



from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 1 / sqrt(2   )   ):
	__lowercase		       =			tau * frequency / samplerate
	__lowercase		       =			sin(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			cos(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			_sin / (2 * q_factor)

	__lowercase		       =			(1 - _cos) / 2
	__lowercase		       =			1 - _cos

	__lowercase		       =			1 + alpha
	__lowercase		       =			-2 * _cos
	__lowercase		       =			1 - alpha

	__lowercase		       =			IIRFilter(2   )
	filt.set_coefficients([aa, aa, aa]  ,	[ba, ba, ba]   )
	return filt



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 1 / sqrt(2   )   ):
	__lowercase		       =			tau * frequency / samplerate
	__lowercase		       =			sin(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			cos(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			_sin / (2 * q_factor)

	__lowercase		       =			(1 + _cos) / 2
	__lowercase		       =			-1 - _cos

	__lowercase		       =			1 + alpha
	__lowercase		       =			-2 * _cos
	__lowercase		       =			1 - alpha

	__lowercase		       =			IIRFilter(2   )
	filt.set_coefficients([aa, aa, aa]  ,	[ba, ba, ba]   )
	return filt



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 1 / sqrt(2   )   ):
	__lowercase		       =			tau * frequency / samplerate
	__lowercase		       =			sin(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			cos(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			_sin / (2 * q_factor)

	__lowercase		       =			_sin / 2
	__lowercase		       =			0
	__lowercase		       =			-ba

	__lowercase		       =			1 + alpha
	__lowercase		       =			-2 * _cos
	__lowercase		       =			1 - alpha

	__lowercase		       =			IIRFilter(2   )
	filt.set_coefficients([aa, aa, aa]  ,	[ba, ba, ba]   )
	return filt



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 1 / sqrt(2   )   ):
	__lowercase		       =			tau * frequency / samplerate
	__lowercase		       =			sin(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			cos(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			_sin / (2 * q_factor)

	__lowercase		       =			1 - alpha
	__lowercase		       =			-2 * _cos
	__lowercase		       =			1 + alpha

	__lowercase		       =			IIRFilter(2   )
	filt.set_coefficients([ba, ba, ba]  ,	[ba, ba, ba]   )
	return filt



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 1 / sqrt(2   )  ,	):
	__lowercase		       =			tau * frequency / samplerate
	__lowercase		       =			sin(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			cos(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			_sin / (2 * q_factor)
	__lowercase		       =			1_0 ** (gain_db / 4_0)

	__lowercase		       =			1 + alpha * big_a
	__lowercase		       =			-2 * _cos
	__lowercase		       =			1 - alpha * big_a
	__lowercase		       =			1 + alpha / big_a
	__lowercase		       =			-2 * _cos
	__lowercase		       =			1 - alpha / big_a

	__lowercase		       =			IIRFilter(2   )
	filt.set_coefficients([aa, aa, aa]  ,	[ba, ba, ba]   )
	return filt



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 1 / sqrt(2   )  ,	):
	__lowercase		       =			tau * frequency / samplerate
	__lowercase		       =			sin(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			cos(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			_sin / (2 * q_factor)
	__lowercase		       =			1_0 ** (gain_db / 4_0)
	__lowercase		       =			(big_a + 1) - (big_a - 1) * _cos
	__lowercase		       =			(big_a + 1) + (big_a - 1) * _cos
	__lowercase		       =			(big_a - 1) - (big_a + 1) * _cos
	__lowercase		       =			(big_a - 1) + (big_a + 1) * _cos
	__lowercase		       =			2 * sqrt(_SCREAMING_SNAKE_CASE   ) * alpha

	__lowercase		       =			big_a * (pmc + aaa)
	__lowercase		       =			2 * big_a * mpc
	__lowercase		       =			big_a * (pmc - aaa)
	__lowercase		       =			ppmc + aaa
	__lowercase		       =			-2 * pmpc
	__lowercase		       =			ppmc - aaa

	__lowercase		       =			IIRFilter(2   )
	filt.set_coefficients([aa, aa, aa]  ,	[ba, ba, ba]   )
	return filt



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 1 / sqrt(2   )  ,	):
	__lowercase		       =			tau * frequency / samplerate
	__lowercase		       =			sin(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			cos(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			_sin / (2 * q_factor)
	__lowercase		       =			1_0 ** (gain_db / 4_0)
	__lowercase		       =			(big_a + 1) - (big_a - 1) * _cos
	__lowercase		       =			(big_a + 1) + (big_a - 1) * _cos
	__lowercase		       =			(big_a - 1) - (big_a + 1) * _cos
	__lowercase		       =			(big_a - 1) + (big_a + 1) * _cos
	__lowercase		       =			2 * sqrt(_SCREAMING_SNAKE_CASE   ) * alpha

	__lowercase		       =			big_a * (ppmc + aaa)
	__lowercase		       =			-2 * big_a * pmpc
	__lowercase		       =			big_a * (ppmc - aaa)
	__lowercase		       =			pmc + aaa
	__lowercase		       =			2 * mpc
	__lowercase		       =			pmc - aaa

	__lowercase		       =			IIRFilter(2   )
	filt.set_coefficients([aa, aa, aa]  ,	[ba, ba, ba]   )
	return filt








import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class   _A   ( _lowercase  ,						_lowercase	):


						'''simple docstring'''





						@register_to_config
						def __init__(	self	:							Optional[Any]							,      *,
						    lowerCamelCase	:							int = 4							,      lowerCamelCase	:							int = 768							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Optional[int]							,      ):






							'''simple docstring'''



							super().__init__()

							__lowercase		       =			nn.Parameter(torch.zeros(lowerCamelCase	)	)

							# parameters for additional clip time embeddings
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)

							# parameters for encoder hidden states
							__lowercase		       =			clip_extra_context_tokens
							__lowercase		       =			nn.Linear(
							    lowerCamelCase							,      self.clip_extra_context_tokens * cross_attention_dim	)
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			nn.LayerNorm(lowerCamelCase	)



						def     _snake_case	(	self	:							Union[str, Any]							,      *, lowerCamelCase	:							Any							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							Tuple	):






							'''simple docstring'''



							if do_classifier_free_guidance:
								# Add the classifier free guidance embeddings to the image embeddings
								__lowercase		       =			image_embeddings.shape[0]
								__lowercase		       =			self.learned_classifier_free_guidance_embeddings.unsqueeze(0	)
								__lowercase		       =			classifier_free_guidance_embeddings.expand(
								    lowerCamelCase							,      -1	)
								__lowercase		       =			torch.cat([classifier_free_guidance_embeddings, image_embeddings]							,      dim=0	)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							__lowercase		       =			prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							__lowercase		       =			self.embedding_proj(lowerCamelCase	)
							__lowercase		       =			self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase	)
							__lowercase		       =			time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							__lowercase		       =			self.clip_extra_context_tokens_proj(lowerCamelCase	)
							__lowercase		       =			clip_extra_context_tokens.reshape(lowerCamelCase							,      -1							,      self.clip_extra_context_tokens	)
							__lowercase		       =			clip_extra_context_tokens.permute(0							,      2							,      1	)

							__lowercase		       =			self.encoder_hidden_states_proj(lowerCamelCase	)
							__lowercase		       =			self.text_encoder_hidden_states_norm(lowerCamelCase	)
							__lowercase		       =			torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]							,      dim=1	)

							return text_encoder_hidden_states, additive_clip_time_embeddings



