from __future__ import annotations



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :List[Any]    						=	len(SCREAMING_SNAKE_CASE			)
    # We need to create solution object to save path.
    __UpperCamelCase  :Optional[Any]    						=	[[0 for _ in range(SCREAMING_SNAKE_CASE			)] for _ in range(SCREAMING_SNAKE_CASE			)]
    __UpperCamelCase  :List[str]    						=	run_maze(SCREAMING_SNAKE_CASE						,    0						,    0						,    SCREAMING_SNAKE_CASE			)
    if solved:
        print('''\n'''.join(str(SCREAMING_SNAKE_CASE			) for row in solutions			)			)
    else:
        print('''No solution exists!'''			)
    return solved



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Any    						=	len(SCREAMING_SNAKE_CASE			)
    # Final check point.
    if i == j == (size - 1):
        __UpperCamelCase  :Optional[int]    						=	1
        return True

    __UpperCamelCase  :int    						=	(not i < 0) and (not j < 0)  # Check lower bounds
    __UpperCamelCase  :Union[str, Any]    						=	(i < size) and (j < size)  # Check upper bounds

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

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

            __UpperCamelCase  :str    						=	0
            return False
    return False


if __name__ == "__main__":
    import doctest

    doctest.testmod()
import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
    import optax
    from flax.training.common_utils import onehot

    from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
    from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    @slow
    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :List[str]    						=	FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''')
        __UpperCamelCase  :Dict    						=	AutoTokenizer.from_pretrained('''google/mt5-small''')

        __UpperCamelCase  :Optional[Any]    						=	tokenizer('''Hello there'''				,				return_tensors='''np''').input_ids
        __UpperCamelCase  :List[str]    						=	tokenizer('''Hi I am'''				,				return_tensors='''np''').input_ids

        __UpperCamelCase  :Optional[int]    						=	shift_tokens_right(__lowercase				,				model.config.pad_token_id				,				model.config.decoder_start_token_id)

        __UpperCamelCase  :Tuple    						=	model(__lowercase				,				decoder_input_ids=__lowercase).logits
        __UpperCamelCase  :Any    						=	optax.softmax_cross_entropy(__lowercase				,				onehot(__lowercase				,				logits.shape[-1])).mean()

        __UpperCamelCase  :str    						=	-(labels.shape[-1] * loss.item())

        __UpperCamelCase  :Optional[Any]    						=	-84.91_27
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1E-4)





