
'''simple docstring'''







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 			lowerCAmelCase (				__UpperCAmelCase		,     __UpperCAmelCase			):




		@register_to_config
		def __init__(				self					,						UpperCamelCase = 128					,						UpperCamelCase = 256					,						UpperCamelCase = 20_00.0					,						UpperCamelCase = 768					,						UpperCamelCase = 12					,						UpperCamelCase = 12					,						UpperCamelCase = 64					,						UpperCamelCase = 2_048					,						UpperCamelCase = 0.1					,						):
								super().__init__()

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

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

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

								_SCREAMING_SNAKE_CASE				= nn.Dropout(p=UpperCamelCase      )

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

								_SCREAMING_SNAKE_CASE				= TaLayerNorm(UpperCamelCase      )

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




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= torch.mul(query_input.unsqueeze(-1      )					,						key_input.unsqueeze(-2      )      )
								return mask.unsqueeze(-3      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_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(UpperCamelCase      ).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(UpperCamelCase					,						device=decoder_input_tokens.device      )					,						(batch, seq_length)					,						)

								_SCREAMING_SNAKE_CASE				= self.position_encoding(UpperCamelCase      )

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

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

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

								_SCREAMING_SNAKE_CASE				= self.spec_out(UpperCamelCase      )
								return spec_out


class 			lowerCAmelCase (				nn.Module			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=1e-6      ):
								super().__init__()
								_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						):
								_SCREAMING_SNAKE_CASE				= self.layer[0](
								    UpperCamelCase					,						conditioning_emb=UpperCamelCase					,						attention_mask=UpperCamelCase					,						)

								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](
														    UpperCamelCase					,						key_value_states=UpperCamelCase					,						attention_mask=UpperCamelCase					,						)

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

								return (hidden_states,)


class 			lowerCAmelCase (				nn.Module			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								super().__init__()
								_SCREAMING_SNAKE_CASE				= TaLayerNorm(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= TaFiLMLayer(in_features=d_model * 4					,						out_features=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= Attention(query_dim=UpperCamelCase					,						heads=UpperCamelCase					,						dim_head=UpperCamelCase					,						out_bias=UpperCamelCase					,						scale_qk=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= nn.Dropout(UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase=None					,						UpperCamelCase=None					,						):
								# pre_self_attention_layer_norm
								_SCREAMING_SNAKE_CASE				= self.layer_norm(UpperCamelCase      )

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

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

								_SCREAMING_SNAKE_CASE				= hidden_states + self.dropout(UpperCamelCase      )

								return hidden_states


class 			lowerCAmelCase (				nn.Module			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								super().__init__()
								_SCREAMING_SNAKE_CASE				= Attention(query_dim=UpperCamelCase					,						heads=UpperCamelCase					,						dim_head=UpperCamelCase					,						out_bias=UpperCamelCase					,						scale_qk=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= TaLayerNorm(UpperCamelCase					,						eps=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= nn.Dropout(UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase=None					,						UpperCamelCase=None					,						):
								_SCREAMING_SNAKE_CASE				= self.layer_norm(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= self.attention(
								    UpperCamelCase					,						encoder_hidden_states=UpperCamelCase					,						attention_mask=attention_mask.squeeze(1      )					,						)
								_SCREAMING_SNAKE_CASE				= hidden_states + self.dropout(UpperCamelCase      )
								return layer_output


class 			lowerCAmelCase (				nn.Module			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								super().__init__()
								_SCREAMING_SNAKE_CASE				= TaDenseGatedActDense(d_model=UpperCamelCase					,						d_ff=UpperCamelCase					,						dropout_rate=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= TaFiLMLayer(in_features=d_model * 4					,						out_features=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= TaLayerNorm(UpperCamelCase					,						eps=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= nn.Dropout(UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase=None      ):
								_SCREAMING_SNAKE_CASE				= self.layer_norm(UpperCamelCase      )
								if conditioning_emb is not None:
														_SCREAMING_SNAKE_CASE				= self.film(UpperCamelCase					,						UpperCamelCase      )

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


class 			lowerCAmelCase (				nn.Module			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								super().__init__()
								_SCREAMING_SNAKE_CASE				= nn.Linear(UpperCamelCase					,						UpperCamelCase					,						bias=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= nn.Linear(UpperCamelCase					,						UpperCamelCase					,						bias=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= nn.Linear(UpperCamelCase					,						UpperCamelCase					,						bias=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= nn.Dropout(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= NewGELUActivation()




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= self.act(self.wi_a(UpperCamelCase      )      )
								_SCREAMING_SNAKE_CASE				= self.wi_a(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= hidden_gelu * hidden_linear
								_SCREAMING_SNAKE_CASE				= self.dropout(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= self.wo(UpperCamelCase      )
								return hidden_states


class 			lowerCAmelCase (				nn.Module			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase=1e-6      ):
								super().__init__()
								_SCREAMING_SNAKE_CASE				= nn.Parameter(torch.ones(UpperCamelCase      )      )
								_SCREAMING_SNAKE_CASE				= eps




		def     lowercase       (				self					,						UpperCamelCase      ):
								# 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=UpperCamelCase      )
								_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 			lowerCAmelCase (				nn.Module			):




		def     lowercase       (				self					,						UpperCamelCase      ):
								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 			lowerCAmelCase (				nn.Module			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase      ):
								super().__init__()
								_SCREAMING_SNAKE_CASE				= nn.Linear(UpperCamelCase					,						out_features * 2					,						bias=UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= self.scale_bias(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= torch.chunk(UpperCamelCase					,						2					,						-1      )
								_SCREAMING_SNAKE_CASE				= x * (1 + scale) + shift
								return x

'''simple docstring'''







from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Dict       =					DistilBertTokenizer
		a							:					Tuple       =					DistilBertTokenizerFast
		a							:					List[str]       =					True




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= DistilBertTokenizer.from_pretrained("distilbert-base-uncased"      )

								_SCREAMING_SNAKE_CASE				= tokenizer.encode("sequence builders"					,						add_special_tokens=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= tokenizer.encode("multi-sequence build"					,						add_special_tokens=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase					,						UpperCamelCase      )

								assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
								assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
								    tokenizer.sep_token_id
								]