

import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
      a_     :    str     		=   "pt"
elif is_tf_available():
      a_     :    Any     		=   "tf"
else:
      a_     :    List[str]     		=   "jax"




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE						,      unittest.TestCase ):
    __UpperCamelCase    =PerceiverTokenizer
    __UpperCamelCase    =False

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     super().setUp()
     SCREAMING_SNAKE_CASE		    =      PerceiverTokenizer()
     tokenizer.save_pretrained(self.tmpdirname     )

    @cached_property
    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'     )

    def UpperCamelCase      (      self			:   List[str]				,     **snake_case__			:   Union[str, Any]     ):

     """simple docstring"""






     return self.tokenizer_class.from_pretrained(self.tmpdirname				,     **snake_case__     )

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   str				,     snake_case__			:   Optional[Any]=False				,     snake_case__			:   Optional[Any]=2_0				,     snake_case__			:   str=5     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      []
     for i in range(len(snake_case__     )     ):
      try:
       SCREAMING_SNAKE_CASE		    =      tokenizer.decode([i]				,     clean_up_tokenization_spaces=snake_case__     )
      except UnicodeDecodeError:
       pass
      toks.append((i, tok)     )

     SCREAMING_SNAKE_CASE		    =      list(filter(lambda snake_case__     : re.match(r'^[ a-zA-Z]+$'				,     t[1]     )				,     snake_case__     )     )
     SCREAMING_SNAKE_CASE		    =      list(filter(lambda snake_case__     : [t[0]] == tokenizer.encode(t[1]				,     add_special_tokens=snake_case__     )				,     snake_case__     )     )
     if max_length is not None and len(snake_case__     ) > max_length:
      SCREAMING_SNAKE_CASE		    =      toks[:max_length]
     if min_length is not None and len(snake_case__     ) < min_length and len(snake_case__     ) > 0:
      while len(snake_case__     ) < min_length:
       SCREAMING_SNAKE_CASE		    =      toks + toks
        # toks_str = [t[1] for t in toks]
     SCREAMING_SNAKE_CASE		    =      [t[0] for t in toks]

     # Ensure consistency
     SCREAMING_SNAKE_CASE		    =      tokenizer.decode(snake_case__				,     clean_up_tokenization_spaces=snake_case__     )
     if " " not in output_txt and len(snake_case__     ) > 1:
      SCREAMING_SNAKE_CASE		    =      (
          tokenizer.decode([toks_ids[0]]				,     clean_up_tokenization_spaces=snake_case__     )
          + ' '
          + tokenizer.decode(toks_ids[1:]				,     clean_up_tokenization_spaces=snake_case__     )
      )
     if with_prefix_space:
      SCREAMING_SNAKE_CASE		    =      ' ' + output_txt
     SCREAMING_SNAKE_CASE		    =      tokenizer.encode(snake_case__				,     add_special_tokens=snake_case__     )
     return output_txt, output_ids

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.perceiver_tokenizer
     SCREAMING_SNAKE_CASE		    =      'Unicode €.'
     SCREAMING_SNAKE_CASE		    =      tokenizer(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
     self.assertEqual(encoded['input_ids']				,     snake_case__     )

     # decoding
     SCREAMING_SNAKE_CASE		    =      tokenizer.decode(snake_case__     )
     self.assertEqual(snake_case__				,     '[CLS]Unicode €.[SEP]'     )

     SCREAMING_SNAKE_CASE		    =      tokenizer('e è é ê ë'     )
     SCREAMING_SNAKE_CASE		    =      [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
     self.assertEqual(encoded['input_ids']				,     snake_case__     )
     # decoding
     SCREAMING_SNAKE_CASE		    =      tokenizer.decode(snake_case__     )
     self.assertEqual(snake_case__				,     '[CLS]e è é ê ë[SEP]'     )

     # encode/decode, but with `encode` instead of `__call__`
     self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'     )     )				,     '[CLS]e è é ê ë[SEP]'     )

