



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  __magic_name__   (							A__,  A__						):






		@register_to_config
		def __init__(						self      :     List[Any]				,		UpperCamelCase__      :     int = 1_28				,		UpperCamelCase__      :     int = 2_56				,		UpperCamelCase__      :     float = 20_00.0				,		UpperCamelCase__      :     int = 7_68				,		UpperCamelCase__      :     int = 12				,		UpperCamelCase__      :     int = 12				,		UpperCamelCase__      :     int = 64				,		UpperCamelCase__      :     int = 20_48				,		UpperCamelCase__      :     float = 0.1				,		)				->					Optional[int]:
								'''simple docstring'''







								super().__init__()

								UpperCAmelCase         =     nn.Sequential(
								    nn.Linear(UpperCamelCase__				,		d_model * 4				,		bias=UpperCamelCase__      )				,		nn.SiLU()				,		nn.Linear(d_model * 4				,		d_model * 4				,		bias=UpperCamelCase__      )				,		nn.SiLU()				,		)

								UpperCAmelCase         =     nn.Embedding(UpperCamelCase__				,		UpperCamelCase__      )
								UpperCAmelCase         =     False

								UpperCAmelCase         =     nn.Linear(UpperCamelCase__				,		UpperCamelCase__				,		bias=UpperCamelCase__      )

								UpperCAmelCase         =     nn.Dropout(p=UpperCamelCase__      )

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

								UpperCAmelCase         =     TaLayerNorm(UpperCamelCase__      )

								UpperCAmelCase         =     nn.Dropout(p=UpperCamelCase__      )
								UpperCAmelCase         =     nn.Linear(UpperCamelCase__				,		UpperCamelCase__				,		bias=UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Optional[Any]      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     torch.mul(query_input.unsqueeze(-1      )				,		key_input.unsqueeze(-2      )      )
								return mask.unsqueeze(-3      )







		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     List[Any]      )				->					List[str]:
								'''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(UpperCamelCase__      ).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(UpperCamelCase__				,		device=decoder_input_tokens.device      )				,		(batch, seq_length)				,		)

								UpperCAmelCase         =     self.position_encoding(UpperCamelCase__      )

								UpperCAmelCase         =     self.continuous_inputs_projection(UpperCamelCase__      )
								inputs += position_encodings
								UpperCAmelCase         =     self.dropout(UpperCamelCase__      )

								# 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(UpperCamelCase__				,		UpperCamelCase__      )) 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(
														    UpperCamelCase__				,		conditioning_emb=UpperCamelCase__				,		encoder_hidden_states=UpperCamelCase__				,		encoder_attention_mask=UpperCamelCase__				,		)[0]

								UpperCAmelCase         =     self.decoder_norm(UpperCamelCase__      )
								UpperCAmelCase         =     self.post_dropout(UpperCamelCase__      )

								UpperCAmelCase         =     self.spec_out(UpperCamelCase__      )
								return spec_out





class  __magic_name__   (							nn.Module						):






		def __init__(						self      :     Tuple				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Tuple=1e-6      )				->					Optional[int]:
								'''simple docstring'''







								super().__init__()
								UpperCAmelCase         =     nn.ModuleList()

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

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

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







		def SCREAMING_SNAKE_CASE_			(						self      :     int				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Union[str, Any]=None				,		UpperCamelCase__      :     Tuple=None				,		UpperCamelCase__      :     Optional[Any]=None				,		UpperCamelCase__      :     Union[str, Any]=None				,		UpperCamelCase__      :     int=None				,		)				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     self.layer[0](
								    UpperCamelCase__				,		conditioning_emb=UpperCamelCase__				,		attention_mask=UpperCamelCase__				,		)

								if encoder_hidden_states is not None:
														UpperCAmelCase         =     torch.where(encoder_attention_mask > 0				,		0				,		-1e1_0      ).to(
														    encoder_hidden_states.dtype      )

														UpperCAmelCase         =     self.layer[1](
														    UpperCamelCase__				,		key_value_states=UpperCamelCase__				,		attention_mask=UpperCamelCase__				,		)

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

								return (hidden_states,)





class  __magic_name__   (							nn.Module						):






		def __init__(						self      :     str				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     int      )				->					Optional[Any]:
								'''simple docstring'''







								super().__init__()
								UpperCAmelCase         =     TaLayerNorm(UpperCamelCase__      )
								UpperCAmelCase         =     TaFiLMLayer(in_features=d_model * 4				,		out_features=UpperCamelCase__      )
								UpperCAmelCase         =     Attention(query_dim=UpperCamelCase__				,		heads=UpperCamelCase__				,		dim_head=UpperCamelCase__				,		out_bias=UpperCamelCase__				,		scale_qk=UpperCamelCase__      )
								UpperCAmelCase         =     nn.Dropout(UpperCamelCase__      )







		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Tuple=None				,		UpperCamelCase__      :     List[Any]=None				,		)				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     self.layer_norm(UpperCamelCase__      )

								if conditioning_emb is not None:
														UpperCAmelCase         =     self.FiLMLayer(UpperCamelCase__				,		UpperCamelCase__      )

