"""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
  __UpperCamelCase       :    Optional[int]         =		"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   __UpperCAmelCase     (	_snake_case		:       Any,				_snake_case		:       List[str],				_snake_case		:       List[Any]=None,				_snake_case		:       int=None,				_snake_case		:       Optional[int]=None,				_snake_case		:       int=None,				_snake_case		:       Optional[int]=None,				_snake_case		:       int=None,				):
   if attention_mask is None:
      _lowercase							     =       np.where(input_ids != config.pad_token_id,				1,				0       )
   if decoder_attention_mask is None:
      _lowercase							     =       np.where(decoder_input_ids != config.pad_token_id,				1,				0       )
   if head_mask is None:
      _lowercase							     =       np.ones((config.encoder_layers, config.encoder_attention_heads)       )
   if decoder_head_mask is None:
      _lowercase							     =       np.ones((config.decoder_layers, config.decoder_attention_heads)       )
   if cross_attn_head_mask is None:
      _lowercase							     =       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   UpperCAmelCase_	(				unittest.TestCase   ):
    def __init__(	self			:       Any   ,						_lowercase			:       Union[str, Any]   ,						_lowercase			:       List[str]=1_3   ,						_lowercase			:       int=7   ,						_lowercase			:       Dict=True   ,						_lowercase			:       Dict=False   ,						_lowercase			:       Optional[int]=9_9   ,						_lowercase			:       Optional[int]=1_6   ,						_lowercase			:       Tuple=2   ,						_lowercase			:       Dict=4   ,						_lowercase			:       List[Any]=4   ,						_lowercase			:       Optional[int]="gelu"   ,						_lowercase			:       str=0.1   ,						_lowercase			:       Dict=0.1   ,						_lowercase			:       Optional[Any]=3_2   ,						_lowercase			:       Tuple=2   ,						_lowercase			:       Optional[Any]=1   ,						_lowercase			:       Tuple=0   ,						_lowercase			:       Optional[int]=2   ,						_lowercase			:       str=0.02   ,						)						->    Any:
       _lowercase							     =       parent
       _lowercase							     =       batch_size
       _lowercase							     =       seq_length
       _lowercase							     =       is_training
       _lowercase							     =       use_labels
       _lowercase							     =       vocab_size
       _lowercase							     =       hidden_size
       _lowercase							     =       num_hidden_layers
       _lowercase							     =       num_attention_heads
       _lowercase							     =       intermediate_size
       _lowercase							     =       hidden_act
       _lowercase							     =       hidden_dropout_prob
       _lowercase							     =       attention_probs_dropout_prob
       _lowercase							     =       max_position_embeddings
       _lowercase							     =       eos_token_id
       _lowercase							     =       pad_token_id
       _lowercase							     =       bos_token_id
       _lowercase							     =       decoder_start_token_id
       _lowercase							     =       initializer_range
    def 		_lowerCamelCase      (	self			:       Tuple		)						->    List[Any]:
       _lowercase							     =       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,						self.vocab_size		)   ,						3   ,						self.vocab_size		)
       _lowercase							     =       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,						dtype=np.intaa		))   ,						-1		)

       _lowercase							     =       shift_tokens_right(lowerCamelCase__   ,						1		)

       _lowercase							     =       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=lowerCamelCase__   ,						)
       _lowercase							     =       prepare_mbart_inputs_dict(lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__		)
       return config, inputs_dict
    def 		_lowerCamelCase      (	self			:       Tuple		)						->    List[str]:
       _lowercase							     =       self.prepare_config_and_inputs()
       return config, inputs_dict
    def 		_lowerCamelCase      (	self			:       int   ,						_lowercase			:       int   ,						_lowercase			:       str   ,						_lowercase			:       Any		)						->    Optional[Any]:
       _lowercase							     =       2_0
       _lowercase							     =       model_class_name(lowerCamelCase__		)

       _lowercase							     =       model.encode(inputs_dict["input_ids"]		)

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

       _lowercase							     =       model.init_cache(decoder_input_ids.shape[0]   ,						lowerCamelCase__   ,						lowerCamelCase__		)
       _lowercase							     =       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,						dtype="i4"		)

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

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

       _lowercase							     =       model.decode(lowerCamelCase__   ,						lowerCamelCase__		)

       _lowercase							     =       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 		_lowerCamelCase      (	self			:       List[str]   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Tuple   ,						_lowercase			:       int		)						->    Optional[Any]:
       _lowercase							     =       2_0
       _lowercase							     =       model_class_name(lowerCamelCase__		)

       _lowercase							     =       model.encode(inputs_dict["input_ids"]		)

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

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

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

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

       _lowercase							     =       model.decode(lowerCamelCase__   ,						lowerCamelCase__   ,						decoder_attention_mask=lowerCamelCase__		)

