




import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil







@pytest.fixture
def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Union[str, Any]:



       """simple docstring"""

       A__       =    tmp_path / '''file.csv'''
       A__       =    textwrap.dedent(
           '''\
        header1,header2
        1,2
        10,20
        '''						)
       with open(lowercase_ ,				'''w'''						) as f:
              f.write(lowercase_						)
       return str(lowercase_						)







@pytest.fixture
def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

       A__       =    tmp_path / '''malformed_file.csv'''
       A__       =    textwrap.dedent(
           '''\
        header1,header2
        1,2
        10,20,
        '''						)
       with open(lowercase_ ,				'''w'''						) as f:
              f.write(lowercase_						)
       return str(lowercase_						)







@pytest.fixture
def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       str:



       """simple docstring"""

       A__       =    tmp_path / '''csv_with_image.csv'''
       A__       =    textwrap.dedent(
           f"""\
        image
        {image_file}
        """						)
       with open(lowercase_ ,				'''w'''						) as f:
              f.write(lowercase_						)
       return str(lowercase_						)







@pytest.fixture
def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       List[str]:



       """simple docstring"""

       A__       =    tmp_path / '''csv_with_label.csv'''
       A__       =    textwrap.dedent(
           '''\
        label
        good
        bad
        good
        '''						)
       with open(lowercase_ ,				'''w'''						) as f:
              f.write(lowercase_						)
       return str(lowercase_						)







@pytest.fixture
def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Tuple:



       """simple docstring"""

       A__       =    tmp_path / '''csv_with_int_list.csv'''
       A__       =    textwrap.dedent(
           '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        '''						)
       with open(lowercase_ ,				'''w'''						) as f:
              f.write(lowercase_						)
       return str(lowercase_						)







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_						)   ->       Dict:



       """simple docstring"""

       A__       =    Csv()
       A__       =    csv._generate_tables([[csv_file, malformed_csv_file]]						)
       with pytest.raises(lowercase_ ,				match='''Error tokenizing data'''						):
              for _ in generator:
                     pass
       assert any(
           record.levelname == '''ERROR'''
           and '''Failed to read file''' in record.message
           and os.path.basename(lowercase_						) in record.message
           for record in caplog.records						)







@require_pil
def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Union[str, Any]:



       """simple docstring"""

       with open(lowercase_ ,				encoding='''utf-8'''						) as f:
              A__       =    f.read().splitlines()[1]
       A__       =    Csv(encoding='''utf-8''' ,				features=Features({'''image''': Image()}						)						)
       A__       =    csv._generate_tables([[csv_file_with_image]]						)
       A__       =    pa.concat_tables([table for _, table in generator]						)
       assert pa_table.schema.field('''image'''						).type == Image()()
       A__       =    pa_table.to_pydict()['''image''']
       assert generated_content == [{"path": image_file, "bytes": None}]







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Tuple:



       """simple docstring"""

       with open(lowercase_ ,				encoding='''utf-8'''						) as f:
              A__       =    f.read().splitlines()[1:]
       A__       =    Csv(encoding='''utf-8''' ,				features=Features({'''label''': ClassLabel(names=['''good''', '''bad''']						)}						)						)
       A__       =    csv._generate_tables([[csv_file_with_label]]						)
       A__       =    pa.concat_tables([table for _, table in generator]						)
       assert pa_table.schema.field('''label'''						).type == ClassLabel(names=['''good''', '''bad''']						)()
       A__       =    pa_table.to_pydict()['''label''']
       assert generated_content == [ClassLabel(names=['''good''', '''bad''']						).straint(lowercase_						) for label in labels]







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Tuple:



       """simple docstring"""