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.perceiver_tokenizer
     SCREAMING_SNAKE_CASE		    =      ['A long paragraph for summarization.', 'Another paragraph for summarization.']
     # fmt: off
     SCREAMING_SNAKE_CASE		    =      [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
     # fmt: on
     SCREAMING_SNAKE_CASE		    =      tokenizer(snake_case__				,     padding=snake_case__				,     return_tensors=snake_case__     )
     self.assertIsInstance(snake_case__				,     snake_case__     )

     if FRAMEWORK != "jax":
      SCREAMING_SNAKE_CASE		    =      list(batch.input_ids.numpy()[0]     )
     else:
      SCREAMING_SNAKE_CASE		    =      list(batch.input_ids.tolist()[0]     )

     self.assertListEqual(snake_case__				,     snake_case__     )

     self.assertEqual((2, 3_8)				,     batch.input_ids.shape     )
     self.assertEqual((2, 3_8)				,     batch.attention_mask.shape     )

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.perceiver_tokenizer
     SCREAMING_SNAKE_CASE		    =      ['A long paragraph for summarization.', 'Another paragraph for summarization.']
     SCREAMING_SNAKE_CASE		    =      tokenizer(snake_case__				,     padding=snake_case__				,     return_tensors=snake_case__     )
     # check if input_ids are returned and no decoder_input_ids
     self.assertIn('input_ids'				,     snake_case__     )
     self.assertIn('attention_mask'				,     snake_case__     )
     self.assertNotIn('decoder_input_ids'				,     snake_case__     )
     self.assertNotIn('decoder_attention_mask'				,     snake_case__     )

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.perceiver_tokenizer
     SCREAMING_SNAKE_CASE		    =      [
         'Summary of the text.',
         'Another summary.',
     ]
     SCREAMING_SNAKE_CASE		    =      tokenizer(
         text_target=snake_case__				,     max_length=3_2				,     padding='max_length'				,     truncation=snake_case__				,     return_tensors=snake_case__     )
     self.assertEqual(3_2				,     targets['input_ids'].shape[1]     )

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.get_tokenizers()
     for tokenizer in tokenizers:
      with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
       self.assertNotEqual(tokenizer.model_max_length				,     4_2     )

        # Now let's start the test
     SCREAMING_SNAKE_CASE		    =      self.get_tokenizers()
     for tokenizer in tokenizers:
      with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
       # Isolate this from the other tests because we save additional tokens/etc
       SCREAMING_SNAKE_CASE		    =      tempfile.mkdtemp()

       SCREAMING_SNAKE_CASE		    =      ' He is very happy, UNwant\u00E9d,running'
       SCREAMING_SNAKE_CASE		    =      tokenizer.encode(snake_case__				,     add_special_tokens=snake_case__     )
       tokenizer.save_pretrained(snake_case__     )

       SCREAMING_SNAKE_CASE		    =      tokenizer.__class__.from_pretrained(snake_case__     )
       SCREAMING_SNAKE_CASE		    =      after_tokenizer.encode(snake_case__				,     add_special_tokens=snake_case__     )
       self.assertListEqual(snake_case__				,     snake_case__     )

       shutil.rmtree(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      self.get_tokenizers(model_max_length=4_2     )
     for tokenizer in tokenizers:
      with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
       # Isolate this from the other tests because we save additional tokens/etc
       SCREAMING_SNAKE_CASE		    =      tempfile.mkdtemp()

       SCREAMING_SNAKE_CASE		    =      ' He is very happy, UNwant\u00E9d,running'
       tokenizer.add_tokens(['bim', 'bambam']     )
       SCREAMING_SNAKE_CASE		    =      tokenizer.additional_special_tokens
       additional_special_tokens.append('new_additional_special_token'     )
       tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}     )
       SCREAMING_SNAKE_CASE		    =      tokenizer.encode(snake_case__				,     add_special_tokens=snake_case__     )
       tokenizer.save_pretrained(snake_case__     )

