



"""simple docstring"""
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
			import os

			# The slow tests are often failing with OOM error on GPU
			# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
			# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
			__SCREAMING_SNAKE_CASE     :  Dict										=					'''platform'''

			import jax
			import jax.numpy as jnp

			from transformers import AutoTokenizer
			from transformers.models.mbart.modeling_flax_mbart import (
			    FlaxMBartForConditionalGeneration,
			    FlaxMBartForQuestionAnswering,
			    FlaxMBartForSequenceClassification,
			    FlaxMBartModel,
			    shift_tokens_right,
			)



def 				lowerCAmelCase_(      lowercase_   :							Any  ,      lowercase_   :							List[str]  ,      lowercase_   :							Optional[Any]=None  ,      lowercase_   :							Tuple=None  ,      lowercase_   :							Dict=None  ,      lowercase_   :							Tuple=None  ,      lowercase_   :							Any=None  ,      lowercase_   :							List[Any]=None  ,      )		->				Tuple:

				if attention_mask is None:
								_lowerCamelCase					     = np.where(input_ids != config.pad_token_id  ,      1  ,      0							)
				if decoder_attention_mask is None:
								_lowerCamelCase					     = np.where(decoder_input_ids != config.pad_token_id  ,      1  ,      0							)
				if head_mask is None:
								_lowerCamelCase					     = np.ones((config.encoder_layers, config.encoder_attention_heads)							)
				if decoder_head_mask is None:
								_lowerCamelCase					     = np.ones((config.decoder_layers, config.decoder_attention_heads)							)
				if cross_attn_head_mask is None:
								_lowerCamelCase					     = np.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,
				}


class 							lowerCamelCase_(      unittest.TestCase			):





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__=1_3 ,				lowerCamelCase__=7 ,				lowerCamelCase__=True ,				lowerCamelCase__=False ,				lowerCamelCase__=9_9 ,				lowerCamelCase__=1_6 ,				lowerCamelCase__=2 ,				lowerCamelCase__=4 ,				lowerCamelCase__=4 ,				lowerCamelCase__="gelu" ,				lowerCamelCase__=0.1 ,				lowerCamelCase__=0.1 ,				lowerCamelCase__=3_2 ,				lowerCamelCase__=2 ,				lowerCamelCase__=1 ,				lowerCamelCase__=0 ,				lowerCamelCase__=2 ,				lowerCamelCase__=0.0_2 ,				):
									_lowerCamelCase					     = parent
									_lowerCamelCase					     = batch_size
									_lowerCamelCase					     = seq_length
									_lowerCamelCase					     = is_training
									_lowerCamelCase					     = use_labels
									_lowerCamelCase					     = vocab_size
									_lowerCamelCase					     = hidden_size
									_lowerCamelCase					     = num_hidden_layers
									_lowerCamelCase					     = num_attention_heads
									_lowerCamelCase					     = intermediate_size
									_lowerCamelCase					     = hidden_act
									_lowerCamelCase					     = hidden_dropout_prob
									_lowerCamelCase					     = attention_probs_dropout_prob
									_lowerCamelCase					     = max_position_embeddings
									_lowerCamelCase					     = eos_token_id
									_lowerCamelCase					     = pad_token_id
									_lowerCamelCase					     = bos_token_id
									_lowerCamelCase					     = decoder_start_token_id
									_lowerCamelCase					     = initializer_range

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,				self.vocab_size      ) ,				3 ,				self.vocab_size      )
									_lowerCamelCase					     = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,				dtype=np.intaa      )) ,				-1      )

									_lowerCamelCase					     = shift_tokens_right(lowercase__ ,				1      )

									_lowerCamelCase					     = MBartConfig(
									    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_id=self.eos_token_id ,				bos_token_id=self.bos_token_id ,				pad_token_id=self.pad_token_id ,				decoder_start_token_id=self.decoder_start_token_id ,				initializer_range=self.initializer_range ,				use_cache=lowercase__ ,				)
									_lowerCamelCase					     = prepare_mbart_inputs_dict(lowercase__ ,				lowercase__ ,				lowercase__      )
									return config, inputs_dict

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.prepare_config_and_inputs()
									return config, inputs_dict

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = 2_0
									_lowerCamelCase					     = model_class_name(lowercase__      )

									_lowerCamelCase					     = model.encode(inputs_dict['''input_ids''']      )

									_lowerCamelCase					     = (
									    inputs_dict["decoder_input_ids"],
									    inputs_dict["decoder_attention_mask"],
									)

