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
     lowercase_  							=			"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				(		__SCREAMING_SNAKE_CASE       :					Union[str, Any]		,							__SCREAMING_SNAKE_CASE       :					int		,							__SCREAMING_SNAKE_CASE       :					int=None		,							__SCREAMING_SNAKE_CASE       :					str=None		,							__SCREAMING_SNAKE_CASE       :					Tuple=None		,							__SCREAMING_SNAKE_CASE       :					Union[str, Any]=None		,							__SCREAMING_SNAKE_CASE       :					List[str]=None		,							__SCREAMING_SNAKE_CASE       :					Any=None		,							):







    '''simple docstring'''
    if attention_mask is None:
        __snake_case       :				Union[str, Any] 			=    np.where(input_ids != config.pad_token_id		,							1		,							0   )
    if decoder_attention_mask is None:
        __snake_case       :				Dict 			=    np.where(decoder_input_ids != config.pad_token_id		,							1		,							0   )
    if head_mask is None:
        __snake_case       :				str 			=    np.ones((config.encoder_layers, config.encoder_attention_heads)   )
    if decoder_head_mask is None:
        __snake_case       :				Optional[Any] 			=    np.ones((config.decoder_layers, config.decoder_attention_heads)   )
    if cross_attn_head_mask is None:
        __snake_case       :				List[str] 			=    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      SCREAMING_SNAKE_CASE__					(  unittest.TestCase    ):

       def __init__(							self					:     List[str]			,			_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     List[Any]=13			,			_lowerCAmelCase					:     List[Any]=7			,			_lowerCAmelCase					:     int=True			,			_lowerCAmelCase					:     Union[str, Any]=False			,			_lowerCAmelCase					:     str=99			,			_lowerCAmelCase					:     int=16			,			_lowerCAmelCase					:     Optional[int]=2			,			_lowerCAmelCase					:     Union[str, Any]=4			,			_lowerCAmelCase					:     Dict=4			,			_lowerCAmelCase					:     Optional[Any]="gelu"			,			_lowerCAmelCase					:     Dict=0.1			,			_lowerCAmelCase					:     Any=0.1			,			_lowerCAmelCase					:     List[str]=32			,			_lowerCAmelCase					:     int=2			,			_lowerCAmelCase					:     Tuple=1			,			_lowerCAmelCase					:     Tuple=0			,			_lowerCAmelCase					:     Dict=2			,			_lowerCAmelCase					:     Union[str, Any]=0.02			,			):
           __snake_case       :				Union[str, Any] 			=    parent
           __snake_case       :				str 			=    batch_size
           __snake_case       :				Dict 			=    seq_length
           __snake_case       :				Dict 			=    is_training
           __snake_case       :				List[Any] 			=    use_labels
           __snake_case       :				Optional[Any] 			=    vocab_size
           __snake_case       :				List[Any] 			=    hidden_size
           __snake_case       :				Dict 			=    num_hidden_layers
           __snake_case       :				Any 			=    num_attention_heads
           __snake_case       :				Any 			=    intermediate_size
           __snake_case       :				Tuple 			=    hidden_act
           __snake_case       :				Any 			=    hidden_dropout_prob
           __snake_case       :				Union[str, Any] 			=    attention_probs_dropout_prob
           __snake_case       :				Any 			=    max_position_embeddings
           __snake_case       :				Any 			=    eos_token_id
           __snake_case       :				Dict 			=    pad_token_id
           __snake_case       :				Union[str, Any] 			=    bos_token_id
           __snake_case       :				Union[str, Any] 			=    decoder_start_token_id
           __snake_case       :				Optional[int] 			=    initializer_range

       def 			snake_case__    (							self					:     str			):
           __snake_case       :				Union[str, Any] 			=    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,			self.vocab_size			)			,			3			,			self.vocab_size			)
           __snake_case       :				Optional[int] 			=    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,			dtype=np.intaa			))			,			-1			)

           __snake_case       :				List[Any] 			=    shift_tokens_right(_lowerCAmelCase			,			1			)

           __snake_case       :				Optional[int] 			=    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			,			)
           __snake_case       :				int 			=    prepare_mbart_inputs_dict(_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			)
           return config, inputs_dict

       def 			snake_case__    (							self					:     List[Any]			):
           __snake_case			, __snake_case       :				Dict 			=    self.prepare_config_and_inputs()
           return config, inputs_dict

       def 			snake_case__    (							self					:     Any			,			_lowerCAmelCase					:     List[str]			,			_lowerCAmelCase					:     Union[str, Any]			,			_lowerCAmelCase					:     Dict			):
           __snake_case       :				Any 			=    20
           __snake_case       :				Tuple 			=    model_class_name(_lowerCAmelCase			)

