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()
__UpperCamelCase	: Optional[int]    		=  logging.get_logger(__name__)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							MobileNetVaConfig(layer_norm_eps=0.001		)

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

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

						# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
						# the usual 1000. The first class (index 0) is "background".
						SCREAMING_SNAKE_CASE							:					str           =							1_001
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''imagenet-1k-id2label.json'''
						SCREAMING_SNAKE_CASE							:					List[str]           =							'''huggingface/label-files'''
						SCREAMING_SNAKE_CASE							:					Dict           =							json.load(open(hf_hub_download(_UpperCamelCase	,						_UpperCamelCase	,						repo_type='''dataset'''		)	,						'''r'''		)		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							{int(_UpperCamelCase		) + 1: v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'''background'''
						SCREAMING_SNAKE_CASE							:					Tuple           =							idalabel
						SCREAMING_SNAKE_CASE							:					Any           =							{v: k for k, v in idalabel.items()}

						return config
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Any           =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						SCREAMING_SNAKE_CASE							:					List[str]           =							Image.open(requests.get(_UpperCamelCase	,						stream=_UpperCamelCase		).raw		)
						return im
@torch.no_grad()
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase=False		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							get_mobilenet_va_config(_UpperCamelCase		)

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

						assert logits.shape == (1, 1_001)

						if model_name == "mobilenet_v1_1.0_224":
												SCREAMING_SNAKE_CASE							:					Dict           =							torch.tensor([-4.1739, -1.1233, 3.1205]		)
						elif model_name == "mobilenet_v1_0.75_192":
												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.tensor([-3.9440, -2.3141, -0.3333]		)
						else:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							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...'''		)
												SCREAMING_SNAKE_CASE							:					str           =							'''google/''' + model_name
												image_processor.push_to_hub(_UpperCamelCase		)
												model.push_to_hub(_UpperCamelCase		)


if __name__ == "__main__":
					__UpperCamelCase	: str    		=  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.'
					)

					__UpperCamelCase	: Tuple    		=  parser.parse_args()
					convert_movilevit_checkpoint(
					    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)






# 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

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


__UpperCamelCase	: Dict    		=  [
    'EAGER',
    'AOT_EAGER',
    'INDUCTOR',
    'NVFUSER',
    'AOT_NVFUSER',
    'AOT_CUDAGRAPHS',
    'OFI',
    'FX2TRT',
    'ONNXRT',
    'IPEX',
]
def    A			(					_lowercase	,						_lowercase=None	,						_lowercase=None	,						_lowercase=None		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							True
						while ask_again:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input(_lowercase		)
												try:
																		if default is not None and len(_lowercase		) == 0:
																								return default
																		return convert_value(_lowercase		) if convert_value is not None else result
												except Exception:
																		if error_message is not None:
																								print(_lowercase		)
def    A			(					_lowercase	,						_lowercase=[]	,						_lowercase=None	,						_lowercase=0		):
						SCREAMING_SNAKE_CASE							:					Dict           =							BulletMenu(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					str           =							menu.run(default_choice=_lowercase		)
						return convert_value(_lowercase		) if convert_value is not None else result
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		)
						return ComputeEnvironment(['''LOCAL_MACHINE''', '''AMAZON_SAGEMAKER'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Any           =							int(_lowercase		)
						return DistributedType(['''NO''', '''MULTI_CPU''', '''MULTI_XPU''', '''MULTI_GPU''', '''MULTI_NPU''', '''TPU'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							int(_lowercase		)
						return DynamoBackend(DYNAMO_BACKENDS[value]		).value
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							int(_lowercase		)
						return PrecisionType(['''no''', '''fp16''', '''bf16''', '''fp8'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		)
						return SageMakerDistributedType(['''NO''', '''DATA_PARALLEL''', '''MODEL_PARALLEL'''][value]		)
def    A			(					_lowercase		):
						return {"yes": True, "no": False}[value.lower()]





class       lowercase__		(     argparse.RawDescriptionHelpFormatter):


						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							super()._format_usage(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							usage.replace('''<command> [<args>] '''					,	''''''		)
												return usage