									_lowerCamelCase					     = model.init_cache(decoder_input_ids.shape[0] ,				lowercase__ ,				lowercase__      )
									_lowerCamelCase					     = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,				dtype='''i4'''      )

									_lowerCamelCase					     = jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :] ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,				)
									_lowerCamelCase					     = model.decode(
									    decoder_input_ids[:, :-1] ,				lowercase__ ,				decoder_attention_mask=lowercase__ ,				past_key_values=lowercase__ ,				decoder_position_ids=lowercase__ ,				)

									_lowerCamelCase					     = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,				dtype='''i4'''      )
									_lowerCamelCase					     = model.decode(
									    decoder_input_ids[:, -1:] ,				lowercase__ ,				decoder_attention_mask=lowercase__ ,				past_key_values=outputs_cache.past_key_values ,				decoder_position_ids=lowercase__ ,				)

									_lowerCamelCase					     = model.decode(lowercase__ ,				lowercase__      )

									_lowerCamelCase					     = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
									self.parent.assertTrue(diff < 1e-3 ,				msg=F"""Max diff is {diff}"""      )





					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = 2_0
									_lowerCamelCase					     = model_class_name(lowercase__      )

									_lowerCamelCase					     = model.encode(inputs_dict['''input_ids''']      )

									_lowerCamelCase					     = (
									    inputs_dict["decoder_input_ids"],
									    inputs_dict["decoder_attention_mask"],
									)

									_lowerCamelCase					     = jnp.concatenate(
									    [
									        decoder_attention_mask,
									        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])      ),
									    ] ,				axis=-1 ,				)

									_lowerCamelCase					     = model.init_cache(decoder_input_ids.shape[0] ,				lowercase__ ,				lowercase__      )
									_lowerCamelCase					     = jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :] ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,				)

									_lowerCamelCase					     = model.decode(
									    decoder_input_ids[:, :-1] ,				lowercase__ ,				decoder_attention_mask=lowercase__ ,				past_key_values=lowercase__ ,				decoder_position_ids=lowercase__ ,				)
									_lowerCamelCase					     = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,				dtype='''i4'''      )
									_lowerCamelCase					     = model.decode(
									    decoder_input_ids[:, -1:] ,				lowercase__ ,				past_key_values=outputs_cache.past_key_values ,				decoder_attention_mask=lowercase__ ,				decoder_position_ids=lowercase__ ,				)

									_lowerCamelCase					     = model.decode(lowercase__ ,				lowercase__ ,				decoder_attention_mask=lowercase__      )

									_lowerCamelCase					     = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
									self.parent.assertTrue(diff < 1e-3 ,				msg=F"""Max diff is {diff}"""      )


@require_flax
class 							lowerCamelCase_(      unittest.TestCase			):





					'''simple docstring'''



					lowercase__      :				List[Any]        =       99

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = np.array(
									    [
									        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
									        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
									        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
									        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
									        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
									        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
									        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
									        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
									        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
									        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
									        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
									        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
									        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
									    ] ,				dtype=np.intaa ,				)

									_lowerCamelCase					     = input_ids.shape[0]
									_lowerCamelCase					     = MBartConfig(
									    vocab_size=self.vocab_size ,				d_model=2_4 ,				encoder_layers=2 ,				decoder_layers=2 ,				encoder_attention_heads=2 ,				decoder_attention_heads=2 ,				encoder_ffn_dim=3_2 ,				decoder_ffn_dim=3_2 ,				max_position_embeddings=4_8 ,				eos_token_id=2 ,				pad_token_id=1 ,				bos_token_id=0 ,				)
									return config, input_ids, batch_size

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self._get_config_and_data()
									_lowerCamelCase					     = FlaxMBartForSequenceClassification(lowercase__      )
									_lowerCamelCase					     = model(input_ids=lowercase__ ,				decoder_input_ids=lowercase__      )
									_lowerCamelCase					     = (batch_size, config.num_labels)
									self.assertEqual(outputs['''logits'''].shape ,				lowercase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self._get_config_and_data()
									_lowerCamelCase					     = FlaxMBartForQuestionAnswering(lowercase__      )
									_lowerCamelCase					     = model(input_ids=lowercase__      )

									self.assertEqual(outputs['''start_logits'''].shape ,				input_ids.shape      )
									self.assertEqual(outputs['''end_logits'''].shape ,				input_ids.shape      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self._get_config_and_data()
									_lowerCamelCase					     = FlaxMBartForConditionalGeneration(lowercase__      )
									_lowerCamelCase					     = lm_model(input_ids=lowercase__      )
									_lowerCamelCase					     = (batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs['''logits'''].shape ,				lowercase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = MBartConfig(
									    vocab_size=self.vocab_size ,				d_model=1_4 ,				encoder_layers=2 ,				decoder_layers=2 ,				encoder_attention_heads=2 ,				decoder_attention_heads=2 ,				encoder_ffn_dim=8 ,				decoder_ffn_dim=8 ,				max_position_embeddings=4_8 ,				)
									_lowerCamelCase					     = FlaxMBartForConditionalGeneration(lowercase__      )
									_lowerCamelCase					     = np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]] ,				dtype=np.intaa      )
									_lowerCamelCase					     = np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]] ,				dtype=np.intaa      )
									_lowerCamelCase					     = lm_model(input_ids=lowercase__ ,				decoder_input_ids=lowercase__      )
									_lowerCamelCase					     = (*summary.shape, config.vocab_size)
									self.assertEqual(outputs['''logits'''].shape ,				lowercase__      )





					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]] ,				dtype=np.intaa      )
									_lowerCamelCase					     = shift_tokens_right(lowercase__ ,				1      )
									_lowerCamelCase					     = np.equal(lowercase__ ,				1      ).astype(np.floataa      ).sum()
									_lowerCamelCase					     = np.equal(lowercase__ ,				1      ).astype(np.floataa      ).sum()
									self.assertEqual(shifted.shape ,				input_ids.shape      )
									self.assertEqual(lowercase__ ,				n_pad_before - 1      )
									self.assertTrue(np.equal(shifted[:, 0] ,				2      ).all()      )


@require_flax
class 							lowerCamelCase_(      _UpperCAmelCase, unittest.TestCase, _UpperCAmelCase			):





					'''simple docstring'''



					lowercase__      :				int        =       True
					lowercase__      :				Any        =       (
					    (
					        FlaxMBartModel,
					        FlaxMBartForConditionalGeneration,
					        FlaxMBartForSequenceClassification,
					        FlaxMBartForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)
					lowercase__      :				int        =       (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = FlaxMBartModelTester(self      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(lowercase__ ,				lowercase__ ,				lowercase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward_with_attn_mask(lowercase__ ,				lowercase__ ,				lowercase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__      ):
																	_lowerCamelCase					     = self._prepare_for_class(lowercase__ ,				lowercase__      )
																	_lowerCamelCase					     = model_class(lowercase__      )

																	@jax.jit
																	def encode_jitted(lowerCamelCase__ ,				lowerCamelCase__=None ,				**lowerCamelCase__      ):
																					return model.encode(input_ids=lowercase__ ,				attention_mask=lowercase__      )

																	with self.subTest('''JIT Enabled'''      ):
																					_lowerCamelCase					     = encode_jitted(**lowercase__      ).to_tuple()

																	with self.subTest('''JIT Disabled'''      ):
																					with jax.disable_jit():
																									_lowerCamelCase					     = encode_jitted(**lowercase__      ).to_tuple()

																	self.assertEqual(len(lowercase__      ) ,				len(lowercase__      )      )
																	for jitted_output, output in zip(lowercase__ ,				lowercase__      ):
																					self.assertEqual(jitted_output.shape ,				output.shape      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__      ):
																	_lowerCamelCase					     = model_class(lowercase__      )
																	_lowerCamelCase					     = model.encode(inputs_dict['''input_ids'''] ,				inputs_dict['''attention_mask''']      )

																	_lowerCamelCase					     = {
																	    "decoder_input_ids": inputs_dict["decoder_input_ids"],
																	    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
																	    "encoder_outputs": encoder_outputs,
																	}

																	@jax.jit
																	def decode_jitted(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
																					return model.decode(
																					    decoder_input_ids=lowercase__ ,				decoder_attention_mask=lowercase__ ,				encoder_outputs=lowercase__ ,				)

																	with self.subTest('''JIT Enabled'''      ):
																					_lowerCamelCase					     = decode_jitted(**lowercase__      ).to_tuple()

																	with self.subTest('''JIT Disabled'''      ):
																					with jax.disable_jit():
																									_lowerCamelCase					     = decode_jitted(**lowercase__      ).to_tuple()

																	self.assertEqual(len(lowercase__      ) ,				len(lowercase__      )      )
																	for jitted_output, output in zip(lowercase__ ,				lowercase__      ):
																					self.assertEqual(jitted_output.shape ,				output.shape      )





