




'''simple docstring'''


def 						snake_case_       (UpperCamelCase			:    list					,		UpperCamelCase			:    list ):





							'''simple docstring'''


							_validate_point(UpperCamelCase )
							_validate_point(UpperCamelCase )
							if len(UpperCamelCase ) != len(UpperCamelCase ):
														raise ValueError('''Both points must be in the same n-dimensional space''' )

							return float(sum(abs(a - b ) for a, b in zip(UpperCamelCase					,		UpperCamelCase ) ) )
def 						snake_case_       (UpperCamelCase			:    list[float] ):





							'''simple docstring'''


							if point:
														if isinstance(UpperCamelCase					,		UpperCamelCase ):
																					for item in point:
																												if not isinstance(UpperCamelCase					,		(int, float) ):
																																			_a      	=							(
																																			    '''Expected a list of numbers as input, found '''
																																			    f'{type(UpperCamelCase ).__name__}'
																																			)
																																			raise TypeError(UpperCamelCase )
														else:
																					_a      	=							f'Expected a list of numbers as input, found {type(UpperCamelCase ).__name__}'
																					raise TypeError(UpperCamelCase )
							else:
														raise ValueError('''Missing an input''' )
def 						snake_case_       (UpperCamelCase			:    list					,		UpperCamelCase			:    list ):





							'''simple docstring'''


							_validate_point(UpperCamelCase )
							_validate_point(UpperCamelCase )
							if len(UpperCamelCase ) != len(UpperCamelCase ):
														raise ValueError('''Both points must be in the same n-dimensional space''' )

							return float(sum(abs(x - y ) for x, y in zip(UpperCamelCase					,		UpperCamelCase ) ) )


if __name__ == "__main__":
				import doctest

				doctest.testmod()







'''simple docstring'''


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      A       (						unittest.TestCase       ):







							@slow
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''   )
														_a      	=							AutoTokenizer.from_pretrained('''google/mt5-small'''   )

														_a      	=							tokenizer('''Hello there'''				,     return_tensors='''np'''   ).input_ids
														_a      	=							tokenizer('''Hi I am'''				,     return_tensors='''np'''   ).input_ids

														_a      	=							shift_tokens_right(lowerCAmelCase_				,     model.config.pad_token_id				,     model.config.decoder_start_token_id   )

														_a      	=							model(lowerCAmelCase_				,     decoder_input_ids=lowerCAmelCase_   ).logits
														_a      	=							optax.softmax_cross_entropy(lowerCAmelCase_				,     onehot(lowerCAmelCase_				,     logits.shape[-1]   )   ).mean()

														_a      	=							-(labels.shape[-1] * loss.item())

														_a      	=							-8_4.9_1_2_7
														self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1e-4   )

