

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 			_SCREAMING_SNAKE_CASE (   __snake_case					,       __snake_case		):







   '''simple docstring'''




   @register_to_config
   def __init__(       self					:			Any     , __lowerCamelCase					:			int = 128     , __lowerCamelCase					:			int = 256     , __lowerCamelCase					:			float = 2_000.0     , __lowerCamelCase					:			int = 768     , __lowerCamelCase					:			int = 12     , __lowerCamelCase					:			int = 12     , __lowerCamelCase					:			int = 64     , __lowerCamelCase					:			int = 2048     , __lowerCamelCase					:			float = 0.1     , ):
       super().__init__()

       SCREAMING_SNAKE_CASE										=   nn.Sequential(
           nn.Linear(__lowerCamelCase     , d_model * 4     , bias=__lowerCamelCase			)     , nn.SiLU()     , nn.Linear(d_model * 4     , d_model * 4     , bias=__lowerCamelCase			)     , nn.SiLU()     , )

       SCREAMING_SNAKE_CASE										=   nn.Embedding(__lowerCamelCase     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   False

       SCREAMING_SNAKE_CASE										=   nn.Linear(__lowerCamelCase     , __lowerCamelCase     , bias=__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   nn.Dropout(p=__lowerCamelCase			)

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

       SCREAMING_SNAKE_CASE										=   TaLayerNorm(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   nn.Dropout(p=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   nn.Linear(__lowerCamelCase     , __lowerCamelCase     , bias=__lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			List[str]     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   torch.mul(query_input.unsqueeze(-1			)     , key_input.unsqueeze(-2			)			)
       return mask.unsqueeze(-3			)





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			List[str]     , __lowerCamelCase					:			Dict			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   decoder_input_tokens.shape
       assert decoder_noise_time.shape == (batch,)

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

       SCREAMING_SNAKE_CASE										=   self.conditioning_emb(__lowerCamelCase			).unsqueeze(1			)

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

       SCREAMING_SNAKE_CASE										=   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.
       SCREAMING_SNAKE_CASE										=   torch.broadcast_to(
           torch.arange(__lowerCamelCase     , device=decoder_input_tokens.device			)     , (batch, seq_length)     , )

       SCREAMING_SNAKE_CASE										=   self.position_encoding(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   self.continuous_inputs_projection(__lowerCamelCase			)
       inputs += position_encodings
       SCREAMING_SNAKE_CASE										=   self.dropout(__lowerCamelCase			)

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

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

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

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

       SCREAMING_SNAKE_CASE										=   self.decoder_norm(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.post_dropout(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   self.spec_out(__lowerCamelCase			)
       return spec_out





class 			_SCREAMING_SNAKE_CASE (   nn.Module		):







   '''simple docstring'''




   def __init__(       self					:			List[Any]     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			List[str]     , __lowerCamelCase					:			Tuple     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Optional[int]=1e-6			):
       super().__init__()
       SCREAMING_SNAKE_CASE										=   nn.ModuleList()

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

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

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





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			Any=None     , __lowerCamelCase					:			Optional[Any]=None     , __lowerCamelCase					:			Optional[int]=None     , __lowerCamelCase					:			Optional[int]=None     , __lowerCamelCase					:			List[str]=None     , ):
       SCREAMING_SNAKE_CASE										=   self.layer[0](
           __lowerCamelCase     , conditioning_emb=__lowerCamelCase     , attention_mask=__lowerCamelCase     , )

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

           SCREAMING_SNAKE_CASE										=   self.layer[1](
               __lowerCamelCase     , key_value_states=__lowerCamelCase     , attention_mask=__lowerCamelCase     , )

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

       return (hidden_states,)





class 			_SCREAMING_SNAKE_CASE (   nn.Module		):







   '''simple docstring'''




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





   def      _snake_case		(       self					:			List[str]     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Tuple=None     , __lowerCamelCase					:			Optional[Any]=None     , ):
       # pre_self_attention_layer_norm
       SCREAMING_SNAKE_CASE										=   self.layer_norm(__lowerCamelCase			)

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

       # Self-attention block
       SCREAMING_SNAKE_CASE										=   self.attention(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   hidden_states + self.dropout(__lowerCamelCase			)

       return hidden_states





class 			_SCREAMING_SNAKE_CASE (   nn.Module		):







   '''simple docstring'''




