'''simple docstring'''




import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
						import torch

if is_tf_available():
						import tensorflow as tf






@require_vision
@require_torchvision
class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  tempfile.mkdtemp()
					lowerCAmelCase   					=  SamImageProcessor()
					lowerCAmelCase   					=  SamProcessor(__lowerCAmelCase)
					processor.save_pretrained(self.tmpdirname)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					return AutoProcessor.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase).image_processor



			def 							a_       (   self):




					"""simple docstring"""


					shutil.rmtree(self.tmpdirname)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  [np.random.randint(255       ,    size=(3, 30, 400)       ,    dtype=np.uinta)]

					lowerCAmelCase   					=  [Image.fromarray(np.moveaxis(__lowerCAmelCase       ,    0       ,    -1)) for x in image_inputs]

					return image_inputs



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  SamProcessor(image_processor=self.get_image_processor())
					processor.save_pretrained(self.tmpdirname)

					lowerCAmelCase   					=  self.get_image_processor(do_normalize=__lowerCAmelCase       ,    padding_value=1.0)

					lowerCAmelCase   					=  SamProcessor.from_pretrained(self.tmpdirname       ,    do_normalize=__lowerCAmelCase       ,    padding_value=1.0)

					self.assertEqual(processor.image_processor.to_json_string()       ,    image_processor_add_kwargs.to_json_string())
					self.assertIsInstance(processor.image_processor       ,    __lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()

					lowerCAmelCase   					=  SamProcessor(image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  self.prepare_image_inputs()

					lowerCAmelCase   					=  image_processor(__lowerCAmelCase       ,    return_tensors="""np""")
					lowerCAmelCase   					=  processor(images=__lowerCAmelCase       ,    return_tensors="""np""")

					input_feat_extract.pop("""original_sizes""")  # pop original_sizes as it is popped in the processor
					input_feat_extract.pop("""reshaped_input_sizes""")  # pop original_sizes as it is popped in the processor

					for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum()       ,    input_processor[key].sum()       ,    delta=1E-2)







			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()

					lowerCAmelCase   					=  SamProcessor(image_processor=__lowerCAmelCase)
					lowerCAmelCase   					=  [torch.ones((1, 3, 5, 5))]

					lowerCAmelCase   					=  [[1764, 2646]]

					lowerCAmelCase   					=  [[683, 1024]]
					lowerCAmelCase   					=  processor.post_process_masks(__lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase)
					self.assertEqual(masks[0].shape       ,    (1, 3, 1764, 2646))

					lowerCAmelCase   					=  processor.post_process_masks(
					    __lowerCAmelCase       ,    torch.tensor(__lowerCAmelCase)       ,    torch.tensor(__lowerCAmelCase))
					self.assertEqual(masks[0].shape       ,    (1, 3, 1764, 2646))

					# should also work with np
					lowerCAmelCase   					=  [np.ones((1, 3, 5, 5))]
					lowerCAmelCase   					=  processor.post_process_masks(__lowerCAmelCase       ,    np.array(__lowerCAmelCase)       ,    np.array(__lowerCAmelCase))

					self.assertEqual(masks[0].shape       ,    (1, 3, 1764, 2646))

					lowerCAmelCase   					=  [[1, 0], [0, 1]]
					with self.assertRaises(__lowerCAmelCase):
							lowerCAmelCase   					=  processor.post_process_masks(__lowerCAmelCase       ,    np.array(__lowerCAmelCase)       ,    np.array(__lowerCAmelCase))







@require_vision
@require_tf
class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  tempfile.mkdtemp()
					lowerCAmelCase   					=  SamImageProcessor()
					lowerCAmelCase   					=  SamProcessor(__lowerCAmelCase)
					processor.save_pretrained(self.tmpdirname)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					return AutoProcessor.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase).image_processor



			def 							a_       (   self):




					"""simple docstring"""


					shutil.rmtree(self.tmpdirname)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  [np.random.randint(255       ,    size=(3, 30, 400)       ,    dtype=np.uinta)]

					lowerCAmelCase   					=  [Image.fromarray(np.moveaxis(__lowerCAmelCase       ,    0       ,    -1)) for x in image_inputs]

					return image_inputs



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  SamProcessor(image_processor=self.get_image_processor())
					processor.save_pretrained(self.tmpdirname)

					lowerCAmelCase   					=  self.get_image_processor(do_normalize=__lowerCAmelCase       ,    padding_value=1.0)

					lowerCAmelCase   					=  SamProcessor.from_pretrained(self.tmpdirname       ,    do_normalize=__lowerCAmelCase       ,    padding_value=1.0)

