

import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
      )


class      lowerCamelCase_ :




 def __init__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase=13  ,  _UpperCAmelCase=7  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=99  ,  _UpperCAmelCase=32  ,  _UpperCAmelCase=5  ,  _UpperCAmelCase=4  ,  _UpperCAmelCase=37  ,  _UpperCAmelCase="gelu"  ,  _UpperCAmelCase=0.1  ,  _UpperCAmelCase=0.1  ,  _UpperCAmelCase=512  ,  _UpperCAmelCase=16  ,  _UpperCAmelCase=2  ,  _UpperCAmelCase=0.0_2  ,  _UpperCAmelCase=3  ,  _UpperCAmelCase=4  ,  _UpperCAmelCase=None  ,  _UpperCAmelCase=1_000  ,  ):
   __UpperCAmelCase	:  Union[str, Any]		=     parent
   __UpperCAmelCase	:  List[Any]		=     batch_size
   __UpperCAmelCase	:  Tuple		=     seq_length
   __UpperCAmelCase	:  List[str]		=     is_training
   __UpperCAmelCase	:  int		=     use_input_mask
   __UpperCAmelCase	:  Dict		=     use_token_type_ids
   __UpperCAmelCase	:  Optional[Any]		=     use_labels
   __UpperCAmelCase	:  str		=     vocab_size
   __UpperCAmelCase	:  str		=     hidden_size
   __UpperCAmelCase	:  Optional[int]		=     num_hidden_layers
   __UpperCAmelCase	:  str		=     num_attention_heads
   __UpperCAmelCase	:  int		=     intermediate_size
   __UpperCAmelCase	:  List[str]		=     hidden_act
   __UpperCAmelCase	:  Union[str, Any]		=     hidden_dropout_prob
   __UpperCAmelCase	:  Tuple		=     attention_probs_dropout_prob
   __UpperCAmelCase	:  str		=     max_position_embeddings
   __UpperCAmelCase	:  Tuple		=     type_vocab_size
   __UpperCAmelCase	:  Optional[Any]		=     type_sequence_label_size
   __UpperCAmelCase	:  str		=     initializer_range
   __UpperCAmelCase	:  Union[str, Any]		=     num_labels
   __UpperCAmelCase	:  int		=     num_choices
   __UpperCAmelCase	:  str		=     scope
   __UpperCAmelCase	:  Optional[Any]		=     range_bbox




 def     a       (			self							):
   __UpperCAmelCase	:  List[str]		=     ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size							)

   __UpperCAmelCase	:  List[str]		=     ids_tensor([self.batch_size, self.seq_length, 4]  ,  self.range_bbox							)
   # Ensure that bbox is legal
   for i in range(bbox.shape[0]							):
     for j in range(bbox.shape[1]							):
       if bbox[i, j, 3] < bbox[i, j, 1]:
         __UpperCAmelCase	:  Any		=     bbox[i, j, 3]
         __UpperCAmelCase	:  Optional[int]		=     bbox[i, j, 1]
         __UpperCAmelCase	:  Dict		=     t
       if bbox[i, j, 2] < bbox[i, j, 0]:
         __UpperCAmelCase	:  Optional[Any]		=     bbox[i, j, 2]
         __UpperCAmelCase	:  Tuple		=     bbox[i, j, 0]
         __UpperCAmelCase	:  Dict		=     t

   __UpperCAmelCase	:  Optional[int]		=     None
   if self.use_input_mask:
     __UpperCAmelCase	:  Any		=     ids_tensor([self.batch_size, self.seq_length]  ,  vocab_size=2							)

   __UpperCAmelCase	:  Tuple		=     None
   if self.use_token_type_ids:
     __UpperCAmelCase	:  Tuple		=     ids_tensor([self.batch_size, self.seq_length]  ,  self.type_vocab_size							)

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

   __UpperCAmelCase	:  Optional[Any]		=     self.get_config()

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




 def     a       (			self							):
   return LayoutLMConfig(
       vocab_size=self.vocab_size  ,  hidden_size=self.hidden_size  ,  num_hidden_layers=self.num_hidden_layers  ,  num_attention_heads=self.num_attention_heads  ,  intermediate_size=self.intermediate_size  ,  hidden_act=self.hidden_act  ,  hidden_dropout_prob=self.hidden_dropout_prob  ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,  max_position_embeddings=self.max_position_embeddings  ,  type_vocab_size=self.type_vocab_size  ,  initializer_range=self.initializer_range  ,  )




