





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

import numpy as np





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   np.array:







	'''simple docstring'''







	lowerCamelCase__              =	F'{sampling_rate}'
	lowerCamelCase__              =	'''1'''
	lowerCamelCase__              =	'''f32le'''
	lowerCamelCase__              =	[
	    '''ffmpeg''',
	    '''-i''',
	    '''pipe:0''',
	    '''-ac''',
	    ac,
	    '''-ar''',
	    ar,
	    '''-f''',
	    format_for_conversion,
	    '''-hide_banner''',
	    '''-loglevel''',
	    '''quiet''',
	    '''pipe:1''',
	]

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





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case = "f32le" ,)       ->   int:







	'''simple docstring'''







	lowerCamelCase__              =	F'{sampling_rate}'
	lowerCamelCase__              =	'''1'''
	if format_for_conversion == "s16le":
		lowerCamelCase__              =	2
	elif format_for_conversion == "f32le":
		lowerCamelCase__              =	4
	else:
		raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'							)

	lowerCamelCase__              =	platform.system()
	if system == "Linux":
		lowerCamelCase__              =	'''alsa'''
		lowerCamelCase__              =	'''default'''
	elif system == "Darwin":
		lowerCamelCase__              =	'''avfoundation'''
		lowerCamelCase__              =	''':0'''
	elif system == "Windows":
		lowerCamelCase__              =	'''dshow'''
		lowerCamelCase__              =	'''default'''

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





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case = None ,__snake_case = None ,__snake_case = "f32le" ,)       ->   Union[str, Any]:







	'''simple docstring'''







	if stream_chunk_s is not None:
		lowerCamelCase__              =	stream_chunk_s
	else:
		lowerCamelCase__              =	chunk_length_s

	lowerCamelCase__              =	ffmpeg_microphone(a__ ,a__ ,format_for_conversion=a__							)
	if format_for_conversion == "s16le":
		lowerCamelCase__              =	np.intaa
		lowerCamelCase__              =	2
	elif format_for_conversion == "f32le":
		lowerCamelCase__              =	np.floataa
		lowerCamelCase__              =	4
	else:
		raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'							)

	if stride_length_s is None:
		lowerCamelCase__              =	chunk_length_s / 6
	lowerCamelCase__              =	int(round(sampling_rate * chunk_length_s							)							) * size_of_sample
	if isinstance(a__ ,(int, float)							):
		lowerCamelCase__              =	[stride_length_s, stride_length_s]

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





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case = False							)       ->   Union[str, Any]:







	'''simple docstring'''







	lowerCamelCase__              =	b''''''
	lowerCamelCase__       ,			lowerCamelCase__              =	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__              =	0
	for raw in iterator:
		acc += raw
		if stream and len(a__							) < chunk_len:
			lowerCamelCase__              =	(_stride_left, 0)
			yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
		else:
			while len(a__							) >= chunk_len:
				# We are flushing the accumulator
				lowerCamelCase__              =	(_stride_left, stride_right)
				lowerCamelCase__              =	{'''raw''': acc[:chunk_len], '''stride''': stride}
				if stream:
					lowerCamelCase__              =	False
				yield item
				lowerCamelCase__              =	stride_left
				lowerCamelCase__              =	acc[chunk_len - stride_left - stride_right :]
		# Last chunk
	if len(a__							) > stride_left:
		lowerCamelCase__              =	{'''raw''': acc, '''stride''': (_stride_left, 0)}
		if stream:
			lowerCamelCase__              =	False
		yield item





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Optional[Any]:







	'''simple docstring'''







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









from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		42






class       __A					(					lowerCAmelCase    ,       lowerCAmelCase				):

			'''simple docstring'''







			@register_to_config
			def __init__(					self ,						__lowerCAmelCase = 1_6 ,						__lowerCAmelCase = 8_8 ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = 1 ,						__lowerCAmelCase = 0.0 ,						__lowerCAmelCase = 3_2 ,						__lowerCAmelCase = None ,						__lowerCAmelCase = False ,						__lowerCAmelCase = None ,						__lowerCAmelCase = "geglu" ,						__lowerCAmelCase = True ,						__lowerCAmelCase = True ,						):



				'''simple docstring'''







				super().__init__()
				lowerCamelCase__              =	num_attention_heads
				lowerCamelCase__              =	attention_head_dim
				lowerCamelCase__              =	num_attention_heads * attention_head_dim

				lowerCamelCase__              =	in_channels

				lowerCamelCase__              =	torch.nn.GroupNorm(num_groups=__lowerCAmelCase ,						num_channels=__lowerCAmelCase ,						eps=1E-6 ,						affine=__lowerCAmelCase							)
				lowerCamelCase__              =	nn.Linear(__lowerCAmelCase ,						__lowerCAmelCase							)

				# 3. Define transformers blocks
				lowerCamelCase__              =	nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            __lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						dropout=__lowerCAmelCase ,						cross_attention_dim=__lowerCAmelCase ,						activation_fn=__lowerCAmelCase ,						attention_bias=__lowerCAmelCase ,						double_self_attention=__lowerCAmelCase ,						norm_elementwise_affine=__lowerCAmelCase ,						)
				        for d in range(__lowerCAmelCase							)
				    ]							)

				lowerCamelCase__              =	nn.Linear(__lowerCAmelCase ,						__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						__lowerCAmelCase=1 ,						__lowerCAmelCase=None ,						__lowerCAmelCase = True ,						):



				'''simple docstring'''







				lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__              =	hidden_states.shape
				lowerCamelCase__              =	batch_frames // num_frames

				lowerCamelCase__              =	hidden_states

				lowerCamelCase__              =	hidden_states[None, :].reshape(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							)
				lowerCamelCase__              =	hidden_states.permute(0 ,						2 ,						1 ,						3 ,						4							)

				lowerCamelCase__              =	self.norm(__lowerCAmelCase							)
				lowerCamelCase__              =	hidden_states.permute(0 ,						3 ,						4 ,						2 ,						1							).reshape(batch_size * height * width ,						__lowerCAmelCase ,						__lowerCAmelCase							)

				lowerCamelCase__              =	self.proj_in(__lowerCAmelCase							)

				# 2. Blocks
				for block in self.transformer_blocks:
					lowerCamelCase__              =	block(
					    __lowerCAmelCase ,						encoder_hidden_states=__lowerCAmelCase ,						timestep=__lowerCAmelCase ,						cross_attention_kwargs=__lowerCAmelCase ,						class_labels=__lowerCAmelCase ,						)

				# 3. Output
				lowerCamelCase__              =	self.proj_out(__lowerCAmelCase							)
				lowerCamelCase__              =	(
				    hidden_states[None, None, :]
				    .reshape(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							)
				    .permute(0 ,						3 ,						4 ,						1 ,						2							)
				    .contiguous()
				)
				lowerCamelCase__              =	hidden_states.reshape(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							)

				lowerCamelCase__              =	hidden_states + residual

				if not return_dict:
					return (output,)

				return TransformerTemporalModelOutput(sample=__lowerCAmelCase							)



