
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     lowercase (		_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE       ):



		@register_to_config
		def __init__(			self    ,				A_ = 128    ,				A_ = 256    ,				A_ = 2000.0    ,				A_ = 768    ,				A_ = 12    ,				A_ = 12    ,				A_ = 64    ,				A_ = 2_048    ,				A_ = 0.1    ,				)  -> Tuple:
				"""simple docstring"""
				super().__init__()

				UpperCamelCase          =					nn.Sequential(
				    nn.Linear(A_    ,				d_model * 4    ,				bias=A_	)    ,				nn.SiLU()    ,				nn.Linear(d_model * 4    ,				d_model * 4    ,				bias=A_	)    ,				nn.SiLU()    ,				)

				UpperCamelCase          =					nn.Embedding(A_    ,				A_	)
				UpperCamelCase          =					False

				UpperCamelCase          =					nn.Linear(A_    ,				A_    ,				bias=A_	)

				UpperCamelCase          =					nn.Dropout(p=A_	)

				UpperCamelCase          =					nn.ModuleList()
				for lyr_num in range(A_	):
						# FiLM conditional T5 decoder
						UpperCamelCase          =					DecoderLayer(d_model=A_    ,				d_kv=A_    ,				num_heads=A_    ,				d_ff=A_    ,				dropout_rate=A_	)
						self.decoders.append(A_	)

				UpperCamelCase          =					TaLayerNorm(A_	)

				UpperCamelCase          =					nn.Dropout(p=A_	)
				UpperCamelCase          =					nn.Linear(A_    ,				A_    ,				bias=A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					torch.mul(query_input.unsqueeze(-1	)    ,				key_input.unsqueeze(-2	)	)
				return mask.unsqueeze(-3	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> int:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					decoder_input_tokens.shape
				assert decoder_noise_time.shape == (batch,)

				# decoder_noise_time is in [0, 1), so rescale to expected timing range.
				UpperCamelCase          =					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	)

				UpperCamelCase          =					self.conditioning_emb(A_	).unsqueeze(1	)

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

				UpperCamelCase          =					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.
				UpperCamelCase          =					torch.broadcast_to(
				    torch.arange(A_    ,				device=decoder_input_tokens.device	)    ,				(batch, seq_length)    ,				)

				UpperCamelCase          =					self.position_encoding(A_	)

				UpperCamelCase          =					self.continuous_inputs_projection(A_	)
				inputs += position_encodings
				UpperCamelCase          =					self.dropout(A_	)

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

				# Translate encoding masks to encoder-decoder masks.
				UpperCamelCase          =					[(x, self.encoder_decoder_mask(A_    ,				A_	)) for x, y in encodings_and_masks]

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

				for lyr in self.decoders:
						UpperCamelCase          =					lyr(
						    A_    ,				conditioning_emb=A_    ,				encoder_hidden_states=A_    ,				encoder_attention_mask=A_    ,				)[0]

				UpperCamelCase          =					self.decoder_norm(A_	)
				UpperCamelCase          =					self.post_dropout(A_	)

				UpperCamelCase          =					self.spec_out(A_	)
				return spec_out






class     lowercase (		nn.Module       ):



		def __init__(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_=1e-6	)  -> Union[str, Any]:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase          =					nn.ModuleList()

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

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

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



		def __UpperCamelCase			(			self    ,				A_    ,				A_=None    ,				A_=None    ,				A_=None    ,				A_=None    ,				A_=None    ,				)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					self.layer[0](
				    A_    ,				conditioning_emb=A_    ,				attention_mask=A_    ,				)

				if encoder_hidden_states is not None:
						UpperCamelCase          =					torch.where(encoder_attention_mask > 0    ,				0    ,				-1e10	).to(
						    encoder_hidden_states.dtype	)

						UpperCamelCase          =					self.layer[1](
						    A_    ,				key_value_states=A_    ,				attention_mask=A_    ,				)

				# Apply Film Conditional Feed Forward layer
				UpperCamelCase          =					self.layer[-1](A_    ,				A_	)

				return (hidden_states,)






class     lowercase (		nn.Module       ):



		def __init__(			self    ,				A_    ,				A_    ,				A_    ,				A_	)  -> int:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase          =					TaLayerNorm(A_	)
				UpperCamelCase          =					TaFiLMLayer(in_features=d_model * 4    ,				out_features=A_	)
				UpperCamelCase          =					Attention(query_dim=A_    ,				heads=A_    ,				dim_head=A_    ,				out_bias=A_    ,				scale_qk=A_	)
				UpperCamelCase          =					nn.Dropout(A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_=None    ,				A_=None    ,				)  -> List[str]:
				"""simple docstring"""
				# pre_self_attention_layer_norm
				UpperCamelCase          =					self.layer_norm(A_	)

				if conditioning_emb is not None:
						UpperCamelCase          =					self.FiLMLayer(A_    ,				A_	)

