



'''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_							: List[Any]						 =	logging.get_logger(__name__)




def     snake_case_					(     lowerCAmelCase_		)->		Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[Any]			     = MobileNetVaConfig(layer_norm_eps=0.0_0_1		)

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

       _UpperCAmelCase  :						int			     = re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""       ,   _lowercase		)
       if matches:
              _UpperCAmelCase  :						Tuple			     = float(matches[1]		)
              _UpperCAmelCase  :						Optional[Any]			     = int(matches[2]		)

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

       return config




def     snake_case_					(     )->		str:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[Any]			     = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
       _UpperCAmelCase  :						str			     = Image.open(requests.get(_lowercase       ,   stream=_lowercase		).raw		)
       return im




@torch.no_grad()
def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_=False		)->		Tuple:





       '''simple docstring'''


       _UpperCAmelCase  :						Dict			     = get_mobilenet_va_config(_lowercase		)

       # Load 🤗 model
       _UpperCAmelCase  :						List[Any]			     = MobileNetVaForImageClassification(_lowercase		).eval()

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

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

       assert logits.shape == (1, 1001)

       if model_name == "mobilenet_v1_1.0_224":
              _UpperCAmelCase  :						Union[str, Any]			     = torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]		)
       elif model_name == "mobilenet_v1_0.75_192":
              _UpperCAmelCase  :						Union[str, Any]			     = torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]		)
       else:
              _UpperCAmelCase  :						Optional[int]			     = None

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

       Path(_lowercase		).mkdir(exist_ok=_lowercase		)
       print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''		)
       model.save_pretrained(_lowercase		)
       print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
       image_processor.save_pretrained(_lowercase		)

       if push_to_hub:
              print("""Pushing to the hub..."""		)
              _UpperCAmelCase  :						Optional[int]			     = '''google/''' + model_name
              image_processor.push_to_hub(_lowercase		)
              model.push_to_hub(_lowercase		)


if __name__ == "__main__":
   A_							: Optional[Any]						 =	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_							: List[str]						 =	parser.parse_args()
   convert_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__UpperCamelCase		:       Tuple									=     {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Optional[Any]									=     [
					    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'SwiftFormerForImageClassification',
					    'SwiftFormerModel',
					    'SwiftFormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_swiftformer import (
					    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SwiftFormerConfig,
					    SwiftFormerOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swiftformer import (
										    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwiftFormerForImageClassification,
										    SwiftFormerModel,
										    SwiftFormerPreTrainedModel,
										)

else:
					import sys

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