       _lowercase							     =       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   UpperCAmelCase_	(				unittest.TestCase   ):
    snake_case_											=      99
    def 		_lowerCamelCase      (	self			:       Union[str, Any]		)						->    str:
       _lowercase							     =       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   ,						)

       _lowercase							     =       input_ids.shape[0]
       _lowercase							     =       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 		_lowerCamelCase      (	self			:       Any		)						->    str:
       _lowercase							     =       self._get_config_and_data()
       _lowercase							     =       FlaxMBartForSequenceClassification(lowerCamelCase__		)
       _lowercase							     =       model(input_ids=lowerCamelCase__   ,						decoder_input_ids=lowerCamelCase__		)
       _lowercase							     =       (batch_size, config.num_labels)
       self.assertEqual(outputs["logits"].shape   ,						lowerCamelCase__		)
    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Union[str, Any]:
       _lowercase							     =       self._get_config_and_data()
       _lowercase							     =       FlaxMBartForQuestionAnswering(lowerCamelCase__		)
       _lowercase							     =       model(input_ids=lowerCamelCase__		)

       self.assertEqual(outputs["start_logits"].shape   ,						input_ids.shape		)
       self.assertEqual(outputs["end_logits"].shape   ,						input_ids.shape		)
    def 		_lowerCamelCase      (	self			:       List[Any]		)						->    Optional[Any]:
       _lowercase							     =       self._get_config_and_data()
       _lowercase							     =       FlaxMBartForConditionalGeneration(lowerCamelCase__		)
       _lowercase							     =       lm_model(input_ids=lowerCamelCase__		)
       _lowercase							     =       (batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs["logits"].shape   ,						lowerCamelCase__		)
    def 		_lowerCamelCase      (	self			:       Dict		)						->    str:
       _lowercase							     =       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   ,						)
       _lowercase							     =       FlaxMBartForConditionalGeneration(lowerCamelCase__		)
       _lowercase							     =       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		)
       _lowercase							     =       np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]   ,						dtype=np.intaa		)
       _lowercase							     =       lm_model(input_ids=lowerCamelCase__   ,						decoder_input_ids=lowerCamelCase__		)
       _lowercase							     =       (*summary.shape, config.vocab_size)
       self.assertEqual(outputs["logits"].shape   ,						lowerCamelCase__		)




    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    Any:
       _lowercase							     =       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		)
       _lowercase							     =       shift_tokens_right(lowerCamelCase__   ,						1		)
       _lowercase							     =       np.equal(lowerCamelCase__   ,						1		).astype(np.floataa		).sum()
       _lowercase							     =       np.equal(lowerCamelCase__   ,						1		).astype(np.floataa		).sum()
       self.assertEqual(shifted.shape   ,						input_ids.shape		)
       self.assertEqual(lowerCamelCase__   ,						n_pad_before - 1		)
       self.assertTrue(np.equal(shifted[:, 0]   ,						2		).all()		)
@require_flax
class   UpperCAmelCase_	(				UpperCAmelCase__,    unittest.TestCase,    UpperCAmelCase__   ):
    snake_case_											=      True
    snake_case_											=      (
        (
            FlaxMBartModel,
            FlaxMBartForConditionalGeneration,
            FlaxMBartForSequenceClassification,
            FlaxMBartForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )
    snake_case_											=      (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()
    def 		_lowerCamelCase      (	self			:       int		)						->    int:
       _lowercase							     =       FlaxMBartModelTester(self		)
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    str:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__		)
    def 		_lowerCamelCase      (	self			:       Any		)						->    List[Any]:
       _lowercase							     =       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(lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__		)
    def 		_lowerCamelCase      (	self			:       Dict		)						->    Any:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__		):
             _lowercase							     =       self._prepare_for_class(lowerCamelCase__   ,						lowerCamelCase__		)
             _lowercase							     =       model_class(lowerCamelCase__		)

             @jax.jit
             def encode_jitted(_lowercase			:       List[Any]   ,						_lowercase			:       int=None   ,						**_lowercase			:       int		):
                return model.encode(input_ids=lowerCamelCase__   ,						attention_mask=lowerCamelCase__		)

             with self.subTest("JIT Enabled"		):
                _lowercase							     =       encode_jitted(**lowerCamelCase__		).to_tuple()

             with self.subTest("JIT Disabled"		):
                with jax.disable_jit():
                   _lowercase							     =       encode_jitted(**lowerCamelCase__		).to_tuple()

             self.assertEqual(len(lowerCamelCase__		)   ,						len(lowerCamelCase__		)		)
             for jitted_output, output in zip(lowerCamelCase__   ,						lowerCamelCase__		):
                self.assertEqual(jitted_output.shape   ,						output.shape		)
    def 		_lowerCamelCase      (	self			:       Dict		)						->    Optional[int]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__		):
             _lowercase							     =       model_class(lowerCamelCase__		)
             _lowercase							     =       model.encode(inputs_dict["input_ids"]   ,						inputs_dict["attention_mask"]		)

