






from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


snake_case								=		logging.get_logger(__name__)


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Any			=	['''input_values''', '''padding_mask''']


     def __init__(				self       :	Tuple					, UpperCAmelCase_       :	int = 1					, UpperCAmelCase_       :	int = 2_4000					, UpperCAmelCase_       :	float = 0.0					, UpperCAmelCase_       :	float = None					, UpperCAmelCase_       :	float = None					, **UpperCAmelCase_       :	Optional[Any]					, ):
       super().__init__(feature_size=UpperCAmelCase_					, sampling_rate=UpperCAmelCase_					, padding_value=UpperCAmelCase_					, **UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      int							=    chunk_length_s
       SCREAMING_SNAKE_CASE       :      List[str]							=    overlap


     @property
     def 				_A       (				self       :	Optional[Any]      ):
       if self.chunk_length_s is None:
         return None
       else:
         return int(self.chunk_length_s * self.sampling_rate      )


     @property
     def 				_A       (				self       :	List[str]      ):
       if self.chunk_length_s is None or self.overlap is None:
         return None
       else:
         return max(1					, int((1.0 - self.overlap) * self.chunk_length      )      )






     def __call__(				self       :	Dict					, UpperCAmelCase_       :	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					, UpperCAmelCase_       :	Optional[Union[bool, str, PaddingStrategy]] = None					, UpperCAmelCase_       :	Optional[bool] = False					, UpperCAmelCase_       :	Optional[int] = None					, UpperCAmelCase_       :	Optional[Union[str, TensorType]] = None					, UpperCAmelCase_       :	Optional[int] = None					, ):
       if sampling_rate is not None:
         if sampling_rate != self.sampling_rate:
           raise ValueError(
               f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
               f''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
               f''' {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."      )

       if padding and truncation:
         raise ValueError("Both padding and truncation were set. Make sure you only set one."      )
       elif padding is None:
         # by default let's pad the inputs
         SCREAMING_SNAKE_CASE       :      int							=    True

       SCREAMING_SNAKE_CASE       :      Tuple							=    bool(
           isinstance(UpperCAmelCase_					, (list, tuple)      ) and (isinstance(raw_audio[0]					, (np.ndarray, tuple, list)      ))      )

       if is_batched:
         SCREAMING_SNAKE_CASE       :      Any							=    [np.asarray(UpperCAmelCase_					, dtype=np.floataa      ).T for audio in raw_audio]
       elif not is_batched and not isinstance(UpperCAmelCase_					, np.ndarray      ):
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    np.asarray(UpperCAmelCase_					, dtype=np.floataa      )
       elif isinstance(UpperCAmelCase_					, np.ndarray      ) and raw_audio.dtype is np.dtype(np.floataa      ):
         SCREAMING_SNAKE_CASE       :      str							=    raw_audio.astype(np.floataa      )

       # always return batch
       if not is_batched:
         SCREAMING_SNAKE_CASE       :      Any							=    [np.asarray(UpperCAmelCase_      ).T]

       # verify inputs are valid
       for idx, example in enumerate(UpperCAmelCase_      ):
         if example.ndim > 2:
           raise ValueError(f'''Expected input shape (channels, length) but got shape {example.shape}'''      )
         if self.feature_size == 1 and example.ndim != 1:
           raise ValueError(f'''Expected mono audio but example has {example.shape[-1]} channels'''      )
         if self.feature_size == 2 and example.shape[-1] != 2:
           raise ValueError(f'''Expected stereo audio but example has {example.shape[-1]} channels'''      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    None
       SCREAMING_SNAKE_CASE       :      List[Any]							=    BatchFeature({"input_values": raw_audio}      )
       if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
         if truncation:
           SCREAMING_SNAKE_CASE       :      Tuple							=    min(array.shape[0] for array in raw_audio      )
           SCREAMING_SNAKE_CASE       :      Dict							=    int(np.floor(max_length / self.chunk_stride      )      )
           SCREAMING_SNAKE_CASE       :      int							=    (nb_step - 1) * self.chunk_stride + self.chunk_length
         elif padding:
           SCREAMING_SNAKE_CASE       :      List[Any]							=    max(array.shape[0] for array in raw_audio      )
           SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    int(np.ceil(max_length / self.chunk_stride      )      )
           SCREAMING_SNAKE_CASE       :      int							=    (nb_step - 1) * self.chunk_stride + self.chunk_length
           SCREAMING_SNAKE_CASE       :      Dict							=    "max_length"
         else:
           SCREAMING_SNAKE_CASE       :      List[str]							=    input_values

