

import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin






class        A_		(       SCREAMING_SNAKE_CASE			,							SCREAMING_SNAKE_CASE				):
							@register_to_config
							def __init__(		self		:			int   ,SCREAMING_SNAKE_CASE__		:			int = 1_2_8   ,SCREAMING_SNAKE_CASE__		:			int = 2_5_6   ,SCREAMING_SNAKE_CASE__		:			float = 2000.0   ,SCREAMING_SNAKE_CASE__		:			int = 7_6_8   ,SCREAMING_SNAKE_CASE__		:			int = 1_2   ,SCREAMING_SNAKE_CASE__		:			int = 1_2   ,SCREAMING_SNAKE_CASE__		:			int = 6_4   ,SCREAMING_SNAKE_CASE__		:			int = 2_0_4_8   ,SCREAMING_SNAKE_CASE__		:			float = 0.1   ,):
								super().__init__()

								__lowerCamelCase				:				Optional[Any]					       = nn.Sequential(
								    nn.Linear(SCREAMING_SNAKE_CASE__   ,d_model * 4   ,bias=SCREAMING_SNAKE_CASE__)   ,nn.SiLU()   ,nn.Linear(d_model * 4   ,d_model * 4   ,bias=SCREAMING_SNAKE_CASE__)   ,nn.SiLU()   ,)

								__lowerCamelCase				:				Tuple					       = nn.Embedding(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Union[str, Any]					       = False

								__lowerCamelCase				:				Union[str, Any]					       = nn.Linear(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,bias=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Dict					       = nn.Dropout(p=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Dict					       = nn.ModuleList()
								for lyr_num in range(SCREAMING_SNAKE_CASE__):
									# FiLM conditional T5 decoder
									__lowerCamelCase				:				Optional[int]					       = DecoderLayer(d_model=SCREAMING_SNAKE_CASE__   ,d_kv=SCREAMING_SNAKE_CASE__   ,num_heads=SCREAMING_SNAKE_CASE__   ,d_ff=SCREAMING_SNAKE_CASE__   ,dropout_rate=SCREAMING_SNAKE_CASE__)
									self.decoders.append(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Any					       = TaLayerNorm(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				str					       = nn.Dropout(p=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				int					       = nn.Linear(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,bias=SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			List[Any]):
								__lowerCamelCase				:				Tuple					       = torch.mul(query_input.unsqueeze(-1)   ,key_input.unsqueeze(-2))
								return mask.unsqueeze(-3)


							def     lowerCAmelCase     (		self		:			int   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			str):
								__lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase				:				str					       = decoder_input_tokens.shape
								assert decoder_noise_time.shape == (batch,)

								# decoder_noise_time is in [0, 1), so rescale to expected timing range.
								__lowerCamelCase				:				Dict					       = get_timestep_embedding(
								    decoder_noise_time * self.config.max_decoder_noise_time   ,embedding_dim=self.config.d_model   ,max_period=self.config.max_decoder_noise_time   ,).to(dtype=self.dtype)

								__lowerCamelCase				:				List[Any]					       = self.conditioning_emb(SCREAMING_SNAKE_CASE__).unsqueeze(1)

								assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

								__lowerCamelCase				:				Any					       = decoder_input_tokens.shape[1]

								# If we want to use relative positions for audio context, we can just offset
								# this sequence by the length of encodings_and_masks.
								__lowerCamelCase				:				Tuple					       = torch.broadcast_to(
								    torch.arange(SCREAMING_SNAKE_CASE__   ,device=decoder_input_tokens.device)   ,(batch, seq_length)   ,)

								__lowerCamelCase				:				Tuple					       = self.position_encoding(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Tuple					       = self.continuous_inputs_projection(SCREAMING_SNAKE_CASE__)
								inputs += position_encodings
								__lowerCamelCase				:				str					       = self.dropout(SCREAMING_SNAKE_CASE__)

								# decoder: No padding present.
								__lowerCamelCase				:				List[str]					       = torch.ones(
								    decoder_input_tokens.shape[:2]   ,device=decoder_input_tokens.device   ,dtype=inputs.dtype)

								# Translate encoding masks to encoder-decoder masks.
								__lowerCamelCase				:				str					       = [(x, self.encoder_decoder_mask(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)) for x, y in encodings_and_masks]

