
import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


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





class       _snake_case					(     _a				):
			UpperCamelCase__          =    ["""input_features"""]







			def __init__(				self						,   _a=80						,   _a=16_000						,   _a=160						,   _a=30						,   _a=400						,   _a=0.0						,   _a=False						,   **_a						,   ):
				super().__init__(
				    feature_size=__UpperCAmelCase						,   sampling_rate=__UpperCAmelCase						,   padding_value=__UpperCAmelCase						,   return_attention_mask=__UpperCAmelCase						,   **__UpperCAmelCase						,   )
				__magic_name__		:		Optional[int]	   =     n_fft
				__magic_name__		:		Union[str, Any]	   =     hop_length
				__magic_name__		:		Optional[int]	   =     chunk_length
				__magic_name__		:		str	   =     chunk_length * sampling_rate
				__magic_name__		:		List[str]	   =     self.n_samples // hop_length
				__magic_name__		:		Dict	   =     sampling_rate
				__magic_name__		:		List[str]	   =     mel_filter_bank(
				    num_frequency_bins=1 + n_fft // 2						,   num_mel_filters=__UpperCAmelCase						,   min_frequency=0.0						,   max_frequency=8_000.0						,   sampling_rate=__UpperCAmelCase						,   norm="slaney"						,   mel_scale="slaney"						,   )







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		List[str]	   =     spectrogram(
				    __UpperCAmelCase						,   window_function(self.n_fft						,   "hann"		)						,   frame_length=self.n_fft						,   hop_length=self.hop_length						,   power=2.0						,   mel_filters=self.mel_filters						,   log_mel="log10"						,   )
				__magic_name__		:		Union[str, Any]	   =     log_spec[:, :-1]
				__magic_name__		:		List[str]	   =     np.maximum(__UpperCAmelCase						,   log_spec.max() - 8.0		)
				__magic_name__		:		Dict	   =     (log_spec + 4.0) / 4.0
				return log_spec







			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def 			SCREAMING_SNAKE_CASE    (				_a						,   _a						,   _a = 0.0		):
				if attention_mask is not None:
					__magic_name__		:		str	   =     np.array(__UpperCAmelCase						,   np.intaa		)
					__magic_name__		:		Dict	   =     []

					for vector, length in zip(__UpperCAmelCase						,   attention_mask.sum(-1		)		):
						__magic_name__		:		Any	   =     (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7		)
						if length < normed_slice.shape[0]:
							__magic_name__		:		Any	   =     padding_value

						normed_input_values.append(__UpperCAmelCase		)
				else:
					__magic_name__		:		List[str]	   =     [(x - x.mean()) / np.sqrt(x.var() + 1e-7		) for x in input_values]

				return normed_input_values







			def __call__(				self						,   _a						,   _a = True						,   _a = None						,   _a = None						,   _a = None						,   _a = "max_length"						,   _a = None						,   _a = None						,   _a = None						,   **_a						,   ):

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

				__magic_name__		:		Tuple	   =     isinstance(__UpperCAmelCase						,   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}'''		)
				__magic_name__		:		str	   =     is_batched_numpy or (
				    isinstance(__UpperCAmelCase						,   (list, tuple)		) and (isinstance(raw_speech[0]						,   (np.ndarray, tuple, list)		))
				)

				if is_batched:
					__magic_name__		:		Optional[int]	   =     [np.asarray([speech]						,   dtype=np.floataa		).T for speech in raw_speech]
				elif not is_batched and not isinstance(__UpperCAmelCase						,   np.ndarray		):
					__magic_name__		:		str	   =     np.asarray(__UpperCAmelCase						,   dtype=np.floataa		)
				elif isinstance(__UpperCAmelCase						,   np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
					__magic_name__		:		Union[str, Any]	   =     raw_speech.astype(np.floataa		)

				# always return batch
				if not is_batched:
					__magic_name__		:		Union[str, Any]	   =     [np.asarray([raw_speech]		).T]

				__magic_name__		:		Any	   =     BatchFeature({"input_features": raw_speech}		)

				# convert into correct format for padding

				__magic_name__		:		List[str]	   =     self.pad(
				    __UpperCAmelCase						,   padding=__UpperCAmelCase						,   max_length=max_length if max_length else self.n_samples						,   truncation=__UpperCAmelCase						,   pad_to_multiple_of=__UpperCAmelCase						,   return_attention_mask=return_attention_mask or do_normalize						,   )

