



'''simple docstring'''

import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
							from PIL import Image
else:

							class 	__magic_name__						:







														@staticmethod
														def 							__lowercase			(							*_UpperCAmelCase					:       Optional[int]	,**_UpperCAmelCase					:       str		):
																pass



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      str:
		_a     :      List[Any]        =							hashlib.mda(image.tobytes()  )
		return m.hexdigest()[:10]



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Dict:
		_a     :      Dict        =							np.array(lowerCAmelCase_  )
		_a     :      List[Any]        =							npimg.shape
		return {"hash": hashimage(lowerCAmelCase_  ), "shape": shape}

@is_pipeline_test
@require_vision
@require_torch
class 	__magic_name__						(    unittest.TestCase   ):
							lowerCAmelCase       :    List[str]   				=					dict(
							    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])   )
							lowerCAmelCase       :    str   				=					dict(
							    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])   )







							def 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Any		):
									_a     :      Union[str, Any]        =							MaskGenerationPipeline(model=_UpperCAmelCase	,image_processor=_UpperCAmelCase		)
									return image_segmenter, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]







							def 							__lowercase			(							self					:       List[Any]	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Any		):
									pass







							@require_tf
							@unittest.skip('Image segmentation not implemented in TF'		)
							def 							__lowercase			(							self					:       Any		):
									pass







							@slow
							@require_torch
							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      int        =							pipeline('mask-generation'	,model='facebook/sam-vit-huge'		)

									_a     :      Union[str, Any]        =							image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'	,points_per_batch=256		)

									# Shortening by hashing
									_a     :      Optional[Any]        =							[]
									for i, o in enumerate(outputs['masks']		):
											new_outupt += [{"mask": mask_to_test_readable(_UpperCAmelCase		), "scores": outputs["scores"][i]}]

									# fmt: off
									self.assertEqual(
									    nested_simplify(_UpperCAmelCase	,decimals=4		)	,[
									        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.04_44},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_21},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.01_67},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.01_32},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.00_53},
									        {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.99_67},
									        {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.9_93},
									        {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.99_09},
									        {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.98_79},
									        {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.98_34},
									        {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.97_16},
									        {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.96_12},
									        {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.95_99},
									        {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.95_52},
									        {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.95_32},
									        {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.95_16},
									        {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.94_99},
									        {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.94_83},
									        {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.94_64},
									        {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.9_43},
									        {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.9_43},
									        {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.94_08},
									        {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.93_35},
									        {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.93_26},
									        {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.92_62},
									        {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.89_99},
									        {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.89_86},
									        {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.89_84},
									        {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.88_73},
									        {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.88_71}
									    ]	,)
									# fmt: on







							@require_torch
							@slow
							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      int        =							'facebook/sam-vit-huge'
									_a     :      int        =							pipeline('mask-generation'	,model=_UpperCAmelCase		)

									_a     :      str        =							image_segmenter(
									    'http://images.cocodataset.org/val2017/000000039769.jpg'	,pred_iou_thresh=1	,points_per_batch=256		)

									# Shortening by hashing
									_a     :      Union[str, Any]        =							[]
									for i, o in enumerate(outputs['masks']		):
											new_outupt += [{"mask": mask_to_test_readable(_UpperCAmelCase		), "scores": outputs["scores"][i]}]

									self.assertEqual(
									    nested_simplify(_UpperCAmelCase	,decimals=4		)	,[
									        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.04_44},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.02_10},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.01_67},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.01_32},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.00_53},
									    ]	,)









'''simple docstring'''

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

import numpy as np



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      np.array:
		_a     :      Optional[int]        =							f"""{sampling_rate}"""
		_a     :      Any        =							'1'
		_a     :      Optional[int]        =							'f32le'
		_a     :      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:
						_a     :      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
		_a     :      int        =							output_stream[0]
		_a     :      List[str]        =							np.frombuffer(lowerCAmelCase_						,			np.floataa  )
		if audio.shape[0] == 0:
				raise ValueError('Malformed soundfile'  )
		return audio



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = "f32le"						,			)			->      Union[str, Any]:
		_a     :      List[str]        =							f"""{sampling_rate}"""
		_a     :      List[str]        =							'1'
		if format_for_conversion == "s16le":
				_a     :      List[Any]        =							2
		elif format_for_conversion == "f32le":
				_a     :      Dict        =							4
		else:
				raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""  )

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

		_a     :      str        =							[
		    '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     :      Union[str, Any]        =							_ffmpeg_stream(lowerCAmelCase_						,			lowerCAmelCase_  )
		for item in iterator:
				yield item



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = "f32le"						,			)			->      str:
		if stream_chunk_s is not None:
				_a     :      str        =							stream_chunk_s
		else:
				_a     :      List[str]        =							chunk_length_s

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

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

		_a     :      str        =							int(round(sampling_rate * stride_length_s[0]  )  ) * size_of_sample
		_a     :      List[str]        =							int(round(sampling_rate * stride_length_s[1]  )  ) * size_of_sample
		_a     :      Any        =							datetime.datetime.now()
		_a     :      Dict        =							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
				_a     :      List[Any]        =							np.frombuffer(item['raw']						,			dtype=lowerCAmelCase_  )
				_a     :      List[str]        =							(
				    item['stride'][0] // size_of_sample,
				    item['stride'][1] // size_of_sample,
				)
				_a     :      Union[str, Any]        =							sampling_rate
				audio_time += delta
				if datetime.datetime.now() > audio_time + 10 * delta:
						# We're late !! SKIP
						continue
				yield item



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



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Tuple:
		_a     :      Optional[Any]        =							2**24  # 16Mo
		try:
				with subprocess.Popen(lowerCAmelCase_						,			stdout=subprocess.PIPE						,			bufsize=lowerCAmelCase_  ) as ffmpeg_process:
						while True:
								_a     :      Any        =							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




