
'''simple docstring'''







import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
						from .features import FeatureType


_snake_case ,		_snake_case ,		_snake_case				:   Any     =				False, False, False


@dataclass
class 			lowerCAmelCase :
		a							:					Optional[int]       =					None
		a							:					bool       =					True
		a							:					bool       =					True
		a							:					Optional[str]       =					None
		# Automatically constructed
		a							:					ClassVar[str]       =					"dict"
		a							:					ClassVar[Any]       =					pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}			)
		a							:					str       =					field(default="""Audio"""		,     init=__UpperCAmelCase		,     repr=__UpperCAmelCase			)




		def __call__(				self      ):
								return self.pa_type




		def     lowercase       (				self					,						UpperCamelCase      ):
								try:
														import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
								except ImportError as err:
														raise ImportError("To support encoding audio data, please install 'soundfile'."      ) from err
								if isinstance(UpperCamelCase					,						UpperCamelCase      ):
														return {"bytes": None, "path": value}
								elif isinstance(UpperCamelCase					,						UpperCamelCase      ):
														return {"bytes": value, "path": None}
								elif "array" in value:
														# convert the audio array to wav bytes
														_SCREAMING_SNAKE_CASE				= BytesIO()
														sf.write(UpperCamelCase					,						value["array"]					,						value["sampling_rate"]					,						format="wav"      )
														return {"bytes": buffer.getvalue(), "path": None}
								elif value.get("path"      ) is not None and os.path.isfile(value["path"]      ):
														# we set "bytes": None to not duplicate the data if they're already available locally
														if value["path"].endswith("pcm"      ):
																				# "PCM" only has raw audio bytes
																				if value.get("sampling_rate"      ) is None:
																										# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
																										raise KeyError("To use PCM files, please specify a 'sampling_rate' in Audio object"      )
																				if value.get("bytes"      ):
																										# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
																										_SCREAMING_SNAKE_CASE				= np.frombuffer(value["bytes"]					,						dtype=np.intaa      ).astype(np.floataa      ) / 32_767
																				else:
																										_SCREAMING_SNAKE_CASE				= np.memmap(value["path"]					,						dtype="h"					,						mode="r"      ).astype(np.floataa      ) / 32_767

																				_SCREAMING_SNAKE_CASE				= BytesIO(bytes()      )
																				sf.write(UpperCamelCase					,						UpperCamelCase					,						value["sampling_rate"]					,						format="wav"      )
																				return {"bytes": buffer.getvalue(), "path": None}
														else:
																				return {"bytes": None, "path": value.get("path"      )}
								elif value.get("bytes"      ) is not None or value.get("path"      ) is not None:
														# store the audio bytes, and path is used to infer the audio format using the file extension
														return {"bytes": value.get("bytes"      ), "path": value.get("path"      )}
								else:
														raise ValueError(
														    F'An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase = None      ):
								if not self.decode:
														raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead."      )

								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= (value["path"], BytesIO(value["bytes"]      )) if value["bytes"] is not None else (value["path"], None)
								if path is None and file is None:
														raise ValueError(F'An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.'      )

								try:
														import librosa
														import soundfile as sf
								except ImportError as err:
														raise ImportError("To support decoding audio files, please install 'librosa' and 'soundfile'."      ) from err

								_SCREAMING_SNAKE_CASE				= xsplitext(UpperCamelCase      )[1][1:].lower() if path is not None else None
								if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
														raise RuntimeError(
														    "Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
														    "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. "      )
								elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
														raise RuntimeError(
														    "Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
														    "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. "      )

								if file is None:
														_SCREAMING_SNAKE_CASE				= token_per_repo_id or {}
														_SCREAMING_SNAKE_CASE				= path.split("::"      )[-1]
														try:
																				_SCREAMING_SNAKE_CASE				= string_to_dict(UpperCamelCase					,						config.HUB_DATASETS_URL      )["repo_id"]
																				_SCREAMING_SNAKE_CASE				= token_per_repo_id[repo_id]
														except (ValueError, KeyError):
																				_SCREAMING_SNAKE_CASE				= None

														with xopen(UpperCamelCase					,						"rb"					,						use_auth_token=UpperCamelCase      ) as f:
																				_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= sf.read(UpperCamelCase      )

								else:
														_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= sf.read(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= array.T
								if self.mono:
														_SCREAMING_SNAKE_CASE				= librosa.to_mono(UpperCamelCase      )
								if self.sampling_rate and self.sampling_rate != sampling_rate:
														_SCREAMING_SNAKE_CASE				= librosa.resample(UpperCamelCase					,						orig_sr=UpperCamelCase					,						target_sr=self.sampling_rate      )
														_SCREAMING_SNAKE_CASE				= self.sampling_rate

								return {"path": path, "array": array, "sampling_rate": sampling_rate}




		def     lowercase       (				self      ):
								from .features import Value

								if self.decode:
														raise ValueError("Cannot flatten a decoded Audio feature."      )
								return {
								    "bytes": Value("binary"      ),
								    "path": Value("string"      ),
								}