								# cross attend style: concat encodings
								__lowerCamelCase				:				Union[str, Any]					       = torch.cat([x[0] for x in encodings_and_encdec_masks]   ,dim=1)
								__lowerCamelCase				:				Optional[Any]					       = torch.cat([x[1] for x in encodings_and_encdec_masks]   ,dim=-1)

								for lyr in self.decoders:
									__lowerCamelCase				:				Tuple					       = lyr(
									    SCREAMING_SNAKE_CASE__   ,conditioning_emb=SCREAMING_SNAKE_CASE__   ,encoder_hidden_states=SCREAMING_SNAKE_CASE__   ,encoder_attention_mask=SCREAMING_SNAKE_CASE__   ,)[0]

								__lowerCamelCase				:				List[str]					       = self.decoder_norm(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Tuple					       = self.post_dropout(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				str					       = self.spec_out(SCREAMING_SNAKE_CASE__)
								return spec_out






class        A_		(       nn.Module				):
							def __init__(		self		:			int   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			Any   ,SCREAMING_SNAKE_CASE__		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Dict   ,SCREAMING_SNAKE_CASE__		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Tuple=1E-6):
								super().__init__()
								__lowerCamelCase				:				Any					       = nn.ModuleList()

								# cond self attention: layer 0
								self.layer.append(
								    TaLayerSelfAttentionCond(d_model=SCREAMING_SNAKE_CASE__   ,d_kv=SCREAMING_SNAKE_CASE__   ,num_heads=SCREAMING_SNAKE_CASE__   ,dropout_rate=SCREAMING_SNAKE_CASE__))

								# cross attention: layer 1
								self.layer.append(
								    TaLayerCrossAttention(
								        d_model=SCREAMING_SNAKE_CASE__   ,d_kv=SCREAMING_SNAKE_CASE__   ,num_heads=SCREAMING_SNAKE_CASE__   ,dropout_rate=SCREAMING_SNAKE_CASE__   ,layer_norm_epsilon=SCREAMING_SNAKE_CASE__   ,))

								# Film Cond MLP + dropout: last layer
								self.layer.append(
								    TaLayerFFCond(d_model=SCREAMING_SNAKE_CASE__   ,d_ff=SCREAMING_SNAKE_CASE__   ,dropout_rate=SCREAMING_SNAKE_CASE__   ,layer_norm_epsilon=SCREAMING_SNAKE_CASE__))


							def     lowerCAmelCase     (		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=None   ,SCREAMING_SNAKE_CASE__		:			int=None   ,SCREAMING_SNAKE_CASE__		:			Tuple=None   ,SCREAMING_SNAKE_CASE__		:			Tuple=None   ,SCREAMING_SNAKE_CASE__		:			str=None   ,):
								__lowerCamelCase				:				Any					       = self.layer[0](
								    SCREAMING_SNAKE_CASE__   ,conditioning_emb=SCREAMING_SNAKE_CASE__   ,attention_mask=SCREAMING_SNAKE_CASE__   ,)

								if encoder_hidden_states is not None:
									__lowerCamelCase				:				Tuple					       = torch.where(encoder_attention_mask > 0   ,0   ,-1E10).to(
									    encoder_hidden_states.dtype)

									__lowerCamelCase				:				Any					       = self.layer[1](
									    SCREAMING_SNAKE_CASE__   ,key_value_states=SCREAMING_SNAKE_CASE__   ,attention_mask=SCREAMING_SNAKE_CASE__   ,)

								# Apply Film Conditional Feed Forward layer
								__lowerCamelCase				:				Tuple					       = self.layer[-1](SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)

								return (hidden_states,)






class        A_		(       nn.Module				):
							def __init__(		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			Dict   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]):
								super().__init__()
								__lowerCamelCase				:				int					       = TaLayerNorm(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				str					       = TaFiLMLayer(in_features=d_model * 4   ,out_features=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Union[str, Any]					       = Attention(query_dim=SCREAMING_SNAKE_CASE__   ,heads=SCREAMING_SNAKE_CASE__   ,dim_head=SCREAMING_SNAKE_CASE__   ,out_bias=SCREAMING_SNAKE_CASE__   ,scale_qk=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				str					       = nn.Dropout(SCREAMING_SNAKE_CASE__)


							def     lowerCAmelCase     (		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=None   ,SCREAMING_SNAKE_CASE__		:			List[str]=None   ,):
								# pre_self_attention_layer_norm
								__lowerCamelCase				:				Dict					       = self.layer_norm(SCREAMING_SNAKE_CASE__)

								if conditioning_emb is not None:
									__lowerCamelCase				:				int					       = self.FiLMLayer(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)