								# Self-attention block
								UpperCAmelCase         =     self.attention(UpperCamelCase__      )

								UpperCAmelCase         =     hidden_states + self.dropout(UpperCamelCase__      )

								return hidden_states





class  __magic_name__   (							nn.Module						):






		def __init__(						self      :     Union[str, Any]				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Optional[int]      )				->					Any:
								'''simple docstring'''







								super().__init__()
								UpperCAmelCase         =     Attention(query_dim=UpperCamelCase__				,		heads=UpperCamelCase__				,		dim_head=UpperCamelCase__				,		out_bias=UpperCamelCase__				,		scale_qk=UpperCamelCase__      )
								UpperCAmelCase         =     TaLayerNorm(UpperCamelCase__				,		eps=UpperCamelCase__      )
								UpperCAmelCase         =     nn.Dropout(UpperCamelCase__      )







		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Optional[int]=None				,		UpperCamelCase__      :     Union[str, Any]=None				,		)				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.layer_norm(UpperCamelCase__      )
								UpperCAmelCase         =     self.attention(
								    UpperCamelCase__				,		encoder_hidden_states=UpperCamelCase__				,		attention_mask=attention_mask.squeeze(1      )				,		)
								UpperCAmelCase         =     hidden_states + self.dropout(UpperCamelCase__      )
								return layer_output





class  __magic_name__   (							nn.Module						):






		def __init__(						self      :     str				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Tuple      )				->					Any:
								'''simple docstring'''







								super().__init__()
								UpperCAmelCase         =     TaDenseGatedActDense(d_model=UpperCamelCase__				,		d_ff=UpperCamelCase__				,		dropout_rate=UpperCamelCase__      )
								UpperCAmelCase         =     TaFiLMLayer(in_features=d_model * 4				,		out_features=UpperCamelCase__      )
								UpperCAmelCase         =     TaLayerNorm(UpperCamelCase__				,		eps=UpperCamelCase__      )
								UpperCAmelCase         =     nn.Dropout(UpperCamelCase__      )







		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     str=None      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     self.layer_norm(UpperCamelCase__      )
								if conditioning_emb is not None:
														UpperCAmelCase         =     self.film(UpperCamelCase__				,		UpperCamelCase__      )

								UpperCAmelCase         =     self.DenseReluDense(UpperCamelCase__      )
								UpperCAmelCase         =     hidden_states + self.dropout(UpperCamelCase__      )
								return hidden_states





class  __magic_name__   (							nn.Module						):






		def __init__(						self      :     List[str]				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Union[str, Any]      )				->					Optional[Any]:
								'''simple docstring'''







								super().__init__()
								UpperCAmelCase         =     nn.Linear(UpperCamelCase__				,		UpperCamelCase__				,		bias=UpperCamelCase__      )
								UpperCAmelCase         =     nn.Linear(UpperCamelCase__				,		UpperCamelCase__				,		bias=UpperCamelCase__      )
								UpperCAmelCase         =     nn.Linear(UpperCamelCase__				,		UpperCamelCase__				,		bias=UpperCamelCase__      )
								UpperCAmelCase         =     nn.Dropout(UpperCamelCase__      )
								UpperCAmelCase         =     NewGELUActivation()







		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     str      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     self.act(self.wi_a(UpperCamelCase__      )      )
								UpperCAmelCase         =     self.wi_a(UpperCamelCase__      )
								UpperCAmelCase         =     hidden_gelu * hidden_linear
								UpperCAmelCase         =     self.dropout(UpperCamelCase__      )

								UpperCAmelCase         =     self.wo(UpperCamelCase__      )
								return hidden_states





class  __magic_name__   (							nn.Module						):






		def __init__(						self      :     List[str]				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Union[str, Any]=1e-6      )				->					Union[str, Any]:
								'''simple docstring'''







								super().__init__()
								UpperCAmelCase         =     nn.Parameter(torch.ones(UpperCamelCase__      )      )
								UpperCAmelCase         =     eps







		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]				,		UpperCamelCase__      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     hidden_states.to(torch.floataa      ).pow(2      ).mean(-1				,		keepdim=UpperCamelCase__      )
								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  __magic_name__   (							nn.Module						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     torch.Tensor      )				->					torch.Tensor:
								'''simple docstring'''







								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi      ) * (input + 0.04_47_15 * torch.pow(UpperCamelCase__				,		3.0      ))      ))





class  __magic_name__   (							nn.Module						):






		def __init__(						self      :     int				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     str      )				->					str:
								'''simple docstring'''







								super().__init__()
								UpperCAmelCase         =     nn.Linear(UpperCamelCase__				,		out_features * 2				,		bias=UpperCamelCase__      )







		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Optional[Any]      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.scale_bias(UpperCamelCase__      )
								UpperCAmelCase       ,		UpperCAmelCase         =     torch.chunk(UpperCamelCase__				,		2				,		-1      )
								UpperCAmelCase         =     x * (1 + scale) + shift
								return x








import fire

from utils import calculate_rouge, save_json

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_=None    ,   **lowerCamelCase_    ) ->       Optional[int]:
						UpperCAmelCase         =     [x.strip() for x in open(lowerCamelCase_    ).readlines()]
						UpperCAmelCase         =     [x.strip() for x in open(lowerCamelCase_    ).readlines()][: len(lowerCamelCase_    )]
						UpperCAmelCase         =     calculate_rouge(lowerCamelCase_    ,   lowerCamelCase_    ,   **lowerCamelCase_    )
						if save_path is not None:
												save_json(lowerCamelCase_    ,   lowerCamelCase_    ,   indent=lowerCamelCase_    )
						return metrics  # these print nicely


if __name__ == "__main__":
		fire.Fire(calculate_rouge_path)