					self.assertEqual(processor.image_processor.to_json_string()       ,    image_processor_add_kwargs.to_json_string())
					self.assertIsInstance(processor.image_processor       ,    __lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()

					lowerCAmelCase   					=  SamProcessor(image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  self.prepare_image_inputs()

					lowerCAmelCase   					=  image_processor(__lowerCAmelCase       ,    return_tensors="""np""")
					lowerCAmelCase   					=  processor(images=__lowerCAmelCase       ,    return_tensors="""np""")

					input_feat_extract.pop("""original_sizes""")  # pop original_sizes as it is popped in the processor
					input_feat_extract.pop("""reshaped_input_sizes""")  # pop reshaped_input_sizes as it is popped in the processor

					for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum()       ,    input_processor[key].sum()       ,    delta=1E-2)







			@require_tf
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()

					lowerCAmelCase   					=  SamProcessor(image_processor=__lowerCAmelCase)
					lowerCAmelCase   					=  [tf.ones((1, 3, 5, 5))]

					lowerCAmelCase   					=  [[1764, 2646]]

					lowerCAmelCase   					=  [[683, 1024]]
					lowerCAmelCase   					=  processor.post_process_masks(__lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    return_tensors="""tf""")
					self.assertEqual(masks[0].shape       ,    (1, 3, 1764, 2646))

					lowerCAmelCase   					=  processor.post_process_masks(
					    __lowerCAmelCase       ,    tf.convert_to_tensor(__lowerCAmelCase)       ,    tf.convert_to_tensor(__lowerCAmelCase)       ,    return_tensors="""tf"""       ,    )
					self.assertEqual(masks[0].shape       ,    (1, 3, 1764, 2646))

					# should also work with np
					lowerCAmelCase   					=  [np.ones((1, 3, 5, 5))]
					lowerCAmelCase   					=  processor.post_process_masks(
					    __lowerCAmelCase       ,    np.array(__lowerCAmelCase)       ,    np.array(__lowerCAmelCase)       ,    return_tensors="""tf""")

					self.assertEqual(masks[0].shape       ,    (1, 3, 1764, 2646))

					lowerCAmelCase   					=  [[1, 0], [0, 1]]
					with self.assertRaises(tf.errors.InvalidArgumentError):
							lowerCAmelCase   					=  processor.post_process_masks(
							    __lowerCAmelCase       ,    np.array(__lowerCAmelCase)       ,    np.array(__lowerCAmelCase)       ,    return_tensors="""tf""")







@require_vision
@require_torchvision
class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  tempfile.mkdtemp()
					lowerCAmelCase   					=  SamImageProcessor()
					lowerCAmelCase   					=  SamProcessor(__lowerCAmelCase)
					processor.save_pretrained(self.tmpdirname)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					return AutoProcessor.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase).image_processor



			def 							a_       (   self):




					"""simple docstring"""


					shutil.rmtree(self.tmpdirname)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  [np.random.randint(255       ,    size=(3, 30, 400)       ,    dtype=np.uinta)]

					lowerCAmelCase   					=  [Image.fromarray(np.moveaxis(__lowerCAmelCase       ,    0       ,    -1)) for x in image_inputs]

					return image_inputs



			@is_pt_tf_cross_test
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()

					lowerCAmelCase   					=  SamProcessor(image_processor=__lowerCAmelCase)
					lowerCAmelCase   					=  np.random.randint(0       ,    2       ,    size=(1, 3, 5, 5)).astype(np.floataa)
					lowerCAmelCase   					=  [tf.convert_to_tensor(__lowerCAmelCase)]
					lowerCAmelCase   					=  [torch.tensor(__lowerCAmelCase)]

					lowerCAmelCase   					=  [[1764, 2646]]

					lowerCAmelCase   					=  [[683, 1024]]
					lowerCAmelCase   					=  processor.post_process_masks(
					    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    return_tensors="""tf""")
					lowerCAmelCase   					=  processor.post_process_masks(
					    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    return_tensors="""pt""")

					self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()))







