




"""simple docstring"""






import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
			import torch






@is_pipeline_test
class lowerCAmelCase__	(     unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			lowerCamelCase__      =			TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Optional[int]    =    TextaTextGenerationPipeline(model=lowercase  ,      tokenizer=lowercase  )
							return generator, ["Something to write", "Something else"]
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			int    =    generator('Something there'  )
							self.assertEqual(lowercase  ,      [{'generated_text': ANY(lowercase  )}]  )
							# These are encoder decoder, they don't just append to incoming string
							self.assertFalse(outputs[0]['generated_text'].startswith('Something there'  )  )

							_lowerCamelCase     :			Optional[Any]    =    generator(['This is great !', 'Something else']  ,      num_return_sequences=2  ,      do_sample=lowercase  )
							self.assertEqual(
							    lowercase  ,      [
							        [{'generated_text': ANY(lowercase  )}, {'generated_text': ANY(lowercase  )}],
							        [{'generated_text': ANY(lowercase  )}, {'generated_text': ANY(lowercase  )}],
							    ]  ,      )

							_lowerCamelCase     :			Union[str, Any]    =    generator(
							    ['This is great !', 'Something else']  ,      num_return_sequences=2  ,      batch_size=2  ,      do_sample=lowercase  )
							self.assertEqual(
							    lowercase  ,      [
							        [{'generated_text': ANY(lowercase  )}, {'generated_text': ANY(lowercase  )}],
							        [{'generated_text': ANY(lowercase  )}, {'generated_text': ANY(lowercase  )}],
							    ]  ,      )

							with self.assertRaises(lowercase  ):
											generator(4  )
			@require_torch
			def 					A_    (  self  ):
							_lowerCamelCase     :			int    =    pipeline('text2text-generation'  ,      model='patrickvonplaten/t5-tiny-random'  ,      framework='pt'  )
							# do_sample=False necessary for reproducibility
							_lowerCamelCase     :			Dict    =    generator('Something there'  ,      do_sample=lowercase  )
							self.assertEqual(lowercase  ,      [{'generated_text': ''}]  )

							_lowerCamelCase     :			str    =    3
							_lowerCamelCase     :			str    =    generator(
							    'Something there'  ,      num_return_sequences=lowercase  ,      num_beams=lowercase  ,      )
							_lowerCamelCase     :			int    =    [
							    {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
							    {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
							    {'generated_text': ''},
							]
							self.assertEqual(lowercase  ,      lowercase  )

							_lowerCamelCase     :			int    =    generator('This is a test'  ,      do_sample=lowercase  ,      num_return_sequences=2  ,      return_tensors=lowercase  )
							self.assertEqual(
							    lowercase  ,      [
							        {'generated_token_ids': ANY(torch.Tensor  )},
							        {'generated_token_ids': ANY(torch.Tensor  )},
							    ]  ,      )
							_lowerCamelCase     :			Optional[int]    =    generator.model.config.eos_token_id
							_lowerCamelCase     :			Union[str, Any]    =    '<pad>'
							_lowerCamelCase     :			List[str]    =    generator(
							    ['This is a test', 'This is a second test']  ,      do_sample=lowercase  ,      num_return_sequences=2  ,      batch_size=2  ,      return_tensors=lowercase  ,      )
							self.assertEqual(
							    lowercase  ,      [
							        [
							            {'generated_token_ids': ANY(torch.Tensor  )},
							            {'generated_token_ids': ANY(torch.Tensor  )},
							        ],
							        [
							            {'generated_token_ids': ANY(torch.Tensor  )},
							            {'generated_token_ids': ANY(torch.Tensor  )},
							        ],
							    ]  ,      )






			@require_tf
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    pipeline('text2text-generation'  ,      model='patrickvonplaten/t5-tiny-random'  ,      framework='tf'  )
							# do_sample=False necessary for reproducibility
							_lowerCamelCase     :			List[str]    =    generator('Something there'  ,      do_sample=lowercase  )
							self.assertEqual(lowercase  ,      [{'generated_text': ''}]  )





"""simple docstring"""






def      _snake_case			(				lowercase__							,    lowercase__		):
				# "extended trapezoidal rule"
				# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
				_lowerCamelCase     :			List[Any]    =    (boundary[1] - boundary[0]) / steps
				_lowerCamelCase     :			Tuple    =    boundary[0]
				_lowerCamelCase     :			Dict    =    boundary[1]
				_lowerCamelCase     :			List[Any]    =    make_points(lowercase__							,    lowercase__							,    lowercase__		)
				_lowerCamelCase     :			List[Any]    =    0.0
				y += (h / 2.0) * f(lowercase__		)
				for i in x_i:
								# print(i)
								y += h * f(lowercase__		)
				y += (h / 2.0) * f(lowercase__		)
				return y







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			str    =    a + h
				while x < (b - h):
								yield x
								_lowerCamelCase     :			int    =    x + h







def      _snake_case			(				lowercase__		):  # enter your function here
				_lowerCamelCase     :			Optional[Any]    =    (x - 0) * (x - 0)
				return y







def      _snake_case			(				):
				_lowerCamelCase     :			int    =    0.0  # Lower bound of integration
				_lowerCamelCase     :			Optional[int]    =    1.0  # Upper bound of integration
				_lowerCamelCase     :			List[str]    =    1_0.0  # define number of steps or resolution
				_lowerCamelCase     :			List[Any]    =    [a, b]  # define boundary of integration
				_lowerCamelCase     :			Optional[Any]    =    method_a(lowercase__							,    lowercase__		)
				print(f'''y = {y}'''		)


if __name__ == "__main__":
			main()