"""simple docstring"""





import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np





def       a_  (   _lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Union[str, Any]  ):





       '''simple docstring'''

       lowercase__    :	Dict     					=			f"""{sampling_rate}"""
       lowercase__    :	Optional[Any]     					=			'''1'''
       lowercase__    :	Union[str, Any]     					=			'''f32le'''
       lowercase__    :	Any     					=			[
           '''ffmpeg''',
           '''-i''',
           '''pipe:0''',
           '''-ac''',
           ac,
           '''-ar''',
           ar,
           '''-f''',
           format_for_conversion,
           '''-hide_banner''',
           '''-loglevel''',
           '''quiet''',
           '''pipe:1''',
       ]

       try:
              with subprocess.Popen(_lowerCAmelCase     ,				stdin=subprocess.PIPE     ,				stdout=subprocess.PIPE  ) as ffmpeg_process:
                     lowercase__    :	Optional[int]     					=			ffmpeg_process.communicate(_lowerCAmelCase  )
       except FileNotFoundError as error:
              raise ValueError('ffmpeg was not found but is required to load audio files from filename'  ) from error
       lowercase__    :	str     					=			output_stream[0]
       lowercase__    :	str     					=			np.frombuffer(_lowerCAmelCase     ,				np.floataa  )
       if audio.shape[0] == 0:
              raise ValueError('Malformed soundfile'  )
       return audio





def       a_  (   _lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				str = "f32le"     ,				):





       '''simple docstring'''

       lowercase__    :	List[Any]     					=			f"""{sampling_rate}"""
       lowercase__    :	List[str]     					=			'''1'''
       if format_for_conversion == "s16le":
              lowercase__    :	Dict     					=			2
       elif format_for_conversion == "f32le":
              lowercase__    :	int     					=			4
       else:
              raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""  )

       lowercase__    :	Optional[Any]     					=			platform.system()
       if system == "Linux":
              lowercase__    :	Union[str, Any]     					=			'''alsa'''
              lowercase__    :	Tuple     					=			'''default'''
       elif system == "Darwin":
              lowercase__    :	List[Any]     					=			'''avfoundation'''
              lowercase__    :	Optional[int]     					=			''':0'''
       elif system == "Windows":
              lowercase__    :	str     					=			'''dshow'''
              lowercase__    :	str     					=			'''default'''

       lowercase__    :	Any     					=			[
           '''ffmpeg''',
           '''-f''',
           format_,
           '''-i''',
           input_,
           '''-ac''',
           ac,
           '''-ar''',
           ar,
           '''-f''',
           format_for_conversion,
           '''-fflags''',
           '''nobuffer''',
           '''-hide_banner''',
           '''-loglevel''',
           '''quiet''',
           '''pipe:1''',
       ]
       lowercase__    :	Any     					=			int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
       lowercase__    :	Any     					=			_ffmpeg_stream(_lowerCAmelCase     ,				_lowerCAmelCase  )
       for item in iterator:
              yield item





def       a_  (   _lowerCAmelCase						:				Dict     ,				_lowerCAmelCase						:				Dict     ,				_lowerCAmelCase						:				Any = None     ,				_lowerCAmelCase						:				Optional[int] = None     ,				_lowerCAmelCase						:				Optional[int] = "f32le"     ,				):





       '''simple docstring'''

       if stream_chunk_s is not None:
              lowercase__    :	Dict     					=			stream_chunk_s
       else:
              lowercase__    :	Dict     					=			chunk_length_s

       lowercase__    :	int     					=			ffmpeg_microphone(_lowerCAmelCase     ,				_lowerCAmelCase     ,				format_for_conversion=_lowerCAmelCase  )
       if format_for_conversion == "s16le":
              lowercase__    :	Dict     					=			np.intaa
              lowercase__    :	Optional[Any]     					=			2
       elif format_for_conversion == "f32le":
              lowercase__    :	Optional[Any]     					=			np.floataa
              lowercase__    :	Dict     					=			4
       else:
              raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""  )

       if stride_length_s is None:
              lowercase__    :	List[Any]     					=			chunk_length_s / 6
       lowercase__    :	str     					=			int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
       if isinstance(_lowerCAmelCase     ,				(int, float)  ):
              lowercase__    :	Optional[int]     					=			[stride_length_s, stride_length_s]

       lowercase__    :	Tuple     					=			int(round(sampling_rate * stride_length_s[0]  )  ) * size_of_sample
       lowercase__    :	str     					=			int(round(sampling_rate * stride_length_s[1]  )  ) * size_of_sample
       lowercase__    :	Union[str, Any]     					=			datetime.datetime.now()
       lowercase__    :	int     					=			datetime.timedelta(seconds=_lowerCAmelCase  )
       for item in chunk_bytes_iter(_lowerCAmelCase     ,				_lowerCAmelCase     ,				stride=(stride_left, stride_right)     ,				stream=_lowerCAmelCase  ):
              # Put everything back in numpy scale
              lowercase__    :	Dict     					=			np.frombuffer(item['raw']     ,				dtype=_lowerCAmelCase  )
              lowercase__    :	List[str]     					=			(
                  item['''stride'''][0] // size_of_sample,
                  item['''stride'''][1] // size_of_sample,
              )
              lowercase__    :	Optional[int]     					=			sampling_rate
              audio_time += delta
              if datetime.datetime.now() > audio_time + 10 * delta:
                     # We're late !! SKIP
                     continue
              yield item





def       a_  (   _lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				int = False  ):





