




from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel


@require_tf
class 				UpperCamelCase_    :

    '''simple docstring'''






    UpperCAmelCase__							  =      MBartConfig
    UpperCAmelCase__							  =      {}
    UpperCAmelCase__							  =      '''gelu'''


    def __init__(			self      :	List[Any]					, UpperCAmelCase__      :	Optional[int]					, UpperCAmelCase__      :	str=13					, UpperCAmelCase__      :	List[str]=7					, UpperCAmelCase__      :	Optional[Any]=True					, UpperCAmelCase__      :	Optional[Any]=False					, UpperCAmelCase__      :	str=99					, UpperCAmelCase__      :	Union[str, Any]=32					, UpperCAmelCase__      :	Optional[Any]=2					, UpperCAmelCase__      :	Any=4					, UpperCAmelCase__      :	Union[str, Any]=37					, UpperCAmelCase__      :	List[Any]=0.1					, UpperCAmelCase__      :	Optional[int]=0.1					, UpperCAmelCase__      :	Any=20					, UpperCAmelCase__      :	Any=2					, UpperCAmelCase__      :	int=1					, UpperCAmelCase__      :	Union[str, Any]=0					, )						->Any:





           '''simple docstring'''

           A__       =    parent
           A__       =    batch_size
           A__       =    seq_length
           A__       =    is_training
           A__       =    use_labels
           A__       =    vocab_size
           A__       =    hidden_size
           A__       =    num_hidden_layers
           A__       =    num_attention_heads
           A__       =    intermediate_size
           A__       =    hidden_dropout_prob
           A__       =    attention_probs_dropout_prob
           A__       =    max_position_embeddings
           A__       =    eos_token_id
           A__       =    pad_token_id
           A__       =    bos_token_id


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->str:





           '''simple docstring'''

           A__       =    ids_tensor([self.batch_size, self.seq_length - 1]					, self.vocab_size)
           A__       =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)					, 1)
           A__       =    tf.concat([input_ids, eos_tensor]					, axis=1)

           A__       =    ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size)

           A__       =    self.config_cls(
               vocab_size=self.vocab_size					, d_model=self.hidden_size					, encoder_layers=self.num_hidden_layers					, decoder_layers=self.num_hidden_layers					, encoder_attention_heads=self.num_attention_heads					, decoder_attention_heads=self.num_attention_heads					, encoder_ffn_dim=self.intermediate_size					, decoder_ffn_dim=self.intermediate_size					, dropout=self.hidden_dropout_prob					, attention_dropout=self.attention_probs_dropout_prob					, max_position_embeddings=self.max_position_embeddings					, eos_token_ids=[2]					, bos_token_id=self.bos_token_id					, pad_token_id=self.pad_token_id					, decoder_start_token_id=self.pad_token_id					, **self.config_updates					, )
           A__       =    prepare_mbart_inputs_dict(UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__)
           return config, inputs_dict





    def        SCREAMING_SNAKE_CASE							(			self      :	str					, UpperCAmelCase__      :	Tuple					, UpperCAmelCase__      :	Optional[Any])						->Union[str, Any]:





           '''simple docstring'''

           A__       =    TFMBartModel(config=UpperCAmelCase__).get_decoder()
           A__       =    inputs_dict['''input_ids''']

           A__       =    input_ids[:1, :]
           A__       =    inputs_dict['''attention_mask'''][:1, :]
           A__       =    inputs_dict['''head_mask''']
           A__       =    1

           # first forward pass
           A__       =    model(UpperCAmelCase__					, attention_mask=UpperCAmelCase__					, head_mask=UpperCAmelCase__					, use_cache=UpperCAmelCase__)

           A__ ,							A__       =    outputs.to_tuple()
           A__       =    past_key_values[1]







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_=None ,				lowercase_=None ,				lowercase_=None ,				lowercase_=None ,				lowercase_=None ,				)   ->       Union[str, Any]:



       """simple docstring"""

       if attention_mask is None:
              A__       =    tf.cast(tf.math.not_equal(lowercase_ ,				config.pad_token_id						) ,				tf.inta						)
       if decoder_attention_mask is None:
              A__       =    tf.concat(
                  [
                      tf.ones(decoder_input_ids[:, :1].shape ,				dtype=tf.inta						),
                      tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,				config.pad_token_id						) ,				tf.inta						),
                  ] ,				axis=-1 ,				)
       if head_mask is None:
              A__       =    tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
       if decoder_head_mask is None:
              A__       =    tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
       if cross_attn_head_mask is None:
              A__       =    tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }


@require_tf
class 				UpperCamelCase_    ( UpperCAmelCase__		,					UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
    UpperCAmelCase__							  =      (TFMBartForConditionalGeneration,) if is_tf_available() else ()
    UpperCAmelCase__							  =      (
        {
            '''conversational''': TFMBartForConditionalGeneration,
            '''feature-extraction''': TFMBartModel,
            '''summarization''': TFMBartForConditionalGeneration,
            '''text2text-generation''': TFMBartForConditionalGeneration,
            '''translation''': TFMBartForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    UpperCAmelCase__							  =      True
    UpperCAmelCase__							  =      False
    UpperCAmelCase__							  =      False


    def        SCREAMING_SNAKE_CASE							(			self      :	str					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	str					, UpperCAmelCase__      :	Any					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Optional[int])						->int:





           '''simple docstring'''

           if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
                  # Exception encountered when calling layer '...'
                  return True

           return False


    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Any:





           '''simple docstring'''

           A__       =    TFMBartModelTester(self)
           A__       =    ConfigTester(self					, config_class=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->Optional[Any]:





           '''simple docstring'''

           self.config_tester.run_common_tests()





    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any])						->List[Any]:





           '''simple docstring'''

           A__       =    self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase__)


@require_sentencepiece
@require_tokenizers
@require_tf
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      [
        ''' UN Chief Says There Is No Military Solution in Syria''',
    ]
    UpperCAmelCase__							  =      [
        '''Şeful ONU declară că nu există o soluţie militară în Siria''',
    ]
    UpperCAmelCase__							  =      '''facebook/mbart-large-en-ro'''


    @cached_property
    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->int:





           '''simple docstring'''

           return AutoTokenizer.from_pretrained(self.model_name)


    @cached_property
    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->Union[str, Any]:





           '''simple docstring'''

           A__       =    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
           return model


    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any]					, **UpperCAmelCase__      :	List[Any])						->Union[str, Any]:





           '''simple docstring'''

           A__       =    self.translate_src_text(**UpperCAmelCase__)
           self.assertListEqual(self.expected_text					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, **UpperCAmelCase__      :	Dict)						->str:





           '''simple docstring'''

           A__       =    self.tokenizer(self.src_text					, **UpperCAmelCase__					, return_tensors='''tf''')
           A__       =    self.model.generate(
               model_inputs.input_ids					, attention_mask=model_inputs.attention_mask					, num_beams=2)
           A__       =    self.tokenizer.batch_decode(UpperCAmelCase__					, skip_special_tokens=UpperCAmelCase__)
           return generated_words





    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->List[Any]:





           '''simple docstring'''

           self._assert_generated_batch_equal_expected()








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)