           __snake_case       :				Optional[int] 			=    model.encode(inputs_dict["""input_ids"""]			)

           __snake_case			, __snake_case       :				str 			=    (
               inputs_dict["""decoder_input_ids"""],
               inputs_dict["""decoder_attention_mask"""],
           )

           __snake_case       :				Union[str, Any] 			=    model.init_cache(decoder_input_ids.shape[0]			,			_lowerCAmelCase			,			_lowerCAmelCase			)
           __snake_case       :				Optional[int] 			=    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,			dtype="""i4"""			)

           __snake_case       :				str 			=    jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]			,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,			)
           __snake_case       :				List[str] 			=    model.decode(
               decoder_input_ids[:, :-1]			,			_lowerCAmelCase			,			decoder_attention_mask=_lowerCAmelCase			,			past_key_values=_lowerCAmelCase			,			decoder_position_ids=_lowerCAmelCase			,			)

           __snake_case       :				Union[str, Any] 			=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,			dtype="""i4"""			)
           __snake_case       :				List[str] 			=    model.decode(
               decoder_input_ids[:, -1:]			,			_lowerCAmelCase			,			decoder_attention_mask=_lowerCAmelCase			,			past_key_values=outputs_cache.past_key_values			,			decoder_position_ids=_lowerCAmelCase			,			)

           __snake_case       :				List[str] 			=    model.decode(_lowerCAmelCase			,			_lowerCAmelCase			)

           __snake_case       :				str 			=    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					:     int			,			_lowerCAmelCase					:     List[str]			,			_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     List[Any]			):
           __snake_case       :				List[Any] 			=    20
           __snake_case       :				Any 			=    model_class_name(_lowerCAmelCase			)

           __snake_case       :				Tuple 			=    model.encode(inputs_dict["""input_ids"""]			)

           __snake_case			, __snake_case       :				Union[str, Any] 			=    (
               inputs_dict["""decoder_input_ids"""],
               inputs_dict["""decoder_attention_mask"""],
           )

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

           __snake_case       :				Tuple 			=    model.init_cache(decoder_input_ids.shape[0]			,			_lowerCAmelCase			,			_lowerCAmelCase			)
           __snake_case       :				List[Any] 			=    jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]			,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,			)

           __snake_case       :				Union[str, Any] 			=    model.decode(
               decoder_input_ids[:, :-1]			,			_lowerCAmelCase			,			decoder_attention_mask=_lowerCAmelCase			,			past_key_values=_lowerCAmelCase			,			decoder_position_ids=_lowerCAmelCase			,			)
           __snake_case       :				Union[str, Any] 			=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,			dtype="""i4"""			)
           __snake_case       :				Optional[Any] 			=    model.decode(
               decoder_input_ids[:, -1:]			,			_lowerCAmelCase			,			past_key_values=outputs_cache.past_key_values			,			decoder_attention_mask=_lowerCAmelCase			,			decoder_position_ids=_lowerCAmelCase			,			)

           __snake_case       :				List[Any] 			=    model.decode(_lowerCAmelCase			,			_lowerCAmelCase			,			decoder_attention_mask=_lowerCAmelCase			)

           __snake_case       :				int 			=    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      SCREAMING_SNAKE_CASE__					(  unittest.TestCase    ):
       A							:	List[Any]          =	99

       def 			snake_case__    (							self					:     Any			):
           __snake_case       :				Any 			=    np.array(
               [
                   [71, 82, 18, 33, 46, 91, 2],
                   [68, 34, 26, 58, 30, 82, 2],
                   [5, 97, 17, 39, 94, 40, 2],
                   [76, 83, 94, 25, 70, 78, 2],
                   [87, 59, 41, 35, 48, 66, 2],
                   [55, 13, 16, 58, 5, 2, 1],  # note padding
                   [64, 27, 31, 51, 12, 75, 2],
                   [52, 64, 86, 17, 83, 39, 2],
                   [48, 61, 9, 24, 71, 82, 2],
                   [26, 1, 60, 48, 22, 13, 2],
                   [21, 5, 62, 28, 14, 76, 2],
                   [45, 98, 37, 86, 59, 48, 2],
                   [70, 70, 50, 9, 28, 0, 2],
               ]			,			dtype=np.intaa			,			)

           __snake_case       :				Optional[Any] 			=    input_ids.shape[0]
           __snake_case       :				int 			=    MBartConfig(
               vocab_size=self.vocab_size			,			d_model=24			,			encoder_layers=2			,			decoder_layers=2			,			encoder_attention_heads=2			,			decoder_attention_heads=2			,			encoder_ffn_dim=32			,			decoder_ffn_dim=32			,			max_position_embeddings=48			,			eos_token_id=2			,			pad_token_id=1			,			bos_token_id=0			,			)
           return config, input_ids, batch_size

       def 			snake_case__    (							self					:     Union[str, Any]			):
           __snake_case			, __snake_case			, __snake_case       :				str 			=    self._get_config_and_data()
           __snake_case       :				Union[str, Any] 			=    FlaxMBartForSequenceClassification(_lowerCAmelCase			)
           __snake_case       :				Union[str, Any] 			=    model(input_ids=_lowerCAmelCase			,			decoder_input_ids=_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    (batch_size, config.num_labels)
           self.assertEqual(outputs["""logits"""].shape			,			_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Any			):
           __snake_case			, __snake_case			, __snake_case       :				Tuple 			=    self._get_config_and_data()
           __snake_case       :				Union[str, Any] 			=    FlaxMBartForQuestionAnswering(_lowerCAmelCase			)
           __snake_case       :				Any 			=    model(input_ids=_lowerCAmelCase			)

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

       def 			snake_case__    (							self					:     Any			):
           __snake_case			, __snake_case			, __snake_case       :				List[Any] 			=    self._get_config_and_data()
           __snake_case       :				str 			=    FlaxMBartForConditionalGeneration(_lowerCAmelCase			)
           __snake_case       :				List[Any] 			=    lm_model(input_ids=_lowerCAmelCase			)
           __snake_case       :				int 			=    (batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs["""logits"""].shape			,			_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Union[str, Any]			):
           __snake_case       :				Optional[int] 			=    MBartConfig(
               vocab_size=self.vocab_size			,			d_model=14			,			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=48			,			)
           __snake_case       :				List[Any] 			=    FlaxMBartForConditionalGeneration(_lowerCAmelCase			)
           __snake_case       :				Any 			=    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]			,			dtype=np.intaa			)
           __snake_case       :				List[Any] 			=    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]			,			dtype=np.intaa			)
           __snake_case       :				Any 			=    lm_model(input_ids=_lowerCAmelCase			,			decoder_input_ids=_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    (*summary.shape, config.vocab_size)
           self.assertEqual(outputs["""logits"""].shape			,			_lowerCAmelCase			)





       def 			snake_case__    (							self					:     Optional[Any]			):
           __snake_case       :				str 			=    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]			,			dtype=np.intaa			)
           __snake_case       :				Dict 			=    shift_tokens_right(_lowerCAmelCase			,			1			)
           __snake_case       :				Union[str, Any] 			=    np.equal(_lowerCAmelCase			,			1			).astype(np.floataa			).sum()
           __snake_case       :				str 			=    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      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase   ,							unittest.TestCase   ,							__UpperCamelCase    ):
       A							:	Union[str, Any]          =	True
       A							:	int          =	(
           (
               FlaxMBartModel,
               FlaxMBartForConditionalGeneration,
               FlaxMBartForSequenceClassification,
               FlaxMBartForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       A							:	Tuple          =	(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()

       def 			snake_case__    (							self					:     Optional[int]			):
           __snake_case       :				Dict 			=    FlaxMBartModelTester(self			)

       def 			snake_case__    (							self					:     Dict			):
           __snake_case			, __snake_case       :				List[Any] 			=    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 			snake_case__    (							self					:     Optional[int]			):
           __snake_case			, __snake_case       :				Dict 			=    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 			snake_case__    (							self					:     Optional[Any]			):
           __snake_case			, __snake_case       :				List[str] 			=    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__			):
                   __snake_case       :				List[Any] 			=    self._prepare_for_class(_lowerCAmelCase			,			_lowerCAmelCase			)
                   __snake_case       :				Tuple 			=    model_class(_lowerCAmelCase			)

