


from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
				import tensorflow as tf

				from transformers import (
				    TF_MODEL_FOR_PRETRAINING_MAPPING,
				    TFMobileBertForMaskedLM,
				    TFMobileBertForMultipleChoice,
				    TFMobileBertForNextSentencePrediction,
				    TFMobileBertForPreTraining,
				    TFMobileBertForQuestionAnswering,
				    TFMobileBertForSequenceClassification,
				    TFMobileBertForTokenClassification,
				    TFMobileBertModel,
				)







@require_tf
class 	__lowercase    (lowerCAmelCase__	,       lowerCAmelCase__	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						(
							    (
							        TFMobileBertModel,
							        TFMobileBertForMaskedLM,
							        TFMobileBertForNextSentencePrediction,
							        TFMobileBertForPreTraining,
							        TFMobileBertForQuestionAnswering,
							        TFMobileBertForSequenceClassification,
							        TFMobileBertForTokenClassification,
							        TFMobileBertForMultipleChoice,
							    )
							    if is_tf_available()
							    else ()
							)
							_UpperCAmelCase      =						(
							    {
							        """feature-extraction""": TFMobileBertModel,
							        """fill-mask""": TFMobileBertForMaskedLM,
							        """question-answering""": TFMobileBertForQuestionAnswering,
							        """text-classification""": TFMobileBertForSequenceClassification,
							        """token-classification""": TFMobileBertForTokenClassification,
							        """zero-shot""": TFMobileBertForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__=False       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					super()._prepare_for_class(__a			,						__a			,						return_labels=__a       )

										if return_labels:
													if model_class in get_values(__a       ):
																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					tf.zeros(self.model_tester.batch_size			,						dtype=tf.intaa       )

										return inputs_dict







							class 	__lowercase    (lowerCAmelCase__  ):





														"""simple docstring"""







														def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__=1_3			,						lowerCAmelCase__=7			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=9_9			,						lowerCAmelCase__=3_2			,						lowerCAmelCase__=3_2			,						lowerCAmelCase__=2			,						lowerCAmelCase__=4			,						lowerCAmelCase__=3_7			,						lowerCAmelCase__="gelu"			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=5_1_2			,						lowerCAmelCase__=1_6			,						lowerCAmelCase__=2			,						lowerCAmelCase__=0.02			,						lowerCAmelCase__=3			,						lowerCAmelCase__=4			,						lowerCAmelCase__=None			,						):





																	"""simple docstring"""




																	SCREAMING_SNAKE_CASE_		:     Optional[int]					=					parent
																	SCREAMING_SNAKE_CASE_		:     Optional[int]					=					batch_size
																	SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					seq_length
																	SCREAMING_SNAKE_CASE_		:     Dict					=					is_training
																	SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					use_input_mask
																	SCREAMING_SNAKE_CASE_		:     Optional[int]					=					use_token_type_ids
																	SCREAMING_SNAKE_CASE_		:     int					=					use_labels
																	SCREAMING_SNAKE_CASE_		:     List[Any]					=					vocab_size
																	SCREAMING_SNAKE_CASE_		:     List[str]					=					hidden_size
																	SCREAMING_SNAKE_CASE_		:     str					=					num_hidden_layers
																	SCREAMING_SNAKE_CASE_		:     str					=					num_attention_heads
																	SCREAMING_SNAKE_CASE_		:     List[Any]					=					intermediate_size
																	SCREAMING_SNAKE_CASE_		:     Tuple					=					hidden_act
																	SCREAMING_SNAKE_CASE_		:     Tuple					=					hidden_dropout_prob
																	SCREAMING_SNAKE_CASE_		:     Tuple					=					attention_probs_dropout_prob
																	SCREAMING_SNAKE_CASE_		:     Optional[int]					=					max_position_embeddings
																	SCREAMING_SNAKE_CASE_		:     Dict					=					type_vocab_size
																	SCREAMING_SNAKE_CASE_		:     List[str]					=					type_sequence_label_size
																	SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					initializer_range
																	SCREAMING_SNAKE_CASE_		:     Dict					=					num_labels
																	SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					num_choices
																	SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					scope
																	SCREAMING_SNAKE_CASE_		:     int					=					embedding_size






														def 					UpperCamelCase__     (      self       ):





																	"""simple docstring"""




																	SCREAMING_SNAKE_CASE_		:     Tuple					=					ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size       )

