



'''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 	UpperCAmelCase	(		__a					,	__a):


 '''simple docstring'''




 @register_to_config
 def __init__(   self							,  lowerCAmelCase_ = 1_2_8							,  lowerCAmelCase_ = 2_5_6							,  lowerCAmelCase_ = 2_0_0_0.0							,  lowerCAmelCase_ = 7_6_8							,  lowerCAmelCase_ = 1_2							,  lowerCAmelCase_ = 1_2							,  lowerCAmelCase_ = 6_4							,  lowerCAmelCase_ = 2_0_4_8							,  lowerCAmelCase_ = 0.1							,  ) ->      Any:



      """simple docstring"""







      super().__init__()

      a_       		=nn.Sequential(
          nn.Linear(lowerCAmelCase_							,  d_model * 4							,  bias=lowerCAmelCase_)							,  nn.SiLU()							,  nn.Linear(d_model * 4							,  d_model * 4							,  bias=lowerCAmelCase_)							,  nn.SiLU()							,  )

      a_       		=nn.Embedding(lowerCAmelCase_							,  lowerCAmelCase_)
      a_       		=False

      a_       		=nn.Linear(lowerCAmelCase_							,  lowerCAmelCase_							,  bias=lowerCAmelCase_)

      a_       		=nn.Dropout(p=lowerCAmelCase_)

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

      a_       		=TaLayerNorm(lowerCAmelCase_)

      a_       		=nn.Dropout(p=lowerCAmelCase_)
      a_       		=nn.Linear(lowerCAmelCase_							,  lowerCAmelCase_							,  bias=lowerCAmelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      Any:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      List[Any]:



      """simple docstring"""







      a_		,						a_		,						a_       		=decoder_input_tokens.shape
      assert decoder_noise_time.shape == (batch,)

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

      a_       		=self.conditioning_emb(lowerCAmelCase_).unsqueeze(1)

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

      a_       		=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.
      a_       		=torch.broadcast_to(
          torch.arange(lowerCAmelCase_							,  device=decoder_input_tokens.device)							,  (batch, seq_length)							,  )

      a_       		=self.position_encoding(lowerCAmelCase_)

      a_       		=self.continuous_inputs_projection(lowerCAmelCase_)
      inputs += position_encodings
      a_       		=self.dropout(lowerCAmelCase_)

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

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

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

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

      a_       		=self.decoder_norm(lowerCAmelCase_)
      a_       		=self.post_dropout(lowerCAmelCase_)

      a_       		=self.spec_out(lowerCAmelCase_)
      return spec_out







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_=1e-6) ->      Any:



      """simple docstring"""







      super().__init__()
      a_       		=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       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  ) ->      List[str]:



      """simple docstring"""







      a_       		=self.layer[0](
          lowerCAmelCase_							,  conditioning_emb=lowerCAmelCase_							,  attention_mask=lowerCAmelCase_							,  )

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

           a_       		=self.layer[1](
               lowerCAmelCase_							,  key_value_states=lowerCAmelCase_							,  attention_mask=lowerCAmelCase_							,  )

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

      return (hidden_states,)







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      List[Any]:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  ) ->      Optional[Any]:



      """simple docstring"""







      a_       		=self.layer_norm(lowerCAmelCase_)

      if conditioning_emb is not None:
           a_       		=self.FiLMLayer(lowerCAmelCase_							,  lowerCAmelCase_)

      # Self-attention block
      a_       		=self.attention(lowerCAmelCase_)

      a_       		=hidden_states + self.dropout(lowerCAmelCase_)

      return hidden_states







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      Any:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  ) ->      str:



      """simple docstring"""







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







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      Optional[int]:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_=None) ->      int:



      """simple docstring"""







      a_       		=self.layer_norm(lowerCAmelCase_)
      if conditioning_emb is not None:
           a_       		=self.film(lowerCAmelCase_							,  lowerCAmelCase_)

      a_       		=self.DenseReluDense(lowerCAmelCase_)
      a_       		=hidden_states + self.dropout(lowerCAmelCase_)
      return hidden_states







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      Optional[Any]:



      """simple docstring"""







      super().__init__()
      a_       		=nn.Linear(lowerCAmelCase_							,  lowerCAmelCase_							,  bias=lowerCAmelCase_)
      a_       		=nn.Linear(lowerCAmelCase_							,  lowerCAmelCase_							,  bias=lowerCAmelCase_)
      a_       		=nn.Linear(lowerCAmelCase_							,  lowerCAmelCase_							,  bias=lowerCAmelCase_)
      a_       		=nn.Dropout(lowerCAmelCase_)
      a_       		=NewGELUActivation()




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Optional[Any]:



      """simple docstring"""







      a_       		=self.act(self.wi_a(lowerCAmelCase_))
      a_       		=self.wi_a(lowerCAmelCase_)
      a_       		=hidden_gelu * hidden_linear
      a_       		=self.dropout(lowerCAmelCase_)

      a_       		=self.wo(lowerCAmelCase_)
      return hidden_states







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_=1e-6) ->      List[str]:



      """simple docstring"""







      super().__init__()
      a_       		=nn.Parameter(torch.ones(lowerCAmelCase_))
      a_       		=eps




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Tuple:



      """simple docstring"""







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

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

      return self.weight * hidden_states







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      torch.Tensor:



      """simple docstring"""







      return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.0_4_4_7_1_5 * torch.pow(lowerCAmelCase_							,  3.0))))







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      Tuple:



      """simple docstring"""







      super().__init__()
      a_       		=nn.Linear(lowerCAmelCase_							,  out_features * 2							,  bias=lowerCAmelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      List[str]:



      """simple docstring"""







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







'''simple docstring'''


import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

lowercase							       =			logging.getLogger()






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     a_       		={}
     a_       		=os.path.join(lowercase__       ,	"all_results.json"       )
     if os.path.exists(lowercase__       ):
          with open(lowercase__       ,	"r"       ) as f:
               a_       		=json.load(lowercase__       )
     else:
          raise ValueError(F"""can't find {path}"""       )
     return results


lowercase							       =			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@require_torch_tpu
class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      import xla_spawn

      a_       		=self.get_auto_remove_tmp_dir()
      a_       		=f"""
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

      with patch.object(lowerCAmelCase_							,  "argv"							,  lowerCAmelCase_):
           a_       		=time()
           xla_spawn.main()
           a_       		=time()

           a_       		=get_results(lowerCAmelCase_)
           self.assertGreaterEqual(result["eval_accuracy"]							,  0.7_5)

           # Assert that the script takes less than 500 seconds to make sure it doesn't hang.
           self.assertLess(end - start							,  5_0_0)




 def       lowercase_  (   self) ->      Tuple:



      """simple docstring"""







      import xla_spawn

      a_       		="\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            ".split()
      with patch.object(lowerCAmelCase_							,  "argv"							,  lowerCAmelCase_):
           xla_spawn.main()