					@slow
					def 	snake_case__					(			self      ):






									for model_class_name in self.all_model_classes:
													_lowerCamelCase					     = model_class_name.from_pretrained('''facebook/mbart-large-cc25''' ,				from_pt=lowercase__      )
													# FlaxMBartForSequenceClassification expects eos token in input_ids
													_lowerCamelCase					     = np.ones((1, 1)      ) * model.config.eos_token_id
													_lowerCamelCase					     = model(lowercase__      )
													self.assertIsNotNone(lowercase__      )


@require_flax
@require_sentencepiece
@require_tokenizers
class 							lowerCamelCase_(      unittest.TestCase			):





					'''simple docstring'''



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

					@cached_property
					def 	snake_case__					(			self      ):
									return AutoTokenizer.from_pretrained(self.model_name      )

					@cached_property
					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = FlaxMBartForConditionalGeneration.from_pretrained(self.model_name ,				from_pt=lowercase__      )
									return model

					def 	snake_case__					(			self ,				**lowerCamelCase__      ):
									_lowerCamelCase					     = self.translate_src_text(**lowercase__      )
									self.assertListEqual(self.expected_text ,				lowercase__      )

					def 	snake_case__					(			self ,				**lowerCamelCase__      ):
									_lowerCamelCase					     = self.tokenizer(self.src_text ,				**lowercase__ ,				return_tensors='''np'''      )
									_lowerCamelCase					     = self.model.generate(
									    model_inputs.input_ids ,				attention_mask=model_inputs.attention_mask ,				decoder_start_token_id=self.tokenizer.lang_code_to_id['''ro_RO'''] ,				early_stopping=lowercase__ ,				num_beams=2 ,				).sequences
									_lowerCamelCase					     = self.tokenizer.batch_decode(lowercase__ ,				skip_special_tokens=lowercase__      )
									return generated_words





					@slow
					def 	snake_case__					(			self      ):
									self._assert_generated_batch_equal_expected()



'''simple docstring'''






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Optional[int]    )							->   str:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Any     =       args.serialization_dir
      SCREAMING_SNAKE_CASE_      :			List[str]     =       args.pruning_method
      SCREAMING_SNAKE_CASE_      :			str     =       args.threshold

      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       torch.load(os.path.join(SCREAMING_SNAKE_CASE_  ,  "pytorch_model.bin"    )  ,  map_location="cpu"    )

      SCREAMING_SNAKE_CASE_      :			List[Any]     =       0  # Number of remaining (not pruned) params in the encoder
      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       0  # Number of params in the encoder

      print("name".ljust(6_0  ,  " "    )  ,  "Remaining Weights %"  ,  "Remaining Weight"    )
      for name, param in st.items():
            if "encoder" not in name:
                  continue

            if "mask_scores" in name:
                  if pruning_method == "topK":
                        SCREAMING_SNAKE_CASE_      :			int     =       TopKBinarizer.apply(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    ).sum().item()
                  elif pruning_method == "sigmoied_threshold":
                        SCREAMING_SNAKE_CASE_      :			Tuple     =       ThresholdBinarizer.apply(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    ).sum().item()
                  elif pruning_method == "l0":
                        SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			Dict     =       -0.1, 1.1
                        SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       torch.sigmoid(SCREAMING_SNAKE_CASE_    )
                        SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       s * (r - l) + l
                        SCREAMING_SNAKE_CASE_      :			str     =       s_bar.clamp(min=0.0  ,  max=1.0    )
                        SCREAMING_SNAKE_CASE_      :			Dict     =       (mask > 0.0).sum().item()
                  else:
                        raise ValueError("Unknown pruning method"    )
                  remaining_count += mask_ones
                  print(name.ljust(6_0  ,  " "    )  ,  str(round(1_0_0 * mask_ones / param.numel()  ,  3    )    ).ljust(2_0  ,  " "    )  ,  str(SCREAMING_SNAKE_CASE_    )    )
            else:
                  encoder_count += param.numel()
                  if "bias" in name or "LayerNorm" in name:
                        remaining_count += param.numel()

      print(""    )
      print("Remaining Weights (global) %: "  ,  1_0_0 * remaining_count / encoder_count    )


if __name__ == "__main__":
      snake_case_       =       argparse.ArgumentParser()

      parser.add_argument(
          '--pruning_method',
          choices=['l0', 'topK', 'sigmoied_threshold'],
          type=str,
          required=True,
          help=(
              'Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'
              ' pruning)'
          ),
      )
      parser.add_argument(
          '--threshold',
          type=float,
          required=False,
          help=(
              'For `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(
          '--serialization_dir',
          type=str,
          required=True,
          help='Folder containing the model that was previously fine-pruned',
      )

      snake_case_       =       parser.parse_args()

      main(args)

