




import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
							import librosa  # noqa: E402

							UpperCamelCase__		=						True
							UpperCamelCase__		=						''
except Exception as e:
							UpperCamelCase__		=						False
							UpperCamelCase__		=						(
							    F'Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'
							)


from PIL import Image  # noqa: E402


class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase       ,					lowerCamelCase  ):
					_lowerCamelCase		  =			"""mel_config.json"""

					@register_to_config
					def __init__(    self			,	__lowercase = 256			,	__lowercase = 256			,	__lowercase = 22_050			,	__lowercase = 2_048			,	__lowercase = 512			,	__lowercase = 80			,	__lowercase = 32			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							hop_length
						lowerCamelCase_							:		Optional[Any]							       =							sample_rate
						lowerCamelCase_							:		Dict							       =							n_fft
						lowerCamelCase_							:		str							       =							top_db
						lowerCamelCase_							:		str							       =							n_iter
						self.set_resolution(__lowercase			,	__lowercase )
						lowerCamelCase_							:		Union[str, Any]							       =							None

						if not _librosa_can_be_imported:
							raise ValueError(_import_error )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							x_res
						lowerCamelCase_							:		Tuple							       =							y_res
						lowerCamelCase_							:		str							       =							self.y_res
						lowerCamelCase_							:		Dict							       =							self.x_res * self.hop_length - 1

					def      lowercase_							(    self			,	__lowercase = None			,	__lowercase = None ):







						'''simple docstring'''




						if audio_file is not None:
							lowerCamelCase_       ,      lowerCamelCase_							:		Union[str, Any]							       =							librosa.load(__lowercase			,	mono=__lowercase			,	sr=self.sr )
						else:
							lowerCamelCase_							:		int							       =							raw_audio

						# Pad with silence if necessary.
						if len(self.audio ) < self.x_res * self.hop_length:
							lowerCamelCase_							:		List[str]							       =							np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio ),) )] )

					def      lowercase_							(    self ):







						'''simple docstring'''




						return len(self.audio ) // self.slice_size

					def      lowercase_							(    self			,	__lowercase = 0 ):







						'''simple docstring'''




						return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]

					def      lowercase_							(    self ):







						'''simple docstring'''




						return self.sr

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							librosa.feature.melspectrogram(
						    y=self.get_audio_slice(__lowercase )			,	sr=self.sr			,	n_fft=self.n_fft			,	hop_length=self.hop_length			,	n_mels=self.n_mels )
						lowerCamelCase_							:		Optional[int]							       =							librosa.power_to_db(__lowercase			,	ref=np.max			,	top_db=self.top_db )
						lowerCamelCase_							:		Optional[int]							       =							(((log_S + self.top_db) * 255 / self.top_db).clip(0			,	255 ) + 0.5).astype(np.uinta )
						lowerCamelCase_							:		str							       =							Image.fromarray(__lowercase )
						return image





					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							np.frombuffer(image.tobytes()			,	dtype='uint8' ).reshape((image.height, image.width) )
						lowerCamelCase_							:		List[Any]							       =							bytedata.astype('float' ) * self.top_db / 255 - self.top_db
						lowerCamelCase_							:		Any							       =							librosa.db_to_power(__lowercase )
						lowerCamelCase_							:		List[str]							       =							librosa.feature.inverse.mel_to_audio(
						    __lowercase			,	sr=self.sr			,	n_fft=self.n_fft			,	hop_length=self.hop_length			,	n_iter=self.n_iter )
						return audio






from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
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.models.mpnet.modeling_tf_mpnet import (
							    TFMPNetForMaskedLM,
							    TFMPNetForMultipleChoice,
							    TFMPNetForQuestionAnswering,
							    TFMPNetForSequenceClassification,
							    TFMPNetForTokenClassification,
							    TFMPNetModel,
							)


class     __SCREAMING_SNAKE_CASE							:

					def __init__(    self			,	__lowercase			,	__lowercase=13			,	__lowercase=7			,	__lowercase=True			,	__lowercase=True			,	__lowercase=False			,	__lowercase=True			,	__lowercase=99			,	__lowercase=64			,	__lowercase=2			,	__lowercase=4			,	__lowercase=64			,	__lowercase="gelu"			,	__lowercase=0.1			,	__lowercase=0.1			,	__lowercase=512			,	__lowercase=16			,	__lowercase=2			,	__lowercase=0.02			,	__lowercase=3			,	__lowercase=4			,	__lowercase=None			,	):







						'''simple docstring'''




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

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							ids_tensor([self.batch_size, self.seq_length]			,	self.vocab_size )

						lowerCamelCase_							:		Optional[int]							       =							None
						if self.use_input_mask:
							lowerCamelCase_							:		List[Any]							       =							random_attention_mask([self.batch_size, self.seq_length] )