								# Self-attention block
								__lowerCamelCase				:				int					       = self.attention(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Union[str, Any]					       = hidden_states + self.dropout(SCREAMING_SNAKE_CASE__)

								return hidden_states






class        A_		(       nn.Module				):
							def __init__(		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			Any   ,SCREAMING_SNAKE_CASE__		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Tuple):
								super().__init__()
								__lowerCamelCase				:				str					       = Attention(query_dim=SCREAMING_SNAKE_CASE__   ,heads=SCREAMING_SNAKE_CASE__   ,dim_head=SCREAMING_SNAKE_CASE__   ,out_bias=SCREAMING_SNAKE_CASE__   ,scale_qk=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				List[Any]					       = TaLayerNorm(SCREAMING_SNAKE_CASE__   ,eps=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[Any]					       = nn.Dropout(SCREAMING_SNAKE_CASE__)


							def     lowerCAmelCase     (		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=None   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=None   ,):
								__lowerCamelCase				:				str					       = self.layer_norm(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Any					       = self.attention(
								    SCREAMING_SNAKE_CASE__   ,encoder_hidden_states=SCREAMING_SNAKE_CASE__   ,attention_mask=attention_mask.squeeze(1)   ,)
								__lowerCamelCase				:				Optional[int]					       = hidden_states + self.dropout(SCREAMING_SNAKE_CASE__)
								return layer_output






class        A_		(       nn.Module				):
							def __init__(		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			Any   ,SCREAMING_SNAKE_CASE__		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Optional[int]):
								super().__init__()
								__lowerCamelCase				:				Union[str, Any]					       = TaDenseGatedActDense(d_model=SCREAMING_SNAKE_CASE__   ,d_ff=SCREAMING_SNAKE_CASE__   ,dropout_rate=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Tuple					       = TaFiLMLayer(in_features=d_model * 4   ,out_features=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[Any]					       = TaLayerNorm(SCREAMING_SNAKE_CASE__   ,eps=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				int					       = nn.Dropout(SCREAMING_SNAKE_CASE__)


							def     lowerCAmelCase     (		self		:			int   ,SCREAMING_SNAKE_CASE__		:			Any   ,SCREAMING_SNAKE_CASE__		:			Dict=None):
								__lowerCamelCase				:				List[Any]					       = self.layer_norm(SCREAMING_SNAKE_CASE__)
								if conditioning_emb is not None:
									__lowerCamelCase				:				int					       = self.film(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Dict					       = self.DenseReluDense(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Any					       = hidden_states + self.dropout(SCREAMING_SNAKE_CASE__)
								return hidden_states






class        A_		(       nn.Module				):
							def __init__(		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			Optional[int]):
								super().__init__()
								__lowerCamelCase				:				Union[str, Any]					       = nn.Linear(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,bias=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Tuple					       = nn.Linear(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,bias=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				str					       = nn.Linear(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,bias=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Union[str, Any]					       = nn.Dropout(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				List[Any]					       = NewGELUActivation()


							def     lowerCAmelCase     (		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Any):
								__lowerCamelCase				:				List[Any]					       = self.act(self.wi_a(SCREAMING_SNAKE_CASE__))
								__lowerCamelCase				:				Optional[int]					       = self.wi_a(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				str					       = hidden_gelu * hidden_linear
								__lowerCamelCase				:				Tuple					       = self.dropout(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				int					       = self.wo(SCREAMING_SNAKE_CASE__)
								return hidden_states






class        A_		(       nn.Module				):
							def __init__(		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Any   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=1E-6):
								super().__init__()
								__lowerCamelCase				:				List[Any]					       = nn.Parameter(torch.ones(SCREAMING_SNAKE_CASE__))
								__lowerCamelCase				:				List[Any]					       = eps


							def     lowerCAmelCase     (		self		:			str   ,SCREAMING_SNAKE_CASE__		:			int):
								# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
								# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
								# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
								# half-precision inputs is done in fp32

