


"""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  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	snake_case__				:  Union[str, Any]    					=			MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
	snake_case__				:  List[Any]    					=			TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




	def   UpperCAmelCase_      ( self			:				List[str]			,			UpperCAmelCase__			:				List[str]			,			UpperCAmelCase__			:				Any			,			UpperCAmelCase__			:				Optional[Any]   )       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				TextaTextGenerationPipeline(model=UpperCAmelCase__			,			tokenizer=UpperCAmelCase__   )
						return generator, ["Something to write", "Something else"]




	def   UpperCAmelCase_      ( self			:				List[str]			,			UpperCAmelCase__			:				Dict			,			UpperCAmelCase__			:				Dict   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				generator("Something there"   )
						self.assertEqual(UpperCAmelCase__			,			[{"generated_text": ANY(UpperCAmelCase__   )}]   )
						# These are encoder decoder, they don't just append to incoming string
						self.assertFalse(outputs[0]["generated_text"].startswith("Something there"   )   )

						__SCREAMING_SNAKE_CASE						   =				generator(["This is great !", "Something else"]			,			num_return_sequences=2			,			do_sample=UpperCAmelCase__   )
						self.assertEqual(
						    UpperCAmelCase__			,			[
						        [{"generated_text": ANY(UpperCAmelCase__   )}, {"generated_text": ANY(UpperCAmelCase__   )}],
						        [{"generated_text": ANY(UpperCAmelCase__   )}, {"generated_text": ANY(UpperCAmelCase__   )}],
						    ]			,			)

						__SCREAMING_SNAKE_CASE						   =				generator(
						    ["This is great !", "Something else"]			,			num_return_sequences=2			,			batch_size=2			,			do_sample=UpperCAmelCase__   )
						self.assertEqual(
						    UpperCAmelCase__			,			[
						        [{"generated_text": ANY(UpperCAmelCase__   )}, {"generated_text": ANY(UpperCAmelCase__   )}],
						        [{"generated_text": ANY(UpperCAmelCase__   )}, {"generated_text": ANY(UpperCAmelCase__   )}],
						    ]			,			)

						with self.assertRaises(UpperCAmelCase__   ):
											generator(4   )




	@require_torch
	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	str:
						__SCREAMING_SNAKE_CASE						   =				pipeline("text2text-generation"			,			model="patrickvonplaten/t5-tiny-random"			,			framework="pt"   )
						# do_sample=False necessary for reproducibility
						__SCREAMING_SNAKE_CASE						   =				generator("Something there"			,			do_sample=UpperCAmelCase__   )
						self.assertEqual(UpperCAmelCase__			,			[{"generated_text": ""}]   )

						__SCREAMING_SNAKE_CASE						   =				3
						__SCREAMING_SNAKE_CASE						   =				generator(
						    "Something there"			,			num_return_sequences=UpperCAmelCase__			,			num_beams=UpperCAmelCase__			,			)
						__SCREAMING_SNAKE_CASE						   =				[
						    {"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(UpperCAmelCase__			,			UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				generator("This is a test"			,			do_sample=UpperCAmelCase__			,			num_return_sequences=2			,			return_tensors=UpperCAmelCase__   )
						self.assertEqual(
						    UpperCAmelCase__			,			[
						        {"generated_token_ids": ANY(torch.Tensor   )},
						        {"generated_token_ids": ANY(torch.Tensor   )},
						    ]			,			)
						__SCREAMING_SNAKE_CASE						   =				generator.model.config.eos_token_id
						__SCREAMING_SNAKE_CASE						   =				"<pad>"
						__SCREAMING_SNAKE_CASE						   =				generator(
						    ["This is a test", "This is a second test"]			,			do_sample=UpperCAmelCase__			,			num_return_sequences=2			,			batch_size=2			,			return_tensors=UpperCAmelCase__			,			)
						self.assertEqual(
						    UpperCAmelCase__			,			[
						        [
						            {"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   UpperCAmelCase_      ( self			:				str   )       ->	Optional[Any]:
						__SCREAMING_SNAKE_CASE						   =				pipeline("text2text-generation"			,			model="patrickvonplaten/t5-tiny-random"			,			framework="tf"   )
						# do_sample=False necessary for reproducibility
						__SCREAMING_SNAKE_CASE						   =				generator("Something there"			,			do_sample=UpperCAmelCase__   )
						self.assertEqual(UpperCAmelCase__			,			[{"generated_text": ""}]   )




"""simple docstring"""







def    UpperCAmelCase__       (lowerCAmelCase_ = 100_0000   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				set(range(3  ,						lowerCAmelCase_  ,						2   )   )
					primes.add(2   )
					for p in range(3  ,						lowerCAmelCase_  ,						2   ):
										if p not in primes:
															continue
										primes.difference_update(set(range(p * p  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )   )   )

					__SCREAMING_SNAKE_CASE						   =				[float(lowerCAmelCase_   ) for n in range(limit + 1   )]

					for p in primes:
										for n in range(lowerCAmelCase_  ,						limit + 1  ,						lowerCAmelCase_   ):
															phi[n] *= 1 - 1 / p

					return int(sum(phi[2:]   )   )


if __name__ == "__main__":
		print(F"{solution() = }")