			@is_pt_tf_cross_test
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()

					lowerCAmelCase   					=  SamProcessor(image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  self.prepare_image_inputs()

					lowerCAmelCase   					=  image_processor(__lowerCAmelCase       ,    return_tensors="""pt""")["""pixel_values"""].numpy()
					lowerCAmelCase   					=  processor(images=__lowerCAmelCase       ,    return_tensors="""pt""")["""pixel_values"""].numpy()

					lowerCAmelCase   					=  image_processor(__lowerCAmelCase       ,    return_tensors="""tf""")["""pixel_values"""].numpy()
					lowerCAmelCase   					=  processor(images=__lowerCAmelCase       ,    return_tensors="""tf""")["""pixel_values"""].numpy()

					self.assertTrue(np.allclose(__lowerCAmelCase       ,    __lowerCAmelCase))
					self.assertTrue(np.allclose(__lowerCAmelCase       ,    __lowerCAmelCase))
					self.assertTrue(np.allclose(__lowerCAmelCase       ,    __lowerCAmelCase))





'''simple docstring'''




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


__lowercase          =  logging.get_logger(__name__)






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Union[str, Any]      =       ['''input_features''']



			def __init__(   self       ,    __lowerCAmelCase=80       ,    __lowerCAmelCase=16000       ,    __lowerCAmelCase=160       ,    __lowerCAmelCase=30       ,    __lowerCAmelCase=400       ,    __lowerCAmelCase=0.0       ,    __lowerCAmelCase=False       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					super().__init__(
					    feature_size=__lowerCAmelCase       ,    sampling_rate=__lowerCAmelCase       ,    padding_value=__lowerCAmelCase       ,    return_attention_mask=__lowerCAmelCase       ,    **__lowerCAmelCase       ,    )
					lowerCAmelCase   					=  n_fft
					lowerCAmelCase   					=  hop_length
					lowerCAmelCase   					=  chunk_length
					lowerCAmelCase   					=  chunk_length * sampling_rate
					lowerCAmelCase   					=  self.n_samples // hop_length
					lowerCAmelCase   					=  sampling_rate
					lowerCAmelCase   					=  mel_filter_bank(
					    num_frequency_bins=1 + n_fft // 2       ,    num_mel_filters=__lowerCAmelCase       ,    min_frequency=0.0       ,    max_frequency=8000.0       ,    sampling_rate=__lowerCAmelCase       ,    norm="""slaney"""       ,    mel_scale="""slaney"""       ,    )



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  spectrogram(
					    __lowerCAmelCase       ,    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"""       ,    )
					lowerCAmelCase   					=  log_spec[:, :-1]
					lowerCAmelCase   					=  np.maximum(__lowerCAmelCase       ,    log_spec.max() - 8.0)
					lowerCAmelCase   					=  (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 							a_       (   __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = 0.0):




					"""simple docstring"""


					if attention_mask is not None:
							lowerCAmelCase   					=  np.array(__lowerCAmelCase       ,    np.intaa)
							lowerCAmelCase   					=  []

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

									normed_input_values.append(__lowerCAmelCase)
					else:
							lowerCAmelCase   					=  [(x - x.mean()) / np.sqrt(x.var() + 1E-7) for x in input_values]

					return normed_input_values



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




					"""simple docstring"""


					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.""")

					lowerCAmelCase   					=  isinstance(__lowerCAmelCase       ,    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(__lowerCAmelCase       ,    (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(__lowerCAmelCase       ,    np.ndarray):
							lowerCAmelCase   					=  np.asarray(__lowerCAmelCase       ,    dtype=np.floataa)
					elif isinstance(__lowerCAmelCase       ,    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]

					lowerCAmelCase   					=  BatchFeature({"""input_features""": raw_speech})

					# convert into correct format for padding

					lowerCAmelCase   					=  self.pad(
					    __lowerCAmelCase       ,    padding=__lowerCAmelCase       ,    max_length=max_length if max_length else self.n_samples       ,    truncation=__lowerCAmelCase       ,    pad_to_multiple_of=__lowerCAmelCase       ,    return_attention_mask=return_attention_mask or do_normalize       ,    )

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

					# make sure list is in array format
					lowerCAmelCase   					=  padded_inputs.get("""input_features""").transpose(2       ,    0       ,    1)

					lowerCAmelCase   					=  [self._np_extract_fbank_features(__lowerCAmelCase) for waveform in input_features[0]]

					if isinstance(input_features[0]       ,    __lowerCAmelCase):
							lowerCAmelCase   					=  [np.asarray(__lowerCAmelCase       ,    dtype=np.floataa) for feature in input_features]
					else:
							lowerCAmelCase   					=  input_features

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

					if return_tensors is not None:
							lowerCAmelCase   					=  padded_inputs.convert_to_tensors(__lowerCAmelCase)

					return padded_inputs







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  copy.deepcopy(self.__dict__)
					lowerCAmelCase   					=  self.__class__.__name__
					if "mel_filters" in output:
							del output["mel_filters"]
					return output