																	SCREAMING_SNAKE_CASE_		:     List[str]					=					None
																	if self.use_input_mask:
																				SCREAMING_SNAKE_CASE_		:     List[Any]					=					random_attention_mask([self.batch_size, self.seq_length]       )

																	SCREAMING_SNAKE_CASE_		:     List[str]					=					None
																	if self.use_token_type_ids:
																				SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					ids_tensor([self.batch_size, self.seq_length]			,						self.type_vocab_size       )

																	SCREAMING_SNAKE_CASE_		:     List[str]					=					None
																	SCREAMING_SNAKE_CASE_		:     List[Any]					=					None
																	SCREAMING_SNAKE_CASE_		:     Optional[int]					=					None
																	if self.use_labels:
																				SCREAMING_SNAKE_CASE_		:     int					=					ids_tensor([self.batch_size]			,						self.type_sequence_label_size       )
																				SCREAMING_SNAKE_CASE_		:     int					=					ids_tensor([self.batch_size, self.seq_length]			,						self.num_labels       )
																				SCREAMING_SNAKE_CASE_		:     Tuple					=					ids_tensor([self.batch_size]			,						self.num_choices       )

																	SCREAMING_SNAKE_CASE_		:     Tuple					=					MobileBertConfig(
																	    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			,						initializer_range=self.initializer_range			,						embedding_size=self.embedding_size			,						)

																	return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






														def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





																	"""simple docstring"""




																	SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					TFMobileBertModel(config=__a       )
																	SCREAMING_SNAKE_CASE_		:     Dict					=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
																	SCREAMING_SNAKE_CASE_		:     int					=					model(__a       )

																	SCREAMING_SNAKE_CASE_		:     Any					=					[input_ids, input_mask]
																	SCREAMING_SNAKE_CASE_		:     int					=					model(__a       )

																	SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					model(__a       )

																	self.parent.assertEqual(
																	    result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)       )
																	self.parent.assertEqual(result.pooler_output.shape			,						(self.batch_size, self.hidden_size)       )






														def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





																	"""simple docstring"""




																	SCREAMING_SNAKE_CASE_		:     List[str]					=					TFMobileBertForMaskedLM(config=__a       )
																	SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
																	SCREAMING_SNAKE_CASE_		:     Any					=					model(__a       )
																	self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size)       )






														def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





																	"""simple docstring"""




																	SCREAMING_SNAKE_CASE_		:     str					=					TFMobileBertForNextSentencePrediction(config=__a       )
																	SCREAMING_SNAKE_CASE_		:     Dict					=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
																	SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					model(__a       )
																	self.parent.assertEqual(result.logits.shape			,						(self.batch_size, 2)       )






														def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





																	"""simple docstring"""




																	SCREAMING_SNAKE_CASE_		:     Tuple					=					TFMobileBertForPreTraining(config=__a       )
																	SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
																	SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					model(__a       )
																	self.parent.assertEqual(
																	    result.prediction_logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size)       )
																	self.parent.assertEqual(result.seq_relationship_logits.shape			,						(self.batch_size, 2)       )






														def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





																	"""simple docstring"""




																	SCREAMING_SNAKE_CASE_		:     Dict					=					self.num_labels
																	SCREAMING_SNAKE_CASE_		:     str					=					TFMobileBertForSequenceClassification(config=__a       )
																	SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
																	SCREAMING_SNAKE_CASE_		:     str					=					model(__a       )
																	self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)       )






														def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





																	"""simple docstring"""




																	SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.num_choices
																	SCREAMING_SNAKE_CASE_		:     List[str]					=					TFMobileBertForMultipleChoice(config=__a       )
																	SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tf.tile(tf.expand_dims(__a			,						1       )			,						(1, self.num_choices, 1)       )
																	SCREAMING_SNAKE_CASE_		:     Optional[int]					=					tf.tile(tf.expand_dims(__a			,						1       )			,						(1, self.num_choices, 1)       )
																	SCREAMING_SNAKE_CASE_		:     Tuple					=					tf.tile(tf.expand_dims(__a			,						1       )			,						(1, self.num_choices, 1)       )
																	SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					{
																	    'input_ids': multiple_choice_inputs_ids,
																	    'attention_mask': multiple_choice_input_mask,
																	    'token_type_ids': multiple_choice_token_type_ids,
																	}
																	SCREAMING_SNAKE_CASE_		:     Optional[int]					=					model(__a       )
																	self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_choices)       )






														def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





																	"""simple docstring"""




