
'''simple docstring'''



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


_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Optional[int]										=  ['''audio_values''', '''audio_mask''']




						def __init__(   self       ,  _lowercase=2_048       ,  _lowercase=1       ,  _lowercase=[16, 16]       ,  _lowercase=128       ,  _lowercase=44_100       ,  _lowercase=86       ,  _lowercase=2_048       ,  _lowercase=0.0       ,  **_lowercase       ,  ):

												"""simple docstring"""



												super().__init__(
												    feature_size=_lowercase       ,  sampling_rate=_lowercase       ,  padding_value=_lowercase       ,  **_lowercase       ,  )

												_lowerCAmelCase    		=					spectrogram_length
												_lowerCAmelCase    		=					num_channels
												_lowerCAmelCase    		=					patch_size
												_lowerCAmelCase    		=					feature_size // self.patch_size[1]
												_lowerCAmelCase    		=					n_fft
												_lowerCAmelCase    		=					sampling_rate // hop_length_to_sampling_rate
												_lowerCAmelCase    		=					sampling_rate
												_lowerCAmelCase    		=					padding_value
												_lowerCAmelCase    		=					mel_filter_bank(
												    num_frequency_bins=1 + n_fft // 2       ,  num_mel_filters=_lowercase       ,  min_frequency=0.0       ,  max_frequency=2_2050.0       ,  sampling_rate=_lowercase       ,  norm="""slaney"""       ,  mel_scale="""slaney"""       ,  ).T




						def    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					spectrogram(
												    _lowercase       ,  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=80.0       ,  )
												_lowerCAmelCase    		=					log_spec[:, :-1]
												_lowerCAmelCase    		=					log_spec - 20.0
												_lowerCAmelCase    		=					np.clip(log_spec / 40.0       ,  -2.0       ,  0.0 ) + 1.0
												return log_spec




						def __call__(   self       ,  _lowercase       ,  _lowercase = None       ,  _lowercase = True       ,  _lowercase = None       ,  _lowercase = False       ,  _lowercase = False       ,  **_lowercase       ,  ):

												"""simple docstring"""



												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.""" )

												_lowerCAmelCase    		=					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}' )
												_lowerCAmelCase    		=					is_batched_numpy or (
												    isinstance(_lowercase       ,  (list, tuple) ) and (isinstance(raw_speech[0]       ,  (np.ndarray, tuple, list) ))
												)
												if is_batched:
																		_lowerCAmelCase    		=					[np.asarray([speech]       ,  dtype=np.floataa ).T for speech in raw_speech]
												elif not is_batched and not isinstance(_lowercase       ,  np.ndarray ):
																		_lowerCAmelCase    		=					np.asarray(_lowercase       ,  dtype=np.floataa )
												elif isinstance(_lowercase       ,  np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
																		_lowerCAmelCase    		=					raw_speech.astype(np.floataa )
												# always return batch
												if not is_batched:
																		_lowerCAmelCase    		=					[np.asarray([raw_speech] ).T]

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

												# Create audio attention mask
												_lowerCAmelCase    		=					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:
																		_lowerCAmelCase    		=					[
																		    (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
																		]
																		_lowerCAmelCase    		=					np.array(_lowercase ).astype(np.floataa )

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

												# return as BatchFeature
												if return_attention_mask:
																		_lowerCAmelCase    		=					{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
												else:
																		_lowerCAmelCase    		=					{"""audio_values""": padded_audio_features}

												_lowerCAmelCase    		=					BatchFeature(data=_lowercase       ,  tensor_type=_lowercase )
												return encoded_inputs


'''simple docstring'''

from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


snake_case									=    ["""text""", """image""", """audio"""]


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   str     		=	[]

 for input_type in input_types:
  if input_type == "text":
   inputs.append("Text input"			)
  elif input_type == "image":
   inputs.append(
       Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			).resize((5_1_2, 5_1_2)			)			)
  elif input_type == "audio":
   inputs.append(torch.ones(3_0_0_0			)			)
  elif isinstance(lowerCamelCase_      ,			lowerCamelCase_			):
   inputs.append(create_inputs(lowerCamelCase_			)			)
  else:
   raise ValueError(f'''Invalid type requested: {input_type}'''			)

 return inputs


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   str     		=	[]

 for output in outputs:
  if isinstance(lowerCamelCase_      ,			(str, AgentText)			):
   output_types.append("text"			)
  elif isinstance(lowerCamelCase_      ,			(Image.Image, AgentImage)			):
   output_types.append("image"			)
  elif isinstance(lowerCamelCase_      ,			(torch.Tensor, AgentAudio)			):
   output_types.append("audio"			)
  else:
   raise ValueError(f'''Invalid output: {output}'''			)

 return output_types







@is_tool_test
class    lowerCAmelCase       :


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  self.assertTrue(hasattr(self.tool			,							"inputs"    )    )
  self.assertTrue(hasattr(self.tool			,							"outputs"    )    )

  lowerCAmelCase__     :   Dict     		=	self.tool.inputs
  for _input in inputs:
   if isinstance(_input			,							a__    ):
    for __input in _input:
     self.assertTrue(__input in authorized_types    )
   else:
    self.assertTrue(_input in authorized_types    )

  lowerCAmelCase__     :   List[Any]     		=	self.tool.outputs
  for _output in outputs:
   self.assertTrue(_output in authorized_types    )


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	create_inputs(self.tool.inputs    )
  lowerCAmelCase__     :   str     		=	self.tool(*a__    )

  # There is a single output
  if len(self.tool.outputs    ) == 1:
   lowerCAmelCase__     :   Union[str, Any]     		=	[outputs]

  self.assertListEqual(output_types(a__    )			,							self.tool.outputs    )


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  self.assertTrue(hasattr(self.tool			,							"description"    )    )
  self.assertTrue(hasattr(self.tool			,							"default_checkpoint"    )    )
  self.assertTrue(self.tool.description.startswith("This is a tool that"    )    )


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	create_inputs(self.tool.inputs    )
  lowerCAmelCase__     :   str     		=	self.tool(*a__    )

  if not isinstance(a__			,							a__    ):
   lowerCAmelCase__     :   List[str]     		=	[outputs]

  self.assertEqual(len(a__    )			,							len(self.tool.outputs    )    )

  for output, output_type in zip(a__			,							self.tool.outputs    ):
   lowerCAmelCase__     :   List[Any]     		=	AGENT_TYPE_MAPPING[output_type]
   self.assertTrue(isinstance(a__			,							a__    )    )


 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	create_inputs(self.tool.inputs    )

  lowerCAmelCase__     :   Dict     		=	[]

  for _input, input_type in zip(a__			,							self.tool.inputs    ):
   if isinstance(a__			,							a__    ):
    _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input    ) for _input_type in input_type]    )
   else:
    _inputs.append(AGENT_TYPE_MAPPING[input_type](_input    )    )

        # Should not raise an error
  lowerCAmelCase__     :   Any     		=	self.tool(*a__    )

  if not isinstance(a__			,							a__    ):
   lowerCAmelCase__     :   Tuple     		=	[outputs]

  self.assertEqual(len(a__    )			,							len(self.tool.outputs    )    )