             _lowercase							     =       {
                 "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(_lowercase			:       int   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Optional[Any]		):
                return model.decode(
                    decoder_input_ids=lowerCamelCase__   ,						decoder_attention_mask=lowerCamelCase__   ,						encoder_outputs=lowerCamelCase__   ,						)

             with self.subTest("JIT Enabled"		):
                _lowercase							     =       decode_jitted(**lowerCamelCase__		).to_tuple()

             with self.subTest("JIT Disabled"		):
                with jax.disable_jit():
                   _lowercase							     =       decode_jitted(**lowerCamelCase__		).to_tuple()

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




    @slow
    def 		_lowerCamelCase      (	self			:       Any		)						->    str:







       for model_class_name in self.all_model_classes:
          _lowercase							     =       model_class_name.from_pretrained("facebook/mbart-large-cc25"   ,						from_pt=lowerCamelCase__		)
          # FlaxMBartForSequenceClassification expects eos token in input_ids
          _lowercase							     =       np.ones((1, 1)		) * model.config.eos_token_id
          _lowercase							     =       model(lowerCamelCase__		)
          self.assertIsNotNone(lowerCamelCase__		)
@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCAmelCase_	(				unittest.TestCase   ):
    snake_case_											=      [
        " UN Chief Says There Is No Military Solution in Syria",
    ]
    snake_case_											=      [
        "Şeful ONU declară că nu există o soluţie militară în Siria",
    ]
    snake_case_											=      "facebook/mbart-large-en-ro"
    @cached_property
    def 		_lowerCamelCase      (	self			:       Any		)						->    Optional[Any]:
       return AutoTokenizer.from_pretrained(self.model_name		)
    @cached_property
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    Optional[int]:
       _lowercase							     =       FlaxMBartForConditionalGeneration.from_pretrained(self.model_name   ,						from_pt=lowerCamelCase__		)
       return model
    def 		_lowerCamelCase      (	self			:       Optional[int]   ,						**_lowercase			:       Dict		)						->    Union[str, Any]:
       _lowercase							     =       self.translate_src_text(**lowerCamelCase__		)
       self.assertListEqual(self.expected_text   ,						lowerCamelCase__		)
    def 		_lowerCamelCase      (	self			:       List[Any]   ,						**_lowercase			:       Tuple		)						->    List[str]:
       _lowercase							     =       self.tokenizer(self.src_text   ,						**lowerCamelCase__   ,						return_tensors="np"		)
       _lowercase							     =       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=lowerCamelCase__   ,						num_beams=2   ,						).sequences
       _lowercase							     =       self.tokenizer.batch_decode(lowerCamelCase__   ,						skip_special_tokens=lowerCamelCase__		)
       return generated_words




    @slow
    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Tuple:
       self._assert_generated_batch_equal_expected()



'''simple docstring'''


import torch
from torch import autograd






class 						lowerCAmelCase__      (      autograd.Function						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float						,	lowerCamelCase__	:  bool			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   inputs.numel()
					_UpperCAmelCase		:							Any             =   int(0.0_0_5 * nb_elems			) + 1
					if sigmoid:
							_UpperCAmelCase		:							List[str]             =   (torch.sigmoid(lowerCamelCase__			) > threshold).type(inputs.type()			)
					else:
							_UpperCAmelCase		:							List[Any]             =   (inputs > threshold).type(inputs.type()			)
					if mask.sum() < nb_min:
							# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
							_UpperCAmelCase		:							str             =   inputs.flatten().kthvalue(max(nb_elems - nb_min						,	1			)			).values
							_UpperCAmelCase		:							str             =   (inputs > k_threshold).type(inputs.type()			)
					return mask






			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  List[Any]						,	lowerCamelCase__	:  Optional[int]			) ->Dict:



					'''simple docstring'''







					return gradOutput, None, None






class 						lowerCAmelCase__      (      autograd.Function						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float			) ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   inputs.clone()
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Tuple             =   inputs.flatten().sort(descending=lowerCamelCase__			)
					_UpperCAmelCase		:							Optional[int]             =   int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					_UpperCAmelCase		:							str             =   mask.flatten()
					_UpperCAmelCase		:							Optional[int]             =   0
					_UpperCAmelCase		:							Optional[Any]             =   1
					return mask






			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  Any			) ->Union[str, Any]:



					'''simple docstring'''







					return gradOutput, None






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float			) ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:							List[Any]             =   inputs.clone()
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Optional[Any]             =   inputs.abs().flatten().sort(descending=lowerCamelCase__			)
					_UpperCAmelCase		:							int             =   int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					_UpperCAmelCase		:							int             =   mask.flatten()
					_UpperCAmelCase		:							List[Any]             =   0
					_UpperCAmelCase		:							int             =   1
					return mask



