


import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class 						A__							(		UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__  ):




						@register_to_config
						def __init__(       self					:      Any				,    _a					:      int				,    _a					:      int				,    _a					:      int				,    _a					:      float				,    _a					:      int				,    _a					:      int				,    _a					:      int				,    _a					:      int				,    _a					:      str				,    _a					:      bool = False				,    )							->     Union[str, Any]:


										"""simple docstring"""





										super().__init__()

										_SCREAMING_SNAKE_CASE			  =nn.Embedding(__UpperCamelCase				,    __UpperCamelCase  )

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

										_SCREAMING_SNAKE_CASE			  =nn.Dropout(p=__UpperCamelCase  )

										_SCREAMING_SNAKE_CASE			  =TaConfig(
										    vocab_size=__UpperCamelCase				,    d_model=__UpperCamelCase				,    num_heads=__UpperCamelCase				,    d_kv=__UpperCamelCase				,    d_ff=__UpperCamelCase				,    dropout_rate=__UpperCamelCase				,    feed_forward_proj=__UpperCamelCase				,    is_decoder=__UpperCamelCase				,    is_encoder_decoder=__UpperCamelCase				,    )

										_SCREAMING_SNAKE_CASE			  =nn.ModuleList()
										for lyr_num in range(__UpperCamelCase  ):
														_SCREAMING_SNAKE_CASE			  =TaBlock(__UpperCamelCase  )
														self.encoders.append(__UpperCamelCase  )

										_SCREAMING_SNAKE_CASE			  =TaLayerNorm(__UpperCamelCase  )
										_SCREAMING_SNAKE_CASE			  =nn.Dropout(p=__UpperCamelCase  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      Optional[int]				,    _a					:      Optional[Any]  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.token_embedder(__UpperCamelCase  )

										_SCREAMING_SNAKE_CASE			  =encoder_input_tokens.shape[1]
										_SCREAMING_SNAKE_CASE			  =torch.arange(__UpperCamelCase				,    device=encoder_input_tokens.device  )
										x += self.position_encoding(__UpperCamelCase  )

										_SCREAMING_SNAKE_CASE			  =self.dropout_pre(__UpperCamelCase  )

										# inverted the attention mask
										_SCREAMING_SNAKE_CASE			  =encoder_input_tokens.size()
										_SCREAMING_SNAKE_CASE			  =self.get_extended_attention_mask(__UpperCamelCase				,    __UpperCamelCase  )

										for lyr in self.encoders:
														_SCREAMING_SNAKE_CASE			  =lyr(__UpperCamelCase				,    __UpperCamelCase  )[0]
										_SCREAMING_SNAKE_CASE			  =self.layer_norm(__UpperCamelCase  )

										return self.dropout_post(__UpperCamelCase  ), encoder_inputs_mask



from datetime import datetime

import matplotlib.pyplot as plt
import torch







def 						lowerCamelCase(   a__):
				for param in module.parameters():
								_SCREAMING_SNAKE_CASE			  =False







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  ='''cuda''' if torch.cuda.is_available() else '''cpu'''
				if torch.backends.mps.is_available() and torch.backends.mps.is_built():
								_SCREAMING_SNAKE_CASE			  ='''mps'''
				if device == "mps":
								print(
								    '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
								    ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
								    ''' with generations.''')
				return device







def 						lowerCamelCase(   a__):
				_SCREAMING_SNAKE_CASE			  =plt.imshow(a__)
				fig.axes.get_xaxis().set_visible(a__)
				fig.axes.get_yaxis().set_visible(a__)
				plt.show()







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =datetime.now()
				_SCREAMING_SNAKE_CASE			  =current_time.strftime('''%H:%M:%S''')
				return timestamp