								__lowerCamelCase				:				List[str]					       = hidden_states.to(torch.floataa).pow(2).mean(-1   ,keepdim=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Any					       = hidden_states * torch.rsqrt(variance + self.variance_epsilon)

								# convert into half-precision if necessary
								if self.weight.dtype in [torch.floataa, torch.bfloataa]:
									__lowerCamelCase				:				int					       = hidden_states.to(self.weight.dtype)

								return self.weight * hidden_states






class        A_		(       nn.Module				):
							def     lowerCAmelCase     (		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			torch.Tensor):
								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(SCREAMING_SNAKE_CASE__   ,3.0))))






class        A_		(       nn.Module				):
							def __init__(		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Tuple):
								super().__init__()
								__lowerCamelCase				:				List[str]					       = nn.Linear(SCREAMING_SNAKE_CASE__   ,out_features * 2   ,bias=SCREAMING_SNAKE_CASE__)


							def     lowerCAmelCase     (		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Dict):
								__lowerCamelCase				:				List[Any]					       = self.scale_bias(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase       , __lowerCamelCase				:				str					       = torch.chunk(SCREAMING_SNAKE_CASE__   ,2   ,-1)
								__lowerCamelCase				:				Tuple					       = x * (1 + scale) + shift
								return x












"""simple docstring"""
import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt







class 			UpperCamelCase_       :





	def __init__(      self					: str						)						->			Dict:
				UpperCAmelCase_     : List[Any]			     =					""
				UpperCAmelCase_     : int			     =					""
				UpperCAmelCase_     : Dict			     =					[]
				UpperCAmelCase_     : int			     =					0
				UpperCAmelCase_     : List[Any]			     =					256
				UpperCAmelCase_     : Dict			     =					0
				UpperCAmelCase_     : List[Any]			     =					0
				UpperCAmelCase_     : str			     =					0
				UpperCAmelCase_     : List[str]			     =					0





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: Dict						)						->			Optional[Any]:
				UpperCAmelCase_     : Dict			     =					cva.imread(lowerCAmelCase_	,	0						)
				UpperCAmelCase_     : Union[str, Any]			     =					copy.deepcopy(self.img						)
				UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : List[Any]			     =					plt.hist(self.img.ravel()	,	256	,	[0, 256]	,	label="x"						)
				UpperCAmelCase_     : List[Any]			     =					np.sum(lowerCAmelCase_						)
				for i in range(len(lowerCAmelCase_						)						):
							UpperCAmelCase_     : List[Any]			     =					x[i] / self.k
							self.sk += prk
							UpperCAmelCase_     : Optional[Any]			     =					(self.L - 1) * self.sk
							if self.rem != 0:
										UpperCAmelCase_     : Any			     =					int(last % last						)
							UpperCAmelCase_     : List[str]			     =					int(last + 1 if self.rem >= 0.5 else last						)
							self.last_list.append(lowerCAmelCase_						)
							UpperCAmelCase_     : Optional[Any]			     =					int(np.ma.count(self.img						) / self.img[1].size						)
							UpperCAmelCase_     : Dict			     =					self.img[1].size
				for i in range(self.number_of_cols						):
							for j in range(self.number_of_rows						):
										UpperCAmelCase_     : Any			     =					self.img[j][i]
										if num != self.last_list[num]:
													UpperCAmelCase_     : Tuple			     =					self.last_list[num]
				cva.imwrite("output_data/output.jpg"	,	self.img						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			Union[str, Any]:
				plt.hist(self.img.ravel()	,	256	,	[0, 256]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			str:
				cva.imshow("Output-Image"	,	self.img						)
				cva.imshow("Input-Image"	,	self.original_image						)
				cva.waitKey(5_000						)
				cva.destroyAllWindows()


if __name__ == "__main__":
			lowerCamelCase_					=      os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
			lowerCamelCase_					=      ConstantStretch()
			stretcher.stretch(file_path)
			stretcher.plot_histogram()
			stretcher.show_image()