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

                   with self.subTest("""JIT Enabled"""			):
                       __snake_case       :				Any 			=    encode_jitted(**_lowerCAmelCase			).to_tuple()

                   with self.subTest("""JIT Disabled"""			):
                       with jax.disable_jit():
                           __snake_case       :				Union[str, Any] 			=    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 			snake_case__    (							self					:     Dict			):
           __snake_case			, __snake_case       :				Dict 			=    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__			):
                   __snake_case       :				Optional[Any] 			=    model_class(_lowerCAmelCase			)
                   __snake_case       :				Optional[int] 			=    model.encode(inputs_dict["""input_ids"""]			,			inputs_dict["""attention_mask"""]			)

                   __snake_case       :				Dict 			=    {
                       """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					:     Tuple			,			_lowerCAmelCase					:     List[Any]			,			_lowerCAmelCase					:     Dict			):
                       return model.decode(
                           decoder_input_ids=_lowerCAmelCase			,			decoder_attention_mask=_lowerCAmelCase			,			encoder_outputs=_lowerCAmelCase			,			)

                   with self.subTest("""JIT Enabled"""			):
                       __snake_case       :				Tuple 			=    decode_jitted(**_lowerCAmelCase			).to_tuple()

                   with self.subTest("""JIT Disabled"""			):
                       with jax.disable_jit():
                           __snake_case       :				Any 			=    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 			snake_case__    (							self					:     List[Any]			):
           for model_class_name in self.all_model_classes:
               __snake_case       :				Optional[int] 			=    model_class_name.from_pretrained("""facebook/mbart-large-cc25"""			,			from_pt=_lowerCAmelCase			)
               # FlaxMBartForSequenceClassification expects eos token in input_ids
               __snake_case       :				Optional[int] 			=    np.ones((1, 1)			) * model.config.eos_token_id
               __snake_case       :				List[str] 			=    model(_lowerCAmelCase			)
               self.assertIsNotNone(_lowerCAmelCase			)







@require_flax
@require_sentencepiece
@require_tokenizers
class      SCREAMING_SNAKE_CASE__					(  unittest.TestCase    ):
       A							:	Union[str, Any]          =	[
           " UN Chief Says There Is No Military Solution in Syria",
       ]
       A							:	Any          =	[
           "Şeful ONU declară că nu există o soluţie militară în Siria",
       ]
       A							:	Optional[int]          =	"facebook/mbart-large-en-ro"

       @cached_property
       def 			snake_case__    (							self					:     Optional[int]			):
           return AutoTokenizer.from_pretrained(self.model_name			)

       @cached_property
       def 			snake_case__    (							self					:     Dict			):
           __snake_case       :				Optional[Any] 			=    FlaxMBartForConditionalGeneration.from_pretrained(self.model_name			,			from_pt=_lowerCAmelCase			)
           return model

       def 			snake_case__    (							self					:     int			,			**_lowerCAmelCase					:     Optional[Any]			):
           __snake_case       :				Dict 			=    self.translate_src_text(**_lowerCAmelCase			)
           self.assertListEqual(self.expected_text			,			_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Union[str, Any]			,			**_lowerCAmelCase					:     List[str]			):
           __snake_case       :				Optional[Any] 			=    self.tokenizer(self.src_text			,			**_lowerCAmelCase			,			return_tensors="""np"""			)
           __snake_case       :				int 			=    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
           __snake_case       :				str 			=    self.tokenizer.batch_decode(_lowerCAmelCase			,			skip_special_tokens=_lowerCAmelCase			)
           return generated_words





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




import torch
from torch import autograd







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			,			_lowerCAmelCase					:     bool			):
           __snake_case       :				Optional[int] 			=    inputs.numel()
           __snake_case       :				str 			=    int(0.005 * nb_elems			) + 1
           if sigmoid:
               __snake_case       :				Union[str, Any] 			=    (torch.sigmoid(_lowerCAmelCase			) > threshold).type(inputs.type()			)
           else:
               __snake_case       :				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
               __snake_case       :				Dict 			=    inputs.flatten().kthvalue(max(nb_elems - nb_min			,			1			)			).values
               __snake_case       :				List[str] 			=    (inputs > k_threshold).type(inputs.type()			)
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     str			,			_lowerCAmelCase					:     Tuple			):
           return gradOutput, None, None







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				str 			=    inputs.clone()
           __snake_case			, __snake_case       :				Dict 			=    inputs.flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    int(threshold * inputs.numel()			)

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





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     Optional[Any]			):
           return gradOutput, None







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				List[str] 			=    inputs.clone()
           __snake_case			, __snake_case       :				str 			=    inputs.abs().flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				List[Any] 			=    int(threshold * inputs.numel()			)

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



