


from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_						:			Optional[int]													=							logging.get_logger(__name__)

snake_case_						:			List[Any]													=							{'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"ctrl"
						UpperCAmelCase             =						["past_key_values"]
						UpperCAmelCase             =						{
						    "max_position_embeddings": "n_positions",
						    "hidden_size": "n_embd",
						    "num_attention_heads": "n_head",
						    "num_hidden_layers": "n_layer",
						}




						def __init__(       self					:      int				,    _a					:      str=24_6534				,    _a					:      Dict=256				,    _a					:      str=1280				,    _a					:      Optional[Any]=8192				,    _a					:      Optional[Any]=48				,    _a					:      Dict=16				,    _a					:      Tuple=0.1				,    _a					:      Dict=0.1				,    _a					:      List[str]=1E-6				,    _a					:      Dict=0.02				,    _a					:      Optional[Any]=True				,    **_a					:      Any				,    )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =vocab_size
										_SCREAMING_SNAKE_CASE			  =n_positions
										_SCREAMING_SNAKE_CASE			  =n_embd
										_SCREAMING_SNAKE_CASE			  =n_layer
										_SCREAMING_SNAKE_CASE			  =n_head
										_SCREAMING_SNAKE_CASE			  =dff
										_SCREAMING_SNAKE_CASE			  =resid_pdrop
										_SCREAMING_SNAKE_CASE			  =embd_pdrop
										_SCREAMING_SNAKE_CASE			  =layer_norm_epsilon
										_SCREAMING_SNAKE_CASE			  =initializer_range

										_SCREAMING_SNAKE_CASE			  =use_cache

										super().__init__(**_a  )



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