																	SCREAMING_SNAKE_CASE_		:     Tuple					=					self.num_labels
																	SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					TFMobileBertForTokenClassification(config=__a       )
																	SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
																	SCREAMING_SNAKE_CASE_		:     List[Any]					=					model(__a       )
																	self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.num_labels)       )






														def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





																	"""simple docstring"""




																	SCREAMING_SNAKE_CASE_		:     int					=					TFMobileBertForQuestionAnswering(config=__a       )
																	SCREAMING_SNAKE_CASE_		:     List[Any]					=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
																	SCREAMING_SNAKE_CASE_		:     Tuple					=					model(__a       )
																	self.parent.assertEqual(result.start_logits.shape			,						(self.batch_size, self.seq_length)       )
																	self.parent.assertEqual(result.end_logits.shape			,						(self.batch_size, self.seq_length)       )






														def 					UpperCamelCase__     (      self       ):





																	"""simple docstring"""




																	SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.prepare_config_and_inputs()
																	(
																	    (
																	    SCREAMING_SNAKE_CASE_      
																	)		,      (
																	    SCREAMING_SNAKE_CASE_      
																	)		,      (
																	    SCREAMING_SNAKE_CASE_      
																	)		,      (
																	    SCREAMING_SNAKE_CASE_      
																	)		,      (
																	    SCREAMING_SNAKE_CASE_      
																	)		,      (
																	    SCREAMING_SNAKE_CASE_      
																	)		,      (
																	    SCREAMING_SNAKE_CASE_      
																	)		,            
																	)		:     Optional[Any]					=					config_and_inputs
																	SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
																	return config, inputs_dict







							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					TFMobileBertModelTest.TFMobileBertModelTester(self       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					ConfigTester(self			,						config_class=__a			,						hidden_size=3_7       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										self.config_tester.run_common_tests()






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mobilebert_model(*__a       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mobilebert_for_masked_lm(*__a       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__a       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__a       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mobilebert_for_pretraining(*__a       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mobilebert_for_question_answering(*__a       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__a       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mobilebert_for_token_classification(*__a       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for model_name in ["google/mobilebert-uncased"]:
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					TFMobileBertModel.from_pretrained(__a       )
													self.assertIsNotNone(__a       )







@require_tf
class 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					TFMobileBertForPreTraining.from_pretrained('google/mobilebert-uncased'       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					tf.constant([[0, 1, 2, 3, 4, 5]]       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					model(__a       )[0]

										SCREAMING_SNAKE_CASE_		:     int					=					[1, 6, 3_0_5_2_2]
										self.assertEqual(output.shape			,						__a       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					tf.constant(
										    [
										        [
										            [-4.5_919_547, -9.248_295, -9.645_256],
										            [-6.7_306_175, -6.440_284, -6.6_052_837],
										            [-7.2_743_506, -6.7_847_915, -6.024_673],
										        ]
										    ]       )
										tf.debugging.assert_near(output[:, :3, :3]			,						__a			,						atol=1E-4       )









"""simple docstring"""


import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


A	:  Dict            =     get_logger(__name__)


A	:  Dict            =     R"\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n"
class     _UpperCamelCase    :
							'''simple docstring'''







							@add_start_docstrings(__a      )
							def __call__(	self       ,  __a       ,  __a      ):
										raise NotImplementedError(
										    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."      )
class     _UpperCamelCase    :
							'''simple docstring'''







							@add_start_docstrings(__a      )
							def __call__(	self       ,  __a       ,  __a      ):
										raise NotImplementedError(
										    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."      )
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							@add_start_docstrings(__a      )
							def __call__(	self       ,  __a       ,  __a       ,  __a       ,  **__a      ):
										for processor in self:
													__lowerCAmelCase							       =     inspect.signature(processor.__call__      ).parameters
													if len(__a      ) > 3:
																if not all(arg in kwargs for arg in list(function_args.keys()      )[2:]      ):
																			raise ValueError(
																			    f"Make sure that all the required parameters: {list(function_args.keys()      )} for "
																			    f"{processor.__class__} are passed to the logits processor."      )
																__lowerCAmelCase							       =     processor(__a       ,  __a       ,  __a       ,  **__a      )
													else:
																__lowerCAmelCase							       =     processor(__a       ,  __a       ,  __a      )
										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										if not isinstance(__a       ,  __a      ) or not (temperature > 0):
													raise ValueError(f"`temperature` has to be a strictly positive float, but is {temperature}"      )

