"""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  =				logging.get_logger(__name__)


def    lowercase_   (   _lowerCamelCase:	Optional[Any]      ) ->      Tuple:



       '''simple docstring'''

       __lowerCamelCase					:		str		   =     MobileNetVaConfig(layer_norm_eps=0.001      )

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

       __lowerCamelCase					:		List[Any]		   =     re.match(r"^mobilenet_v1_([^_]*)_([^_]*)$"						,   lowerCAmelCase__      )
       if matches:
              __lowerCamelCase					:		Union[str, Any]		   =     float(matches[1]      )
              __lowerCamelCase					:		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".
       __lowerCamelCase					:		Union[str, Any]		   =     1001
       __lowerCamelCase					:		List[Any]		   =     'imagenet-1k-id2label.json'
       __lowerCamelCase					:		List[Any]		   =     'huggingface/label-files'
       __lowerCamelCase					:		Union[str, Any]		   =     json.load(open(hf_hub_download(lowerCAmelCase__						,   lowerCAmelCase__						,   repo_type="dataset"      )						,   "r"      )      )
       __lowerCamelCase					:		Union[str, Any]		   =     {int(lowerCAmelCase__      ) + 1: v for k, v in idalabel.items()}
       __lowerCamelCase					:		Optional[Any]		   =     'background'
       __lowerCamelCase					:		Optional[int]		   =     idalabel
       __lowerCamelCase					:		Optional[int]		   =     {v: k for k, v in idalabel.items()}

       return config


def    lowercase_   (   ) ->      Any:



       '''simple docstring'''

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


@torch.no_grad()
def    lowercase_   (   _lowerCamelCase:	str						,   _lowerCamelCase:	Tuple						,   _lowerCamelCase:	Any						,   _lowerCamelCase:	Optional[Any]=False      ) ->      Union[str, Any]:



       '''simple docstring'''

       __lowerCamelCase					:		List[Any]		   =     get_mobilenet_va_config(lowerCAmelCase__      )

       # Load 🤗 model
       __lowerCamelCase					:		Dict		   =     MobileNetVaForImageClassification(lowerCAmelCase__      ).eval()

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

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

       assert logits.shape == (1, 1001)

       if model_name == "mobilenet_v1_1.0_224":
              __lowerCamelCase					:		List[Any]		   =     torch.tensor([-4.1739, -1.1233, 3.1205]      )
       elif model_name == "mobilenet_v1_0.75_192":
              __lowerCamelCase					:		Dict		   =     torch.tensor([-3.9440, -2.3141, -0.3333]      )
       else:
              __lowerCamelCase					:		Union[str, Any]		   =     None

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

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

       if push_to_hub:
              print("Pushing to the hub..."      )
              __lowerCamelCase					:		Union[str, Any]		   =     'google/' + model_name
              image_processor.push_to_hub(lowerCAmelCase__      )
              model.push_to_hub(lowerCAmelCase__      )


if __name__ == "__main__":
    __A  =				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  =				parser.parse_args()
    convert_movilevit_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )
"""simple docstring"""





# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__A  =				abspath(join(dirname(__file__), '''src'''))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)


def    lowercase_   (   _lowerCamelCase:	List[Any]      ) ->      Union[str, Any]:



							'''simple docstring'''

							config.addinivalue_line(
							    "markers"						,   "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested"      )
							config.addinivalue_line(
							    "markers"						,   "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested"      )
							config.addinivalue_line("markers"						,   "is_pipeline_test: mark test to run only when pipelines are tested"      )
							config.addinivalue_line("markers"						,   "is_staging_test: mark test to run only in the staging environment"      )
							config.addinivalue_line("markers"						,   "accelerate_tests: mark test that require accelerate"      )
							config.addinivalue_line("markers"						,   "tool_tests: mark the tool tests that are run on their specific schedule"      )


def    lowercase_   (   _lowerCamelCase:	Union[str, Any]      ) ->      Any:



							'''simple docstring'''

							from transformers.testing_utils import pytest_addoption_shared

							pytest_addoption_shared(_lowerCamelCase      )


def    lowercase_   (   _lowerCamelCase:	Optional[int]      ) ->      Tuple:



							'''simple docstring'''

							from transformers.testing_utils import pytest_terminal_summary_main

							__lowerCamelCase					:		Any		   =     terminalreporter.config.getoption("--make-reports"      )
							if make_reports:
														pytest_terminal_summary_main(_lowerCamelCase						,   id=_lowerCamelCase      )


def    lowercase_   (   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	Tuple      ) ->      Dict:



							'''simple docstring'''

							if exitstatus == 5:
														__lowerCamelCase					:		Optional[int]		   =     0


# Doctest custom flag to ignore output.
__A  =				doctest.register_optionflag('''IGNORE_RESULT''')

__A  =				doctest.OutputChecker
class  _snake_case     (					a__		):






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			int    ):
													if IGNORE_RESULT & optionflags:
																				return True
													return OutputChecker.check_output(self							,					UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    )

__A  =				CustomOutputChecker
__A  =				HfDoctestModule







__A  =				HfDocTestParser