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





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			Dict=None     , __lowerCamelCase					:			Dict=None     , ):
       SCREAMING_SNAKE_CASE										=   self.layer_norm(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.attention(
           __lowerCamelCase     , encoder_hidden_states=__lowerCamelCase     , attention_mask=attention_mask.squeeze(1			)     , )
       SCREAMING_SNAKE_CASE										=   hidden_states + self.dropout(__lowerCamelCase			)
       return layer_output





class 			_SCREAMING_SNAKE_CASE (   nn.Module		):







   '''simple docstring'''




   def __init__(       self					:			Dict     , __lowerCamelCase					:			Union[str, Any]     , __lowerCamelCase					:			Any     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			Union[str, Any]			):
       super().__init__()
       SCREAMING_SNAKE_CASE										=   TaDenseGatedActDense(d_model=__lowerCamelCase     , d_ff=__lowerCamelCase     , dropout_rate=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   TaFiLMLayer(in_features=d_model * 4     , out_features=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   TaLayerNorm(__lowerCamelCase     , eps=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   nn.Dropout(__lowerCamelCase			)





   def      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Tuple=None			):
       SCREAMING_SNAKE_CASE										=   self.layer_norm(__lowerCamelCase			)
       if conditioning_emb is not None:
           SCREAMING_SNAKE_CASE										=   self.film(__lowerCamelCase     , __lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   self.DenseReluDense(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   hidden_states + self.dropout(__lowerCamelCase			)
       return hidden_states





class 			_SCREAMING_SNAKE_CASE (   nn.Module		):







   '''simple docstring'''




   def __init__(       self					:			Optional[int]     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Any     , __lowerCamelCase					:			Optional[Any]			):
       super().__init__()
       SCREAMING_SNAKE_CASE										=   nn.Linear(__lowerCamelCase     , __lowerCamelCase     , bias=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   nn.Linear(__lowerCamelCase     , __lowerCamelCase     , bias=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   nn.Linear(__lowerCamelCase     , __lowerCamelCase     , bias=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   nn.Dropout(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   NewGELUActivation()





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   self.act(self.wi_a(__lowerCamelCase			)			)
       SCREAMING_SNAKE_CASE										=   self.wi_a(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   hidden_gelu * hidden_linear
       SCREAMING_SNAKE_CASE										=   self.dropout(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   self.wo(__lowerCamelCase			)
       return hidden_states





class 			_SCREAMING_SNAKE_CASE (   nn.Module		):







   '''simple docstring'''




   def __init__(       self					:			List[Any]     , __lowerCamelCase					:			List[str]     , __lowerCamelCase					:			Union[str, Any]=1e-6			):
       super().__init__()
       SCREAMING_SNAKE_CASE										=   nn.Parameter(torch.ones(__lowerCamelCase			)			)
       SCREAMING_SNAKE_CASE										=   eps





   def      _snake_case		(       self					:			List[str]     , __lowerCamelCase					:			str			):
       # 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

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

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

       return self.weight * hidden_states





class 			_SCREAMING_SNAKE_CASE (   nn.Module		):







   '''simple docstring'''




   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			torch.Tensor			):
       return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi			) * (input + 0.044_715 * torch.pow(__lowerCamelCase     , 3.0			))			))





class 			_SCREAMING_SNAKE_CASE (   nn.Module		):







   '''simple docstring'''




   def __init__(       self					:			int     , __lowerCamelCase					:			List[Any]     , __lowerCamelCase					:			List[Any]			):
       super().__init__()
       SCREAMING_SNAKE_CASE										=   nn.Linear(__lowerCamelCase     , out_features * 2     , bias=__lowerCamelCase			)





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




from __future__ import annotations






class     __a			:



	def __init__(    self				:     List[Any]					,						snake_case_				:     str					,						snake_case_				:     str)->				Optional[int]:
						__lowerCAmelCase ,						__lowerCAmelCase						 =text, pattern
						__lowerCAmelCase ,						__lowerCAmelCase						 =len(snake_case_), len(snake_case_)



	def 		UpperCamelCase				(    self				:     List[Any]					,						snake_case_				:     str)->				int:

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



	def 		UpperCamelCase				(    self				:     List[str]					,						snake_case_				:     int)->				int:

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


lowercase_													=      '''ABAABA'''
lowercase_													=      '''AB'''
lowercase_													=      BoyerMooreSearch(text, pattern)
lowercase_													=      bms.bad_character_heuristic()

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




