



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 				snake_case (UpperCamelCase		,      UpperCamelCase   ):



       @register_to_config
       def __init__(				self     ,UpperCAmelCase_ = 128     ,UpperCAmelCase_ = 256     ,UpperCAmelCase_ = 2_000.0     ,UpperCAmelCase_ = 768     ,UpperCAmelCase_ = 12     ,UpperCAmelCase_ = 12     ,UpperCAmelCase_ = 64     ,UpperCAmelCase_ = 2_048     ,UpperCAmelCase_ = 0.1     ,)  ->							List[Any]:
           super().__init__()

           lowercase__		       =						nn.Sequential(
               nn.Linear(UpperCAmelCase_     ,d_model * 4     ,bias=UpperCAmelCase_      )     ,nn.SiLU()     ,nn.Linear(d_model * 4     ,d_model * 4     ,bias=UpperCAmelCase_      )     ,nn.SiLU()     ,)

           lowercase__		       =						nn.Embedding(UpperCAmelCase_     ,UpperCAmelCase_      )
           lowercase__		       =						False

           lowercase__		       =						nn.Linear(UpperCAmelCase_     ,UpperCAmelCase_     ,bias=UpperCAmelCase_      )

           lowercase__		       =						nn.Dropout(p=UpperCAmelCase_      )

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

           lowercase__		       =						TaLayerNorm(UpperCAmelCase_      )

           lowercase__		       =						nn.Dropout(p=UpperCAmelCase_      )
           lowercase__		       =						nn.Linear(UpperCAmelCase_     ,UpperCAmelCase_     ,bias=UpperCAmelCase_      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							List[str]:
           lowercase__		       =						torch.mul(query_input.unsqueeze(-1      )     ,key_input.unsqueeze(-2      )      )
           return mask.unsqueeze(-3      )







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							List[str]:
           lowercase__       ,       lowercase__       ,       lowercase__		       =						decoder_input_tokens.shape
           assert decoder_noise_time.shape == (batch,)

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

           lowercase__		       =						self.conditioning_emb(UpperCAmelCase_      ).unsqueeze(1      )

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

           lowercase__		       =						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.
           lowercase__		       =						torch.broadcast_to(
               torch.arange(UpperCAmelCase_     ,device=decoder_input_tokens.device      )     ,(batch, seq_length)     ,)

           lowercase__		       =						self.position_encoding(UpperCAmelCase_      )

           lowercase__		       =						self.continuous_inputs_projection(UpperCAmelCase_      )
           inputs += position_encodings
           lowercase__		       =						self.dropout(UpperCAmelCase_      )

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

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

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

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

           lowercase__		       =						self.decoder_norm(UpperCAmelCase_      )
           lowercase__		       =						self.post_dropout(UpperCAmelCase_      )

           lowercase__		       =						self.spec_out(UpperCAmelCase_      )
           return spec_out





class 				snake_case (nn.Module   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_=1E-6      )  ->							Dict:
           super().__init__()
           lowercase__		       =						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 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,)  ->							Tuple:
           lowercase__		       =						self.layer[0](
               UpperCAmelCase_     ,conditioning_emb=UpperCAmelCase_     ,attention_mask=UpperCAmelCase_     ,)

           if encoder_hidden_states is not None:
               lowercase__		       =						torch.where(encoder_attention_mask > 0     ,0     ,-1E1_0      ).to(
                   encoder_hidden_states.dtype      )

               lowercase__		       =						self.layer[1](
                   UpperCAmelCase_     ,key_value_states=UpperCAmelCase_     ,attention_mask=UpperCAmelCase_     ,)

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

           return (hidden_states,)





class 				snake_case (nn.Module   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Dict:
           super().__init__()
           lowercase__		       =						TaLayerNorm(UpperCAmelCase_      )
           lowercase__		       =						TaFiLMLayer(in_features=d_model * 4     ,out_features=UpperCAmelCase_      )
           lowercase__		       =						Attention(query_dim=UpperCAmelCase_     ,heads=UpperCAmelCase_     ,dim_head=UpperCAmelCase_     ,out_bias=UpperCAmelCase_     ,scale_qk=UpperCAmelCase_      )
           lowercase__		       =						nn.Dropout(UpperCAmelCase_      )







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,)  ->							List[str]:
           # pre_self_attention_layer_norm
           lowercase__		       =						self.layer_norm(UpperCAmelCase_      )

           if conditioning_emb is not None:
               lowercase__		       =						self.FiLMLayer(UpperCAmelCase_     ,UpperCAmelCase_      )

