

"""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     :		Tuple											=					logging.get_logger(__name__)





def        a_  (   _lowerCAmelCase						:				Optional[int]  ):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			MobileNetVaConfig(layer_norm_eps=0.0_0_1  )

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

       lowercase__    :	Union[str, Any]     					=			re.match(R'^mobilenet_v1_([^_]*)_([^_]*)$'     ,				lowerCamelCase__  )
       if matches:
              lowercase__    :	Union[str, Any]     					=			float(matches[1]  )
              lowercase__    :	Tuple     					=			int(matches[2]  )

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

       return config





def        a_  (   ):





       '''simple docstring'''

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





@torch.no_grad()
def        a_  (   _lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				Union[str, Any]     ,				_lowerCAmelCase						:				Tuple     ,				_lowerCAmelCase						:				Dict=False  ):





       '''simple docstring'''

       lowercase__    :	int     					=			get_mobilenet_va_config(lowerCamelCase__  )

       # Load 🤗 model
       lowercase__    :	Union[str, Any]     					=			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
       lowercase__    :	Union[str, Any]     					=			MobileNetVaImageProcessor(
           crop_size={'width': config.image_size, 'height': config.image_size}     ,				size={'shortest_edge': config.image_size + 32}     ,				)
       lowercase__    :	Any     					=			image_processor(images=prepare_img()     ,				return_tensors='pt'  )
       lowercase__    :	int     					=			model(**lowerCamelCase__  )
       lowercase__    :	List[Any]     					=			outputs.logits

       assert logits.shape == (1, 1001)

       if model_name == "mobilenet_v1_1.0_224":
              lowercase__    :	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":
              lowercase__    :	Any     					=			torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]  )
       else:
              lowercase__    :	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...'  )
              lowercase__    :	Union[str, Any]     					=			'google/' + model_name
              image_processor.push_to_hub(lowerCamelCase__  )
              model.push_to_hub(lowerCamelCase__  )


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


"""simple docstring"""





import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


_UpperCamelCase     :		Any											=					[
    {"dataset": "wikipedia", "config_name": "20220301.de"},
    {"dataset": "wikipedia", "config_name": "20220301.en"},
    {"dataset": "wikipedia", "config_name": "20220301.fr"},
    {"dataset": "wikipedia", "config_name": "20220301.frr"},
    {"dataset": "wikipedia", "config_name": "20220301.it"},
    {"dataset": "wikipedia", "config_name": "20220301.simple"},
    {"dataset": "snli", "config_name": "plain_text"},
    {"dataset": "eli5", "config_name": "LFQA_reddit"},
    {"dataset": "wiki40b", "config_name": "en"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.compressed"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.no_index"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.multiset.no_index"},
    {"dataset": "natural_questions", "config_name": "default"},
]





def        a_  (   _lowerCAmelCase						:				Optional[Any]=True  ):





       '''simple docstring'''

       if with_config:
              return [
                  {
                      "testcase_name": d["dataset"] + "/" + d["config_name"],
                      "dataset": d["dataset"],
                      "config_name": d["config_name"],
                  }
                  for d in DATASETS_ON_HF_GCP
              ]
       else:
              return [
                  {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
              ]


@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_a))
class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					str										=						None
      lowerCamelCase__	:					Optional[Any]										=						None


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      List[Any]:
             with TemporaryDirectory() as tmp_dir:
                    lowercase__    :	List[str]     					=			dataset_module_factory(a	,    cache_dir=a							)

                    lowercase__    :	List[Any]     					=			import_main_class(dataset_module.module_path	,    dataset=a							)

                    lowercase__    :	DatasetBuilder     					=			builder_cls(
                        cache_dir=a	,    config_name=a	,    hash=dataset_module.hash	,    )

                    lowercase__    :	Union[str, Any]     					=			'/'.join(
                        [
                            HF_GCP_BASE_URL,
                            builder_instance._relative_data_dir(with_hash=a							).replace(os.sep	,    '/'							),
                            config.DATASET_INFO_FILENAME,
                        ]							)
                    lowercase__    :	Union[str, Any]     					=			cached_path(a	,    cache_dir=a							)
                    self.assertTrue(os.path.exists(a							)							)





@pytest.mark.integration
def        a_  (   _lowerCAmelCase						:				str  ):





       '''simple docstring'''

       lowercase__    :	Union[str, Any]     					=			tmp_path_factory.mktemp('test_hf_gcp'  ) / 'test_wikipedia_simple'
       lowercase__    :	int     					=			dataset_module_factory('wikipedia'     ,				cache_dir=_lowerCAmelCase  )
       lowercase__    :	Optional[int]     					=			import_main_class(dataset_module.module_path  )
       lowercase__    :	DatasetBuilder     					=			builder_cls(
           cache_dir=_lowerCAmelCase     ,				config_name='20220301.frr'     ,				hash=dataset_module.hash     ,				)
       # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
       lowercase__    :	Optional[int]     					=			None
       builder_instance.download_and_prepare()
       lowercase__    :	Optional[int]     					=			builder_instance.as_dataset()
       assert ds






@pytest.mark.integration
def        a_  (   _lowerCAmelCase						:				Optional[Any]  ):





       '''simple docstring'''

       lowercase__    :	Optional[int]     					=			dataset_module_factory('wikipedia'     ,				cache_dir=_lowerCAmelCase  )
       lowercase__    :	List[str]     					=			import_main_class(dataset_module.module_path     ,				dataset=_lowerCAmelCase  )
       lowercase__    :	DatasetBuilder     					=			builder_cls(
           cache_dir=_lowerCAmelCase     ,				config_name='20220301.frr'     ,				hash=dataset_module.hash     ,				)
       lowercase__    :	Union[str, Any]     					=			builder_instance.as_streaming_dataset()
       assert ds
       assert isinstance(_lowerCAmelCase     ,				_lowerCAmelCase  )
       assert "train" in ds
       assert isinstance(ds['train']     ,				_lowerCAmelCase  )
       assert next(iter(ds['train']  )  )

