





"""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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_					:						Optional[Any]      = logging.get_logger(__name__)





def 						UpperCAmelCase__		(					_UpperCAmelCase ):






  """simple docstring"""





  A_  :	Dict    						=  MobileNetVaConfig(layer_norm_eps=0.001 )

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

  A_  :	Any    						=  re.match(R'^.*mobilenet_v2_([^_]*)_([^_]*)$'			, _UpperCAmelCase )
  if matches:
    A_  :	Optional[Any]    						=  float(matches[1] )
    A_  :	List[str]    						=  int(matches[2] )

  if model_name.startswith('deeplabv3_' ):
    A_  :	int    						=  8
    A_  :	List[str]    						=  21
    A_  :	Optional[int]    						=  'pascal-voc-id2label.json'
  else:
    # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
    # of the usual 1000. The first class (index 0) is "background".
    A_  :	Union[str, Any]    						=  1001
    A_  :	Tuple    						=  'imagenet-1k-id2label.json'

  A_  :	Optional[int]    						=  'huggingface/label-files'
  A_  :	List[Any]    						=  json.load(open(hf_hub_download(_UpperCAmelCase			, _UpperCAmelCase			, repo_type='dataset' )			, 'r' ) )

  if config.num_labels == 1001:
    A_  :	int    						=  {int(_UpperCAmelCase ) + 1: v for k, v in idalabel.items()}
    A_  :	Dict    						=  'background'
  else:
    A_  :	Optional[Any]    						=  {int(_UpperCAmelCase ): v for k, v in idalabel.items()}

  A_  :	str    						=  idalabel
  A_  :	Dict    						=  {v: k for k, v in idalabel.items()}

  return config





def 						UpperCAmelCase__		(					):






  """simple docstring"""





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





@torch.no_grad()
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase=False ):






  """simple docstring"""





  A_  :	int    						=  get_mobilenet_va_config(_UpperCAmelCase )

  # Load 🤗 model
  if model_name.startswith('deeplabv3_' ):
    A_  :	List[Any]    						=  MobileNetVaForSemanticSegmentation(_UpperCAmelCase ).eval()
  else:
    A_  :	Optional[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 MobileNetV2ImageProcessor
  A_  :	List[Any]    						=  MobileNetVaImageProcessor(
      crop_size={'width': config.image_size, 'height': config.image_size}			, size={'shortest_edge': config.image_size + 32}			, )
  A_  :	List[str]    						=  image_processor(images=prepare_img()			, return_tensors='pt' )
  A_  :	List[Any]    						=  model(**_UpperCAmelCase )
  A_  :	Tuple    						=  outputs.logits

  if model_name.startswith('deeplabv3_' ):
    assert logits.shape == (1, 21, 65, 65)

    if model_name == "deeplabv3_mobilenet_v2_1.0_513":
      A_  :	str    						=  torch.tensor(
          [
              [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
              [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
              [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
          ] )

    else:
      raise ValueError(f"""Unknown model name: {model_name}""" )

    assert torch.allclose(logits[0, :3, :3, :3]			, _UpperCAmelCase			, atol=1E-4 )
  else:
    assert logits.shape == (1, 1001)

    if model_name == "mobilenet_v2_1.4_224":
      A_  :	str    						=  torch.tensor([0.0_181, -1.0_015, 0.4_688] )
    elif model_name == "mobilenet_v2_1.0_224":
      A_  :	Optional[int]    						=  torch.tensor([0.2_445, -1.1_993, 0.1_905] )
    elif model_name == "mobilenet_v2_0.75_160":
      A_  :	Union[str, Any]    						=  torch.tensor([0.2_482, 0.4_136, 0.6_669] )
    elif model_name == "mobilenet_v2_0.35_96":
      A_  :	Union[str, Any]    						=  torch.tensor([0.1_451, -0.4_624, 0.7_192] )
    else:
      A_  :	Dict    						=  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...' )
    A_  :	str    						=  'google/' + model_name
    image_processor.push_to_hub(_UpperCAmelCase )
    model.push_to_hub(_UpperCAmelCase )


if __name__ == "__main__":
   lowerCamelCase_					:						List[Any]      = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='mobilenet_v2_1.0_224',
       type=str,
       help='Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<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_					:						List[str]      = 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,
    is_vision_available,
)


lowerCamelCase_					:						Any      = {
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_					:						Union[str, Any]      = [
       'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TvltModel',
       'TvltForPreTraining',
       'TvltForAudioVisualClassification',
       'TvltPreTrainedModel',
   ]

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_					:						List[Any]      = ['TvltImageProcessor']


if TYPE_CHECKING:
   from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
   from .processing_tvlt import TvltProcessor
   from .feature_extraction_tvlt import TvltFeatureExtractor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tvlt import (
          TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TvltForAudioVisualClassification,
          TvltForPreTraining,
          TvltModel,
          TvltPreTrainedModel,
      )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_tvlt import TvltImageProcessor


else:
   import sys

   lowerCamelCase_					:						Optional[int]      = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)