										__lowerCAmelCase							       =     temperature



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     scores / self.temperature
										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a = -float("Inf"      )       ,  __a = 1      ):
										if not isinstance(__a       ,  __a      ) or (top_p < 0 or top_p > 1.0):
													raise ValueError(f"`top_p` has to be a float > 0 and < 1, but is {top_p}"      )
										if not isinstance(__a       ,  __a      ) or (min_tokens_to_keep < 1):
													raise ValueError(f"`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}"      )

										__lowerCAmelCase							       =     top_p
										__lowerCAmelCase							       =     filter_value
										__lowerCAmelCase							       =     min_tokens_to_keep



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     lax.top_k(__a       ,  scores.shape[-1]      )

										__lowerCAmelCase							       =     jnp.full_like(__a       ,  self.filter_value      )
										__lowerCAmelCase							       =     jax.nn.softmax(__a       ,  axis=-1      ).cumsum(axis=-1      )
										__lowerCAmelCase							       =     cumulative_probs < self.top_p

										# include the token that is higher than top_p as well
										__lowerCAmelCase							       =     jnp.roll(__a       ,  1      )
										score_mask |= score_mask.at[:, 0].set(__a      )

										# min tokens to keep
										__lowerCAmelCase							       =     score_mask.at[:, : self.min_tokens_to_keep].set(__a      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  __a       ,  __a      )
										__lowerCAmelCase							       =     jax.lax.sort_key_val(__a       ,  __a      )[-1]

										return next_scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a = -float("Inf"      )       ,  __a = 1      ):
										if not isinstance(__a       ,  __a      ) or top_k <= 0:
													raise ValueError(f"`top_k` has to be a strictly positive integer, but is {top_k}"      )

										__lowerCAmelCase							       =     max(__a       ,  __a      )
										__lowerCAmelCase							       =     filter_value



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     scores.shape
										__lowerCAmelCase							       =     jnp.full(batch_size * vocab_size       ,  self.filter_value      )

										__lowerCAmelCase							       =     min(self.top_k       ,  scores.shape[-1]      )  # Safety check
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     lax.top_k(__a       ,  __a      )
										__lowerCAmelCase							       =     jnp.broadcast_to((jnp.arange(__a      ) * vocab_size)[:, None]       ,  (batch_size, topk)      ).flatten()
										__lowerCAmelCase							       =     topk_scores.flatten()
										__lowerCAmelCase							       =     topk_indices.flatten() + shift

										__lowerCAmelCase							       =     next_scores_flat.at[topk_indices_flat].set(__a      )
										__lowerCAmelCase							       =     next_scores_flat.reshape(__a       ,  __a      )
										return next_scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     bos_token_id



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     jnp.full(scores.shape       ,  -float("inf"      )      )

										__lowerCAmelCase							       =     1 - jnp.bool_(cur_len - 1      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  new_scores.at[:, self.bos_token_id].set(0      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     max_length
										__lowerCAmelCase							       =     eos_token_id



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     jnp.full(scores.shape       ,  -float("inf"      )      )

										__lowerCAmelCase							       =     1 - jnp.bool_(cur_len - self.max_length + 1      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  new_scores.at[:, self.eos_token_id].set(0      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a      ):
										if not isinstance(__a       ,  __a      ) or min_length < 0:
													raise ValueError(f"`min_length` has to be a positive integer, but is {min_length}"      )

										if not isinstance(__a       ,  __a      ) or eos_token_id < 0:
													raise ValueError(f"`eos_token_id` has to be a positive integer, but is {eos_token_id}"      )

										__lowerCAmelCase							       =     min_length
										__lowerCAmelCase							       =     eos_token_id



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										# create boolean flag to decide if min length penalty should be applied
										__lowerCAmelCase							       =     1 - jnp.clip(cur_len - self.min_length       ,  0       ,  1      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  scores.at[:, self.eos_token_id].set(-float("inf"      )      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     list(__a      )
										__lowerCAmelCase							       =     begin_index



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     1 - jnp.bool_(cur_len - self.begin_index      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  scores.at[:, self.begin_suppress_tokens].set(-float("inf"      )      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     list(__a      )



