

import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
      import jax.numpy as jnp

      from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormModel,
      )




class   UpperCamelCase			(  unittest.TestCase ):

    def __init__(      self			:   int				,     snake_case__			:   Tuple				,     snake_case__			:   Optional[int]=1_3				,     snake_case__			:   str=7				,     snake_case__			:   Tuple=True				,     snake_case__			:   Dict=True				,     snake_case__			:   Dict=True				,     snake_case__			:   List[str]=True				,     snake_case__			:   int=9_9				,     snake_case__			:   int=3_2				,     snake_case__			:   Union[str, Any]=5				,     snake_case__			:   str=4				,     snake_case__			:   Dict=3_7				,     snake_case__			:   str="gelu"				,     snake_case__			:   Tuple=0.1				,     snake_case__			:   Optional[Any]=0.1				,     snake_case__			:   List[str]=5_1_2				,     snake_case__			:   Optional[int]=1_6				,     snake_case__			:   Dict=2				,     snake_case__			:   int=0.02				,     snake_case__			:   str=4				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      parent
     SCREAMING_SNAKE_CASE		    =      batch_size
     SCREAMING_SNAKE_CASE		    =      seq_length
     SCREAMING_SNAKE_CASE		    =      is_training
     SCREAMING_SNAKE_CASE		    =      use_attention_mask
     SCREAMING_SNAKE_CASE		    =      use_token_type_ids
     SCREAMING_SNAKE_CASE		    =      use_labels
     SCREAMING_SNAKE_CASE		    =      vocab_size
     SCREAMING_SNAKE_CASE		    =      hidden_size
     SCREAMING_SNAKE_CASE		    =      num_hidden_layers
     SCREAMING_SNAKE_CASE		    =      num_attention_heads
     SCREAMING_SNAKE_CASE		    =      intermediate_size
     SCREAMING_SNAKE_CASE		    =      hidden_act
     SCREAMING_SNAKE_CASE		    =      hidden_dropout_prob
     SCREAMING_SNAKE_CASE		    =      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE		    =      max_position_embeddings
     SCREAMING_SNAKE_CASE		    =      type_vocab_size
     SCREAMING_SNAKE_CASE		    =      type_sequence_label_size
     SCREAMING_SNAKE_CASE		    =      initializer_range
     SCREAMING_SNAKE_CASE		    =      num_choices

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE		    =      None
     if self.use_attention_mask:
      SCREAMING_SNAKE_CASE		    =      random_attention_mask([self.batch_size, self.seq_length]     )

     SCREAMING_SNAKE_CASE		    =      None
     if self.use_token_type_ids:
      SCREAMING_SNAKE_CASE		    =      ids_tensor([self.batch_size, self.seq_length]				,     self.type_vocab_size     )

     SCREAMING_SNAKE_CASE		    =      RobertaPreLayerNormConfig(
         vocab_size=self.vocab_size				,     hidden_size=self.hidden_size				,     num_hidden_layers=self.num_hidden_layers				,     num_attention_heads=self.num_attention_heads				,     intermediate_size=self.intermediate_size				,     hidden_act=self.hidden_act				,     hidden_dropout_prob=self.hidden_dropout_prob				,     attention_probs_dropout_prob=self.attention_probs_dropout_prob				,     max_position_embeddings=self.max_position_embeddings				,     type_vocab_size=self.type_vocab_size				,     is_decoder=snake_case__				,     initializer_range=self.initializer_range				,     )

     return config, input_ids, token_type_ids, attention_mask

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      config_and_inputs
     SCREAMING_SNAKE_CASE		    =      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
     return config, inputs_dict




    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      config_and_inputs

     SCREAMING_SNAKE_CASE		    =      True
     SCREAMING_SNAKE_CASE		    =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
     SCREAMING_SNAKE_CASE		    =      ids_tensor([self.batch_size, self.seq_length]				,     vocab_size=2     )

     return (
         config,
         input_ids,
         token_type_ids,
         encoder_hidden_states,
         encoder_attention_mask,
     )




@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class   UpperCamelCase			(  SCREAMING_SNAKE_CASE						,      unittest.TestCase ):
    __UpperCamelCase    =True

    __UpperCamelCase    =(
        (
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      FlaxRobertaPreLayerNormModelTester(self     )




    @slow
    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     for model_class_name in self.all_model_classes:
      SCREAMING_SNAKE_CASE		    =      model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'				,     from_pt=snake_case__     )
      SCREAMING_SNAKE_CASE		    =      model(np.ones((1, 1)     )     )
      self.assertIsNotNone(snake_case__     )




@require_flax
class   UpperCamelCase			(  unittest.TestCase ):

    @slow
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'				,     from_pt=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]				,     dtype=jnp.intaa     )
     SCREAMING_SNAKE_CASE		    =      model(snake_case__     )[0]
     SCREAMING_SNAKE_CASE		    =      [1, 1_1, 5_0_2_6_5]
     self.assertEqual(list(output.shape     )				,     snake_case__     )
     # compare the actual values for a slice.
     SCREAMING_SNAKE_CASE		    =      np.array(
         [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]				,     dtype=np.floataa     )
     self.assertTrue(np.allclose(output[:, :3, :3]				,     snake_case__				,     atol=1E-4     )     )




    @slow
    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'				,     from_pt=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]				,     dtype=jnp.intaa     )
     SCREAMING_SNAKE_CASE		    =      model(snake_case__     )[0]
     # compare the actual values for a slice.
     SCREAMING_SNAKE_CASE		    =      np.array(
         [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]				,     dtype=np.floataa     )
     self.assertTrue(np.allclose(output[:, :3, :3]				,     snake_case__				,     atol=1E-4     )     )




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)

