


'''simple docstring'''



from __future__ import annotations

import math
from collections.abc import Callable







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__ = 1_0_0	, ):
			UpperCAmelCase__					:   Optional[Any]       				=       x_start
			UpperCAmelCase__					:   List[Any]       				=       fnc(__lowerCamelCase   )
			UpperCAmelCase__					:   int       				=       0.0

			for _ in range(__lowerCamelCase   ):
						# Approximates curve as a sequence of linear lines and sums their length
						UpperCAmelCase__					:   Tuple       				=       (x_end - x_start) / steps + xa
						UpperCAmelCase__					:   Optional[Any]       				=       fnc(__lowerCamelCase   )
						length += math.hypot(xa - xa	, fxa - fxa   )

						# Increment step
						UpperCAmelCase__					:   List[str]       				=       xa
						UpperCAmelCase__					:   int       				=       fxa

			return length


if __name__ == "__main__":







		def       _UpperCamelCase (						UpperCamelCase__   ):
					return math.sin(1_0 * x   )

		print('f(x) = sin(10 * x)')
		print('The length of the curve from x = -10 to x = 10 is:')
		__A		=10
		while i <= 10_00_00:
				print(f"""With {i} steps: {line_length(f, -10, 10, i)}""")
				i *= 10



'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
		import tensorflow as tf

		from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM


@require_tf
@require_sentencepiece
@require_tokenizers
class 						_snake_case   (       unittest.TestCase		):




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small""")
								UpperCAmelCase__					:   str       				=       AutoTokenizer.from_pretrained("""google/mt5-small""")

								UpperCAmelCase__					:   Optional[Any]       				=       tokenizer("""Hello there"""							,							return_tensors="""tf""").input_ids
								UpperCAmelCase__					:   Tuple       				=       tokenizer("""Hi I am"""							,							return_tensors="""tf""").input_ids

								UpperCAmelCase__					:   List[Any]       				=       model(_lowerCamelCase							,							labels=_lowerCamelCase).loss
								UpperCAmelCase__					:   Tuple       				=       -tf.math.reduce_mean(_lowerCamelCase).numpy()

								UpperCAmelCase__					:   Optional[int]       				=       -21.228168
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 2e-4)