       '''simple docstring'''

       lowercase__    :	str     					=			b''''''
       lowercase__    :	List[Any]     					=			stride
       if stride_left + stride_right >= chunk_len:
              raise ValueError(
                  f"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}"""  )
       lowercase__    :	List[str]     					=			0
       for raw in iterator:
              acc += raw
              if stream and len(_lowerCAmelCase  ) < chunk_len:
                     lowercase__    :	Optional[Any]     					=			(_stride_left, 0)
                     yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
              else:
                     while len(_lowerCAmelCase  ) >= chunk_len:
                            # We are flushing the accumulator
                            lowercase__    :	Dict     					=			(_stride_left, stride_right)
                            lowercase__    :	Optional[Any]     					=			{'''raw''': acc[:chunk_len], '''stride''': stride}
                            if stream:
                                   lowercase__    :	Tuple     					=			False
                            yield item
                            lowercase__    :	Union[str, Any]     					=			stride_left
                            lowercase__    :	Tuple     					=			acc[chunk_len - stride_left - stride_right :]
    # Last chunk
       if len(_lowerCAmelCase  ) > stride_left:
              lowercase__    :	Tuple     					=			{'''raw''': acc, '''stride''': (_stride_left, 0)}
              if stream:
                     lowercase__    :	Dict     					=			False
              yield item






def       a_  (   _lowerCAmelCase						:				List[str]     ,				_lowerCAmelCase						:				List[str]  ):





       '''simple docstring'''

       lowercase__    :	Tuple     					=			2**24  # 16Mo
       try:
              with subprocess.Popen(_lowerCAmelCase     ,				stdout=subprocess.PIPE     ,				bufsize=_lowerCAmelCase  ) as ffmpeg_process:
                     while True:
                            lowercase__    :	Optional[int]     					=			ffmpeg_process.stdout.read(_lowerCAmelCase  )
                            if raw == b"":
                                   break
                            yield raw
       except FileNotFoundError as error:
              raise ValueError('ffmpeg was not found but is required to stream audio files from filename'  ) from error


"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import 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 MobileViTImageProcessor


class   UpperCAmelCase_			(					unittest.TestCase):


      def __init__(     self	,    a	,    a=7	,    a=3	,    a=1_8	,    a=3_0	,    a=4_0_0	,    a=True	,    a=None	,    a=True	,    a=None	,    a=True	,    ) ->      List[str]:
             lowercase__    :	Tuple     					=			size if size is not None else {'shortest_edge': 2_0}
             lowercase__    :	Union[str, Any]     					=			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
             lowercase__    :	Optional[int]     					=			parent
             lowercase__    :	Optional[int]     					=			batch_size
             lowercase__    :	str     					=			num_channels
             lowercase__    :	Any     					=			image_size
             lowercase__    :	Optional[Any]     					=			min_resolution
             lowercase__    :	int     					=			max_resolution
             lowercase__    :	List[Any]     					=			do_resize
             lowercase__    :	List[str]     					=			size
             lowercase__    :	str     					=			do_center_crop
             lowercase__    :	List[Any]     					=			crop_size
             lowercase__    :	Union[str, Any]     					=			do_flip_channel_order


      def 	_UpperCAmelCase (     self							) ->      int:
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_flip_channel_order": self.do_flip_channel_order,
             }





@require_torch
@require_vision
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Optional[Any]										=						MobileViTImageProcessor if is_vision_available() else None


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Tuple     					=			MobileViTImageProcessingTester(self							)


      @property
      def 	_UpperCAmelCase (     self							) ->      int:
             return self.image_processor_tester.prepare_image_processor_dict()


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	List[str]     					=			self.image_processing_class(**self.image_processor_dict							)
             self.assertTrue(hasattr(a	,    'do_resize'							)							)
             self.assertTrue(hasattr(a	,    'size'							)							)
             self.assertTrue(hasattr(a	,    'do_center_crop'							)							)
             self.assertTrue(hasattr(a	,    'center_crop'							)							)
             self.assertTrue(hasattr(a	,    'do_flip_channel_order'							)							)


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Optional[int]     					=			self.image_processing_class.from_dict(self.image_processor_dict							)
             self.assertEqual(image_processor.size	,    {'shortest_edge': 2_0}							)
             self.assertEqual(image_processor.crop_size	,    {'height': 1_8, 'width': 1_8}							)

             lowercase__    :	str     					=			self.image_processing_class.from_dict(self.image_processor_dict	,    size=4_2	,    crop_size=8_4							)
             self.assertEqual(image_processor.size	,    {'shortest_edge': 4_2}							)
             self.assertEqual(image_processor.crop_size	,    {'height': 8_4, 'width': 8_4}							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             pass


      def 	_UpperCAmelCase (     self							) ->      str:
             # Initialize image_processing
             lowercase__    :	Any     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PIL images
             lowercase__    :	List[str]     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    Image.Image							)

             # Test not batched input
             lowercase__    :	Union[str, 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.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )

             # Test batched
             lowercase__    :	List[Any]     					=			image_processing(a	,    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.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             # Initialize image_processing
             lowercase__    :	Dict     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random numpy tensors
             lowercase__    :	Tuple     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a	,    numpify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    np.ndarray							)

             # Test not batched input
             lowercase__    :	List[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.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )

             # Test batched
             lowercase__    :	Any     					=			image_processing(a	,    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.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )


      def 	_UpperCAmelCase (     self							) ->      Dict:
             # Initialize image_processing
             lowercase__    :	str     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PyTorch tensors
             lowercase__    :	Optional[Any]     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a	,    torchify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    torch.Tensor							)

             # Test not batched input
             lowercase__    :	Union[str, 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.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )

             # Test batched
             lowercase__    :	Tuple     					=			image_processing(a	,    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.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )

