




'''simple docstring'''







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, 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			   ='platform'

   import jax
   import jax.numpy as jnp

   from transformers import MarianTokenizer
   from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right





def 						snake_case_							(_a     :							int   ,       _a     :							Any   ,       _a     :							Optional[int]=None   ,       _a     :							int=None   ,       _a     :							Optional[int]=None   ,       _a     :							Tuple=None   ,       _a     :							List[Any]=None   ,       _a     :							List[str]=None   ,       ):
    if attention_mask is None:
        UpperCAmelCase      	=		np.where(input_ids != config.pad_token_id   ,       1   ,       0					)
    if decoder_attention_mask is None:
        UpperCAmelCase      	=		np.where(decoder_input_ids != config.pad_token_id   ,       1   ,       0					)
    if head_mask is None:
        UpperCAmelCase      	=		np.ones((config.encoder_layers, config.encoder_attention_heads)					)
    if decoder_head_mask is None:
        UpperCAmelCase      	=		np.ones((config.decoder_layers, config.decoder_attention_heads)					)
    if cross_attn_head_mask is None:
        UpperCAmelCase      	=		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": attention_mask,
    }


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



           '''simple docstring'''

           UpperCAmelCase      	=		parent
           UpperCAmelCase      	=		batch_size
           UpperCAmelCase      	=		seq_length
           UpperCAmelCase      	=		is_training
           UpperCAmelCase      	=		use_labels
           UpperCAmelCase      	=		vocab_size
           UpperCAmelCase      	=		hidden_size
           UpperCAmelCase      	=		num_hidden_layers
           UpperCAmelCase      	=		num_attention_heads
           UpperCAmelCase      	=		intermediate_size
           UpperCAmelCase      	=		hidden_act
           UpperCAmelCase      	=		hidden_dropout_prob
           UpperCAmelCase      	=		attention_probs_dropout_prob
           UpperCAmelCase      	=		max_position_embeddings
           UpperCAmelCase      	=		eos_token_id
           UpperCAmelCase      	=		pad_token_id
           UpperCAmelCase      	=		bos_token_id
           UpperCAmelCase      	=		initializer_range
       def    A				(     self						:     Optional[int]    ):



           '''simple docstring'''

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

           UpperCAmelCase      	=		shift_tokens_right(lowercase       ,    1       ,    2    )

           UpperCAmelCase      	=		MarianConfig(
               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       ,    initializer_range=self.initializer_range       ,    use_cache=lowercase       ,    )
           UpperCAmelCase      	=		prepare_marian_inputs_dict(lowercase       ,    lowercase       ,    lowercase    )
           return config, inputs_dict
       def    A				(     self						:     Any    ):



           '''simple docstring'''

           UpperCAmelCase	,					UpperCAmelCase      	=		self.prepare_config_and_inputs()
           return config, inputs_dict
       def    A				(     self						:     Any       ,    lowercase						:     List[str]       ,    lowercase						:     Optional[int]       ,    lowercase						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase      	=		20
           UpperCAmelCase      	=		model_class_name(lowercase    )

           UpperCAmelCase      	=		model.encode(inputs_dict['''input_ids''']    )

           UpperCAmelCase	,					UpperCAmelCase      	=		(
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

           UpperCAmelCase      	=		model.init_cache(decoder_input_ids.shape[0]       ,    lowercase       ,    lowercase    )
           UpperCAmelCase      	=		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,    dtype='''i4'''    )

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

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

           UpperCAmelCase      	=		model.decode(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		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    A				(     self						:     Dict       ,    lowercase						:     Tuple       ,    lowercase						:     str       ,    lowercase						:     List[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		20
           UpperCAmelCase      	=		model_class_name(lowercase    )

           UpperCAmelCase      	=		model.encode(inputs_dict['''input_ids''']    )

           UpperCAmelCase	,					UpperCAmelCase      	=		(
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

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

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

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

           UpperCAmelCase      	=		model.decode(lowercase       ,    lowercase       ,    decoder_attention_mask=lowercase    )

           UpperCAmelCase      	=		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       _a  (     __a							,							unittest.TestCase							,							__a			):
       __a    :					Optional[int]           = True
       __a    :					List[Any]           = (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
       __a    :					Optional[Any]           = (FlaxMarianMTModel,) if is_flax_available() else ()
       def    A				(     self						:     List[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		FlaxMarianModelTester(self    )
       def    A				(     self						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase	,					UpperCAmelCase      	=		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    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase	,					UpperCAmelCase      	=		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    A				(     self						:     Tuple    ):