       SCREAMING_SNAKE_CASE		    =      tokenizer.__class__.from_pretrained(snake_case__     )
       SCREAMING_SNAKE_CASE		    =      after_tokenizer.encode(snake_case__				,     add_special_tokens=snake_case__     )
       self.assertListEqual(snake_case__				,     snake_case__     )
       self.assertIn('new_additional_special_token'				,     after_tokenizer.additional_special_tokens     )
       self.assertEqual(after_tokenizer.model_max_length				,     4_2     )

       SCREAMING_SNAKE_CASE		    =      tokenizer.__class__.from_pretrained(snake_case__				,     model_max_length=4_3     )
       self.assertEqual(tokenizer.model_max_length				,     4_3     )

       shutil.rmtree(snake_case__     )

    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      []
     if self.test_slow_tokenizer:
      tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())     )

     if self.test_rust_tokenizer:
      tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())     )

     for tokenizer_class, tokenizer_utils in tokenizer_list:
      with tempfile.TemporaryDirectory() as tmp_dir:
       tokenizer_utils.save_pretrained(snake_case__     )

       with open(os.path.join(snake_case__				,     'special_tokens_map.json'     )				,     encoding='utf-8'     ) as json_file:
        SCREAMING_SNAKE_CASE		    =      json.load(snake_case__     )

       with open(os.path.join(snake_case__				,     'tokenizer_config.json'     )				,     encoding='utf-8'     ) as json_file:
        SCREAMING_SNAKE_CASE		    =      json.load(snake_case__     )

       SCREAMING_SNAKE_CASE		    =      [F"""<extra_id_{i}>""" for i in range(1_2_5     )]

       SCREAMING_SNAKE_CASE		    =      added_tokens_extra_ids + [
           'an_additional_special_token'
       ]
       SCREAMING_SNAKE_CASE		    =      added_tokens_extra_ids + [
           'an_additional_special_token'
       ]

       with open(os.path.join(snake_case__				,     'special_tokens_map.json'     )				,     'w'				,     encoding='utf-8'     ) as outfile:
        json.dump(snake_case__				,     snake_case__     )
       with open(os.path.join(snake_case__				,     'tokenizer_config.json'     )				,     'w'				,     encoding='utf-8'     ) as outfile:
        json.dump(snake_case__				,     snake_case__     )

       # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
       # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
       # "special_tokens_map.json" files
       SCREAMING_SNAKE_CASE		    =      tokenizer_class.from_pretrained(
           snake_case__				,     )
       self.assertIn(
           'an_additional_special_token'				,     tokenizer_without_change_in_init.additional_special_tokens     )
       self.assertEqual(
           ['an_additional_special_token']				,     tokenizer_without_change_in_init.convert_ids_to_tokens(
               tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']     )     )				,     )

       # Now we test that we can change the value of additional_special_tokens in the from_pretrained
       SCREAMING_SNAKE_CASE		    =      added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'				,     lstrip=snake_case__     )]
       SCREAMING_SNAKE_CASE		    =      tokenizer_class.from_pretrained(
           snake_case__				,     additional_special_tokens=snake_case__				,     )

       self.assertIn('a_new_additional_special_token'				,     tokenizer.additional_special_tokens     )
       self.assertEqual(
           ['a_new_additional_special_token']				,     tokenizer.convert_ids_to_tokens(
               tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']     )     )				,     )

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.perceiver_tokenizer
     self.assertEqual(tokenizer.decode([1_7_8]     )				,     '�'     )

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     pass

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     pass

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     pass

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     pass




    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.get_tokenizers(fast=snake_case__				,     do_lower_case=snake_case__     )
     for tokenizer in tokenizers:
      with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
       SCREAMING_SNAKE_CASE		    =      ['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
       SCREAMING_SNAKE_CASE		    =      tokenizer.convert_tokens_to_string(snake_case__     )

       self.assertIsInstance(snake_case__				,     snake_case__     )




import argparse
import os
import shutil

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      args.pruning_method
 SCREAMING_SNAKE_CASE		    =      args.threshold

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

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

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

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

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

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


if __name__ == "__main__":
      a_     :    Union[str, 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",
      )

      a_     :    Dict     		=   parser.parse_args()

      main(args)

