






'''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()
SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)







def 					lowercase__				(   __UpperCamelCase					)->				Optional[int]:
      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_([^_]*)_([^_]*)$"""	,		a_					)
      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       						=      1001
      UpperCamelCase       						=      '''imagenet-1k-id2label.json'''
      UpperCamelCase       						=      '''huggingface/label-files'''
      UpperCamelCase       						=      json.load(open(hf_hub_download(a_	,		a_	,		repo_type="""dataset"""					)	,		"""r"""					)					)
      UpperCamelCase       						=      {int(a_					) + 1: v for k, v in idalabel.items()}
      UpperCamelCase       						=      '''background'''
      UpperCamelCase       						=      idalabel
      UpperCamelCase       						=      {v: k for k, v in idalabel.items()}

      return config







def 					lowercase__				(   )->				List[str]:
      UpperCamelCase       						=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      UpperCamelCase       						=      Image.open(requests.get(a_	,		stream=a_					).raw					)
      return im







@torch.no_grad()
def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=False					)->				List[Any]:
      UpperCamelCase       						=      get_mobilenet_va_config(a_					)

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

      assert logits.shape == (1, 1001)

      if model_name == "mobilenet_v1_1.0_224":
            UpperCamelCase       						=      torch.tensor([-4.1_739, -1.1_233, 3.1_205]					)
      elif model_name == "mobilenet_v1_0.75_192":
            UpperCamelCase       						=      torch.tensor([-3.9_440, -2.3_141, -0.3_333]					)
      else:
            UpperCamelCase       						=      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..."""					)
            UpperCamelCase       						=      '''google/''' + model_name
            image_processor.push_to_hub(a_					)
            model.push_to_hub(a_					)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     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.'
     )

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()
     convert_movilevit_checkpoint(
         args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )








'''simple docstring'''

import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand







def 					lowercase__				(   __UpperCamelCase					)->				str:
      return EnvironmentCommand()







def 					lowercase__				(   __UpperCamelCase					)->				str:
      return EnvironmentCommand(args.accelerate_config_file					)






class        a_		(    lowerCamelCase       ):

  @staticmethod
  def 						A__		(   _SCREAMING_SNAKE_CASE   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      parser.add_parser("""env"""   )
        download_parser.set_defaults(func=_SCREAMING_SNAKE_CASE   )
        download_parser.add_argument(
            """--accelerate-config_file"""    ,	default=_SCREAMING_SNAKE_CASE    ,	help="""The accelerate config file to use for the default values in the launching script."""    ,	)
        download_parser.set_defaults(func=_SCREAMING_SNAKE_CASE   )

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	*_SCREAMING_SNAKE_CASE   )						->   None:



        """simple docstring"""






        UpperCamelCase       						=      accelerate_config_file

  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      """not installed"""
        if is_safetensors_available():
              import safetensors

              UpperCamelCase       						=      safetensors.__version__
        elif importlib.util.find_spec("""safetensors"""   ) is not None:
              import safetensors

              UpperCamelCase       						=      F"{safetensors.__version__} but is ignored because of PyTorch version too old."

        UpperCamelCase       						=      """not installed"""
        UpperCamelCase       						=      UpperCamelCase       						=      """not found"""
        if is_accelerate_available():
              import accelerate
              from accelerate.commands.config import default_config_file, load_config_from_file

              UpperCamelCase       						=      accelerate.__version__
              # Get the default from the config file.
              if self._accelerate_config_file is not None or os.path.isfile(_SCREAMING_SNAKE_CASE   ):
                    UpperCamelCase       						=      load_config_from_file(self._accelerate_config_file   ).to_dict()

              UpperCamelCase       						=      (
                  """\n""".join([F"\t- {prop}: {val}" for prop, val in accelerate_config.items()]   )
                  if isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
                  else F"\t{accelerate_config}"
              )

        UpperCamelCase       						=      """not installed"""
        UpperCamelCase       						=      """NA"""
        if is_torch_available():
              import torch

              UpperCamelCase       						=      torch.__version__
              UpperCamelCase       						=      torch.cuda.is_available()

        UpperCamelCase       						=      """not installed"""
        UpperCamelCase       						=      """NA"""
        if is_tf_available():
              import tensorflow as tf

              UpperCamelCase       						=      tf.__version__
              try:
                    # deprecated in v2.1
                    UpperCamelCase       						=      tf.test.is_gpu_available()
              except AttributeError:
                    # returns list of devices, convert to bool
                    UpperCamelCase       						=      bool(tf.config.list_physical_devices("""GPU"""   )   )

        UpperCamelCase       						=      """not installed"""
        UpperCamelCase       						=      """not installed"""
        UpperCamelCase       						=      """not installed"""
        UpperCamelCase       						=      """NA"""
        if is_flax_available():
              import flax
              import jax
              import jaxlib

              UpperCamelCase       						=      flax.__version__
              UpperCamelCase       						=      jax.__version__
              UpperCamelCase       						=      jaxlib.__version__
              UpperCamelCase       						=      jax.lib.xla_bridge.get_backend().platform

        UpperCamelCase       						=      {
            """`transformers` version""": version,
            """Platform""": platform.platform(),
            """Python version""": platform.python_version(),
            """Huggingface_hub version""": huggingface_hub.__version__,
            """Safetensors version""": F"{safetensors_version}",
            """Accelerate version""": F"{accelerate_version}",
            """Accelerate config""": F"{accelerate_config_str}",
            """PyTorch version (GPU?)""": F"{pt_version} ({pt_cuda_available})",
            """Tensorflow version (GPU?)""": F"{tf_version} ({tf_cuda_available})",
            """Flax version (CPU?/GPU?/TPU?)""": F"{flax_version} ({jax_backend})",
            """Jax version""": F"{jax_version}",
            """JaxLib version""": F"{jaxlib_version}",
            """Using GPU in script?""": """<fill in>""",
            """Using distributed or parallel set-up in script?""": """<fill in>""",
        }

        print("""\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"""   )
        print(self.format_dict(_SCREAMING_SNAKE_CASE   )   )

        return info


  @staticmethod
  def 						A__		(   _SCREAMING_SNAKE_CASE   )						->   Dict:



        """simple docstring"""






        return "\n".join([F"- {prop}: {val}" for prop, val in d.items()]   ) + "\n"
