





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   A			:
								'''simple docstring'''




								@staticmethod
								def    a_       (			*__lowerCAmelCase   :    Optional[int]							,	**__lowerCAmelCase   :    str   )	->	Dict:







										"""simple docstring"""






										pass







def    __lowerCamelCase					(							__a   :Optional[int]    )		->				str:






		"""simple docstring"""

		A__        =   hashlib.mda(image.tobytes()    )
		return m.hexdigest()[:1_0]







def    __lowerCamelCase					(							__a   :Dict    )		->				Dict:






		"""simple docstring"""

		A__        =   np.array(lowerCAmelCase_    )
		A__        =   npimg.shape
		return {"hash": hashimage(lowerCAmelCase_    ), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class   A			(unittest.TestCase    ):
			'''simple docstring'''




			__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    a_       (			self   :    Any							,	__lowerCAmelCase   :    List[Any]							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    Any   )	->	Any:







					"""simple docstring"""






					A__        =   MaskGenerationPipeline(model=_UpperCAmelCase							,	image_processor=_UpperCAmelCase   )
					return image_segmenter, [
					    "./tests/fixtures/tests_samples/COCO/000000039769.png",
					    "./tests/fixtures/tests_samples/COCO/000000039769.png",
					]






			def    a_       (			self   :    List[Any]							,	__lowerCAmelCase   :    List[str]							,	__lowerCAmelCase   :    Any   )	->	Union[str, Any]:







					"""simple docstring"""






					pass






			@require_tf
			@unittest.skip("""Image segmentation not implemented in TF"""   )
			def    a_       (			self   :    Any   )	->	int:







					"""simple docstring"""






					pass






			@slow
			@require_torch
			def    a_       (			self   :    Optional[int]   )	->	str:







					"""simple docstring"""






					A__        =   pipeline("""mask-generation"""							,	model="""facebook/sam-vit-huge"""   )

					A__        =   image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""							,	points_per_batch=2_56   )

					# Shortening by hashing
					A__        =   []
					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""": (4_80, 6_40)}, """scores""": 1.0_4_4_4},
					        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.0_2_1},
					        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_6_7},
					        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_3_2},
					        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0_0_5_3},
					        {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (4_80, 6_40)}, """scores""": 0.9_9_6_7},
					        {"""mask""": {"""hash""": """453c7844bd""", """shape""": (4_80, 6_40)}, """scores""": 0.9_9_3},
					        {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (4_80, 6_40)}, """scores""": 0.9_9_0_9},
					        {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (4_80, 6_40)}, """scores""": 0.9_8_7_9},
					        {"""mask""": {"""hash""": """801064ff79""", """shape""": (4_80, 6_40)}, """scores""": 0.9_8_3_4},
					        {"""mask""": {"""hash""": """6172f276ef""", """shape""": (4_80, 6_40)}, """scores""": 0.9_7_1_6},
					        {"""mask""": {"""hash""": """b49e60e084""", """shape""": (4_80, 6_40)}, """scores""": 0.9_6_1_2},
					        {"""mask""": {"""hash""": """a811e775fd""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_9_9},
					        {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_5_2},
					        {"""mask""": {"""hash""": """9d8257e080""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_3_2},
					        {"""mask""": {"""hash""": """32de6454a8""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_1_6},
					        {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_9_9},
					        {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_8_3},
					        {"""mask""": {"""hash""": """c290813fb9""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_6_4},
					        {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_3},
					        {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_3},
					        {"""mask""": {"""hash""": """c749b25868""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_0_8},
					        {"""mask""": {"""hash""": """efb6cab859""", """shape""": (4_80, 6_40)}, """scores""": 0.9_3_3_5},
					        {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (4_80, 6_40)}, """scores""": 0.9_3_2_6},
					        {"""mask""": {"""hash""": """788b798e24""", """shape""": (4_80, 6_40)}, """scores""": 0.9_2_6_2},
					        {"""mask""": {"""hash""": """abea804f0e""", """shape""": (4_80, 6_40)}, """scores""": 0.8_9_9_9},
					        {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (4_80, 6_40)}, """scores""": 0.8_9_8_6},
					        {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (4_80, 6_40)}, """scores""": 0.8_9_8_4},
					        {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (4_80, 6_40)}, """scores""": 0.8_8_7_3},
					        {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (4_80, 6_40)}, """scores""": 0.8_8_7_1}
					    ]							,	)
					# fmt: on







			@require_torch
			@slow
			def    a_       (			self   :    List[Any]   )	->	Tuple:







					"""simple docstring"""






					A__        =   'facebook/sam-vit-huge'
					A__        =   pipeline("""mask-generation"""							,	model=_UpperCAmelCase   )

					A__        =   image_segmenter(
					    """http://images.cocodataset.org/val2017/000000039769.jpg"""							,	pred_iou_thresh=1							,	points_per_batch=2_56   )

					# Shortening by hashing
					A__        =   []
					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""": (4_80, 6_40)}, """scores""": 1.0_4_4_4},
					        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.0_2_1_0},
					        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_6_7},
					        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_3_2},
					        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0_0_5_3},
					    ]							,	)








'''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




