




'''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()
a		:	str       =      logging.get_logger(__name__)





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Tuple							       =      MobileNetVaConfig(layer_norm_eps=0.001						)

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

    _a				:							Dict							       =      re.match(r"""^.*mobilenet_v2_([^_]*)_([^_]*)$"""   ,	UpperCAmelCase_						)
    if matches:
        _a				:							Optional[int]							       =      float(matches[1]						)
        _a				:							List[str]							       =      int(matches[2]						)

    if model_name.startswith("""deeplabv3_"""						):
        _a				:							Any							       =      8
        _a				:							Tuple							       =      21
        _a				:							Tuple							       =      """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				:							str							       =      1_001
        _a				:							List[str]							       =      """imagenet-1k-id2label.json"""

    _a				:							Union[str, Any]							       =      """huggingface/label-files"""
    _a				:							Dict							       =      json.load(open(hf_hub_download(UpperCAmelCase_   ,	UpperCAmelCase_   ,	repo_type="""dataset"""						)   ,	"""r"""						)						)

    if config.num_labels == 1_001:
        _a				:							Union[str, Any]							       =      {int(UpperCAmelCase_						) + 1: v for k, v in idalabel.items()}
        _a				:							Tuple							       =      """background"""
    else:
        _a				:							Dict							       =      {int(UpperCAmelCase_						): v for k, v in idalabel.items()}

    _a				:							Any							       =      idalabel
    _a				:							Optional[Any]							       =      {v: k for k, v in idalabel.items()}

    return config





def UpperCAmelCase    (       ):
    _a				:							List[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						):
    _a				:							Any							       =      get_mobilenet_va_config(UpperCAmelCase_						)

    # Load 🤗 model
    if model_name.startswith("""deeplabv3_"""						):
        _a				:							Dict							       =      MobileNetVaForSemanticSegmentation(UpperCAmelCase_						).eval()
    else:
        _a				:							Optional[int]							       =      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				:							Optional[int]							       =      MobileNetVaImageProcessor(
        crop_size={"""width""": config.image_size, """height""": config.image_size}   ,	size={"""shortest_edge""": config.image_size + 32}   ,	)
    _a				:							List[Any]							       =      image_processor(images=prepare_img()   ,	return_tensors="""pt"""						)
    _a				:							Optional[int]							       =      model(**UpperCAmelCase_						)
    _a				:							int							       =      outputs.logits

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

        if model_name == "deeplabv3_mobilenet_v2_1.0_513":
            _a				:							Union[str, Any]							       =      torch.tensor(
                [
                    [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
                    [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
                    [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
                ]						)

        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, 1_001)

        if model_name == "mobilenet_v2_1.4_224":
            _a				:							Tuple							       =      torch.tensor([0.0181, -1.0015, 0.4688]						)
        elif model_name == "mobilenet_v2_1.0_224":
            _a				:							Optional[Any]							       =      torch.tensor([0.2445, -1.1993, 0.1905]						)
        elif model_name == "mobilenet_v2_0.75_160":
            _a				:							Dict							       =      torch.tensor([0.2482, 0.4136, 0.6669]						)
        elif model_name == "mobilenet_v2_0.35_96":
            _a				:							Optional[Any]							       =      torch.tensor([0.1451, -0.4624, 0.7192]						)
        else:
            _a				:							int							       =      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				:							List[str]							       =      """google/""" + model_name
        image_processor.push_to_hub(UpperCAmelCase_						)
        model.push_to_hub(UpperCAmelCase_						)


if __name__ == "__main__":
  a		:	Optional[int]       =      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.'''
  )

  a		:	Tuple       =      parser.parse_args()
  convert_movilevit_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )








'''simple docstring'''





import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def UpperCAmelCase    (       ):
    def filter_log(UpperCAmelCase_						):
        return False if "This IS expected if you are initializing" in record.msg else True

    _a				:							Optional[Any]							       =      datasets.utils.logging.get_logger("""transformers.modeling_utils"""						)
    logger.addFilter(UpperCAmelCase_						)
    try:
        yield
    finally:
        logger.removeFilter(UpperCAmelCase_						)


a		:	Tuple       =      '''\
@inproceedings{bert-score,
  title={BERTScore: Evaluating Text Generation with BERT},
  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=SkeHuCVFDr}
}
'''

a		:	List[str]       =      '''\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.
'''

a		:	Dict       =      '''
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. \'en\').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bertscore = datasets.load_metric("bertscore")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")
    >>> print([round(v, 2) for v in results["f1"]])
    [1.0, 1.0]
'''





