






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


UpperCAmelCase__      			=						{'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__      			=						['''GPTNeoXTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__      			=						[
			    '''GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''GPTNeoXForCausalLM''',
			    '''GPTNeoXForQuestionAnswering''',
			    '''GPTNeoXForSequenceClassification''',
			    '''GPTNeoXForTokenClassification''',
			    '''GPTNeoXLayer''',
			    '''GPTNeoXModel''',
			    '''GPTNeoXPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_neox import (
						    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTNeoXForCausalLM,
						    GPTNeoXForQuestionAnswering,
						    GPTNeoXForSequenceClassification,
						    GPTNeoXForTokenClassification,
						    GPTNeoXLayer,
						    GPTNeoXModel,
						    GPTNeoXPreTrainedModel,
						)


else:
			import sys

			UpperCAmelCase__      			=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)













def     a_   (__A				,       __A				,       __A				,       __A      )				->						int:



					"""simple docstring"""
					__a      ,			__a					:						Any      =					len(__A      ), len(grid[0]      )
					if (
					    min(__A				,       __A      ) < 0
					    or row == row_length
					    or col == col_length
					    or (row, col) in visit
					    or grid[row][col] == 1
					):
										return 0
					if row == row_length - 1 and col == col_length - 1:
										return 1

					visit.add((row, col)      )

					__a					:						Dict      =					0
					count += depth_first_search(__A				,       row + 1				,       __A				,       __A      )
					count += depth_first_search(__A				,       row - 1				,       __A				,       __A      )
					count += depth_first_search(__A				,       __A				,       col + 1				,       __A      )
					count += depth_first_search(__A				,       __A				,       col - 1				,       __A      )

					visit.remove((row, col)      )
					return count


if __name__ == "__main__":
			import doctest

			doctest.testmod()