           '''simple docstring'''

           UpperCAmelCase	,					UpperCAmelCase      	=		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__    ):
                   UpperCAmelCase      	=		self._prepare_for_class(lowercase       ,    lowercase    )
                   UpperCAmelCase      	=		model_class(lowercase    )

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

                   with self.subTest('''JIT Enabled'''    ):
                       UpperCAmelCase      	=		encode_jitted(**lowercase    ).to_tuple()

                   with self.subTest('''JIT Disabled'''    ):
                       with jax.disable_jit():
                           UpperCAmelCase      	=		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    A				(     self						:     Optional[Any]    ):



           '''simple docstring'''

           UpperCAmelCase	,					UpperCAmelCase      	=		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__    ):
                   UpperCAmelCase      	=		model_class(lowercase    )
                   UpperCAmelCase      	=		model.encode(inputs_dict['''input_ids''']       ,    inputs_dict['''attention_mask''']    )

                   UpperCAmelCase      	=		{
                       '''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						:     str       ,    lowercase						:     int       ,    lowercase						:     List[str]    ):
                       return model.decode(
                           decoder_input_ids=lowercase       ,    decoder_attention_mask=lowercase       ,    encoder_outputs=lowercase       ,    )

                   with self.subTest('''JIT Enabled'''    ):
                       UpperCAmelCase      	=		decode_jitted(**lowercase    ).to_tuple()

                   with self.subTest('''JIT Disabled'''    ):
                       with jax.disable_jit():
                           UpperCAmelCase      	=		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    A				(     self						:     Optional[int]    ):



           '''simple docstring'''

           for model_class_name in self.all_model_classes:
               UpperCAmelCase      	=		model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de'''    )
               # FlaxMarianForSequenceClassification expects eos token in input_ids
               UpperCAmelCase      	=		np.ones((1, 1)    ) * model.config.eos_token_id
               UpperCAmelCase      	=		model(lowercase    )
               self.assertIsNotNone(lowercase    )






@require_flax
@require_sentencepiece
@require_tokenizers
class       _a  (     unittest.TestCase			):
       __a    :					Dict           = None
       __a    :					List[Any]           = None
       @classmethod
       def    A				(     cls						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase      	=		f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
           return cls
       @cached_property
       def    A				(     self						:     Dict    ):



           '''simple docstring'''

           return MarianTokenizer.from_pretrained(self.model_name    )
       @property
       def    A				(     self						:     Any    ):



           '''simple docstring'''

           return self.tokenizer.eos_token_id
       @cached_property
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		FlaxMarianMTModel.from_pretrained(self.model_name    )
           self.assertEqual(model.config.decoder_start_token_id       ,    model.config.pad_token_id    )
           return model
       def    A				(     self						:     int       ,    **lowercase						:     List[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.translate_src_text(**lowercase    )
           self.assertListEqual(self.expected_text       ,    lowercase    )







       def    A				(     self						:     str       ,    **lowercase						:     List[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer(self.src_text       ,    padding=lowercase       ,    return_tensors='''np'''       ,    **lowercase    )
           UpperCAmelCase      	=		self.model.generate(
               model_inputs.input_ids       ,    attention_mask=model_inputs.attention_mask       ,    num_beams=2       ,    max_length=128       ,    ).sequences
           UpperCAmelCase      	=		self.tokenizer.batch_decode(lowercase       ,    skip_special_tokens=lowercase    )
           return generated_words






@require_flax
@require_sentencepiece
@require_tokenizers
class       _a  (     __a			):
       __a    :					List[str]           = """en"""
       __a    :					List[str]           = """fr"""
       __a    :					int           = [
           """I am a small frog.""",
           """Now I can forget the 100 words of german that I know.""",
       ]
       __a    :					Optional[Any]           = [
           """Je suis une petite grenouille.""",
           """Maintenant, je peux oublier les 100 mots d'allemand que je connais.""",
       ]
       @slow
       def    A				(     self						:     Optional[int]    ):



           '''simple docstring'''

