

'''simple docstring'''



import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


_UpperCamelCase						:  Tuple							       =				logging.get_logger(__name__)



class 				_snake_case					(   snake_case__			):
  SCREAMING_SNAKE_CASE			:       Optional[Any]				=     """linear"""
  SCREAMING_SNAKE_CASE			:       Union[str, Any]				=     """cosine"""
  SCREAMING_SNAKE_CASE			:       Any				=     """cosine_with_restarts"""
  SCREAMING_SNAKE_CASE			:       Union[str, Any]				=     """polynomial"""
  SCREAMING_SNAKE_CASE			:       Tuple				=     """constant"""
  SCREAMING_SNAKE_CASE			:       Dict				=     """constant_with_warmup"""
  SCREAMING_SNAKE_CASE			:       int				=     """piecewise_constant"""



def       snake_case   (	snake_case      : Optimizer					,		snake_case      : int = -1			) ->  Any:


    """simple docstring"""


    return LambdaLR(snake_case					,		lambda snake_case			: 1					,		last_epoch=snake_case			)


def       snake_case   (	snake_case      : Optimizer					,		snake_case      : int					,		snake_case      : int = -1			) ->  str:


    """simple docstring"""


    def lr_lambda(snake_case      : int			):
        if current_step < num_warmup_steps:
            return float(snake_case			) / float(max(1.0					,		snake_case			)			)
        return 1.0

    return LambdaLR(snake_case					,		snake_case					,		last_epoch=snake_case			)


def       snake_case   (	snake_case      : Optimizer					,		snake_case      : str					,		snake_case      : int = -1			) ->  List[str]:


    """simple docstring"""


    lowerCAmelCase 						=							{}
    lowerCAmelCase 						=							step_rules.split(','			)
    for rule_str in rule_list[:-1]:
        lowerCAmelCase 						=							rule_str.split(':'			)
        lowerCAmelCase 						=							int(snake_case			)
        lowerCAmelCase 						=							float(snake_case			)
        lowerCAmelCase 						=							value
    lowerCAmelCase 						=							float(rule_list[-1]			)

    def create_rules_function(snake_case      : Optional[int]					,		snake_case      : Tuple			):
        def rule_func(snake_case      : int			) -> float:
            lowerCAmelCase 						=							sorted(rules_dict.keys()			)
            for i, sorted_step in enumerate(snake_case			):
                if steps < sorted_step:
                    return rules_dict[sorted_steps[i]]
            return last_lr_multiple

        return rule_func

    lowerCAmelCase 						=							create_rules_function(snake_case					,		snake_case			)

    return LambdaLR(snake_case					,		snake_case					,		last_epoch=snake_case			)


def       snake_case   (	snake_case      : Dict					,		snake_case      : Optional[int]					,		snake_case      : List[Any]					,		snake_case      : Dict=-1			) ->  List[Any]:


    """simple docstring"""


    def lr_lambda(snake_case      : int			):
        if current_step < num_warmup_steps:
            return float(snake_case			) / float(max(1					,		snake_case			)			)
        return max(
            0.0					,		float(num_training_steps - current_step			) / float(max(1					,		num_training_steps - num_warmup_steps			)			)			)

    return LambdaLR(snake_case					,		snake_case					,		snake_case			)


def       snake_case   (	snake_case      : Optimizer					,		snake_case      : int					,		snake_case      : int					,		snake_case      : float = 0.5					,		snake_case      : int = -1			) ->  Optional[Any]:


    """simple docstring"""


    def lr_lambda(snake_case      : int			):
        if current_step < num_warmup_steps:
            return float(snake_case			) / float(max(1					,		snake_case			)			)
        lowerCAmelCase 						=							float(current_step - num_warmup_steps			) / float(max(1					,		num_training_steps - num_warmup_steps			)			)
        return max(0.0					,		0.5 * (1.0 + math.cos(math.pi * float(snake_case			) * 2.0 * progress			))			)

    return LambdaLR(snake_case					,		snake_case					,		snake_case			)


def       snake_case   (	snake_case      : Optimizer					,		snake_case      : int					,		snake_case      : int					,		snake_case      : int = 1					,		snake_case      : int = -1			) ->  Tuple:


    """simple docstring"""


    def lr_lambda(snake_case      : Tuple			):
        if current_step < num_warmup_steps:
            return float(snake_case			) / float(max(1					,		snake_case			)			)
        lowerCAmelCase 						=							float(current_step - num_warmup_steps			) / float(max(1					,		num_training_steps - num_warmup_steps			)			)
        if progress >= 1.0:
            return 0.0
        return max(0.0					,		0.5 * (1.0 + math.cos(math.pi * ((float(snake_case			) * progress) % 1.0)			))			)

    return LambdaLR(snake_case					,		snake_case					,		snake_case			)


def       snake_case   (	snake_case      : str					,		snake_case      : str					,		snake_case      : List[str]					,		snake_case      : Any=1e-7					,		snake_case      : List[Any]=1.0					,		snake_case      : Tuple=-1			) ->  Union[str, Any]:


