


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


snake_case_						:			str													=							logging.get_logger(__name__)

snake_case_						:			Dict													=							{
    '''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}

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




						def __init__(       self					:      Tuple				,    _a					:      Union[str, Any]=5_0400				,    _a					:      Optional[int]=2048				,    _a					:      Dict=4096				,    _a					:      Optional[Any]=28				,    _a					:      List[Any]=16				,    _a					:      List[str]=64				,    _a					:      Optional[int]=None				,    _a					:      Union[str, Any]="gelu_new"				,    _a					:      Dict=0.0				,    _a					:      str=0.0				,    _a					:      List[str]=0.0				,    _a					:      Dict=1E-5				,    _a					:      Optional[int]=0.02				,    _a					:      Dict=True				,    _a					:      List[Any]=5_0256				,    _a					:      Union[str, Any]=5_0256				,    _a					:      Tuple=False				,    **_a					:      str				,    )							->     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			  =n_inner
										_SCREAMING_SNAKE_CASE			  =rotary_dim
										_SCREAMING_SNAKE_CASE			  =activation_function
										_SCREAMING_SNAKE_CASE			  =resid_pdrop
										_SCREAMING_SNAKE_CASE			  =embd_pdrop
										_SCREAMING_SNAKE_CASE			  =attn_pdrop
										_SCREAMING_SNAKE_CASE			  =layer_norm_epsilon
										_SCREAMING_SNAKE_CASE			  =initializer_range
										_SCREAMING_SNAKE_CASE			  =use_cache

										_SCREAMING_SNAKE_CASE			  =bos_token_id
										_SCREAMING_SNAKE_CASE			  =eos_token_id

										super().__init__(
										    bos_token_id=_a				,    eos_token_id=_a				,    tie_word_embeddings=_a				,    **_a  )

class 						A__							(		UpperCamelCase__  ):




						def __init__(       self					:      Union[str, Any]				,    _a					:      PretrainedConfig				,    _a					:      str = "default"				,    _a					:      List[PatchingSpec] = None				,    _a					:      bool = False				,    )							->     Union[str, Any]:


										"""simple docstring"""





										super().__init__(_a				,    task=_a				,    patching_specs=_a				,    use_past=_a  )
										if not getattr(self._config				,    '''pad_token_id'''				,    _a  ):
														# TODO: how to do that better?
														_SCREAMING_SNAKE_CASE			  =0




						@property
						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}  )
										if self.use_past:
														self.fill_with_past_key_values_(_a				,    direction='''inputs'''  )
														_SCREAMING_SNAKE_CASE			  ={0: '''batch''', 1: '''past_sequence + sequence'''}
										else:
														_SCREAMING_SNAKE_CASE			  ={0: '''batch''', 1: '''sequence'''}

										return common_inputs




						@property
						def 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     int:


										"""simple docstring"""





										return self._config.n_layer




						@property
						def 							__UpperCamelCase				(       self					:      str  )							->     int:


										"""simple docstring"""





										return self._config.n_head




						def 							__UpperCamelCase				(       self					:      int				,    _a					:      PreTrainedTokenizer				,    _a					:      int = -1				,    _a					:      int = -1				,    _a					:      bool = False				,    _a					:      Optional[TensorType] = None				,    )							->     Mapping[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =super(_a				,    self  ).generate_dummy_inputs(
										    _a				,    batch_size=_a				,    seq_length=_a				,    is_pair=_a				,    framework=_a  )

										# We need to order the input in the way they appears in the forward()
										_SCREAMING_SNAKE_CASE			  =OrderedDict({'''input_ids''': common_inputs['''input_ids''']}  )

										# Need to add the past_keys
										if self.use_past:
														if not is_torch_available():
																		raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''  )
														else:
																		import torch

																		_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =common_inputs['''input_ids'''].shape
																		# Not using the same length for past_key_values
																		_SCREAMING_SNAKE_CASE			  =seqlen + 2
																		_SCREAMING_SNAKE_CASE			  =(
																		    batch,
																		    self.num_attention_heads,
																		    past_key_values_length,
																		    self._config.hidden_size // self.num_attention_heads,
																		)
																		_SCREAMING_SNAKE_CASE			  =[
																		    (torch.zeros(_a  ), torch.zeros(_a  )) for _ in range(self.num_layers  )
																		]

										_SCREAMING_SNAKE_CASE			  =common_inputs['''attention_mask''']
										if self.use_past:
														_SCREAMING_SNAKE_CASE			  =ordered_inputs['''attention_mask'''].dtype
														_SCREAMING_SNAKE_CASE			  =torch.cat(
														    [ordered_inputs['''attention_mask'''], torch.ones(_a				,    _a				,    dtype=_a  )]				,    dim=1  )

										return ordered_inputs




						@property
						def 							__UpperCamelCase				(       self					:      int  )							->     int:


										"""simple docstring"""





										return 13



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