCase__    =							default_yaml_config_file

				if config_file.endswith('.json' ):
								config.to_json_file(lowerCamelCase )
				else:
								config.to_yaml_file(lowerCamelCase )
				print(f'''accelerate configuration saved at {config_file}''' )



def 						a_		(      ):
				UpperCAmelCase__    =							config_command_parser()
				UpperCAmelCase__    =							parser.parse_args()
				config_command(lowerCamelCase )


if __name__ == "__main__":
						main()



