





from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

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


if is_tf_available():
			import tensorflow as tf

			from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Union[str, Any]   ,    _lowercase     : Any   ,    _lowercase     : List[str]=None   ,    _lowercase     : int=None			)   ->							Tuple:




		'''simple docstring'''
		if attention_mask is None:
				lowercase__					:      List[Any]      =		tf.cast(tf.math.not_equal(_lowercase   ,    config.pad_token_id			)   ,    tf.inta			)
		return {"input_ids": input_ids, "attention_mask": attention_mask}


@require_tf
class       __lowerCAmelCase					:


	'''simple docstring'''





	_A													=				OPTConfig
	_A													=				{}
	_A													=				"gelu"

	def __init__(						self:						Optional[Any],					lowerCamelCase_:						Any,					lowerCamelCase_:						str=13,					lowerCamelCase_:						Optional[int]=7,					lowerCamelCase_:						List[str]=True,					lowerCamelCase_:						str=False,					lowerCamelCase_:						Tuple=99,					lowerCamelCase_:						Dict=16,					lowerCamelCase_:						str=2,					lowerCamelCase_:						int=4,					lowerCamelCase_:						List[Any]=4,					lowerCamelCase_:						Tuple="gelu",					lowerCamelCase_:						Union[str, Any]=0.1,					lowerCamelCase_:						List[Any]=0.1,					lowerCamelCase_:						Any=20,					lowerCamelCase_:						List[Any]=2,					lowerCamelCase_:						Optional[Any]=1,					lowerCamelCase_:						Dict=0,					lowerCamelCase_:						List[Any]=16,					lowerCamelCase_:						Optional[int]=16,					):
			lowercase__					:      int      =		parent
			lowercase__					:      Dict      =		batch_size
			lowercase__					:      Tuple      =		seq_length
			lowercase__					:      Dict      =		is_training
			lowercase__					:      Tuple      =		use_labels
			lowercase__					:      Any      =		vocab_size
			lowercase__					:      Optional[int]      =		hidden_size
			lowercase__					:      Any      =		num_hidden_layers
			lowercase__					:      List[str]      =		num_attention_heads
			lowercase__					:      Tuple      =		intermediate_size
			lowercase__					:      str      =		hidden_act
			lowercase__					:      Union[str, Any]      =		hidden_dropout_prob
			lowercase__					:      Tuple      =		attention_probs_dropout_prob
			lowercase__					:      Union[str, Any]      =		max_position_embeddings
			lowercase__					:      Any      =		eos_token_id
			lowercase__					:      str      =		pad_token_id
			lowercase__					:      List[str]      =		bos_token_id
			lowercase__					:      Tuple      =		embed_dim
			lowercase__					:      Dict      =		word_embed_proj_dim
			lowercase__					:      Tuple      =		False

	def    snake_case__(						self:						Optional[Any]			):
			lowercase__					:      Tuple      =		ids_tensor([self.batch_size, self.seq_length - 1],					self.vocab_size			)
			lowercase__					:      Optional[Any]      =		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			),					1			)
			lowercase__					:      Dict      =		tf.concat([input_ids, eos_tensor],					axis=1			)

			lowercase__					:      int      =		self.config_cls(
			    vocab_size=self.vocab_size,					hidden_size=self.hidden_size,					num_hidden_layers=self.num_hidden_layers,					num_attention_heads=self.num_attention_heads,					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,					embed_dim=self.embed_dim,					word_embed_proj_dim=self.word_embed_proj_dim,					is_encoder_decoder=lowerCamelCase_,					**self.config_updates,					)
			lowercase__					:      Dict      =		prepare_opt_inputs_dict(lowerCamelCase_,					lowerCamelCase_			)
			return config, inputs_dict





	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						int,					lowerCamelCase_:						Union[str, Any]			):
			lowercase__					:      str      =		TFOPTModel(config=lowerCamelCase_			)
			lowercase__					:      Tuple      =		inputs_dict['input_ids']

			lowercase__					:      Optional[int]      =		input_ids[:1, :]
			lowercase__					:      int      =		inputs_dict['attention_mask'][:1, :]
			lowercase__					:      Dict      =		1