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     scores.at[..., self.suppress_tokens].set(-float("inf"      )      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     dict(__a      )
										# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
										# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
										# Indexes without forced tokens will have a negative value.
										__lowerCAmelCase							       =     jnp.ones((max(force_token_map.keys()      ) + 1)       ,  dtype=jnp.intaa      ) * -1
										for index, token in force_token_map.items():
													if token is not None:
																__lowerCAmelCase							       =     force_token_array.at[index].set(__a      )
										__lowerCAmelCase							       =     jnp.intaa(__a      )



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										def _force_token(__a      ):
													__lowerCAmelCase							       =     scores.shape[0]
													__lowerCAmelCase							       =     self.force_token_array[generation_idx]

													__lowerCAmelCase							       =     jnp.ones_like(__a       ,  dtype=scores.dtype      ) * -float("inf"      )
													__lowerCAmelCase							       =     jnp.zeros((batch_size, 1)       ,  dtype=scores.dtype      )
													__lowerCAmelCase							       =     lax.dynamic_update_slice(__a       ,  __a       ,  (0, current_token)      )
													return new_scores

										__lowerCAmelCase							       =     lax.cond(
										    cur_len >= self.force_token_array.shape[0]       ,  lambda: scores       ,  lambda: lax.cond(
										        self.force_token_array[cur_len] >= 0       ,  lambda: _force_token(__a      )       ,  lambda: scores       ,  )       ,  )
										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     generate_config.eos_token_id
										__lowerCAmelCase							       =     generate_config.no_timestamps_token_id
										__lowerCAmelCase							       =     generate_config.no_timestamps_token_id + 1

										__lowerCAmelCase							       =     decoder_input_length + 1

										if generate_config.is_multilingual:
													# room for language token and task token
													self.begin_index += 2
										if hasattr(__a       ,  "max_initial_timestamp_index"      ):
													__lowerCAmelCase							       =     generate_config.max_initial_timestamp_index
										else:
													__lowerCAmelCase							       =     model_config.vocab_size
										if self.max_initial_timestamp_index is None:
													__lowerCAmelCase							       =     model_config.vocab_size



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										# suppress <|notimestamps|> which is handled by without_timestamps
										__lowerCAmelCase							       =     scores.at[:, self.no_timestamps_token_id].set(-float("inf"      )      )

										def handle_pairs(__a       ,  __a      ):
													__lowerCAmelCase							       =     jnp.where((cur_len - self.begin_index) >= 1       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     jnp.where(
													    input_ids_k[cur_len - 1] >= self.timestamp_begin       ,  True and last_was_timestamp       ,  __a       ,  )

													__lowerCAmelCase							       =     jnp.where((cur_len - self.begin_index) < 2       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     jnp.where(
													    input_ids_k[cur_len - 2] >= self.timestamp_begin       ,  __a       ,  __a       ,  )

													return jnp.where(
													    __a       ,  jnp.where(
													        penultimate_was_timestamp > 0       ,  scores_k.at[self.timestamp_begin :].set(-float("inf"      )      )       ,  scores_k.at[: self.eos_token_id].set(-float("inf"      )      )       ,  )       ,  __a       ,  )

										__lowerCAmelCase							       =     jax.vmap(__a      )(__a       ,  __a      )

										__lowerCAmelCase							       =     jnp.where(cur_len == self.begin_index       ,  __a       ,  __a      )
										__lowerCAmelCase							       =     jnp.where(
										    self.max_initial_timestamp_index is not None       ,  True and apply_max_initial_timestamp       ,  __a       ,  )

										__lowerCAmelCase							       =     self.timestamp_begin + self.max_initial_timestamp_index

										__lowerCAmelCase							       =     jnp.where(
										    __a       ,  scores.at[:, last_allowed + 1 :].set(-float("inf"      )      )       ,  __a       ,  )

										# if sum of probability over timestamps is above any other token, sample timestamp
										__lowerCAmelCase							       =     jax.nn.log_softmax(__a       ,  axis=-1      )

										def handle_cumulative_probs(__a       ,  __a      ):
													__lowerCAmelCase							       =     jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]       ,  axis=-1      )
													__lowerCAmelCase							       =     jnp.max(logprobs_k[: self.timestamp_begin]      )
													return jnp.where(
													    timestamp_logprob > max_text_token_logprob       ,  scores_k.at[: self.timestamp_begin].set(-float("inf"      )      )       ,  __a       ,  )

										__lowerCAmelCase							       =     jax.vmap(__a      )(__a       ,  __a      )

										return scores