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Tuple		=     LayoutLMModel(config=_UpperCAmelCase							)
   model.to(_UpperCAmelCase							)
   model.eval()
   __UpperCAmelCase	:  Dict		=     model(_UpperCAmelCase  ,  _UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase							)
   __UpperCAmelCase	:  Optional[int]		=     model(_UpperCAmelCase  ,  _UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase							)
   __UpperCAmelCase	:  Union[str, Any]		=     model(_UpperCAmelCase  ,  _UpperCAmelCase							)
   self.parent.assertEqual(result.last_hidden_state.shape  ,  (self.batch_size, self.seq_length, self.hidden_size)							)
   self.parent.assertEqual(result.pooler_output.shape  ,  (self.batch_size, self.hidden_size)							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Union[str, Any]		=     LayoutLMForMaskedLM(config=_UpperCAmelCase							)
   model.to(_UpperCAmelCase							)
   model.eval()
   __UpperCAmelCase	:  List[str]		=     model(_UpperCAmelCase  ,  _UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase  ,  labels=_UpperCAmelCase							)
   self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.vocab_size)							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  str		=     self.num_labels
   __UpperCAmelCase	:  str		=     LayoutLMForSequenceClassification(_UpperCAmelCase							)
   model.to(_UpperCAmelCase							)
   model.eval()
   __UpperCAmelCase	:  str		=     model(
       _UpperCAmelCase  ,  _UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase  ,  labels=_UpperCAmelCase							)
   self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_labels)							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  int		=     self.num_labels
   __UpperCAmelCase	:  int		=     LayoutLMForTokenClassification(config=_UpperCAmelCase							)
   model.to(_UpperCAmelCase							)
   model.eval()
   __UpperCAmelCase	:  str		=     model(_UpperCAmelCase  ,  _UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase  ,  labels=_UpperCAmelCase							)
   self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.num_labels)							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Optional[Any]		=     LayoutLMForQuestionAnswering(config=_UpperCAmelCase							)
   model.to(_UpperCAmelCase							)
   model.eval()
   __UpperCAmelCase	:  Optional[Any]		=     model(
       _UpperCAmelCase  ,  bbox=_UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase  ,  start_positions=_UpperCAmelCase  ,  end_positions=_UpperCAmelCase  ,  )
   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     a       (			self							):
   __UpperCAmelCase	:  int		=     self.prepare_config_and_inputs()
   (
       (
       __UpperCAmelCase
   ),			(
       __UpperCAmelCase
   ),			(
       __UpperCAmelCase
   ),			(
       __UpperCAmelCase
   ),			(
       __UpperCAmelCase
   ),			(
       __UpperCAmelCase
   ),			(
       __UpperCAmelCase
   ),			(
       __UpperCAmelCase
   ),			
   )	:  Union[str, Any]		=     config_and_inputs
   __UpperCAmelCase	:  Tuple		=     {
       'input_ids': input_ids,
       'bbox': bbox,
       'token_type_ids': token_type_ids,
       'attention_mask': input_mask,
   }
   return config, inputs_dict



@require_torch
class      lowerCamelCase_ (			_lowerCAmelCase	,				_lowerCAmelCase	,				unittest.TestCase):
 _lowerCAmelCase									=	(
     (
         LayoutLMModel,
         LayoutLMForMaskedLM,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
         LayoutLMForQuestionAnswering,
     )
     if is_torch_available()
     else None
 )
 _lowerCAmelCase									=	(
     {
         '''document-question-answering''': LayoutLMForQuestionAnswering,
         '''feature-extraction''': LayoutLMModel,
         '''fill-mask''': LayoutLMForMaskedLM,
         '''text-classification''': LayoutLMForSequenceClassification,
         '''token-classification''': LayoutLMForTokenClassification,
         '''zero-shot''': LayoutLMForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 _lowerCAmelCase									=	True




 def     a       (			self							):
   __UpperCAmelCase	:  int		=     LayoutLMModelTester(self							)
   __UpperCAmelCase	:  Tuple		=     ConfigTester(self  ,  config_class=_UpperCAmelCase  ,  hidden_size=37							)




 def     a       (			self							):
   self.config_tester.run_common_tests()




 def     a       (			self							):
   __UpperCAmelCase	:  List[Any]		=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*_UpperCAmelCase							)




 def     a       (			self							):
   __UpperCAmelCase	:  int		=     self.model_tester.prepare_config_and_inputs()
   for type in ["absolute", "relative_key", "relative_key_query"]:
     __UpperCAmelCase	:  int		=     type
     self.model_tester.create_and_check_model(*_UpperCAmelCase							)




 def     a       (			self							):
   __UpperCAmelCase	:  Dict		=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase							)




 def     a       (			self							):
   __UpperCAmelCase	:  str		=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase							)




 def     a       (			self							):
   __UpperCAmelCase	:  List[Any]		=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase							)




 def     a       (			self							):
   __UpperCAmelCase	:  List[str]		=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase							)




 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
 def     a       (			self							):
   pass





def  _snake_case		()							->		Tuple:
  # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
  # fmt: off
  __UpperCAmelCase	:  List[str]		=     torch.tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]    ,       device=snake_case_  )  # noqa: E231
  __UpperCAmelCase	:  Union[str, Any]		=     torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]    ,       device=snake_case_  )  # noqa: E231
  __UpperCAmelCase	:  List[Any]		=     torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]    ,       device=snake_case_  )  # noqa: E231
  __UpperCAmelCase	:  Optional[Any]		=     torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]    ,       device=snake_case_  )  # noqa: E231
  # these are sequence labels (i.e. at the token level)
  __UpperCAmelCase	:  Union[str, Any]		=     torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]    ,       device=snake_case_  )  # noqa: E231
  # fmt: on

  return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class      lowerCamelCase_ (			unittest.TestCase):




 @slow
 def     a       (			self							):
   __UpperCAmelCase	:  Tuple		=     LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'							).to(_UpperCAmelCase							)

   __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Union[str, Any]		=     prepare_layoutlm_batch_inputs()

   # forward pass
   __UpperCAmelCase	:  Any		=     model(input_ids=_UpperCAmelCase  ,  bbox=_UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase							)

   # test the sequence output on [0, :3, :3]
   __UpperCAmelCase	:  str		=     torch.tensor(
       [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]  ,  device=_UpperCAmelCase  ,  )

   self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]  ,  _UpperCAmelCase  ,  atol=1E-3							)							)

   # test the pooled output on [1, :3]
   __UpperCAmelCase	:  Any		=     torch.tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]  ,  device=_UpperCAmelCase							)

   self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]  ,  _UpperCAmelCase  ,  atol=1E-3							)							)




 @slow
 def     a       (			self							):
   # initialize model with randomly initialized sequence classification head
   __UpperCAmelCase	:  List[str]		=     LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'  ,  num_labels=2							).to(
       _UpperCAmelCase							)

   __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Dict		=     prepare_layoutlm_batch_inputs()

   # forward pass
   __UpperCAmelCase	:  Optional[Any]		=     model(
       input_ids=_UpperCAmelCase  ,  bbox=_UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase  ,  labels=torch.tensor([1, 1]  ,  device=_UpperCAmelCase							)  ,  )

   # test whether we get a loss as a scalar
   __UpperCAmelCase	:  str		=     outputs.loss
   __UpperCAmelCase	:  List[str]		=     torch.Size([]							)
   self.assertEqual(loss.shape  ,  _UpperCAmelCase							)

   # test the shape of the logits
   __UpperCAmelCase	:  Union[str, Any]		=     outputs.logits
   __UpperCAmelCase	:  int		=     torch.Size((2, 2)							)
   self.assertEqual(logits.shape  ,  _UpperCAmelCase							)




 @slow
 def     a       (			self							):
   # initialize model with randomly initialized token classification head
   __UpperCAmelCase	:  List[Any]		=     LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'  ,  num_labels=13							).to(
       _UpperCAmelCase							)

   __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  List[str]		=     prepare_layoutlm_batch_inputs()

   # forward pass
   __UpperCAmelCase	:  Optional[int]		=     model(
       input_ids=_UpperCAmelCase  ,  bbox=_UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase  ,  labels=_UpperCAmelCase							)

   # test the loss calculation to be around 2.65
   # expected_loss = torch.tensor(2.65, device=torch_device)

   # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
   # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

   # test the shape of the logits
   __UpperCAmelCase	:  List[str]		=     outputs.logits
   __UpperCAmelCase	:  Optional[Any]		=     torch.Size((2, 25, 13)							)
   self.assertEqual(logits.shape  ,  _UpperCAmelCase							)