        # normal padding on batch
       if padded_inputs is None:
         SCREAMING_SNAKE_CASE       :      Tuple							=    self.pad(
             UpperCAmelCase_					, max_length=UpperCAmelCase_					, truncation=UpperCAmelCase_					, padding=UpperCAmelCase_					, return_attention_mask=UpperCAmelCase_					, )
         if padding:
           SCREAMING_SNAKE_CASE       :      Optional[Any]							=    padded_inputs.pop("attention_mask"      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    []
       for example in padded_inputs.pop("input_values"      ):
         if self.feature_size == 1:
           SCREAMING_SNAKE_CASE       :      Optional[Any]							=    example[..., None]
         input_values.append(example.T      )

       SCREAMING_SNAKE_CASE       :      Tuple							=    input_values
       if return_tensors is not None:
         SCREAMING_SNAKE_CASE       :      Any							=    padded_inputs.convert_to_tensors(UpperCAmelCase_      )

       return padded_inputs














import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import DonutImageProcessor


class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     def __init__(				self       :	int					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Tuple=7					, UpperCAmelCase_       :	Dict=3					, UpperCAmelCase_       :	Any=18					, UpperCAmelCase_       :	Optional[int]=30					, UpperCAmelCase_       :	Any=400					, UpperCAmelCase_       :	Optional[int]=True					, UpperCAmelCase_       :	Union[str, Any]=None					, UpperCAmelCase_       :	int=True					, UpperCAmelCase_       :	Optional[int]=False					, UpperCAmelCase_       :	List[Any]=True					, UpperCAmelCase_       :	Optional[Any]=True					, UpperCAmelCase_       :	Any=[0.5, 0.5, 0.5]					, UpperCAmelCase_       :	Dict=[0.5, 0.5, 0.5]					, ):
       SCREAMING_SNAKE_CASE       :      Any							=    parent
       SCREAMING_SNAKE_CASE       :      int							=    batch_size
       SCREAMING_SNAKE_CASE       :      Dict							=    num_channels
       SCREAMING_SNAKE_CASE       :      str							=    image_size
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    min_resolution
       SCREAMING_SNAKE_CASE       :      Any							=    max_resolution
       SCREAMING_SNAKE_CASE       :      int							=    do_resize
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    size if size is not None else {"height": 18, "width": 20}
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    do_thumbnail
       SCREAMING_SNAKE_CASE       :      Tuple							=    do_align_axis
       SCREAMING_SNAKE_CASE       :      List[str]							=    do_pad
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    do_normalize
       SCREAMING_SNAKE_CASE       :      Any							=    image_mean
       SCREAMING_SNAKE_CASE       :      Any							=    image_std






     def 				_A       (				self       :	str      ):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_thumbnail": self.do_thumbnail,
           "do_align_long_axis": self.do_align_axis,
           "do_pad": self.do_pad,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }





@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		str			=	DonutImageProcessor if is_vision_available() else None


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    DonutImageProcessingTester(self      )


     @property
     def 				_A       (				self       :	Tuple      ):
       return self.image_processor_tester.prepare_image_processor_dict()


     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_resize"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "size"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_thumbnail"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_align_long_axis"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_pad"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_normalize"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "image_mean"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "image_std"      )      )


     def 				_A       (				self       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Any							=    self.image_processing_class.from_dict(self.image_processor_dict      )
       self.assertEqual(image_processor.size					, {"height": 18, "width": 20}      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.image_processing_class.from_dict(self.image_processor_dict					, size=42      )
       self.assertEqual(image_processor.size					, {"height": 42, "width": 42}      )

       # Previous config had dimensions in (width, height) order
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.image_processing_class.from_dict(self.image_processor_dict					, size=(42, 84)      )
       self.assertEqual(image_processor.size					, {"height": 84, "width": 42}      )


     def 				_A       (				self       :	Dict      ):
       pass


     @is_flaky()
     def 				_A       (				self       :	Optional[Any]      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       SCREAMING_SNAKE_CASE       :      Tuple							=    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_      )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_					, Image.Image      )

       # Test not batched input
       SCREAMING_SNAKE_CASE       :      List[str]							=    image_processing(image_inputs[0]					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )

       # Test batched
       SCREAMING_SNAKE_CASE       :      Dict							=    image_processing(UpperCAmelCase_					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )


     @is_flaky()
     def 				_A       (				self       :	List[str]      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      Dict							=    self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       SCREAMING_SNAKE_CASE       :      List[Any]							=    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_					, numpify=UpperCAmelCase_      )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_					, np.ndarray      )

       # Test not batched input
       SCREAMING_SNAKE_CASE       :      Any							=    image_processing(image_inputs[0]					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )

       # Test batched
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    image_processing(UpperCAmelCase_					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )






     @is_flaky()
     def 				_A       (				self       :	Dict      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE       :      Tuple							=    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_					, torchify=UpperCAmelCase_      )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_					, torch.Tensor      )

       # Test not batched input
       SCREAMING_SNAKE_CASE       :      int							=    image_processing(image_inputs[0]					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )

       # Test batched
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    image_processing(UpperCAmelCase_					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )






