




'''simple docstring'''


def 						snake_case_       (UpperCamelCase			:    Optional[int]					,		UpperCamelCase			:    Optional[Any] ):





							'''simple docstring'''


							_a      	=							(boundary[1] - boundary[0]) / steps
							_a      	=							boundary[0]
							_a      	=							boundary[1]
							_a      	=							make_points(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
							_a      	=							0.0
							y += (h / 2.0) * f(UpperCamelCase )
							for i in x_i:
														# print(i)
														y += h * f(UpperCamelCase )
							y += (h / 2.0) * f(UpperCamelCase )
							return y
def 						snake_case_       (UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    List[str] ):





							'''simple docstring'''


							_a      	=							a + h
							while x < (b - h):
														yield x
														_a      	=							x + h
def 						snake_case_       (UpperCamelCase			:    str ):  # enter your function here





							'''simple docstring'''


							_a      	=							(x - 0) * (x - 0)
							return y
def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							0.0  # Lower bound of integration
							_a      	=							1.0  # Upper bound of integration
							_a      	=							10.0  # define number of steps or resolution
							_a      	=							[a, b]  # define boundary of integration
							_a      	=							method_a(UpperCamelCase					,		UpperCamelCase )
							print(f'y = {y}' )


if __name__ == "__main__":
				main()







'''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   )