 @slow
 def     a       (			self							):
   # initialize model with randomly initialized token classification head
   __UpperCAmelCase	:  Any		=     LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'							).to(_UpperCAmelCase							)

   __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Optional[Any]		=     prepare_layoutlm_batch_inputs()

   # forward pass
   __UpperCAmelCase	:  Optional[Any]		=     model(input_ids=_UpperCAmelCase  ,  bbox=_UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  token_type_ids=_UpperCAmelCase							)

   # test the shape of the logits
   __UpperCAmelCase	:  List[str]		=     torch.Size((2, 25)							)
   self.assertEqual(outputs.start_logits.shape  ,  _UpperCAmelCase							)
   self.assertEqual(outputs.end_logits.shape  ,  _UpperCAmelCase							)











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

_lowerCAmelCase    : List[str]		= logging.get_logger(__name__)



def     lowerCAmelCase		(		_lowerCAmelCase				: bytes    ,				_lowerCAmelCase				: int						):




 """simple docstring"""







 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(_lowerCAmelCase    ,				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(_lowerCAmelCase						)
 UpperCAmelCase__							=    output_stream[0]

 UpperCAmelCase__							=    np.frombuffer(_lowerCAmelCase    ,				np.floataa						)
 if audio.shape[0] == 0:
  raise ValueError("Malformed soundfile"						)
 return audio


@add_end_docstrings(lowerCAmelCase		)
class       _UpperCamelCase   (					lowerCAmelCase		):
      def __init__(      self :Any			,   *lowerCamelCase :Any			,   **lowerCamelCase :Optional[Any]   )  ->      Optional[int]:
       # Default, might be overriden by the model.config.
       UpperCAmelCase__							=    5
       super().__init__(*lowerCamelCase			,   **lowerCamelCase   )

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

       self.check_model_type(lowerCamelCase   )
      def __call__(      self :Optional[Any]			,   lowerCamelCase :Union[np.ndarray, bytes, str]			,   **lowerCamelCase :List[Any]			,   )  ->      Tuple:
       return super().__call__(lowerCamelCase			,   **lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :str			,   lowerCamelCase :Dict=None			,   **lowerCamelCase :str   )  ->      int:
       # No parameters on this pipeline right now
       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 						UpperCAmelCase_    (      self :Optional[Any]			,   lowerCamelCase :Dict   )  ->      Optional[int]:
       if isinstance(lowerCamelCase			,   lowerCamelCase   ):
        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(lowerCamelCase   ).content
        else:
         with open(lowerCamelCase			,   "rb"   ) as f:
          UpperCAmelCase__							=    f.read()

       if isinstance(lowerCamelCase			,   lowerCamelCase   ):
        UpperCAmelCase__							=    ffmpeg_read(lowerCamelCase			,   self.feature_extractor.sampling_rate   )

       if isinstance(lowerCamelCase			,   lowerCamelCase   ):
        # 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"			,   lowerCamelCase   )
        if _inputs is None:
         # Remove path which will not be used from `datasets`.
         inputs.pop("path"			,   lowerCamelCase   )
         UpperCAmelCase__							=    inputs.pop("array"			,   lowerCamelCase   )
        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(lowerCamelCase   )			,   lowerCamelCase			,   self.feature_extractor.sampling_rate   ).numpy()

       if not isinstance(lowerCamelCase			,   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(
           lowerCamelCase			,   sampling_rate=self.feature_extractor.sampling_rate			,   return_tensors="pt"   )
       return processed
      def 						UpperCAmelCase_    (      self :Tuple			,   lowerCamelCase :Union[str, Any]   )  ->      List[Any]:
       UpperCAmelCase__							=    self.model(**lowerCamelCase   )
       return model_outputs







      def 						UpperCAmelCase_    (      self :List[str]			,   lowerCamelCase :Optional[Any]			,   lowerCamelCase :List[Any]=5   )  ->      Tuple:
       UpperCAmelCase__							=    model_outputs.logits[0].softmax(-1   )
       UpperCAmelCase__			,			UpperCAmelCase__							=    probs.topk(lowerCamelCase   )

       UpperCAmelCase__							=    scores.tolist()
       UpperCAmelCase__							=    ids.tolist()

       UpperCAmelCase__							=    [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase			,   lowerCamelCase   )]

       return labels