           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class       _a  (     __a			):
       __a    :					List[Any]           = """fr"""
       __a    :					Union[str, Any]           = """en"""
       __a    :					str           = [
           """Donnez moi le micro.""",
           """Tom et Mary étaient assis à une table.""",  # Accents
       ]
       __a    :					List[Any]           = [
           """Give me the microphone.""",
           """Tom and Mary were sitting at a table.""",
       ]
       @slow
       def    A				(     self						:     int    ):



           '''simple docstring'''

           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class       _a  (     __a			):
       __a    :					Any           = """mt"""
       __a    :					int           = """en"""
       __a    :					Union[str, Any]           = ["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
       __a    :					Tuple           = ["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]
       @slow
       def    A				(     self						:     Any    ):



           '''simple docstring'''

           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class       _a  (     __a			):
       __a    :					Any           = """en"""
       __a    :					Union[str, Any]           = """de"""
       __a    :					Optional[Any]           = [
           """I am a small frog.""",
           """Now I can forget the 100 words of german that I know.""",
           """Tom asked his teacher for advice.""",
           """That's how I would do it.""",
           """Tom really admired Mary's courage.""",
           """Turn around and close your eyes.""",
       ]
       __a    :					int           = [
           """Ich bin ein kleiner Frosch.""",
           """Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.""",
           """Tom bat seinen Lehrer um Rat.""",
           """So würde ich das machen.""",
           """Tom bewunderte Marias Mut wirklich.""",
           """Drehen Sie sich um und schließen Sie die Augen.""",
       ]
       @slow
       def    A				(     self						:     str    ):



           '''simple docstring'''

           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class       _a  (     __a			):
       __a    :					Optional[Any]           = """en"""
       __a    :					int           = """zh"""
       __a    :					Optional[Any]           = ["""My name is Wolfgang and I live in Berlin"""]
       __a    :					Union[str, Any]           = ["""我叫沃尔夫冈 我住在柏林"""]
       @slow
       def    A				(     self						:     List[Any]    ):



           '''simple docstring'''

           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class       _a  (     __a			):
       __a    :					List[str]           = """ru"""
       __a    :					Tuple           = """fr"""
       __a    :					List[Any]           = ["""Он показал мне рукопись своей новой пьесы."""]
       __a    :					str           = ["""Il m'a montré le manuscrit de sa nouvelle pièce."""]
       @slow
       def    A				(     self						:     Dict    ):



           '''simple docstring'''

           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class       _a  (     __a			):
       __a    :					List[Any]           = """en"""
       __a    :					int           = """ROMANCE"""
       __a    :					Dict           = [
           """>>fr<< Don't spend so much time watching TV.""",
           """>>pt<< Your message has been sent.""",
           """>>es<< He's two years older than me.""",
       ]
       __a    :					int           = [
           """Ne passez pas autant de temps à regarder la télé.""",
           """A sua mensagem foi enviada.""",
           """Es dos años más viejo que yo.""",
       ]
       @slow
       def    A				(     self						:     Union[str, Any]    ):



           '''simple docstring'''

           self._assert_generated_batch_equal_expected()











'''simple docstring'''







import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

A			   =logging.get_logger(__name__)





def 						snake_case_							(_a     :							bytes   ,       _a     :							int					):
    UpperCAmelCase      	=		F"{sampling_rate}"
    UpperCAmelCase      	=		'''1'''
    UpperCAmelCase      	=		'''f32le'''
    UpperCAmelCase      	=		[
        '''ffmpeg''',
        '''-i''',
        '''pipe:0''',
        '''-ac''',
        ac,
        '''-ar''',
        ar,
        '''-f''',
        format_for_conversion,
        '''-hide_banner''',
        '''-loglevel''',
        '''quiet''',
        '''pipe:1''',
    ]

    try:
        UpperCAmelCase      	=		subprocess.Popen(_a   ,       stdin=subprocess.PIPE   ,       stdout=subprocess.PIPE					)
    except FileNotFoundError:
        raise ValueError('''ffmpeg was not found but is required to load audio files from filename'''					)
    UpperCAmelCase      	=		ffmpeg_process.communicate(_a					)
    UpperCAmelCase      	=		output_stream[0]

    UpperCAmelCase      	=		np.frombuffer(_a   ,       np.floataa					)
    if audio.shape[0] == 0:
        raise ValueError('''Malformed soundfile'''					)
    return audio







@add_end_docstrings(__a			)
class       _a  (     __a			):
       def __init__(     self						:     Dict       ,    *lowercase						:     Dict       ,    **lowercase						:     Any    ):



           '''simple docstring'''

           UpperCAmelCase      	=		5
           super().__init__(*lowercase       ,    **lowercase    )

           if self.framework != "pt":
               raise ValueError(f"The {self.__class__} is only available in PyTorch."    )

           self.check_model_type(lowercase    )
       def __call__(     self						:     List[Any]       ,    lowercase						:     Union[np.ndarray, bytes, str]       ,    **lowercase						:     Dict       ,    ):



           '''simple docstring'''

           return super().__call__(lowercase       ,    **lowercase    )
       def    A				(     self						:     Tuple       ,    lowercase						:     Dict=None       ,    **lowercase						:     List[str]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		{}
           if top_k is not None:
               if top_k > self.model.config.num_labels:
                   UpperCAmelCase      	=		self.model.config.num_labels
               UpperCAmelCase      	=		top_k
           return {}, {}, postprocess_params
       def    A				(     self						:     List[Any]       ,    lowercase						:     Union[str, Any]    ):



           '''simple docstring'''

           if isinstance(lowercase       ,    lowercase    ):
               if inputs.startswith('''http://'''    ) or inputs.startswith('''https://'''    ):
                   # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                   # like http_huggingface_co.png
                   UpperCAmelCase      	=		requests.get(lowercase    ).content
               else:
                   with open(lowercase       ,    '''rb'''    ) as f:
                       UpperCAmelCase      	=		f.read()

           if isinstance(lowercase       ,    lowercase    ):
               UpperCAmelCase      	=		ffmpeg_read(lowercase       ,    self.feature_extractor.sampling_rate    )

           if isinstance(lowercase       ,    lowercase    ):
               # Accepting `"array"` which is the key defined in `datasets` for
               # better integration
               if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
                   raise ValueError(
                       '''When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a '''
                       '''"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '''
                       '''containing the sampling_rate associated with that array'''    )

               UpperCAmelCase      	=		inputs.pop('''raw'''       ,    lowercase    )
               if _inputs is None:
                   # Remove path which will not be used from `datasets`.
                   inputs.pop('''path'''       ,    lowercase    )
                   UpperCAmelCase      	=		inputs.pop('''array'''       ,    lowercase    )
               UpperCAmelCase      	=		inputs.pop('''sampling_rate'''    )
               UpperCAmelCase      	=		_inputs
               if in_sampling_rate != self.feature_extractor.sampling_rate:
                   import torch

                   if is_torchaudio_available():
                       from torchaudio import functional as F
                   else:
                       raise ImportError(
                           '''torchaudio is required to resample audio samples in AudioClassificationPipeline. '''
                           '''The torchaudio package can be installed through: `pip install torchaudio`.'''    )

                   UpperCAmelCase      	=		F.resample(
                       torch.from_numpy(lowercase    )       ,    lowercase       ,    self.feature_extractor.sampling_rate    ).numpy()

           if not isinstance(lowercase       ,    np.ndarray    ):
               raise ValueError('''We expect a numpy ndarray as input'''    )
           if len(inputs.shape    ) != 1:
               raise ValueError('''We expect a single channel audio input for AudioClassificationPipeline'''    )

           UpperCAmelCase      	=		self.feature_extractor(
               lowercase       ,    sampling_rate=self.feature_extractor.sampling_rate       ,    return_tensors='''pt'''    )
           return processed
       def    A				(     self						:     Optional[Any]       ,    lowercase						:     List[str]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.model(**lowercase    )
           return model_outputs







       def    A				(     self						:     Union[str, Any]       ,    lowercase						:     Optional[Any]       ,    lowercase						:     List[str]=5    ):



           '''simple docstring'''

           UpperCAmelCase      	=		model_outputs.logits[0].softmax(-1    )
           UpperCAmelCase	,					UpperCAmelCase      	=		probs.topk(lowercase    )

           UpperCAmelCase      	=		scores.tolist()
           UpperCAmelCase      	=		ids.tolist()

           UpperCAmelCase      	=		[{'''score''': score, '''label''': self.model.config.idalabel[_id]} for score, _id in zip(lowercase       ,    lowercase    )]

           return labels




