






'''simple docstring'''
import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY






@is_pipeline_test
@require_torch_or_tf
class   __SCREAMING_SNAKE_CASE   (      unittest.TestCase					):
 snake_case_					=				MODEL_FOR_CAUSAL_LM_MAPPING
 snake_case_					=				TF_MODEL_FOR_CAUSAL_LM_MAPPING




 @require_torch
 def   __magic_name__						(   self						:				Optional[Any]   )						->    Tuple:
       SCREAMING_SNAKE_CASE__  :					Tuple						=pipeline(task='''text-generation'''       ,   model='''sshleifer/tiny-ctrl'''       ,   framework='''pt'''   )
       # Using `do_sample=False` to force deterministic output
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=text_generator('''This is a test'''       ,   do_sample=__lowercase   )
       self.assertEqual(
           __lowercase       ,   [
               {
                   '''generated_text''': (
                       '''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
                       ''' oscope. FiliFili@@'''
                   )
               }
           ]       ,   )

       SCREAMING_SNAKE_CASE__  :					str						=text_generator(['''This is a test''', '''This is a second test''']   )
       self.assertEqual(
           __lowercase       ,   [
               [
                   {
                       '''generated_text''': (
                           '''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
                           ''' oscope. FiliFili@@'''
                       )
                   }
               ],
               [
                   {
                       '''generated_text''': (
                           '''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
                           ''' oscope. oscope. FiliFili@@'''
                       )
                   }
               ],
           ]       ,   )

       SCREAMING_SNAKE_CASE__  :					Tuple						=text_generator('''This is a test'''       ,   do_sample=__lowercase       ,   num_return_sequences=2       ,   return_tensors=__lowercase   )
       self.assertEqual(
           __lowercase       ,   [
               {'''generated_token_ids''': ANY(__lowercase   )},
               {'''generated_token_ids''': ANY(__lowercase   )},
           ]       ,   )
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=text_generator.model.config.eos_token_id
       SCREAMING_SNAKE_CASE__  :					List[Any]						='<pad>'
       SCREAMING_SNAKE_CASE__  :					Optional[int]						=text_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(__lowercase   )},
                   {'''generated_token_ids''': ANY(__lowercase   )},
               ],
               [
                   {'''generated_token_ids''': ANY(__lowercase   )},
                   {'''generated_token_ids''': ANY(__lowercase   )},
               ],
           ]       ,   )




 @require_tf
 def   __magic_name__						(   self						:				Tuple   )						->    Dict:
       SCREAMING_SNAKE_CASE__  :					Any						=pipeline(task='''text-generation'''       ,   model='''sshleifer/tiny-ctrl'''       ,   framework='''tf'''   )

       # Using `do_sample=False` to force deterministic output
       SCREAMING_SNAKE_CASE__  :					Dict						=text_generator('''This is a test'''       ,   do_sample=__lowercase   )
       self.assertEqual(
           __lowercase       ,   [
               {
                   '''generated_text''': (
                       '''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
                       ''' please,'''
                   )
               }
           ]       ,   )

       SCREAMING_SNAKE_CASE__  :					int						=text_generator(['''This is a test''', '''This is a second test''']       ,   do_sample=__lowercase   )
       self.assertEqual(
           __lowercase       ,   [
               [
                   {
                       '''generated_text''': (
                           '''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
                           ''' please,'''
                       )
                   }
               ],
               [
                   {
                       '''generated_text''': (
                           '''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
                           ''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
                       )
                   }
               ],
           ]       ,   )




 def   __magic_name__						(   self						:				Dict       ,   __lowercase						:				Dict       ,   __lowercase						:				int       ,   __lowercase						:				Union[str, Any]   )						->    int:
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=TextGenerationPipeline(model=__lowercase       ,   tokenizer=__lowercase   )
       return text_generator, ["This is a test", "Another test"]




 def   __magic_name__						(   self						:				List[str]   )						->    Tuple:
       SCREAMING_SNAKE_CASE__  :					List[str]						='Hello I believe in'
       SCREAMING_SNAKE_CASE__  :					Any						=pipeline('''text-generation'''       ,   model='''hf-internal-testing/tiny-random-gpt2'''   )
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=text_generator(__lowercase   )
       self.assertEqual(
           __lowercase       ,   [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}]       ,   )

       SCREAMING_SNAKE_CASE__  :					List[str]						=text_generator(__lowercase       ,   stop_sequence=''' fe'''   )
       self.assertEqual(__lowercase       ,   [{'''generated_text''': '''Hello I believe in fe'''}]   )




 def   __magic_name__						(   self						:				Tuple       ,   __lowercase						:				Any       ,   __lowercase						:				Any   )						->    Optional[int]:
       SCREAMING_SNAKE_CASE__  :					List[str]						=text_generator.model
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=text_generator.tokenizer

