


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

import numpy as np





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     str		):





	"""simple docstring"""







	__magic_name__	:   Dict					     =					f'{sampling_rate}'
	__magic_name__	:   Any					     =					"""1"""
	__magic_name__	:   Tuple					     =					"""f32le"""
	__magic_name__	:   Optional[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:
			__magic_name__	:   Dict					     =					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
	__magic_name__	:   str					     =					output_stream[0]
	__magic_name__	:   Optional[int]					     =					np.frombuffer(__lowerCAmelCase		,		np.floataa		)
	if audio.shape[0] == 0:
		raise ValueError('Malformed soundfile'		)
	return audio





def lowerCamelCase      (      lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     int		,		lowerCAmelCase    :     int = "f32le"		,		):





	"""simple docstring"""







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

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

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





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     Union[str, Any] = None		,		lowerCAmelCase    :     int = None		,		lowerCAmelCase    :     List[str] = "f32le"		,		):





	"""simple docstring"""







	if stream_chunk_s is not None:
		__magic_name__	:   Dict					     =					stream_chunk_s
	else:
		__magic_name__	:   Union[str, Any]					     =					chunk_length_s

	__magic_name__	:   List[str]					     =					ffmpeg_microphone(__lowerCAmelCase		,		__lowerCAmelCase		,		format_for_conversion=__lowerCAmelCase		)
	if format_for_conversion == "s16le":
		__magic_name__	:   int					     =					np.intaa
		__magic_name__	:   List[str]					     =					2
	elif format_for_conversion == "f32le":
		__magic_name__	:   int					     =					np.floataa
		__magic_name__	:   Dict					     =					4
	else:
		raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'		)

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

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





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     Union[str, Any] = False		):





	"""simple docstring"""







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





def lowerCamelCase      (      lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     List[str]		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					2**24  # 16Mo
	try:
		with subprocess.Popen(__lowerCAmelCase		,		stdout=subprocess.PIPE		,		bufsize=__lowerCAmelCase		) as ffmpeg_process:
			while True:
				__magic_name__	:   Union[str, 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



'''simple docstring'''

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
							from PIL import Image
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      Any				   =    """Salesforce/blip-image-captioning-base"""
		A_     :      Any				   =    (
		    """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
		    """image to caption, and returns a text that contains the description in English."""
		)
		A_     :      List[str]				   =    """image_captioner"""
		A_     :      Optional[Any]				   =    AutoModelForVisionaSeq

		A_     :      Any				   =    ["""image"""]
		A_     :      Union[str, Any]				   =    ["""text"""]







		def __init__( self		:						Dict  ,					*_A		:						Dict  ,					**_A		:						Any      )  ->			Dict:
			requires_backends(self  ,					['vision']      )
			super().__init__(*_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						Tuple  ,					_A		:						"Image"      )  ->			Union[str, Any]:
			return self.pre_processor(images=_A  ,					return_tensors='pt'      )







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						str      )  ->			List[Any]:
			return self.model.generate(**_A      )







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						Optional[int]      )  ->			Union[str, Any]:
			return self.pre_processor.batch_decode(_A  ,					skip_special_tokens=_A      )[0].strip()