





def 				SCREAMING_SNAKE_CASE__    (     __a ):
	# must be
	snake_case_		:      str           =						[list(__a ) for row in matrix]
	if matrix and isinstance(__a ,			__a ):
		if isinstance(matrix[0] ,			__a ):
			snake_case_		:      Union[str, Any]           =						0
			for row in matrix:
				if prev_len == 0:
					snake_case_		:      str           =						len(__a )
					snake_case_		:      Union[str, Any]           =						True
				else:
					snake_case_		:      List[str]           =						prev_len == len(__a )
		else:
			snake_case_		:      List[Any]           =						True
	else:
		snake_case_		:      Optional[Any]           =						False

	return result


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	if check_matrix(__a ) and len(__a ) > 0:
		snake_case_		:      Tuple           =						[list(__a ) for row in a]
		snake_case_		:      Dict           =						len(__a )
		if isinstance(a[0] ,			__a ):
			snake_case_		:      Any           =						len(a[0] )
		else:
			for dat in a:
				print(__a )
			return

		# horizotal printing increasing
		for i in range(0 ,			__a ):
			print(a[0][i] )
		# vertical printing down
		for i in range(1 ,			__a ):
			print(a[i][mat_col - 1] )
		# horizotal printing decreasing
		if mat_row > 1:
			for i in range(mat_col - 2 ,			-1 ,			-1 ):
				print(a[mat_row - 1][i] )
        # vertical printing up
		for i in range(mat_row - 2 ,			0 ,			-1 ):
			print(a[i][0] )
		snake_case_		:      int           =						[row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
		if len(__a ) > 0:
			spiral_print_clockwise(__a )
		else:
			return
	else:
		print('Not a valid matrix' )
		return


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	if matrix:
		return list(matrix.pop(0 ) ) + spiral_traversal(list(zip(*__a ) )[::-1] )
	else:
		return []


# driver code
if __name__ == "__main__":
						import doctest

						doctest.testmod()

						_SCREAMING_SNAKE_CASE   						=		[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
						spiral_print_clockwise(a)



'''simple docstring'''






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


__lowercase	=    logging.get_logger(__name__)

__lowercase	=    {
    '''google/pegasus-x-base''': '''https://huggingface.co/google/pegasus-x-base/resolve/main/config.json''',
    '''google/pegasus-x-large''': '''https://huggingface.co/google/pegasus-x-large/resolve/main/config.json''',
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}

class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  Dict								=		'''pegasus_x'''
	_UpperCamelCase							:  Optional[int]								=		['''past_key_values''']
	_UpperCamelCase							:  str								=		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




	def __init__(						self				: Any					,						UpperCamelCase_				: Tuple=96103					,						UpperCamelCase_				: List[str]=16384					,						UpperCamelCase_				: Any=16					,						UpperCamelCase_				: Dict=4096					,						UpperCamelCase_				: str=16					,						UpperCamelCase_				: Union[str, Any]=16					,						UpperCamelCase_				: str=4096					,						UpperCamelCase_				: Any=16					,						UpperCamelCase_				: str=0.0					,						UpperCamelCase_				: Dict=0.0					,						UpperCamelCase_				: Union[str, Any]=True					,						UpperCamelCase_				: Union[str, Any]=True					,						UpperCamelCase_				: List[str]="gelu"					,						UpperCamelCase_				: Dict=1024					,						UpperCamelCase_				: str=0.1					,						UpperCamelCase_				: str=0.0					,						UpperCamelCase_				: Optional[Any]=0.0					,						UpperCamelCase_				: str=0.0_2					,						UpperCamelCase_				: Optional[Any]=0					,						UpperCamelCase_				: Tuple=True					,						UpperCamelCase_				: Tuple=0					,						UpperCamelCase_				: Optional[int]=1					,						UpperCamelCase_				: List[Any]=1					,						UpperCamelCase_				: List[Any]=32					,						UpperCamelCase_				: Optional[int]=512					,						UpperCamelCase_				: Any=True					,						**UpperCamelCase_				: Dict					,						):
			lowerCAmelCase_   :       List[Any]    							=vocab_size
			lowerCAmelCase_   :       Any    							=max_position_embeddings
			lowerCAmelCase_   :       int    							=d_model
			lowerCAmelCase_   :       List[Any]    							=encoder_ffn_dim
			lowerCAmelCase_   :       Union[str, Any]    							=encoder_layers
			lowerCAmelCase_   :       Tuple    							=encoder_attention_heads
			lowerCAmelCase_   :       List[Any]    							=decoder_ffn_dim
			lowerCAmelCase_   :       List[Any]    							=decoder_layers
			lowerCAmelCase_   :       List[str]    							=decoder_attention_heads
			lowerCAmelCase_   :       str    							=dropout
			lowerCAmelCase_   :       Any    							=attention_dropout
			lowerCAmelCase_   :       Any    							=activation_dropout
			lowerCAmelCase_   :       Optional[int]    							=activation_function
			lowerCAmelCase_   :       List[Any]    							=init_std
			lowerCAmelCase_   :       List[str]    							=encoder_layerdrop
			lowerCAmelCase_   :       Optional[int]    							=decoder_layerdrop
			lowerCAmelCase_   :       int    							=use_cache
			lowerCAmelCase_   :       Union[str, Any]    							=encoder_layers
			lowerCAmelCase_   :       Tuple    							=scale_embedding  # scale factor will be sqrt(d_model) if True

			lowerCAmelCase_   :       Optional[int]    							=num_global_tokens
			lowerCAmelCase_   :       List[str]    							=block_size
			lowerCAmelCase_   :       Optional[int]    							=stagger_local_blocks

			super().__init__(
			    pad_token_id=UpperCamelCase_					,						eos_token_id=UpperCamelCase_					,						is_encoder_decoder=UpperCamelCase_					,						decoder_start_token_id=UpperCamelCase_					,						forced_eos_token_id=UpperCamelCase_					,						**UpperCamelCase_					,						)




	@property
	def 	__A      (						self				: List[str]      ):
			return self.encoder_attention_heads





	@property
	def 	__A      (						self				: int      ):
			return self.d_model






