




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
	__A						:		Optional[int]          =						"platform"

	import jax
	import jax.numpy as jnp

	from transformers import AutoTokenizer
	from transformers.models.mbart.modeling_flax_mbart import (
	    FlaxMBartForConditionalGeneration,
	    FlaxMBartForQuestionAnswering,
	    FlaxMBartForSequenceClassification,
	    FlaxMBartModel,
	    shift_tokens_right,
	)





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			)     ->     str:







				'''simple docstring'''


				if attention_mask is None:
								__lowerCAmelCase					=  np.where(input_ids != config.pad_token_id						,			1						,			0   )
				if decoder_attention_mask is None:
								__lowerCAmelCase					=  np.where(decoder_input_ids != config.pad_token_id						,			1						,			0   )
				if head_mask is None:
								__lowerCAmelCase					=  np.ones((config.encoder_layers, config.encoder_attention_heads)   )
				if decoder_head_mask is None:
								__lowerCAmelCase					=  np.ones((config.decoder_layers, config.decoder_attention_heads)   )
				if cross_attn_head_mask is None:
								__lowerCAmelCase					=  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 		lowercase_       (	unittest.TestCase				):




					def __init__(   self:		Dict,     _lowercase:		List[Any],     _lowercase:		Any=13,     _lowercase:		int=7,     _lowercase:		str=True,     _lowercase:		List[str]=False,     _lowercase:		Any=99,     _lowercase:		str=16,     _lowercase:		Optional[int]=2,     _lowercase:		List[str]=4,     _lowercase:		List[str]=4,     _lowercase:		Optional[int]="gelu",     _lowercase:		Any=0.1,     _lowercase:		Union[str, Any]=0.1,     _lowercase:		Dict=32,     _lowercase:		int=2,     _lowercase:		Union[str, Any]=1,     _lowercase:		Any=0,     _lowercase:		int=2,     _lowercase:		List[Any]=0.02,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  parent
									__lowerCAmelCase					=  batch_size
									__lowerCAmelCase					=  seq_length
									__lowerCAmelCase					=  is_training
									__lowerCAmelCase					=  use_labels
									__lowerCAmelCase					=  vocab_size
									__lowerCAmelCase					=  hidden_size
									__lowerCAmelCase					=  num_hidden_layers
									__lowerCAmelCase					=  num_attention_heads
									__lowerCAmelCase					=  intermediate_size
									__lowerCAmelCase					=  hidden_act
									__lowerCAmelCase					=  hidden_dropout_prob
									__lowerCAmelCase					=  attention_probs_dropout_prob
									__lowerCAmelCase					=  max_position_embeddings
									__lowerCAmelCase					=  eos_token_id
									__lowerCAmelCase					=  pad_token_id
									__lowerCAmelCase					=  bos_token_id
									__lowerCAmelCase					=  decoder_start_token_id
									__lowerCAmelCase					=  initializer_range




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  np.clip(ids_tensor([self.batch_size, self.seq_length - 1],     self.vocab_size),     3,     self.vocab_size)
									__lowerCAmelCase					=  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),     dtype=np.intaa)),     -1)

									__lowerCAmelCase					=  shift_tokens_right(_lowercase,     1)

									__lowerCAmelCase					=  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=_lowercase,     )
									__lowerCAmelCase					=  prepare_mbart_inputs_dict(_lowercase,     _lowercase,     _lowercase)
									return config, inputs_dict




					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.prepare_config_and_inputs()
									return config, inputs_dict




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		Union[str, Any],     _lowercase:		Dict,     _lowercase:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  20
									__lowerCAmelCase					=  model_class_name(_lowercase)

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

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

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

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

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

									__lowerCAmelCase					=  model.decode(_lowercase,     _lowercase)

									__lowerCAmelCase					=  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     _lowercase						(   self:		List[str],     _lowercase:		int,     _lowercase:		str,     _lowercase:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  20
									__lowerCAmelCase					=  model_class_name(_lowercase)

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

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

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

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

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

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

									__lowerCAmelCase					=  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 		lowercase_       (	unittest.TestCase				):
					__UpperCamelCase     	= 99




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  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,     )