       A__       =    Csv(encoding='''utf-8''' ,				sep=''',''' ,				converters={'''int_list''': lambda lowercase_						: [int(lowercase_						) for i in x.split()]}						)
       A__       =    csv._generate_tables([[csv_file_with_int_list]]						)
       A__       =    pa.concat_tables([table for _, table in generator]						)
       assert pa.types.is_list(pa_table.schema.field('''int_list'''						).type						)
       A__       =    pa_table.to_pydict()['''int_list''']
       assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]








import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Dict:



       """simple docstring"""

       A__       =    args.pruning_method
       A__       =    args.threshold

       A__       =    args.model_name_or_path.rstrip('''/'''						)
       A__       =    args.target_model_path

       print(f"""Load fine-pruned model from {model_name_or_path}"""						)
       A__       =    torch.load(os.path.join(lowercase_ ,				'''pytorch_model.bin'''						)						)
       A__       =    {}

       for name, tensor in model.items():
              if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
                     A__       =    tensor
                     print(f"""Copied layer {name}"""						)
              elif "classifier" in name or "qa_output" in name:
                     A__       =    tensor
                     print(f"""Copied layer {name}"""						)
              elif "bias" in name:
                     A__       =    tensor
                     print(f"""Copied layer {name}"""						)
              else:
                     if pruning_method == "magnitude":
                            A__       =    MagnitudeBinarizer.apply(inputs=lowercase_ ,				threshold=lowercase_						)
                            A__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     elif pruning_method == "topK":
                            if "mask_scores" in name:
                                   continue
                            A__       =    name[:-6]
                            A__       =    model[f"""{prefix_}mask_scores"""]
                            A__       =    TopKBinarizer.apply(lowercase_ ,				lowercase_						)
                            A__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     elif pruning_method == "sigmoied_threshold":
                            if "mask_scores" in name:
                                   continue
                            A__       =    name[:-6]
                            A__       =    model[f"""{prefix_}mask_scores"""]
                            A__       =    ThresholdBinarizer.apply(lowercase_ ,				lowercase_ ,				lowercase_						)
                            A__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     elif pruning_method == "l0":
                            if "mask_scores" in name:
                                   continue
                            A__       =    name[:-6]
                            A__       =    model[f"""{prefix_}mask_scores"""]
                            A__ ,							A__       =    -0.1, 1.1
                            A__       =    torch.sigmoid(lowercase_						)
                            A__       =    s * (r - l) + l
                            A__       =    s_bar.clamp(min=0.0 ,				max=1.0						)
                            A__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     else:
                            raise ValueError('''Unknown pruning method'''						)

       if target_model_path is None:
              A__       =    os.path.join(
                  os.path.dirname(lowercase_						) ,				f"""bertarized_{os.path.basename(lowercase_						)}"""						)

       if not os.path.isdir(lowercase_						):
              shutil.copytree(lowercase_ ,				lowercase_						)
              print(f"""\nCreated folder {target_model_path}"""						)

       torch.save(lowercase_ ,				os.path.join(lowercase_ ,				'''pytorch_model.bin'''						)						)
       print('''\nPruned model saved! See you later!'''						)


if __name__ == "__main__":
  _lowerCamelCase	:		Optional[Any]							       =   argparse.ArgumentParser()

  parser.add_argument(
      """--pruning_method""",
      choices=["""l0""", """magnitude""", """topK""", """sigmoied_threshold"""],
      type=str,
      required=True,
      help=(
          """Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"""
          """ sigmoied_threshold = Soft movement pruning)"""
      ),
  )
  parser.add_argument(
      """--threshold""",
      type=float,
      required=False,
      help=(
          """For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."""
          """For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."""
          """Not needed for `l0`"""
      ),
  )
  parser.add_argument(
      """--model_name_or_path""",
      type=str,
      required=True,
      help="""Folder containing the model that was previously fine-pruned""",
  )
  parser.add_argument(
      """--target_model_path""",
      default=None,
      type=str,
      required=False,
      help="""Folder containing the model that was previously fine-pruned""",
  )

  _lowerCamelCase	:		int							       =   parser.parse_args()

  main(args)


