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    (						lowerCamelCase_       ,		lowerCamelCase_		):




   @register_to_config
   def __init__(   self      :    Optional[int]	,     _lowercase      :    int = 1_28	,     _lowercase      :    int = 2_56	,     _lowercase      :    float = 20_00.0	,     _lowercase      :    int = 7_68	,     _lowercase      :    int = 12	,     _lowercase      :    int = 12	,     _lowercase      :    int = 64	,     _lowercase      :    int = 20_48	,     _lowercase      :    float = 0.1	,     ):



     """simple docstring"""






     super().__init__()

     SCREAMING_SNAKE_CASE__      							=				nn.Sequential(
         nn.Linear(_lowercase	,     d_model * 4	,     bias=_lowercase				)	,     nn.SiLU()	,     nn.Linear(d_model * 4	,     d_model * 4	,     bias=_lowercase				)	,     nn.SiLU()	,     )

     SCREAMING_SNAKE_CASE__      							=				nn.Embedding(_lowercase	,     _lowercase				)
     SCREAMING_SNAKE_CASE__      							=				False

     SCREAMING_SNAKE_CASE__      							=				nn.Linear(_lowercase	,     _lowercase	,     bias=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				nn.Dropout(p=_lowercase				)

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

     SCREAMING_SNAKE_CASE__      							=				TaLayerNorm(_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				nn.Dropout(p=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				nn.Linear(_lowercase	,     _lowercase	,     bias=_lowercase				)




   def      __a						(   self      :    str	,     _lowercase      :    Any	,     _lowercase      :    Tuple				):



     """simple docstring"""






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




   def      __a						(   self      :    Optional[int]	,     _lowercase      :    int	,     _lowercase      :    Tuple	,     _lowercase      :    Union[str, Any]				):



     """simple docstring"""






     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(_lowercase				).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(_lowercase	,     device=decoder_input_tokens.device				)	,     (batch, seq_length)	,     )

     SCREAMING_SNAKE_CASE__      							=				self.position_encoding(_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				self.continuous_inputs_projection(_lowercase				)
     inputs += position_encodings
     SCREAMING_SNAKE_CASE__      							=				self.dropout(_lowercase				)

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

     SCREAMING_SNAKE_CASE__      							=				self.decoder_norm(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				self.post_dropout(_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				self.spec_out(_lowercase				)
     return spec_out







class __snake_case    (						nn.Module		):




   def __init__(   self      :    List[str]	,     _lowercase      :    str	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    List[str]	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    Optional[Any]	,     _lowercase      :    Tuple=1E-6				):



     """simple docstring"""






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

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

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

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




   def      __a						(   self      :    List[Any]	,     _lowercase      :    int	,     _lowercase      :    Union[str, Any]=None	,     _lowercase      :    Optional[int]=None	,     _lowercase      :    int=None	,     _lowercase      :    Tuple=None	,     _lowercase      :    List[str]=None	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.layer[0](
         _lowercase	,     conditioning_emb=_lowercase	,     attention_mask=_lowercase	,     )

     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](
           _lowercase	,     key_value_states=_lowercase	,     attention_mask=_lowercase	,     )

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

     return (hidden_states,)







class __snake_case    (						nn.Module		):




   def __init__(   self      :    List[str]	,     _lowercase      :    Optional[int]	,     _lowercase      :    Optional[Any]	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    List[Any]				):



     """simple docstring"""






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




   def      __a						(   self      :    List[str]	,     _lowercase      :    Dict	,     _lowercase      :    str=None	,     _lowercase      :    Tuple=None	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.layer_norm(_lowercase				)

     if conditioning_emb is not None:
       SCREAMING_SNAKE_CASE__      							=				self.FiLMLayer(_lowercase	,     _lowercase				)

     # Self-attention block
     SCREAMING_SNAKE_CASE__      							=				self.attention(_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				hidden_states + self.dropout(_lowercase				)

     return hidden_states







class __snake_case    (						nn.Module		):




   def __init__(   self      :    List[Any]	,     _lowercase      :    List[Any]	,     _lowercase      :    Any	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    Optional[int]	,     _lowercase      :    List[Any]				):



     """simple docstring"""






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




   def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    int	,     _lowercase      :    str=None	,     _lowercase      :    List[Any]=None	,     ):



     """simple docstring"""






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







class __snake_case    (						nn.Module		):




   def __init__(   self      :    Tuple	,     _lowercase      :    List[Any]	,     _lowercase      :    List[str]	,     _lowercase      :    Optional[Any]	,     _lowercase      :    Optional[Any]				):



     """simple docstring"""






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




   def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    int	,     _lowercase      :    Any=None				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.layer_norm(_lowercase				)
     if conditioning_emb is not None:
       SCREAMING_SNAKE_CASE__      							=				self.film(_lowercase	,     _lowercase				)

     SCREAMING_SNAKE_CASE__      							=				self.DenseReluDense(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				hidden_states + self.dropout(_lowercase				)
     return hidden_states







class __snake_case    (						nn.Module		):




   def __init__(   self      :    int	,     _lowercase      :    Optional[Any]	,     _lowercase      :    str	,     _lowercase      :    Tuple				):



     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE__      							=				nn.Linear(_lowercase	,     _lowercase	,     bias=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				nn.Linear(_lowercase	,     _lowercase	,     bias=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				nn.Linear(_lowercase	,     _lowercase	,     bias=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				nn.Dropout(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				NewGELUActivation()




   def      __a						(   self      :    List[Any]	,     _lowercase      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.act(self.wi_a(_lowercase				)				)
     SCREAMING_SNAKE_CASE__      							=				self.wi_a(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				hidden_gelu * hidden_linear
     SCREAMING_SNAKE_CASE__      							=				self.dropout(_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				self.wo(_lowercase				)
     return hidden_states







class __snake_case    (						nn.Module		):




   def __init__(   self      :    Tuple	,     _lowercase      :    Any	,     _lowercase      :    int=1E-6				):



     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE__      							=				nn.Parameter(torch.ones(_lowercase				)				)
     SCREAMING_SNAKE_CASE__      							=				eps




   def      __a						(   self      :    List[str]	,     _lowercase      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				hidden_states.to(torch.floataa				).pow(2				).mean(-1	,     keepdim=_lowercase				)
     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 __snake_case    (						nn.Module		):




   def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    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(_lowercase	,     3.0				))				))







class __snake_case    (						nn.Module		):




   def __init__(   self      :    str	,     _lowercase      :    Dict	,     _lowercase      :    int				):



     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE__      							=				nn.Linear(_lowercase	,     out_features * 2	,     bias=_lowercase				)




   def      __a						(   self      :    Dict	,     _lowercase      :    str	,     _lowercase      :    Union[str, Any]				):



     """simple docstring"""






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




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case    (						unittest.TestCase		):




   @slow
   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""	,     return_dict=_lowercase				).to(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""google/mt5-small"""				)

     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hello there"""	,     return_tensors="""pt"""				).input_ids
     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hi I am"""	,     return_tensors="""pt"""				).input_ids

     SCREAMING_SNAKE_CASE__      							=				model(input_ids.to(_lowercase				)	,     labels=labels.to(_lowercase				)				).loss
     SCREAMING_SNAKE_CASE__      							=				-(labels.shape[-1] * loss.item())

     SCREAMING_SNAKE_CASE__      							=				-84.91_27
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE				) < 1E-4				)