									__lowerCAmelCase					=  input_ids.shape[0]
									__lowerCAmelCase					=  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     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  self._get_config_and_data()
									__lowerCAmelCase					=  FlaxMBartForSequenceClassification(_lowercase)
									__lowerCAmelCase					=  model(input_ids=_lowercase,     decoder_input_ids=_lowercase)
									__lowerCAmelCase					=  (batch_size, config.num_labels)
									self.assertEqual(outputs["""logits"""].shape,     _lowercase)




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  self._get_config_and_data()
									__lowerCAmelCase					=  FlaxMBartForQuestionAnswering(_lowercase)
									__lowerCAmelCase					=  model(input_ids=_lowercase)

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




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  self._get_config_and_data()
									__lowerCAmelCase					=  FlaxMBartForConditionalGeneration(_lowercase)
									__lowerCAmelCase					=  lm_model(input_ids=_lowercase)
									__lowerCAmelCase					=  (batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs["""logits"""].shape,     _lowercase)




					def     _lowercase						(   self:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  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,     )
									__lowerCAmelCase					=  FlaxMBartForConditionalGeneration(_lowercase)
									__lowerCAmelCase					=  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],     dtype=np.intaa)
									__lowerCAmelCase					=  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],     dtype=np.intaa)
									__lowerCAmelCase					=  lm_model(input_ids=_lowercase,     decoder_input_ids=_lowercase)
									__lowerCAmelCase					=  (*summary.shape, config.vocab_size)
									self.assertEqual(outputs["""logits"""].shape,     _lowercase)







					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									__lowerCAmelCase					=  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],     dtype=np.intaa)
									__lowerCAmelCase					=  shift_tokens_right(_lowercase,     1)
									__lowerCAmelCase					=  np.equal(_lowercase,     1).astype(np.floataa).sum()
									__lowerCAmelCase					=  np.equal(_lowercase,     1).astype(np.floataa).sum()
									self.assertEqual(shifted.shape,     input_ids.shape)
									self.assertEqual(_lowercase,     n_pad_before - 1)
									self.assertTrue(np.equal(shifted[:, 0],     2).all())





@require_flax
class 		lowercase_       (	lowerCAmelCase__						,							unittest.TestCase						,							lowerCAmelCase__				):
					__UpperCamelCase     	= True
					__UpperCamelCase     	= (
					    (
					        FlaxMBartModel,
					        FlaxMBartForConditionalGeneration,
					        FlaxMBartForSequenceClassification,
					        FlaxMBartForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)
					__UpperCamelCase     	= (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  FlaxMBartModelTester(self)




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(_lowercase,     _lowercase,     _lowercase)




					def     _lowercase						(   self:		str):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  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(_lowercase,     _lowercase,     _lowercase)




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

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

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




					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

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

																	__lowerCAmelCase					=  {
																	    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
																	    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
																	    """encoder_outputs""": encoder_outputs,
																	}

																	@jax.jit
																	def decode_jitted(_lowercase:		Union[str, Any],     _lowercase:		str,     _lowercase:		Optional[Any]):
																					return model.decode(
																					    decoder_input_ids=_lowercase,     decoder_attention_mask=_lowercase,     encoder_outputs=_lowercase,     )

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

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

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







					@slow
					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									for model_class_name in self.all_model_classes:
													__lowerCAmelCase					=  model_class_name.from_pretrained("""facebook/mbart-large-cc25""",     from_pt=_lowercase)
													# FlaxMBartForSequenceClassification expects eos token in input_ids
													__lowerCAmelCase					=  np.ones((1, 1)) * model.config.eos_token_id
													__lowerCAmelCase					=  model(_lowercase)
													self.assertIsNotNone(_lowercase)





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




					@cached_property
					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									return AutoTokenizer.from_pretrained(self.model_name)




					@cached_property
					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  FlaxMBartForConditionalGeneration.from_pretrained(self.model_name,     from_pt=_lowercase)
									return model




					def     _lowercase						(   self:		Tuple,     **_lowercase:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  self.translate_src_text(**_lowercase)
									self.assertListEqual(self.expected_text,     _lowercase)




					def     _lowercase						(   self:		Optional[Any],     **_lowercase:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.tokenizer(self.src_text,     **_lowercase,     return_tensors="""np""")
									__lowerCAmelCase					=  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=_lowercase,     num_beams=2,     ).sequences
									__lowerCAmelCase					=  self.tokenizer.batch_decode(_lowercase,     skip_special_tokens=_lowercase)
									return generated_words







					@slow
					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									self._assert_generated_batch_equal_expected()













'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


__A   :							int     	=						logging.get_logger(__name__)






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     _snake_case							   =	['input_values', 'attention_mask']






     def __init__(self     :						Optional[int]					,					lowercase__     :						Tuple=1					,					lowercase__     :						Optional[int]=16000					,					lowercase__     :						Any=128					,					lowercase__     :						Optional[Any]=1024					,					lowercase__     :						int=0.0					,					lowercase__     :						List[str]=True					,					lowercase__     :						Dict=-4.2_67_73_93					,					lowercase__     :						Tuple=4.5_68_99_74					,					lowercase__     :						Optional[int]=False					,					**lowercase__     :						Optional[int]					,					)	->							Tuple:
           super().__init__(feature_size=lowercase__					,					sampling_rate=lowercase__					,					padding_value=lowercase__					,					**lowercase__       )
           __A		 =   num_mel_bins
           __A		 =   max_length
           __A		 =   do_normalize
           __A		 =   mean
           __A		 =   std
           __A		 =   return_attention_mask






     def      _UpperCamelCase (self     :						List[Any]					,					lowercase__     :						np.ndarray					,					lowercase__     :						int					,					)	->							np.ndarray:
           __A		 =   torch.from_numpy(lowercase__       ).unsqueeze(0       )
           __A		 =   ta_kaldi.fbank(
               lowercase__					,					htk_compat=lowercase__					,					sample_frequency=self.sampling_rate					,					use_energy=lowercase__					,					window_type="hanning"					,					num_mel_bins=self.num_mel_bins					,					dither=0.0					,					frame_shift=10					,					)

           __A		 =   fbank.shape[0]
           __A		 =   max_length - n_frames

           # pad or truncate, depending on difference
           if difference > 0:
                 __A		 =   torch.nn.ZeroPadad((0, 0, 0, difference)       )
                 __A		 =   pad_module(lowercase__       )
           elif difference < 0:
                 __A		 =   fbank[0:max_length, :]

           __A		 =   fbank.numpy()

           return fbank






     def      _UpperCamelCase (self     :						int					,					lowercase__     :						np.ndarray       )	->							np.ndarray:
           return (input_values - (self.mean)) / (self.std * 2)






     def __call__(self     :						str					,					lowercase__     :						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,					lowercase__     :						Optional[int] = None					,					lowercase__     :						Optional[Union[str, TensorType]] = None					,					**lowercase__     :						List[Any]					,					)	->							BatchFeature:

           if sampling_rate is not None:
                 if sampling_rate != self.sampling_rate:
                       raise ValueError(
                           f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
                           f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
                           f" {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."       )

           __A		 =   isinstance(lowercase__					,					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}"       )
           __A		 =   is_batched_numpy or (
               isinstance(lowercase__					,					(list, tuple)       ) and (isinstance(raw_speech[0]					,					(np.ndarray, tuple, list)       ))
           )

           if is_batched:
                 __A		 =   [np.asarray(lowercase__					,					dtype=np.floataa       ) for speech in raw_speech]
           elif not is_batched and not isinstance(lowercase__					,					np.ndarray       ):
                 __A		 =   np.asarray(lowercase__					,					dtype=np.floataa       )
           elif isinstance(lowercase__					,					np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
                 __A		 =   raw_speech.astype(np.floataa       )

           # always return batch
           if not is_batched:
                 __A		 =   [raw_speech]

           # extract fbank features and pad/truncate to max_length
           __A		 =   [self._extract_fbank_features(lowercase__					,					max_length=self.max_length       ) for waveform in raw_speech]

           # convert into BatchFeature
           __A		 =   BatchFeature({"input_values": features}       )

           # make sure list is in array format
           __A		 =   padded_inputs.get("input_values"       )
           if isinstance(input_values[0]					,					lowercase__       ):
                 __A		 =   [np.asarray(lowercase__					,					dtype=np.floataa       ) for feature in input_values]

           # normalization
           if self.do_normalize:
                 __A		 =   [self.normalize(lowercase__       ) for feature in input_values]

           if return_tensors is not None:
                 __A		 =   padded_inputs.convert_to_tensors(lowercase__       )

           return padded_inputs