           # Self-attention block
           lowercase__		       =						self.attention(UpperCAmelCase_      )

           lowercase__		       =						hidden_states + self.dropout(UpperCAmelCase_      )

           return hidden_states





class 				snake_case (nn.Module   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Union[str, Any]:
           super().__init__()
           lowercase__		       =						Attention(query_dim=UpperCAmelCase_     ,heads=UpperCAmelCase_     ,dim_head=UpperCAmelCase_     ,out_bias=UpperCAmelCase_     ,scale_qk=UpperCAmelCase_      )
           lowercase__		       =						TaLayerNorm(UpperCAmelCase_     ,eps=UpperCAmelCase_      )
           lowercase__		       =						nn.Dropout(UpperCAmelCase_      )







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,)  ->							int:
           lowercase__		       =						self.layer_norm(UpperCAmelCase_      )
           lowercase__		       =						self.attention(
               UpperCAmelCase_     ,encoder_hidden_states=UpperCAmelCase_     ,attention_mask=attention_mask.squeeze(1      )     ,)
           lowercase__		       =						hidden_states + self.dropout(UpperCAmelCase_      )
           return layer_output





class 				snake_case (nn.Module   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							str:
           super().__init__()
           lowercase__		       =						TaDenseGatedActDense(d_model=UpperCAmelCase_     ,d_ff=UpperCAmelCase_     ,dropout_rate=UpperCAmelCase_      )
           lowercase__		       =						TaFiLMLayer(in_features=d_model * 4     ,out_features=UpperCAmelCase_      )
           lowercase__		       =						TaLayerNorm(UpperCAmelCase_     ,eps=UpperCAmelCase_      )
           lowercase__		       =						nn.Dropout(UpperCAmelCase_      )







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=None      )  ->							int:
           lowercase__		       =						self.layer_norm(UpperCAmelCase_      )
           if conditioning_emb is not None:
               lowercase__		       =						self.film(UpperCAmelCase_     ,UpperCAmelCase_      )

           lowercase__		       =						self.DenseReluDense(UpperCAmelCase_      )
           lowercase__		       =						hidden_states + self.dropout(UpperCAmelCase_      )
           return hidden_states





class 				snake_case (nn.Module   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Any:
           super().__init__()
           lowercase__		       =						nn.Linear(UpperCAmelCase_     ,UpperCAmelCase_     ,bias=UpperCAmelCase_      )
           lowercase__		       =						nn.Linear(UpperCAmelCase_     ,UpperCAmelCase_     ,bias=UpperCAmelCase_      )
           lowercase__		       =						nn.Linear(UpperCAmelCase_     ,UpperCAmelCase_     ,bias=UpperCAmelCase_      )
           lowercase__		       =						nn.Dropout(UpperCAmelCase_      )
           lowercase__		       =						NewGELUActivation()







       def 							_a      (				self     ,UpperCAmelCase_      )  ->							Any:
           lowercase__		       =						self.act(self.wi_a(UpperCAmelCase_      )      )
           lowercase__		       =						self.wi_a(UpperCAmelCase_      )
           lowercase__		       =						hidden_gelu * hidden_linear
           lowercase__		       =						self.dropout(UpperCAmelCase_      )

           lowercase__		       =						self.wo(UpperCAmelCase_      )
           return hidden_states





class 				snake_case (nn.Module   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_=1E-6      )  ->							Tuple:
           super().__init__()
           lowercase__		       =						nn.Parameter(torch.ones(UpperCAmelCase_      )      )
           lowercase__		       =						eps







       def 							_a      (				self     ,UpperCAmelCase_      )  ->							Tuple:
           # 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

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

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

           return self.weight * hidden_states





class 				snake_case (nn.Module   ):



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							torch.Tensor:
           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 				snake_case (nn.Module   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[int]:
           super().__init__()
           lowercase__		       =						nn.Linear(UpperCAmelCase_     ,out_features * 2     ,bias=UpperCAmelCase_      )







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							int:
           lowercase__		       =						self.scale_bias(UpperCAmelCase_      )
           lowercase__       ,       lowercase__		       =						torch.chunk(UpperCAmelCase_     ,2     ,-1      )
           lowercase__		       =						x * (1 + scale) + shift
           return x









'''simple docstring'''





import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger


class 				snake_case (UpperCamelCase   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							int:
           lowercase__		       =						params

           lowercase__		       =						np.array(UpperCAmelCase_      )
           lowercase__		       =						np.array([len(UpperCAmelCase_      ) for t in data]      )

           self.check()
           self.remove_long_sequences()
           self.remove_empty_sequences()
           self.remove_unknown_sequences()
           self.check()
           self.print_statistics()



       def __getitem__(				self     ,UpperCAmelCase_      )  ->							Optional[int]:
           return (self.token_ids[index], self.lengths[index])



       def __len__(				self      )  ->							Union[str, Any]:
           return len(self.lengths      )



       def 							_a      (				self      )  ->							int:
           assert len(self.token_ids      ) == len(self.lengths      )
           assert all(self.lengths[i] == len(self.token_ids[i]      ) for i in range(len(self.lengths      )      )      )



       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__		       =						self.params.max_model_input_size
           lowercase__		       =						self.lengths > max_len
           logger.info(F'''Splitting {sum(UpperCAmelCase_      )} too long sequences.'''      )

           def divide_chunks(UpperCAmelCase_     ,UpperCAmelCase_      ):
               return [l[i : i + n] for i in range(0     ,len(UpperCAmelCase_      )     ,UpperCAmelCase_      )]

           lowercase__		       =						[]
           lowercase__		       =						[]
           if self.params.mlm:
               lowercase__       ,       lowercase__		       =						self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
           else:
               lowercase__       ,       lowercase__		       =						self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]

           for seq_, len_ in zip(self.token_ids     ,self.lengths      ):
               assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
               if len_ <= max_len:
                   new_tok_ids.append(seq_      )
                   new_lengths.append(len_      )
               else:
                   lowercase__		       =						[]
                   for sub_s in divide_chunks(seq_     ,max_len - 2      ):
                       if sub_s[0] != cls_id:
                           lowercase__		       =						np.insert(UpperCAmelCase_     ,0     ,UpperCAmelCase_      )
                       if sub_s[-1] != sep_id:
                           lowercase__		       =						np.insert(UpperCAmelCase_     ,len(UpperCAmelCase_      )     ,UpperCAmelCase_      )
                       assert len(UpperCAmelCase_      ) <= max_len
                       assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                       sub_seqs.append(UpperCAmelCase_      )

                   new_tok_ids.extend(UpperCAmelCase_      )
                   new_lengths.extend([len(UpperCAmelCase_      ) for l in sub_seqs]      )

           lowercase__		       =						np.array(UpperCAmelCase_      )
           lowercase__		       =						np.array(UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						len(self      )
           lowercase__		       =						self.lengths > 11
           lowercase__		       =						self.token_ids[indices]
           lowercase__		       =						self.lengths[indices]
           lowercase__		       =						len(self      )
           logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''      )



       def 							_a      (				self      )  ->							List[Any]:
           if "unk_token" not in self.params.special_tok_ids:
               return
           else:
               lowercase__		       =						self.params.special_tok_ids["unk_token"]
           lowercase__		       =						len(self      )
           lowercase__		       =						np.array([np.count_nonzero(a == unk_token_id      ) for a in self.token_ids]      )
           lowercase__		       =						(unk_occs / self.lengths) < 0.5
           lowercase__		       =						self.token_ids[indices]
           lowercase__		       =						self.lengths[indices]
           lowercase__		       =						len(self      )
           logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''      )



       def 							_a      (				self      )  ->							Optional[int]:
           if not self.params.is_master:
               return
           logger.info(F'''{len(self      )} sequences'''      )
           # data_len = sum(self.lengths)
           # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
           # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

           # unk_idx = self.params.special_tok_ids['unk_token']
           # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
           # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')







       def 							_a      (				self     ,UpperCAmelCase_      )  ->							List[str]:
           lowercase__		       =						[t[0] for t in batch]
           lowercase__		       =						[t[1] for t in batch]
           assert len(UpperCAmelCase_      ) == len(UpperCAmelCase_      )

           # Max for paddings
           lowercase__		       =						max(UpperCAmelCase_      )

           # Pad token ids
           if self.params.mlm:
               lowercase__		       =						self.params.special_tok_ids["pad_token"]
           else:
               lowercase__		       =						self.params.special_tok_ids["unk_token"]
           lowercase__		       =						[list(t.astype(UpperCAmelCase_      )      ) + [pad_idx] * (max_seq_len_ - len(UpperCAmelCase_      )) for t in token_ids]
           assert len(tk_      ) == len(UpperCAmelCase_      )
           assert all(len(UpperCAmelCase_      ) == max_seq_len_ for t in tk_      )

           lowercase__		       =						torch.tensor(tk_      )  # (bs, max_seq_len_)
           lowercase__		       =						torch.tensor(UpperCAmelCase_      )  # (bs)
           return tk_t, lg_t