		def     lowercase       (				self					,						UpperCamelCase      ):
								if pa.types.is_string(storage.type      ):
														_SCREAMING_SNAKE_CASE				= pa.array([None] * len(UpperCamelCase      )					,						type=pa.binary()      )
														_SCREAMING_SNAKE_CASE				= pa.StructArray.from_arrays([bytes_array, storage]					,						["bytes", "path"]					,						mask=storage.is_null()      )
								elif pa.types.is_binary(storage.type      ):
														_SCREAMING_SNAKE_CASE				= pa.array([None] * len(UpperCamelCase      )					,						type=pa.string()      )
														_SCREAMING_SNAKE_CASE				= pa.StructArray.from_arrays([storage, path_array]					,						["bytes", "path"]					,						mask=storage.is_null()      )
								elif pa.types.is_struct(storage.type      ) and storage.type.get_all_field_indices("array"      ):
														_SCREAMING_SNAKE_CASE				= pa.array([Audio().encode_example(UpperCamelCase      ) if x is not None else None for x in storage.to_pylist()]      )
								elif pa.types.is_struct(storage.type      ):
														if storage.type.get_field_index("bytes"      ) >= 0:
																				_SCREAMING_SNAKE_CASE				= storage.field("bytes"      )
														else:
																				_SCREAMING_SNAKE_CASE				= pa.array([None] * len(UpperCamelCase      )					,						type=pa.binary()      )
														if storage.type.get_field_index("path"      ) >= 0:
																				_SCREAMING_SNAKE_CASE				= storage.field("path"      )
														else:
																				_SCREAMING_SNAKE_CASE				= pa.array([None] * len(UpperCamelCase      )					,						type=pa.string()      )
														_SCREAMING_SNAKE_CASE				= pa.StructArray.from_arrays([bytes_array, path_array]					,						["bytes", "path"]					,						mask=storage.is_null()      )
								return array_cast(UpperCamelCase					,						self.pa_type      )




		def     lowercase       (				self					,						UpperCamelCase      ):

								@no_op_if_value_is_null
								def path_to_bytes(UpperCamelCase      ):
														with xopen(UpperCamelCase					,						"rb"      ) as f:
																				_SCREAMING_SNAKE_CASE				= f.read()
														return bytes_

								_SCREAMING_SNAKE_CASE				= pa.array(
								    [
								        (path_to_bytes(x["path"]      ) if x["bytes"] is None else x["bytes"]) if x is not None else None
								        for x in storage.to_pylist()
								    ]					,						type=pa.binary()					,						)
								_SCREAMING_SNAKE_CASE				= pa.array(
								    [os.path.basename(UpperCamelCase      ) if path is not None else None for path in storage.field("path"      ).to_pylist()]					,						type=pa.string()					,						)
								_SCREAMING_SNAKE_CASE				= pa.StructArray.from_arrays([bytes_array, path_array]					,						["bytes", "path"]					,						mask=bytes_array.is_null()      )
								return array_cast(UpperCamelCase					,						self.pa_type      )

'''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 GLPNImageProcessor


class 			lowerCAmelCase (				unittest.TestCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase=7					,						UpperCamelCase=3					,						UpperCamelCase=18					,						UpperCamelCase=30					,						UpperCamelCase=400					,						UpperCamelCase=True					,						UpperCamelCase=32					,						UpperCamelCase=True					,						):
								_SCREAMING_SNAKE_CASE				= parent
								_SCREAMING_SNAKE_CASE				= batch_size
								_SCREAMING_SNAKE_CASE				= num_channels
								_SCREAMING_SNAKE_CASE				= image_size
								_SCREAMING_SNAKE_CASE				= min_resolution
								_SCREAMING_SNAKE_CASE				= max_resolution
								_SCREAMING_SNAKE_CASE				= do_resize
								_SCREAMING_SNAKE_CASE				= size_divisor
								_SCREAMING_SNAKE_CASE				= do_rescale




		def     lowercase       (				self      ):
								return {
								    "do_resize": self.do_resize,
								    "size_divisor": self.size_divisor,
								    "do_rescale": self.do_rescale,
								}


@require_torch
@require_vision
class 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Dict       =					GLPNImageProcessor if is_vision_available() else None




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= GLPNImageProcessingTester(self      )




		@property
		def     lowercase       (				self      ):
								return self.image_processor_tester.prepare_image_processor_dict()




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								self.assertTrue(hasattr(UpperCamelCase					,						"do_resize"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"size_divisor"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"resample"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"do_rescale"      )      )




		def     lowercase       (				self      ):
								pass




		def     lowercase       (				self      ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random PIL images
								_SCREAMING_SNAKE_CASE				= prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase					,						Image.Image      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= image_processing(image_inputs[0]					,						return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )




		def     lowercase       (				self      ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random numpy tensors
								_SCREAMING_SNAKE_CASE				= 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 (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= image_processing(image_inputs[0]					,						return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )




		def     lowercase       (				self      ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random PyTorch tensors
								_SCREAMING_SNAKE_CASE				= 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 (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= image_processing(image_inputs[0]					,						return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )