

'''simple docstring'''





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


UpperCamelCase_							=					logging.get_logger(__name__)

UpperCamelCase_							=					{
    '''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 		__SCREAMING_SNAKE_CASE       ( a__			):
				lowerCamelCase_							=						"""gptj"""
				lowerCamelCase_							=						{
				    """max_position_embeddings""": """n_positions""",
				    """hidden_size""": """n_embd""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}







				def __init__(					self    :		str		,				UpperCAmelCase__    :		Tuple=50400		,				UpperCAmelCase__    :		str=2048		,				UpperCAmelCase__    :		Optional[int]=4096		,				UpperCAmelCase__    :		str=28		,				UpperCAmelCase__    :		Tuple=16		,				UpperCAmelCase__    :		Optional[int]=64		,				UpperCAmelCase__    :		Tuple=None		,				UpperCAmelCase__    :		int="gelu_new"		,				UpperCAmelCase__    :		Optional[int]=0.0		,				UpperCAmelCase__    :		Tuple=0.0		,				UpperCAmelCase__    :		List[str]=0.0		,				UpperCAmelCase__    :		List[Any]=1E-5		,				UpperCAmelCase__    :		List[str]=0.02		,				UpperCAmelCase__    :		int=True		,				UpperCAmelCase__    :		Union[str, Any]=50256		,				UpperCAmelCase__    :		str=50256		,				UpperCAmelCase__    :		Optional[int]=False		,				**UpperCAmelCase__    :		int		,				):
										'''simple docstring'''
										lowercase   :       int								=vocab_size
										lowercase   :       Tuple								=n_positions
										lowercase   :       List[Any]								=n_embd
										lowercase   :       int								=n_layer
										lowercase   :       Union[str, Any]								=n_head
										lowercase   :       Optional[int]								=n_inner
										lowercase   :       Any								=rotary_dim
										lowercase   :       Any								=activation_function
										lowercase   :       Optional[int]								=resid_pdrop
										lowercase   :       Optional[int]								=embd_pdrop
										lowercase   :       int								=attn_pdrop
										lowercase   :       Optional[Any]								=layer_norm_epsilon
										lowercase   :       Optional[int]								=initializer_range
										lowercase   :       Union[str, Any]								=use_cache

										lowercase   :       Union[str, Any]								=bos_token_id
										lowercase   :       Dict								=eos_token_id

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





class 		__SCREAMING_SNAKE_CASE       ( a__			):







				def __init__(					self    :		Any		,				UpperCAmelCase__    :		Any		,				UpperCAmelCase__    :		str = "default"		,				UpperCAmelCase__    :		Optional[Any] = None		,				UpperCAmelCase__    :		Optional[int] = False		,				):
										'''simple docstring'''
										super().__init__(lowerCAmelCase__		,				task=lowerCAmelCase__		,				patching_specs=lowerCAmelCase__		,				use_past=lowerCAmelCase__      )
										if not getattr(self._config		,				'''pad_token_id'''		,				lowerCAmelCase__      ):
																# TODO: how to do that better?
																lowercase   :       Optional[Any]								=0







				@property
				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}      )
										if self.use_past:
																self.fill_with_past_key_values_(lowerCAmelCase__		,				direction='''inputs'''      )
																lowercase   :       Tuple								={0: "batch", 1: "past_sequence + sequence"}
										else:
																lowercase   :       List[Any]								={0: "batch", 1: "sequence"}

										return common_inputs







				@property
				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										return self._config.n_layer







				@property
				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										return self._config.n_head







				def    lowerCamelCase_  (					self    :		Any		,				UpperCAmelCase__    :		Tuple		,				UpperCAmelCase__    :		Dict = -1		,				UpperCAmelCase__    :		str = -1		,				UpperCAmelCase__    :		Tuple = False		,				UpperCAmelCase__    :		Dict = None		,				):
										'''simple docstring'''
										lowercase   :       List[Any]								=super(lowerCAmelCase__		,				self      ).generate_dummy_inputs(
										    lowerCAmelCase__		,				batch_size=lowerCAmelCase__		,				seq_length=lowerCAmelCase__		,				is_pair=lowerCAmelCase__		,				framework=lowerCAmelCase__      )

										# We need to order the input in the way they appears in the forward()
										lowercase   :       Dict								=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

																						lowercase   :       Optional[int]								=common_inputs["input_ids"].shape
																						# Not using the same length for past_key_values
																						lowercase   :       Optional[int]								=seqlen + 2
																						lowercase   :       Optional[int]								=(
																						    batch,
																						    self.num_attention_heads,
																						    past_key_values_length,
																						    self._config.hidden_size // self.num_attention_heads,
																						)
																						lowercase   :       List[Any]								=[
																						    (torch.zeros(lowerCAmelCase__      ), torch.zeros(lowerCAmelCase__      )) for _ in range(self.num_layers      )
																						]

										lowercase   :       List[str]								=common_inputs["attention_mask"]
										if self.use_past:
																lowercase   :       List[str]								=ordered_inputs["attention_mask"].dtype
																lowercase   :       Union[str, Any]								=torch.cat(
																    [ordered_inputs['''attention_mask'''], torch.ones(lowerCAmelCase__		,				lowerCAmelCase__		,				dtype=lowerCAmelCase__      )]		,				dim=1      )

										return ordered_inputs







				@property
				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										return 13








'''simple docstring'''





from __future__ import annotations






def 							_lowerCAmelCase (      __magic_name__	:    list[list[int]]	)							->     bool:
						lowercase   :       str								=len(__magic_name__	)
						# We need to create solution object to save path.
						lowercase   :       int								=[[0 for _ in range(__magic_name__	)] for _ in range(__magic_name__	)]
						lowercase   :       List[Any]								=run_maze(__magic_name__      ,   0      ,   0      ,   __magic_name__	)
						if solved:
												print('''\n'''.join(str(__magic_name__	) for row in solutions	)	)
						else:
												print('''No solution exists!'''	)
						return solved






def 							_lowerCAmelCase (      __magic_name__	:    list[list[int]]      ,   __magic_name__	:    int      ,   __magic_name__	:    int      ,   __magic_name__	:    list[list[int]]	)							->     bool:
						lowercase   :       Optional[int]								=len(__magic_name__	)
						# Final check point.
						if i == j == (size - 1):
												lowercase   :       Optional[int]								=1
												return True

						lowercase   :       Optional[int]								=(not i < 0) and (not j < 0)  # Check lower bounds
						lowercase   :       Tuple								=(i < size) and (j < size)  # Check upper bounds

						if lower_flag and upper_flag:
												# check for already visited and block points.
												lowercase   :       Union[str, Any]								=(not solutions[i][j]) and (not maze[i][j])
												if block_flag:
																		# check visited
																		lowercase   :       Union[str, Any]								=1

																		# check for directions
																		if (
																		    run_maze(__magic_name__      ,   i + 1      ,   __magic_name__      ,   __magic_name__	)
																		    or run_maze(__magic_name__      ,   __magic_name__      ,   j + 1      ,   __magic_name__	)
																		    or run_maze(__magic_name__      ,   i - 1      ,   __magic_name__      ,   __magic_name__	)
																		    or run_maze(__magic_name__      ,   __magic_name__      ,   j - 1      ,   __magic_name__	)
																		):
																								return True

																		lowercase   :       str								=0
																		return False
						return False


if __name__ == "__main__":
							import doctest

							doctest.testmod()





