


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      ["""pixel_values"""]


     def __init__(	self  ,       __A = True  ,       __A = None  ,       __A = PILImageResampling.BILINEAR  ,       __A = True  ,       __A = None  ,       __A = True  ,       __A = 1 / 255  ,       __A = True  ,       __A = None  ,       __A = None  ,       **__A  ,       ):
        super().__init__(**__A    )
        __a					=  size if size is not None else {"""shortest_edge""": 256}
        __a					=  get_size_dict(__A  ,       default_to_square=__A    )
        __a					=  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        __a					=  get_size_dict(__A    )
        __a					=  do_resize
        __a					=  size
        __a					=  resample
        __a					=  do_center_crop
        __a					=  crop_size
        __a					=  do_rescale
        __a					=  rescale_factor
        __a					=  do_normalize
        __a					=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __a					=  image_std if image_std is not None else IMAGENET_STANDARD_STD


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A = PILImageResampling.BICUBIC  ,       __A = None  ,       **__A  ,       ):
        __a					=  get_size_dict(__A  ,       default_to_square=__A    )
        if "shortest_edge" not in size:
           raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''    )
        __a					=  get_resize_output_image_size(__A  ,       size=size["""shortest_edge"""]  ,       default_to_square=__A    )
        return resize(__A  ,       size=__A  ,       resample=__A  ,       data_format=__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A = None  ,       **__A  ,       ):
        __a					=  get_size_dict(__A    )
        return center_crop(__A  ,       size=(size["""height"""], size["""width"""])  ,       data_format=__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A = None  ,       **__A    ):
        return rescale(__A  ,       scale=__A  ,       data_format=__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A = None  ,       **__A  ,       ):
        return normalize(__A  ,       mean=__A  ,       std=__A  ,       data_format=__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = ChannelDimension.FIRST  ,       **__A  ,       ):
        __a					=  do_resize if do_resize is not None else self.do_resize
        __a					=  size if size is not None else self.size
        __a					=  get_size_dict(__A  ,       default_to_square=__A    )
        __a					=  resample if resample is not None else self.resample
        __a					=  do_center_crop if do_center_crop is not None else self.do_center_crop
        __a					=  crop_size if crop_size is not None else self.crop_size
        __a					=  get_size_dict(__A    )
        __a					=  do_rescale if do_rescale is not None else self.do_rescale
        __a					=  rescale_factor if rescale_factor is not None else self.rescale_factor
        __a					=  do_normalize if do_normalize is not None else self.do_normalize
        __a					=  image_mean if image_mean is not None else self.image_mean
        __a					=  image_std if image_std is not None else self.image_std

        __a					=  make_list_of_images(__A    )

        if not valid_images(__A    ):
           raise ValueError(
               """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
               """torch.Tensor, tf.Tensor or jax.ndarray."""    )

        if do_resize and size is None:
           raise ValueError("""Size must be specified if do_resize is True."""    )

        if do_center_crop and crop_size is None:
           raise ValueError("""Crop size must be specified if do_center_crop is True."""    )

        if do_rescale and rescale_factor is None:
           raise ValueError("""Rescale factor must be specified if do_rescale is True."""    )

        if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("""Image mean and std must be specified if do_normalize is True."""    )

        # All transformations expect numpy arrays.
        __a					=  [to_numpy_array(__A    ) for image in images]

        if do_resize:
           __a					=  [self.resize(image=__A  ,       size=__A  ,       resample=__A    ) for image in images]

        if do_center_crop:
           __a					=  [self.center_crop(image=__A  ,       size=__A    ) for image in images]

        if do_rescale:
           __a					=  [self.rescale(image=__A  ,       scale=__A    ) for image in images]

        if do_normalize:
           __a					=  [self.normalize(image=__A  ,       mean=__A  ,       std=__A    ) for image in images]

        __a					=  [to_channel_dimension_format(__A  ,       __A    ) for image in images]

        __a					=  {"""pixel_values""": images}
        return BatchFeature(data=__A  ,       tensor_type=__A    )






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

import numpy as np



def   __UpperCAmelCase					(      __a :     bytes						,__a :     int      )     ->	np.array:

					"""simple docstring"""

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

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



def   __UpperCAmelCase					(      __a :     int						,__a :     float						,__a :     str = "f32le"						,)     ->	str:

					"""simple docstring"""

					_a      :       Dict   =						F"""{sampling_rate}"""
					_a      :       Optional[Any]   =						'''1'''
					if format_for_conversion == "s16le":
										_a      :       Dict   =						2
					elif format_for_conversion == "f32le":
										_a      :       Optional[Any]   =						4
					else:
										raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""      )

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

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



def   __UpperCAmelCase					(      __a :     int						,__a :     float						,__a :     Optional[int] = None						,__a :     Optional[Union[Tuple[float, float], float]] = None						,__a :     str = "f32le"						,)     ->	Optional[int]:

					"""simple docstring"""

					if stream_chunk_s is not None:
										_a      :       Tuple   =						stream_chunk_s
					else:
										_a      :       Tuple   =						chunk_length_s

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

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

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



def   __UpperCAmelCase					(      __a :     Optional[int]						,__a :     int						,__a :     Tuple[int, int]						,__a :     bool = False      )     ->	Optional[int]:

					"""simple docstring"""

					_a      :       Any   =						b''''''
					_a ,   _a      :       List[str]   =						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}"""      )
					_a      :       List[str]   =						0
					for raw in iterator:
										acc += raw
										if stream and len(__a      ) < chunk_len:
															_a      :       Dict   =						(_stride_left, 0)
															yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
										else:
															while len(__a      ) >= chunk_len:
																				# We are flushing the accumulator
																				_a      :       List[str]   =						(_stride_left, stride_right)
																				_a      :       List[Any]   =						{'''raw''': acc[:chunk_len], '''stride''': stride}
																				if stream:
																									_a      :       List[Any]   =						False
																				yield item
																				_a      :       Optional[Any]   =						stride_left
																				_a      :       Optional[Any]   =						acc[chunk_len - stride_left - stride_right :]
    # Last chunk
					if len(__a      ) > stride_left:
										_a      :       Optional[Any]   =						{'''raw''': acc, '''stride''': (_stride_left, 0)}
										if stream:
															_a      :       Dict   =						False
										yield item







def   __UpperCAmelCase					(      __a :     int						,__a :     int      )     ->	Tuple:

					"""simple docstring"""

					_a      :       Dict   =						2**24  # 16Mo
					try:
										with subprocess.Popen(__a						,stdout=subprocess.PIPE						,bufsize=__a      ) as ffmpeg_process:
															while True:
																				_a      :       int   =						ffmpeg_process.stdout.read(__a      )
																				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