			# first forward pass
			lowercase__					:      Any      =		model(lowerCamelCase_,					attention_mask=lowerCamelCase_,					use_cache=lowerCamelCase_			)

			lowercase__   ,				lowercase__					:      Any      =		outputs.to_tuple()

			# create hypothetical next token and extent to next_input_ids
			lowercase__					:      Dict      =		ids_tensor((self.batch_size, 3),					config.vocab_size			)
			lowercase__					:      str      =		tf.cast(ids_tensor((self.batch_size, 3),					2			),					tf.inta			)

			# append to next input_ids and
			lowercase__					:      int      =		tf.concat([input_ids, next_tokens],					axis=-1			)
			lowercase__					:      Union[str, Any]      =		tf.concat([attention_mask, next_attn_mask],					axis=-1			)

			lowercase__					:      Tuple      =		model(lowerCamelCase_,					attention_mask=lowerCamelCase_			)[0]
			lowercase__					:      Tuple      =		model(lowerCamelCase_,					attention_mask=lowerCamelCase_,					past_key_values=lowerCamelCase_			)[0]

			self.parent.assertEqual(next_tokens.shape[1],					output_from_past.shape[1]			)

			# select random slice
			lowercase__					:      Any      =		int(ids_tensor((1,),					output_from_past.shape[-1]			)			)
			lowercase__					:      Dict      =		output_from_no_past[:, -3:, random_slice_idx]
			lowercase__					:      Optional[int]      =		output_from_past[:, :, random_slice_idx]

			# test that outputs are equal for slice
			tf.debugging.assert_near(lowerCamelCase_,					lowerCamelCase_,					rtol=1E-3			)


@require_tf
class       __lowerCAmelCase					(       _UpperCamelCase   ,    _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





	_A													=				(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
	_A													=				(TFOPTForCausalLM,) if is_tf_available() else ()
	_A													=				(
	    {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
	)
	_A													=				False
	_A													=				False
	_A													=				False
	_A													=				10

	def    snake_case__(						self:						str			):
			lowercase__					:      Union[str, Any]      =		TFOPTModelTester(self			)
			lowercase__					:      Optional[int]      =		ConfigTester(self,					config_class=lowerCamelCase_			)

	def    snake_case__(						self:						Union[str, Any]			):
			self.config_tester.run_common_tests()

	def    snake_case__(						self:						int			):
			lowercase__					:      Optional[Any]      =		self.model_tester.prepare_config_and_inputs_for_common()
			self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase_			)





	def    snake_case__(						self:						List[Any]			):
			lowercase__   ,				lowercase__					:      int      =		self.model_tester.prepare_config_and_inputs_for_common()

			def _get_word_embedding_weight(lowerCamelCase_:						List[str],					lowerCamelCase_:						Optional[int]			):
					if hasattr(lowerCamelCase_,					'weight'			):
							return embedding_layer.weight
					else:
							# Here we build the word embeddings weights if not exists.
							# And then we retry to get the attribute once built.
							model.build()
							if hasattr(lowerCamelCase_,					'weight'			):
									return embedding_layer.weight
							else:
									return None

			for model_class in self.all_model_classes:
					for size in [config.vocab_size - 10, config.vocab_size + 10]:
							# build the embeddings
							lowercase__					:      str      =		model_class(config=lowerCamelCase_			)
							lowercase__					:      Dict      =		_get_word_embedding_weight(lowerCamelCase_,					model.get_input_embeddings()			)
							lowercase__					:      Any      =		_get_word_embedding_weight(lowerCamelCase_,					model.get_output_embeddings()			)

							# reshape the embeddings
							model.resize_token_embeddings(lowerCamelCase_			)
							lowercase__					:      Optional[Any]      =		_get_word_embedding_weight(lowerCamelCase_,					model.get_input_embeddings()			)
							lowercase__					:      Optional[Any]      =		_get_word_embedding_weight(lowerCamelCase_,					model.get_output_embeddings()			)

							# check that the resized embeddings size matches the desired size.
							lowercase__					:      List[Any]      =		size if size is not None else config.vocab_size

							self.assertEqual(new_input_embeddings.shape[0],					lowerCamelCase_			)

							# check that weights remain the same after resizing
							lowercase__					:      Optional[int]      =		True
							for pa, pa in zip(old_input_embeddings.value(),					new_input_embeddings.value()			):
									if tf.math.reduce_sum(tf.math.abs(pa - pa			)			) > 0:
											lowercase__					:      Tuple      =		False
							self.assertTrue(lowerCamelCase_			)

							if old_output_embeddings is not None and new_output_embeddings is not None:
									self.assertEqual(new_output_embeddings.shape[0],					lowerCamelCase_			)

									lowercase__					:      Tuple      =		True
									for pa, pa in zip(old_output_embeddings.value(),					new_output_embeddings.value()			):
											if tf.math.reduce_sum(tf.math.abs(pa - pa			)			) > 0:
													lowercase__					:      Tuple      =		False
									self.assertTrue(lowerCamelCase_			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]			)   ->							List[Any]:




		'''simple docstring'''
		return tf.constant(_lowercase   ,    dtype=tf.intaa			)


@require_tf
class       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	_A													=				99

	def    snake_case__(						self:						List[str]			):
			lowercase__					:      int      =		tf.ones((4, 1),					dtype=tf.intaa			) * 2
			lowercase__					:      str      =		tf.concat([ids_tensor((4, 6),					self.vocab_size - 3			) + 3, eos_column_vector],					axis=1			)
			lowercase__					:      Tuple      =		input_ids.shape[0]
			lowercase__					:      int      =		OPTConfig(
			    vocab_size=self.vocab_size,					hidden_size=24,					num_hidden_layers=2,					num_attention_heads=2,					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


@require_sentencepiece
@require_tf
class       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	@slow
	def    snake_case__(						self:						Any			):
			lowercase__					:      Optional[Any]      =		TFOPTModel.from_pretrained('facebook/opt-350m'			)
			lowercase__					:      List[str]      =		_long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]			)
			lowercase__					:      Any      =		tf.not_equal(lowerCamelCase_,					model.config.pad_token_id			)
			with tf.GradientTape():
					lowercase__					:      Tuple      =		model(input_ids=lowerCamelCase_,					attention_mask=lowerCamelCase_			).last_hidden_state
			lowercase__					:      Dict      =		(1, 11, 512)
			self.assertEqual(output.shape,					lowerCamelCase_			)
			lowercase__					:      Dict      =		tf.constant(
			    [[-0.2_8_7_3, -1.9_2_1_8, -0.3_0_3_3], [-1.2_7_1_0, -0.1_3_3_8, -0.1_9_0_2], [0.4_0_9_5, 0.1_2_1_4, -1.3_1_2_1]]			)
			self.assertTrue(np.allclose(output[:, :3, :3],					lowerCamelCase_,					atol=4E-3			)			)

			lowercase__					:      Tuple      =		tf.function(lowerCamelCase_,					jit_compile=lowerCamelCase_			)
			lowercase__					:      List[Any]      =		xla_generate(lowerCamelCase_,					lowerCamelCase_			)[0]
			self.assertTrue(np.allclose(output[:, :3, :3],					lowerCamelCase_,					atol=4E-2			)			)


@require_tf
@slow
class       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	def    snake_case__(						self:						List[str]			):
			super().setUp()
			lowercase__					:      List[Any]      =		'facebook/opt-350m'





	def    snake_case__(						self:						int			):
			lowercase__					:      Optional[int]      =		TFOPTForCausalLM.from_pretrained(self.path_model			)
			lowercase__					:      Union[str, Any]      =		GPTaTokenizer.from_pretrained(self.path_model			)

			lowercase__					:      Any      =		[
			    'Today is a beautiful day and I want to',
			    'In the city of',
			    'Paris is the capital of France and',
			    'Computers and mobile phones have taken',
			]
			# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
			lowercase__					:      Optional[int]      =		tokenizer(lowerCamelCase_,					return_tensors='tf',					padding=lowerCamelCase_,					add_special_tokens=lowerCamelCase_			)
			lowercase__					:      Any      =		tf.math.reduce_mean(model(inputs.input_ids,					attention_mask=inputs.attention_mask			)[0],					axis=-1			)
			lowercase__					:      List[str]      =		tf.constant(
			    [
			        [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
			        [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
			        [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
			        [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
			    ]			)
			self.assertTrue(np.allclose(lowerCamelCase_,					lowerCamelCase_,					atol=1E-4			)			)

			lowercase__					:      str      =		tf.function(lowerCamelCase_,					jit_compile=lowerCamelCase_			)
			lowercase__					:      Optional[int]      =		tf.math.reduce_mean(xla_generate(inputs.input_ids,					attention_mask=inputs.attention_mask			)[0],					axis=-1			)
			self.assertTrue(np.allclose(lowerCamelCase_,					lowerCamelCase_,					atol=1E-4			)			)


@require_tf
@slow
class       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	@property
	def    snake_case__(						self:						Optional[Any]			):
			return [
			    "Today is a beautiful day and I want",
			    "In the city of",
			    "Paris is the capital of France and",
			    "Computers and mobile phones have taken",
			]

	def    snake_case__(						self:						Any			):
			lowercase__					:      List[str]      =		'facebook/opt-125m'

			lowercase__					:      List[Any]      =		[
			    'Today is a beautiful day and I want to',
			    'In the city of New York, the city',
			    'Paris is the capital of France and the capital',
			    'Computers and mobile phones have taken over the',
			]

			lowercase__					:      Optional[int]      =		[]
			lowercase__					:      Tuple      =		GPTaTokenizer.from_pretrained(lowerCamelCase_			)
			lowercase__					:      Tuple      =		TFOPTForCausalLM.from_pretrained(lowerCamelCase_			)

			for prompt in self.prompts:
					lowercase__					:      Dict      =		tokenizer(lowerCamelCase_,					return_tensors='tf'			).input_ids

					lowercase__					:      List[Any]      =		model.generate(lowerCamelCase_,					max_length=10			)

					lowercase__					:      Any      =		tokenizer.batch_decode(lowerCamelCase_,					skip_special_tokens=lowerCamelCase_			)
					predicted_outputs += generated_string

			self.assertListEqual(lowerCamelCase_,					lowerCamelCase_			)

	def    snake_case__(						self:						Tuple			):
			lowercase__					:      str      =		'facebook/opt-350m'

			lowercase__					:      int      =		GPTaTokenizer.from_pretrained(lowerCamelCase_			)
			lowercase__					:      str      =		TFOPTForCausalLM.from_pretrained(lowerCamelCase_			)

			lowercase__					:      Dict      =		'left'

			# use different length sentences to test batching
			lowercase__					:      List[str]      =		[
			    'Hello, my dog is a little',
			    'Today, I',
			]

			lowercase__					:      List[Any]      =		tokenizer(lowerCamelCase_,					return_tensors='tf',					padding=lowerCamelCase_			)
			lowercase__					:      int      =		inputs['input_ids']

			lowercase__					:      Any      =		model.generate(input_ids=lowerCamelCase_,					attention_mask=inputs['attention_mask']			)

			lowercase__					:      Any      =		tokenizer(sentences[0],					return_tensors='tf'			).input_ids
			lowercase__					:      Optional[int]      =		model.generate(input_ids=lowerCamelCase_			)

			lowercase__					:      List[str]      =		inputs_non_padded.shape[-1] - tf.math.reduce_sum(
			    tf.cast(inputs['attention_mask'][-1],					tf.intaa			)			)
			lowercase__					:      Optional[Any]      =		tokenizer(sentences[1],					return_tensors='tf'			).input_ids
			lowercase__					:      Optional[Any]      =		model.generate(input_ids=lowerCamelCase_,					max_length=model.config.max_length - num_paddings			)

			lowercase__					:      Any      =		tokenizer.batch_decode(lowerCamelCase_,					skip_special_tokens=lowerCamelCase_			)
			lowercase__					:      Any      =		tokenizer.decode(output_non_padded[0],					skip_special_tokens=lowerCamelCase_			)
			lowercase__					:      Union[str, Any]      =		tokenizer.decode(output_padded[0],					skip_special_tokens=lowerCamelCase_			)

			lowercase__					:      Optional[int]      =		[
			    'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
			    'Today, I was in the middle of a conversation with a friend about the',
			]
			self.assertListEqual(lowerCamelCase_,					lowerCamelCase_			)
			self.assertListEqual(lowerCamelCase_,					[non_padded_sentence, padded_sentence]			)





	def    snake_case__(						self:						Optional[int]			):
			lowercase__					:      Optional[int]      =		'facebook/opt-350m'

			lowercase__					:      Union[str, Any]      =		[
			    'Today is a beautiful day and I want to',
			    'In the city of San Francisco, the city',
			    'Paris is the capital of France and the capital',
			    'Computers and mobile phones have taken over the',
			]

			lowercase__					:      Tuple      =		[]
			lowercase__					:      Optional[int]      =		GPTaTokenizer.from_pretrained(lowerCamelCase_			)
			lowercase__					:      List[Any]      =		TFOPTForCausalLM.from_pretrained(lowerCamelCase_			)

			for prompt in self.prompts:
					lowercase__					:      Optional[int]      =		tokenizer(lowerCamelCase_,					return_tensors='tf'			).input_ids

					lowercase__					:      Optional[Any]      =		model.generate(lowerCamelCase_,					max_length=10			)

					lowercase__					:      Tuple      =		tokenizer.batch_decode(lowerCamelCase_,					skip_special_tokens=lowerCamelCase_			)
					predicted_outputs += generated_string

			self.assertListEqual(lowerCamelCase_,					lowerCamelCase_			)









from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


__UpperCamelCase:	Optional[int] = logging.get_logger(__name__)


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				["audio_values", "audio_mask"]

	def __init__(						self:						Any,					lowerCamelCase_:						int=2048,					lowerCamelCase_:						str=1,					lowerCamelCase_:						str=[16, 16],					lowerCamelCase_:						Union[str, Any]=128,					lowerCamelCase_:						Tuple=44100,					lowerCamelCase_:						Union[str, Any]=86,					lowerCamelCase_:						Optional[Any]=2048,					lowerCamelCase_:						Dict=0.0,					**lowerCamelCase_:						str,					):
			super().__init__(
			    feature_size=lowerCamelCase_,					sampling_rate=lowerCamelCase_,					padding_value=lowerCamelCase_,					**lowerCamelCase_,					)

			lowercase__					:      List[str]      =		spectrogram_length
			lowercase__					:      Union[str, Any]      =		num_channels
			lowercase__					:      int      =		patch_size
			lowercase__					:      Union[str, Any]      =		feature_size // self.patch_size[1]
			lowercase__					:      Optional[int]      =		n_fft
			lowercase__					:      int      =		sampling_rate // hop_length_to_sampling_rate
			lowercase__					:      str      =		sampling_rate
			lowercase__					:      Union[str, Any]      =		padding_value
			lowercase__					:      Any      =		mel_filter_bank(
			    num_frequency_bins=1 + n_fft // 2,					num_mel_filters=lowerCamelCase_,					min_frequency=0.0,					max_frequency=2_2_0_5_0.0,					sampling_rate=lowerCamelCase_,					norm='slaney',					mel_scale='slaney',					).T

	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						np.array			):
			lowercase__					:      Optional[Any]      =		spectrogram(
			    lowerCamelCase_,					window_function(self.n_fft,					'hann'			),					frame_length=self.n_fft,					hop_length=self.hop_length,					power=2.0,					mel_filters=self.mel_filters.T,					log_mel='dB',					db_range=8_0.0,					)
			lowercase__					:      Optional[Any]      =		log_spec[:, :-1]
			lowercase__					:      int      =		log_spec - 2_0.0
			lowercase__					:      Dict      =		np.clip(log_spec / 4_0.0,					-2.0,					0.0			) + 1.0
			return log_spec





	def __call__(						self:						Dict,					lowerCamelCase_:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],					lowerCamelCase_:						Optional[Union[str, TensorType]] = None,					lowerCamelCase_:						Optional[bool] = True,					lowerCamelCase_:						Optional[int] = None,					lowerCamelCase_:						bool = False,					lowerCamelCase_:						bool = False,					**lowerCamelCase_:						str,					):

			if sampling_rate is not None:
					if sampling_rate != self.sampling_rate:
							raise ValueError(
							    'This feature extractor is set to support sampling rate'
							    F""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
							    F""" with {self.sampling_rate} and not {sampling_rate}."""			)
			else:
					logger.warning(
					    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
					    'Failing to do so can result in silent errors that might be hard to debug.'			)

			lowercase__					:      Any      =		isinstance(lowerCamelCase_,					np.ndarray			) and len(raw_speech.shape			) > 1
			if is_batched_numpy and len(raw_speech.shape			) > 2:
					raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""			)
			lowercase__					:      Optional[Any]      =		is_batched_numpy or (
			    isinstance(lowerCamelCase_,					(list, tuple)			) and (isinstance(raw_speech[0],					(np.ndarray, tuple, list)			))
			)
			if is_batched:
					lowercase__					:      int      =		[np.asarray([speech],					dtype=np.floataa			).T for speech in raw_speech]
			elif not is_batched and not isinstance(lowerCamelCase_,					np.ndarray			):
					lowercase__					:      Optional[int]      =		np.asarray(lowerCamelCase_,					dtype=np.floataa			)
			elif isinstance(lowerCamelCase_,					np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
					lowercase__					:      str      =		raw_speech.astype(np.floataa			)
			# always return batch
			if not is_batched:
					lowercase__					:      Tuple      =		[np.asarray([raw_speech]			).T]

			# Convert audio signals to log mel spectrograms, truncate by time axis
			lowercase__					:      List[str]      =		[
			    self._np_extract_fbank_features(waveform.squeeze()			).T[: self.spectrogram_length] for waveform in raw_speech
			]
			if isinstance(audio_features[0],					lowerCamelCase_			):
					lowercase__					:      str      =		[np.asarray(lowerCamelCase_,					dtype=np.floataa			) for feature in audio_features]

			# Create audio attention mask
			lowercase__					:      Dict      =		max(
			    [ceil(feature.shape[0] / self.patch_size[0]			) * self.freq_len for feature in audio_features]			)  # The maximum number of audio patches in a batch
			if return_attention_mask:
					lowercase__					:      int      =		[
					    (ceil(feature.shape[0] / self.patch_size[0]			) * self.freq_len) * [1]
					    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]			) * self.freq_len) * [0]
					    for feature in audio_features
					]
					lowercase__					:      Any      =		np.array(lowerCamelCase_			).astype(np.floataa			)

			# convert into correct format for padding
			lowercase__					:      Any      =		max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
			lowercase__					:      Tuple      =		np.ones([len(lowerCamelCase_			), 1, max_time_len, self.feature_size]			).astype(np.floataa			)
			lowercase__					:      Dict      =		padded_audio_features * self.padding_value
			for i in range(len(lowerCamelCase_			)			):
					lowercase__					:      Union[str, Any]      =		audio_features[i]
					lowercase__					:      str      =		feature

			# return as BatchFeature
			if return_attention_mask:
					lowercase__					:      Dict      =		{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
			else:
					lowercase__					:      int      =		{'audio_values': padded_audio_features}

			lowercase__					:      List[Any]      =		BatchFeature(data=lowerCamelCase_,					tensor_type=lowerCamelCase_			)
			return encoded_inputs