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,		_KWARGS_DESCRIPTION       )
class      UpperCamelCase       (	datasets.Metric       ):



  def     a_							(			self ):







      '''simple docstring'''


      return datasets.MetricInfo(
          description=_DESCRIPTION		,     citation=_CITATION		,     homepage="""https://github.com/Tiiiger/bert_score"""		,     inputs_description=_KWARGS_DESCRIPTION		,     features=datasets.Features(
              {
                  """predictions""": datasets.Value("""string"""		,     id="""sequence""" ),
                  """references""": datasets.Sequence(datasets.Value("""string"""		,     id="""sequence""" )		,     id="""references""" ),
              } )		,     codebase_urls=["""https://github.com/Tiiiger/bert_score"""]		,     reference_urls=[
              """https://github.com/Tiiiger/bert_score""",
              """https://arxiv.org/abs/1904.09675""",
          ]		,     )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False		,     __lowerCAmelCase=None		,     __lowerCAmelCase=64		,     __lowerCAmelCase=4		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     ):







      '''simple docstring'''


      _a				:							Any							       =      bert_score.utils.get_hash
      _a				:							int							       =      bert_score.BERTScorer

      if version.parse(bert_score.__version__ ) >= version.parse("""0.3.10""" ):
          _a				:							Union[str, Any]							       =      functools.partial(__lowerCAmelCase		,     use_fast_tokenizer=__lowerCAmelCase )
          _a				:							Dict							       =      functools.partial(__lowerCAmelCase		,     use_fast_tokenizer=__lowerCAmelCase )
      elif use_fast_tokenizer:
          raise ImportWarning(
              """To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"""
              """You can install it with `pip install \"bert-score>=0.3.10\"`.""" )

      if model_type is None:
          assert lang is not None, "either lang or model_type should be specified"
          _a				:							List[str]							       =      bert_score.utils.langamodel[lang.lower()]

      if num_layers is None:
          _a				:							Dict							       =      bert_score.utils.modelalayers[model_type]

      _a				:							Union[str, Any]							       =      get_hash(
          model=__lowerCAmelCase		,     num_layers=__lowerCAmelCase		,     idf=__lowerCAmelCase		,     rescale_with_baseline=__lowerCAmelCase		,     use_custom_baseline=baseline_path is not None		,     )

      with filter_logging_context():
          if not hasattr(self		,     """cached_bertscorer""" ) or self.cached_bertscorer.hash != hashcode:
              _a				:							Optional[Any]							       =      scorer(
                  model_type=__lowerCAmelCase		,     num_layers=__lowerCAmelCase		,     batch_size=__lowerCAmelCase		,     nthreads=__lowerCAmelCase		,     all_layers=__lowerCAmelCase		,     idf=__lowerCAmelCase		,     device=__lowerCAmelCase		,     lang=__lowerCAmelCase		,     rescale_with_baseline=__lowerCAmelCase		,     baseline_path=__lowerCAmelCase		,     )

      ((_a)       , (_a)       , (_a))				:							Optional[Any]							       =      self.cached_bertscorer.score(
          cands=__lowerCAmelCase		,     refs=__lowerCAmelCase		,     verbose=__lowerCAmelCase		,     batch_size=__lowerCAmelCase		,     )
      _a				:							Tuple							       =      {
          """precision""": P.tolist(),
          """recall""": R.tolist(),
          """f1""": F.tolist(),
          """hashcode""": hashcode,
      }
      return output_dict



  def     a_							(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     **__lowerCAmelCase ):







      '''simple docstring'''


      if references is not None:
          _a				:							Dict							       =      [[ref] if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) else ref for ref in references]
      super().add_batch(predictions=__lowerCAmelCase		,     references=__lowerCAmelCase		,     **__lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     **__lowerCAmelCase ):







      '''simple docstring'''


      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ):
          _a				:							Optional[Any]							       =      [reference]
      super().add(prediction=__lowerCAmelCase		,     reference=__lowerCAmelCase		,     **__lowerCAmelCase )