				# Self-attention block
				UpperCamelCase          =					self.attention(A_	)

				UpperCamelCase          =					hidden_states + self.dropout(A_	)

				return hidden_states






class     lowercase (		nn.Module       ):



		def __init__(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_	)  -> Dict:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase          =					Attention(query_dim=A_    ,				heads=A_    ,				dim_head=A_    ,				out_bias=A_    ,				scale_qk=A_	)
				UpperCamelCase          =					TaLayerNorm(A_    ,				eps=A_	)
				UpperCamelCase          =					nn.Dropout(A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_=None    ,				A_=None    ,				)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					self.layer_norm(A_	)
				UpperCamelCase          =					self.attention(
				    A_    ,				encoder_hidden_states=A_    ,				attention_mask=attention_mask.squeeze(1	)    ,				)
				UpperCamelCase          =					hidden_states + self.dropout(A_	)
				return layer_output






class     lowercase (		nn.Module       ):



		def __init__(			self    ,				A_    ,				A_    ,				A_    ,				A_	)  -> Tuple:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase          =					TaDenseGatedActDense(d_model=A_    ,				d_ff=A_    ,				dropout_rate=A_	)
				UpperCamelCase          =					TaFiLMLayer(in_features=d_model * 4    ,				out_features=A_	)
				UpperCamelCase          =					TaLayerNorm(A_    ,				eps=A_	)
				UpperCamelCase          =					nn.Dropout(A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_=None	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					self.layer_norm(A_	)
				if conditioning_emb is not None:
						UpperCamelCase          =					self.film(A_    ,				A_	)

				UpperCamelCase          =					self.DenseReluDense(A_	)
				UpperCamelCase          =					hidden_states + self.dropout(A_	)
				return hidden_states






class     lowercase (		nn.Module       ):



		def __init__(			self    ,				A_    ,				A_    ,				A_	)  -> Union[str, Any]:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase          =					nn.Linear(A_    ,				A_    ,				bias=A_	)
				UpperCamelCase          =					nn.Linear(A_    ,				A_    ,				bias=A_	)
				UpperCamelCase          =					nn.Linear(A_    ,				A_    ,				bias=A_	)
				UpperCamelCase          =					nn.Dropout(A_	)
				UpperCamelCase          =					NewGELUActivation()



		def __UpperCamelCase			(			self    ,				A_	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.act(self.wi_a(A_	)	)
				UpperCamelCase          =					self.wi_a(A_	)
				UpperCamelCase          =					hidden_gelu * hidden_linear
				UpperCamelCase          =					self.dropout(A_	)

				UpperCamelCase          =					self.wo(A_	)
				return hidden_states






class     lowercase (		nn.Module       ):



		def __init__(			self    ,				A_    ,				A_=1e-6	)  -> Optional[Any]:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase          =					nn.Parameter(torch.ones(A_	)	)
				UpperCamelCase          =					eps



		def __UpperCamelCase			(			self    ,				A_	)  -> Optional[int]:
				"""simple docstring"""
				# 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

				UpperCamelCase          =					hidden_states.to(torch.floataa	).pow(2	).mean(-1    ,				keepdim=A_	)
				UpperCamelCase          =					hidden_states * torch.rsqrt(variance + self.variance_epsilon	)

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

				return self.weight * hidden_states






class     lowercase (		nn.Module       ):



		def __UpperCamelCase			(			self    ,				A_	)  -> torch.Tensor:
				"""simple docstring"""
				return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi	) * (input + 0.04_4715 * torch.pow(A_    ,				3.0	))	))






class     lowercase (		nn.Module       ):



		def __init__(			self    ,				A_    ,				A_	)  -> Dict:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase          =					nn.Linear(A_    ,				out_features * 2    ,				bias=A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.scale_bias(A_	)
				UpperCamelCase     ,  UpperCamelCase          =					torch.chunk(A_    ,				2    ,				-1	)
				UpperCamelCase          =					x * (1 + scale) + shift
				return x


from __future__ import annotations


class     lowercase :



		def __init__(			self    ,				A_    ,				A_	)  -> Any:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					text, pattern
				UpperCamelCase     ,  UpperCamelCase          =					len(A_	), len(A_	)



		def __UpperCamelCase			(			self    ,				A_	)  -> int:
				"""simple docstring"""

				for i in range(self.patLen - 1    ,				-1    ,				-1	):
						if char == self.pattern[i]:
								return i
				return -1



		def __UpperCamelCase			(			self    ,				A_	)  -> int:
				"""simple docstring"""

				for i in range(self.patLen - 1    ,				-1    ,				-1	):
						if self.pattern[i] != self.text[current_pos + i]:
								return current_pos + i
				return -1



		def __UpperCamelCase			(			self	)  -> list[int]:
				"""simple docstring"""
				# searches pattern in text and returns index positions
				UpperCamelCase          =					[]
				for i in range(self.textLen - self.patLen + 1	):
						UpperCamelCase          =					self.mismatch_in_text(A_	)
						if mismatch_index == -1:
								positions.append(A_	)
						else:
								UpperCamelCase          =					self.match_in_pattern(self.text[mismatch_index]	)
								UpperCamelCase          =					(
								    mismatch_index - match_index
								)  # shifting index lgtm [py/multiple-definition]
				return positions






_UpperCAmelCase :			Union[str, Any]              =	"ABAABA"
_UpperCAmelCase :			Any              =	"AB"
_UpperCAmelCase :			Dict              =	BoyerMooreSearch(text, pattern)
_UpperCAmelCase :			Optional[int]              =	bms.bad_character_heuristic()

if len(positions) == 0:
					print("No match found")
else:
					print("Pattern found in following positions: ")
					print(positions)