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

						lowerCamelCase_							:		Any							       =							MPNetConfig(
						    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			,	initializer_range=self.initializer_range			,	)
						return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							TFMPNetModel(config=__lowercase )
						lowerCamelCase_							:		str							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		str							       =							model(__lowercase )
						lowerCamelCase_							:		Dict							       =							[input_ids, input_mask]
						lowerCamelCase_							:		Dict							       =							model(__lowercase )
						self.parent.assertEqual(result.last_hidden_state.shape			,	(self.batch_size, self.seq_length, self.hidden_size) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							TFMPNetForMaskedLM(config=__lowercase )
						lowerCamelCase_							:		List[Any]							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		int							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.seq_length, self.vocab_size) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							TFMPNetForQuestionAnswering(config=__lowercase )
						lowerCamelCase_							:		Tuple							       =							{
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						}
						lowerCamelCase_							:		Optional[Any]							       =							model(__lowercase )
						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      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							self.num_labels
						lowerCamelCase_							:		Union[str, Any]							       =							TFMPNetForSequenceClassification(__lowercase )
						lowerCamelCase_							:		Union[str, Any]							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		List[Any]							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.num_labels) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							self.num_choices
						lowerCamelCase_							:		Tuple							       =							TFMPNetForMultipleChoice(__lowercase )
						lowerCamelCase_							:		Dict							       =							tf.tile(tf.expand_dims(__lowercase			,	1 )			,	(1, self.num_choices, 1) )
						lowerCamelCase_							:		Any							       =							tf.tile(tf.expand_dims(__lowercase			,	1 )			,	(1, self.num_choices, 1) )
						lowerCamelCase_							:		Optional[Any]							       =							{
						    'input_ids': multiple_choice_inputs_ids,
						    'attention_mask': multiple_choice_input_mask,
						}
						lowerCamelCase_							:		Union[str, Any]							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.num_choices) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							self.num_labels
						lowerCamelCase_							:		str							       =							TFMPNetForTokenClassification(__lowercase )
						lowerCamelCase_							:		Any							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		str							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.seq_length, self.num_labels) )





					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.prepare_config_and_inputs()
						((lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_))							:		Tuple							       =							config_and_inputs
						lowerCamelCase_							:		str							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						return config, inputs_dict


@require_tf
class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase       ,					lowerCamelCase       ,					unittest.TestCase  ):
					_lowerCamelCase		  =			(
					    (
					        TFMPNetForMaskedLM,
					        TFMPNetForMultipleChoice,
					        TFMPNetForQuestionAnswering,
					        TFMPNetForSequenceClassification,
					        TFMPNetForTokenClassification,
					        TFMPNetModel,
					    )
					    if is_tf_available()
					    else ()
					)
					_lowerCamelCase		  =			(
					    {
					        """feature-extraction""": TFMPNetModel,
					        """fill-mask""": TFMPNetForMaskedLM,
					        """question-answering""": TFMPNetForQuestionAnswering,
					        """text-classification""": TFMPNetForSequenceClassification,
					        """token-classification""": TFMPNetForTokenClassification,
					        """zero-shot""": TFMPNetForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					_lowerCamelCase		  =			False
					_lowerCamelCase		  =			False

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							TFMPNetModelTester(self )
						lowerCamelCase_							:		str							       =							ConfigTester(self			,	config_class=__lowercase			,	hidden_size=37 )

					def      lowercase_							(    self ):







						'''simple docstring'''




						self.config_tester.run_common_tests()

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_model(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_masked_lm(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_question_answering(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_token_classification(*__lowercase )





					@slow
					def      lowercase_							(    self ):







						'''simple docstring'''




						for model_name in ["microsoft/mpnet-base"]:
							lowerCamelCase_							:		str							       =							TFMPNetModel.from_pretrained(__lowercase )
							self.assertIsNotNone(__lowercase )


@require_tf
class     __SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

					@slow
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							TFMPNetModel.from_pretrained('microsoft/mpnet-base' )
						lowerCamelCase_							:		Optional[int]							       =							tf.constant([[0, 1, 2, 3, 4, 5]] )
						lowerCamelCase_							:		str							       =							model(__lowercase )[0]

						lowerCamelCase_							:		List[Any]							       =							[1, 6, 768]
						self.assertEqual(output.shape			,	__lowercase )

						lowerCamelCase_							:		Any							       =							tf.constant(
						    [
						        [
						            [-0.1_06_71_72, 0.08_21_64_73, 0.0_02_45_43],
						            [-0.03_46_58_79, 0.8_35_41_18, -0.03_25_22_88],
						            [-0.06_56_94_76, -0.12_42_41_11, -0.0_49_44_36],
						        ]
						    ] )
						tf.debugging.assert_near(output[:, :3, :3]			,	__lowercase			,	atol=1E-4 )