				# zero-mean and unit-variance normalization
				if do_normalize:
					__magic_name__		:		int	   =     self.zero_mean_unit_var_norm(
					    padded_inputs["input_features"]						,   attention_mask=padded_inputs["attention_mask"]						,   padding_value=self.padding_value						,   )
					__magic_name__		:		int	   =     np.stack(padded_inputs["input_features"]						,   axis=0		)

				# make sure list is in array format
				__magic_name__		:		Optional[Any]	   =     padded_inputs.get("input_features"		).transpose(2						,   0						,   1		)

				__magic_name__		:		Optional[int]	   =     [self._np_extract_fbank_features(__UpperCAmelCase		) for waveform in input_features[0]]

				if isinstance(input_features[0]						,   __UpperCAmelCase		):
					__magic_name__		:		Union[str, Any]	   =     [np.asarray(__UpperCAmelCase						,   dtype=np.floataa		) for feature in input_features]
				else:
					__magic_name__		:		Union[str, Any]	   =     input_features

				if return_attention_mask:
					# rescale from sample (48000) to feature (3000)
					__magic_name__		:		Union[str, Any]	   =     padded_inputs["attention_mask"][:, :: self.hop_length]

				if return_tensors is not None:
					__magic_name__		:		int	   =     padded_inputs.convert_to_tensors(__UpperCAmelCase		)

				return padded_inputs







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Tuple	   =     copy.deepcopy(self.__dict__		)
				__magic_name__		:		Optional[int]	   =     self.__class__.__name__
				if "mel_filters" in output:
					del output["mel_filters"]
				return output




"""simple docstring"""







import os
import sys
import unittest


__lowercase    					=      os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__lowercase    					=      os.path.join(git_repo_path, """src""", """diffusers""")






class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    def 	__snake_case							(					self				:    Any):
      a						:		List[Any]      							=			find_backend("    if not is_torch_available():")
      self.assertEqual(__UpperCAmelCase				, "torch")

      # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
      # self.assertEqual(backend_with_underscore, "tensorflow_text")

      a						:		Dict      							=			find_backend("    if not (is_torch_available() and is_transformers_available()):")
      self.assertEqual(__UpperCAmelCase				, "torch_and_transformers")

      # double_backend_with_underscore = find_backend(
      #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
      # )
      # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

      a						:		int      							=			find_backend(
          "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):")
      self.assertEqual(__UpperCAmelCase				, "torch_and_transformers_and_onnx")






    def 	__snake_case							(					self				:    Union[str, Any]):
      a						:		Dict      							=			read_init()
      # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
      self.assertIn("torch"				, __UpperCAmelCase)
      self.assertIn("torch_and_transformers"				, __UpperCAmelCase)
      self.assertIn("flax_and_transformers"				, __UpperCAmelCase)
      self.assertIn("torch_and_transformers_and_onnx"				, __UpperCAmelCase)

      # Likewise, we can't assert on the exact content of a key
      self.assertIn("UNet2DModel"				, objects["torch"])
      self.assertIn("FlaxUNet2DConditionModel"				, objects["flax"])
      self.assertIn("StableDiffusionPipeline"				, objects["torch_and_transformers"])
      self.assertIn("FlaxStableDiffusionPipeline"				, objects["flax_and_transformers"])
      self.assertIn("LMSDiscreteScheduler"				, objects["torch_and_scipy"])
      self.assertIn("OnnxStableDiffusionPipeline"				, objects["torch_and_transformers_and_onnx"])






    def 	__snake_case							(					self				:    Tuple):
      a						:		Optional[int]      							=			create_dummy_object("CONSTANT"				, "'torch'")
      self.assertEqual(__UpperCAmelCase				, "\nCONSTANT = None\n")

      a						:		Dict      							=			create_dummy_object("function"				, "'torch'")
      self.assertEqual(
          __UpperCAmelCase				, "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n")

      a						:		Optional[Any]      							=			"\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n"
      a						:		int      							=			create_dummy_object("FakeClass"				, "'torch'")
      self.assertEqual(__UpperCAmelCase				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    List[str]):
      a						:		List[str]      							=			"# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
      a						:		Tuple      							=			create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]})
      self.assertEqual(dummy_files["torch"]				, __UpperCAmelCase)






