





"""simple docstring"""




import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCamelCase_	 ="""\
@inproceedings{ramshaw-marcus-1995-text,
    title = \"Text Chunking using Transformation-Based Learning\",
    author = \"Ramshaw, Lance  and
      Marcus, Mitch\",
    booktitle = \"Third Workshop on Very Large Corpora\",
    year = \"1995\",
    url = \"https://www.aclweb.org/anthology/W95-0107\",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
"""

UpperCamelCase_	 ="""\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
"""

UpperCamelCase_	 ="""
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode=\"strict\". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        \"warn\". \"warn\" acts as 0, but the warning is raised.

Returns:
    'scores': dict. Summary of the scores for overall and per type
        Overall:
            'accuracy': accuracy,
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure,
        Per type:
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> seqeval = datasets.load_metric(\"seqeval\")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']
    >>> print(results[\"overall_f1\"])
    0.5
    >>> print(results[\"PER\"][\"f1\"])
    1.0
"""




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






				def 							snake_case      (					self						:       List[Any]				)      -> List[str]:




										'''simple docstring'''



										return datasets.MetricInfo(
										    description=_DESCRIPTION,					citation=_CITATION,					homepage='''https://github.com/chakki-works/seqeval''',					inputs_description=_KWARGS_DESCRIPTION,					features=datasets.Features(
										        {
										            '''predictions''': datasets.Sequence(datasets.Value('''string''',					id='''label'''				),					id='''sequence'''				),
										            '''references''': datasets.Sequence(datasets.Value('''string''',					id='''label'''				),					id='''sequence'''				),
										        }				),					codebase_urls=['''https://github.com/chakki-works/seqeval'''],					reference_urls=['''https://github.com/chakki-works/seqeval'''],					)






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       bool = False,					lowerCAmelCase__						:       Optional[str] = None,					lowerCAmelCase__						:       Optional[str] = None,					lowerCAmelCase__						:       Optional[List[int]] = None,					lowerCAmelCase__						:       Union[str, int] = "warn",					)      -> List[str]:




										'''simple docstring'''



										if scheme is not None:
																try:
																						_UpperCamelCase     :   Tuple      =       importlib.import_module('''seqeval.scheme'''				)
																						_UpperCamelCase     :   Union[str, Any]      =       getattr(lowerCAmelCase__,					lowerCAmelCase__				)
																except AttributeError:
																						raise ValueError(f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""				)
										_UpperCamelCase     :   int      =       classification_report(
										    y_true=lowerCAmelCase__,					y_pred=lowerCAmelCase__,					suffix=lowerCAmelCase__,					output_dict=lowerCAmelCase__,					scheme=lowerCAmelCase__,					mode=lowerCAmelCase__,					sample_weight=lowerCAmelCase__,					zero_division=lowerCAmelCase__,					)
										report.pop('''macro avg'''				)
										report.pop('''weighted avg'''				)
										_UpperCamelCase     :   Any      =       report.pop('''micro avg'''				)

										_UpperCamelCase     :   int      =       {
										    type_name: {
										        '''precision''': score['''precision'''],
										        '''recall''': score['''recall'''],
										        '''f1''': score['''f1-score'''],
										        '''number''': score['''support'''],
										    }
										    for type_name, score in report.items()
										}
										_UpperCamelCase     :   Dict      =       overall_score['''precision''']
										_UpperCamelCase     :   int      =       overall_score['''recall''']
										_UpperCamelCase     :   Tuple      =       overall_score['''f1-score''']
										_UpperCamelCase     :   List[Any]      =       accuracy_score(y_true=lowerCAmelCase__,					y_pred=lowerCAmelCase__				)

										return scores








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         =	logging.get_logger(__name__)






def 						_lowerCAmelCase    (					__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						MobileNetVaConfig(layer_norm_eps=0.001  )

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

 __SCREAMING_SNAKE_CASE							:     Tuple							=						re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$"		,						__lowerCamelCase  )
 if matches:
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						float(matches[1]  )
  __SCREAMING_SNAKE_CASE							:     str							=						int(matches[2]  )

 if model_name.startswith("deeplabv3_"  ):
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						8
  __SCREAMING_SNAKE_CASE							:     Dict							=						21
  __SCREAMING_SNAKE_CASE							:     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".
  __SCREAMING_SNAKE_CASE							:     int							=						1001
  __SCREAMING_SNAKE_CASE							:     Dict							=						"imagenet-1k-id2label.json"

 __SCREAMING_SNAKE_CASE							:     List[Any]							=						"huggingface/label-files"
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						json.load(open(hf_hub_download(__lowerCamelCase		,						__lowerCamelCase		,						repo_type="dataset"  )		,						"r"  )  )

 if config.num_labels == 1001:
  __SCREAMING_SNAKE_CASE							:     int							=						{int(__lowerCamelCase  ) + 1: v for k, v in idalabel.items()}
  __SCREAMING_SNAKE_CASE							:     int							=						"background"
 else:
  __SCREAMING_SNAKE_CASE							:     Any							=						{int(__lowerCamelCase  ): v for k, v in idalabel.items()}

 __SCREAMING_SNAKE_CASE							:     str							=						idalabel
 __SCREAMING_SNAKE_CASE							:     Tuple							=						{v: k for k, v in idalabel.items()}

 return config






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						"http://images.cocodataset.org/val2017/000000039769.jpg"
 __SCREAMING_SNAKE_CASE							:     Any							=						Image.open(requests.get(__lowerCamelCase		,						stream=__lowerCamelCase  ).raw  )
 return im






@torch.no_grad()
def 						_lowerCAmelCase    (					__lowerCamelCase :  str		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Tuple=False  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Any							=						get_mobilenet_va_config(__lowerCamelCase  )

 # Load 🤗 model
 if model_name.startswith("deeplabv3_"  ):
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						MobileNetVaForSemanticSegmentation(__lowerCamelCase  ).eval()
 else:
  __SCREAMING_SNAKE_CASE							:     str							=						MobileNetVaForImageClassification(__lowerCamelCase  ).eval()

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

 # Check outputs on an image, prepared by MobileNetV2ImageProcessor
 __SCREAMING_SNAKE_CASE							:     Tuple							=						MobileNetVaImageProcessor(
     crop_size={"width": config.image_size, "height": config.image_size}		,						size={"shortest_edge": config.image_size + 32}		,						)
 __SCREAMING_SNAKE_CASE							:     Any							=						image_processor(images=prepare_img()		,						return_tensors="pt"  )
 __SCREAMING_SNAKE_CASE							:     List[str]							=						model(**__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Any							=						outputs.logits

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

  if model_name == "deeplabv3_mobilenet_v2_1.0_513":
   __SCREAMING_SNAKE_CASE							:     List[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]		,						__lowerCamelCase		,						atol=1E-4  )
 else:
  assert logits.shape == (1, 1001)

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


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