    """simple docstring"""


    lowerCAmelCase 						=							optimizer.defaults['''lr''']
    if not (lr_init > lr_end):
        raise ValueError(F'lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'			)

    def lr_lambda(snake_case      : int			):
        if current_step < num_warmup_steps:
            return float(snake_case			) / float(max(1					,		snake_case			)			)
        elif current_step > num_training_steps:
            return lr_end / lr_init  # as LambdaLR multiplies by lr_init
        else:
            lowerCAmelCase 						=							lr_init - lr_end
            lowerCAmelCase 						=							num_training_steps - num_warmup_steps
            lowerCAmelCase 						=							1 - (current_step - num_warmup_steps) / decay_steps
            lowerCAmelCase 						=							lr_range * pct_remaining**power + lr_end
            return decay / lr_init  # as LambdaLR multiplies by lr_init

    return LambdaLR(snake_case					,		snake_case					,		snake_case			)


_UpperCamelCase						:  Union[str, Any]							       =				{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}


def       snake_case   (	snake_case      : Union[str, SchedulerType]					,		snake_case      : Optimizer					,		snake_case      : Optional[str] = None					,		snake_case      : Optional[int] = None					,		snake_case      : Optional[int] = None					,		snake_case      : int = 1					,		snake_case      : float = 1.0					,		snake_case      : int = -1					,		) ->  List[str]:


    """simple docstring"""


    lowerCAmelCase 						=							SchedulerType(snake_case			)
    lowerCAmelCase 						=							TYPE_TO_SCHEDULER_FUNCTION[name]
    if name == SchedulerType.CONSTANT:
        return schedule_func(snake_case					,		last_epoch=snake_case			)

    if name == SchedulerType.PIECEWISE_CONSTANT:
        return schedule_func(snake_case					,		step_rules=snake_case					,		last_epoch=snake_case			)

    # All other schedulers require `num_warmup_steps`
    if num_warmup_steps is None:
        raise ValueError(F'{name} requires `num_warmup_steps`, please provide that argument.'			)

    if name == SchedulerType.CONSTANT_WITH_WARMUP:
        return schedule_func(snake_case					,		num_warmup_steps=snake_case					,		last_epoch=snake_case			)

    # All other schedulers require `num_training_steps`
    if num_training_steps is None:
        raise ValueError(F'{name} requires `num_training_steps`, please provide that argument.'			)

    if name == SchedulerType.COSINE_WITH_RESTARTS:
        return schedule_func(
            snake_case					,		num_warmup_steps=snake_case					,		num_training_steps=snake_case					,		num_cycles=snake_case					,		last_epoch=snake_case					,		)

    if name == SchedulerType.POLYNOMIAL:
        return schedule_func(
            snake_case					,		num_warmup_steps=snake_case					,		num_training_steps=snake_case					,		power=snake_case					,		last_epoch=snake_case					,		)

    return schedule_func(
        snake_case					,		num_warmup_steps=snake_case					,		num_training_steps=snake_case					,		last_epoch=snake_case			)












"""simple docstring"""





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

import numpy as np



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				bytes     ,    SCREAMING_SNAKE_CASE							:				int ):


							'''simple docstring'''







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

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



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				float     ,    SCREAMING_SNAKE_CASE							:				str = "f32le"     ,    ):


							'''simple docstring'''







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

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

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



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				float     ,    SCREAMING_SNAKE_CASE							:				Optional[int] = None     ,    SCREAMING_SNAKE_CASE							:				Optional[Union[Tuple[float, float], float]] = None     ,    SCREAMING_SNAKE_CASE							:				str = "f32le"     ,    ):


							'''simple docstring'''







							if stream_chunk_s is not None:
														__lowerCamelCase   :   Optional[int]       =stream_chunk_s
							else:
														__lowerCamelCase   :   Tuple       =chunk_length_s

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

							if stride_length_s is None:
														__lowerCamelCase   :   Tuple       =chunk_length_s / 6
							__lowerCamelCase   :   str       =int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
							if isinstance(SCREAMING_SNAKE_CASE     ,    (int, float) ):
														__lowerCamelCase   :   List[str]       =[stride_length_s, stride_length_s]

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



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				Tuple[int, int]     ,    SCREAMING_SNAKE_CASE							:				bool = False ):


							'''simple docstring'''







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



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				int ):


							'''simple docstring'''







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




