
"""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()
UpperCamelCase_					:						int				= logging.get_logger(__name__)
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						MobileNetVaConfig(layer_norm_eps=0.001				)

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

					A_				 =						re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$"				,   __a				)
					if matches:
										A_				 =						float(matches[1]				)
										A_				 =						int(matches[2]				)

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

					return config
def 							A_     ():

					'''simple docstring'''





					A_				 =						"http://images.cocodataset.org/val2017/000000039769.jpg"
					A_				 =						Image.open(requests.get(__a				,   stream=__a				).raw				)
					return im
@torch.no_grad()
def 							A_     (__a				,   __a				,   __a				,   __a=False				):

					'''simple docstring'''





					A_				 =						get_mobilenet_va_config(__a				)

					# Load 🤗 model
					A_				 =						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_				 =						MobileNetVaImageProcessor(
					    crop_size={"width": config.image_size, "height": config.image_size}				,   size={"shortest_edge": config.image_size + 32}				,   )
					A_				 =						image_processor(images=prepare_img()				,   return_tensors="pt"				)
					A_				 =						model(**__a				)
					A_				 =						outputs.logits

					assert logits.shape == (1, 1001)

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


if __name__ == "__main__":
		UpperCamelCase_					:						Dict				= 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_					:						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 2023 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.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_					:						str				= {'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_					:						Union[str, Any]				= ['''TimmBackbone''']


if TYPE_CHECKING:
		from .configuration_timm_backbone import TimmBackboneConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_timm_backbone import TimmBackbone

else:
		import sys

		UpperCamelCase_					:						List[Any]				= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