       SCREAMING_SNAKE_CASE__  :					Tuple						=text_generator('''This is a test'''   )
       self.assertEqual(__lowercase       ,   [{'''generated_text''': ANY(__lowercase   )}]   )
       self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test'''   )   )

       SCREAMING_SNAKE_CASE__  :					Dict						=text_generator('''This is a test'''       ,   return_full_text=__lowercase   )
       self.assertEqual(__lowercase       ,   [{'''generated_text''': ANY(__lowercase   )}]   )
       self.assertNotIn('''This is a test'''       ,   outputs[0]['''generated_text''']   )

       SCREAMING_SNAKE_CASE__  :					Tuple						=pipeline(task='''text-generation'''       ,   model=__lowercase       ,   tokenizer=__lowercase       ,   return_full_text=__lowercase   )
       SCREAMING_SNAKE_CASE__  :					str						=text_generator('''This is a test'''   )
       self.assertEqual(__lowercase       ,   [{'''generated_text''': ANY(__lowercase   )}]   )
       self.assertNotIn('''This is a test'''       ,   outputs[0]['''generated_text''']   )

       SCREAMING_SNAKE_CASE__  :					List[Any]						=text_generator('''This is a test'''       ,   return_full_text=__lowercase   )
       self.assertEqual(__lowercase       ,   [{'''generated_text''': ANY(__lowercase   )}]   )
       self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test'''   )   )

       SCREAMING_SNAKE_CASE__  :					Optional[int]						=text_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   )}],
           ]       ,   )

       if text_generator.tokenizer.pad_token is not None:
             SCREAMING_SNAKE_CASE__  :					Optional[int]						=text_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   ):
             SCREAMING_SNAKE_CASE__  :					Any						=text_generator('''test'''       ,   return_full_text=__lowercase       ,   return_text=__lowercase   )
       with self.assertRaises(__lowercase   ):
             SCREAMING_SNAKE_CASE__  :					Any						=text_generator('''test'''       ,   return_full_text=__lowercase       ,   return_tensors=__lowercase   )
       with self.assertRaises(__lowercase   ):
             SCREAMING_SNAKE_CASE__  :					int						=text_generator('''test'''       ,   return_text=__lowercase       ,   return_tensors=__lowercase   )

       # Empty prompt is slighly special
       # it requires BOS token to exist.
       # Special case for Pegasus which will always append EOS so will
       # work even without BOS.
       if (
           text_generator.tokenizer.bos_token_id is not None
           or "Pegasus" in tokenizer.__class__.__name__
           or "Git" in model.__class__.__name__
       ):
             SCREAMING_SNAKE_CASE__  :					Optional[int]						=text_generator(''''''   )
             self.assertEqual(__lowercase       ,   [{'''generated_text''': ANY(__lowercase   )}]   )
       else:
             with self.assertRaises((ValueError, AssertionError)   ):
                   SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=text_generator(''''''   )

       if text_generator.framework == "tf":
             # TF generation does not support max_new_tokens, and it's impossible
             # to control long generation with only max_length without
             # fancy calculation, dismissing tests for now.
             return
       # We don't care about infinite range models.
       # They already work.
       # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=['RwkvForCausalLM', 'XGLMForCausalLM', 'GPTNeoXForCausalLM']
       if (
           tokenizer.model_max_length < 1_00_00
           and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
       ):
             # Handling of large generations
             with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)   ):
                   text_generator('''This is a test''' * 5_00       ,   max_new_tokens=20   )

             SCREAMING_SNAKE_CASE__  :					int						=text_generator('''This is a test''' * 5_00       ,   handle_long_generation='''hole'''       ,   max_new_tokens=20   )
             # Hole strategy cannot work
             with self.assertRaises(__lowercase   ):
                   text_generator(
                       '''This is a test''' * 5_00       ,   handle_long_generation='''hole'''       ,   max_new_tokens=tokenizer.model_max_length + 10       ,   )




 @require_torch
 @require_accelerate
 @require_torch_gpu
 def   __magic_name__						(   self						:				int   )						->    Optional[int]:
       import torch

       # Classic `model_kwargs`
       SCREAMING_SNAKE_CASE__  :					Optional[int]						=pipeline(
           model='''hf-internal-testing/tiny-random-bloom'''       ,   model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa}       ,   )
       self.assertEqual(pipe.model.device       ,   torch.device(0   )   )
       self.assertEqual(pipe.model.lm_head.weight.dtype       ,   torch.bfloataa   )
       SCREAMING_SNAKE_CASE__  :					List[Any]						=pipe('''This is a test'''   )
       self.assertEqual(
           __lowercase       ,   [
               {
                   '''generated_text''': (
                       '''This is a test test test test test test test test test test test test test test test test'''
                       ''' test'''
                   )
               }
           ]       ,   )

       # Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
       SCREAMING_SNAKE_CASE__  :					int						=pipeline(model='''hf-internal-testing/tiny-random-bloom'''       ,   device_map='''auto'''       ,   torch_dtype=torch.bfloataa   )
       self.assertEqual(pipe.model.device       ,   torch.device(0   )   )
       self.assertEqual(pipe.model.lm_head.weight.dtype       ,   torch.bfloataa   )
       SCREAMING_SNAKE_CASE__  :					List[str]						=pipe('''This is a test'''   )
       self.assertEqual(
           __lowercase       ,   [
               {
                   '''generated_text''': (
                       '''This is a test test test test test test test test test test test test test test test test'''
                       ''' test'''
                   )
               }
           ]       ,   )

       # torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
       SCREAMING_SNAKE_CASE__  :					str						=pipeline(model='''hf-internal-testing/tiny-random-bloom'''       ,   device_map='''auto'''   )
       self.assertEqual(pipe.model.device       ,   torch.device(0   )   )
       self.assertEqual(pipe.model.lm_head.weight.dtype       ,   torch.floataa   )
       SCREAMING_SNAKE_CASE__  :					List[str]						=pipe('''This is a test'''   )
       self.assertEqual(
           __lowercase       ,   [
               {
                   '''generated_text''': (
                       '''This is a test test test test test test test test test test test test test test test test'''
                       ''' test'''
                   )
               }
           ]       ,   )




 @require_torch
 @require_torch_gpu
 def   __magic_name__						(   self						:				Dict   )						->    str:
       import torch

       SCREAMING_SNAKE_CASE__  :					Tuple						=pipeline(model='''hf-internal-testing/tiny-random-bloom'''       ,   device=0       ,   torch_dtype=torch.floataa   )
       pipe('''This is a test'''   )




 @require_torch
 @require_accelerate
 @require_torch_gpu
 def   __magic_name__						(   self						:				str   )						->    Any:
       import torch

       SCREAMING_SNAKE_CASE__  :					str						=pipeline(model='''hf-internal-testing/tiny-random-bloom'''       ,   device_map='''auto'''       ,   torch_dtype=torch.floataa   )
       pipe('''This is a test'''       ,   do_sample=__lowercase       ,   top_p=0.5   )




 def   __magic_name__						(   self						:				List[str]   )						->    Tuple:
       SCREAMING_SNAKE_CASE__  :					List[Any]						='Hello world'
       SCREAMING_SNAKE_CASE__  :					str						=pipeline('''text-generation'''       ,   model='''hf-internal-testing/tiny-random-gpt2'''   )
       if text_generator.model.framework == "tf":
             SCREAMING_SNAKE_CASE__  :					List[str]						=logging.get_logger('''transformers.generation.tf_utils'''   )
       else:
             SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=logging.get_logger('''transformers.generation.utils'''   )
       SCREAMING_SNAKE_CASE__  :					Optional[int]						='Both `max_new_tokens`'  # The beggining of the message to be checked in this test

       # Both are set by the user -> log warning
       with CaptureLogger(__lowercase   ) as cl:
             SCREAMING_SNAKE_CASE__  :					Dict						=text_generator(__lowercase       ,   max_length=10       ,   max_new_tokens=1   )
       self.assertIn(__lowercase       ,   cl.out   )

       # The user only sets one -> no warning
       with CaptureLogger(__lowercase   ) as cl:
             SCREAMING_SNAKE_CASE__  :					Any						=text_generator(__lowercase       ,   max_new_tokens=1   )
       self.assertNotIn(__lowercase       ,   cl.out   )

       with CaptureLogger(__lowercase   ) as cl:
             SCREAMING_SNAKE_CASE__  :					Any						=text_generator(__lowercase       ,   max_length=10   )
       self.assertNotIn(__lowercase       ,   cl.out   )






from __future__ import annotations

import math


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):

		if num <= 0:
				lowerCamelCase_	:     Optional[int] 						=      F"{num}: Invalid input, please enter a positive integer."
				raise ValueError(lowerCAmelCase__						)

		lowerCamelCase_	:     str 						=      [True] * (num + 1)
		lowerCamelCase_	:     List[str] 						=      []
		lowerCamelCase_	:     Optional[int] 						=      2
		lowerCamelCase_	:     List[str] 						=      int(math.sqrt(lowerCAmelCase__						)						)

		while start <= end:
				# If start is a prime
				if sieve[start] is True:
						prime.append(lowerCAmelCase__						)

						# Set multiples of start be False
						for i in range(start * start							,num + 1							,lowerCAmelCase__						):
								if sieve[i] is True:
										lowerCamelCase_	:     Tuple 						=      False

				start += 1

		for j in range(end + 1							,num + 1						):
				if sieve[j] is True:
						prime.append(lowerCAmelCase__						)

		return prime


if __name__ == "__main__":
		print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
