
"""simple docstring"""

import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
      import torch

      from transformers import AutoModelForCausalLM

@require_torch
class __lowerCAmelCase		(				unittest.TestCase      ):





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'				)
      __UpperCamelCase      =	AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	-1

      __UpperCamelCase      =	ids_tensor((1, 5)     ,					vocab_size=model.config.vocab_size				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	model.generate(__UpperCAmelCase     ,					max_new_tokens=10     ,					do_sample=__UpperCAmelCase				)
      __UpperCamelCase      =	tokenizer.decode(greedy_ids[0]				)

      with CaptureStdout() as cs:
       __UpperCamelCase      =	TextStreamer(__UpperCAmelCase				)
       model.generate(__UpperCAmelCase     ,					max_new_tokens=10     ,					do_sample=__UpperCAmelCase     ,					streamer=__UpperCAmelCase				)
      # The greedy text should be printed to stdout, except for the final "\n" in the streamer
      __UpperCamelCase      =	cs.out[:-1]

      self.assertEqual(__UpperCAmelCase     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'				)
      __UpperCamelCase      =	AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	-1

      __UpperCamelCase      =	ids_tensor((1, 5)     ,					vocab_size=model.config.vocab_size				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	model.generate(__UpperCAmelCase     ,					max_new_tokens=10     ,					do_sample=__UpperCAmelCase				)
      __UpperCamelCase      =	tokenizer.decode(greedy_ids[0]				)

      __UpperCamelCase      =	TextIteratorStreamer(__UpperCAmelCase				)
      __UpperCamelCase      =	{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
      __UpperCamelCase      =	Thread(target=model.generate     ,					kwargs=__UpperCAmelCase				)
      thread.start()
      __UpperCamelCase      =	''
      for new_text in streamer:
       streamer_text += new_text

      self.assertEqual(__UpperCAmelCase     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'				)
      __UpperCamelCase      =	AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	-1

      __UpperCamelCase      =	ids_tensor((1, 5)     ,					vocab_size=model.config.vocab_size				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	model.generate(__UpperCAmelCase     ,					max_new_tokens=10     ,					do_sample=__UpperCAmelCase				)
      __UpperCamelCase      =	greedy_ids[:, input_ids.shape[1] :]
      __UpperCamelCase      =	tokenizer.decode(new_greedy_ids[0]				)

      with CaptureStdout() as cs:
       __UpperCamelCase      =	TextStreamer(__UpperCAmelCase     ,					skip_prompt=__UpperCAmelCase				)
       model.generate(__UpperCAmelCase     ,					max_new_tokens=10     ,					do_sample=__UpperCAmelCase     ,					streamer=__UpperCAmelCase				)
      # The greedy text should be printed to stdout, except for the final "\n" in the streamer
      __UpperCamelCase      =	cs.out[:-1]

      self.assertEqual(__UpperCAmelCase     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	AutoTokenizer.from_pretrained('distilgpt2'				)
      __UpperCamelCase      =	AutoModelForCausalLM.from_pretrained('distilgpt2'				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	-1

      __UpperCamelCase      =	torch.ones((1, 5)     ,					device=__UpperCAmelCase				).long() * model.config.bos_token_id
      with CaptureStdout() as cs:
       __UpperCamelCase      =	TextStreamer(__UpperCAmelCase     ,					skip_special_tokens=__UpperCAmelCase				)
       model.generate(__UpperCAmelCase     ,					max_new_tokens=1     ,					do_sample=__UpperCAmelCase     ,					streamer=__UpperCAmelCase				)

      # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
      # re-tokenized, must only contain one token
      __UpperCamelCase      =	cs.out[:-1]  # Remove the final "\n"
      __UpperCamelCase      =	tokenizer(__UpperCAmelCase     ,					return_tensors='pt'				)
      self.assertEqual(streamer_text_tokenized.input_ids.shape     ,					(1, 1)				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'				)
      __UpperCamelCase      =	AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	-1

      __UpperCamelCase      =	ids_tensor((1, 5)     ,					vocab_size=model.config.vocab_size				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	TextIteratorStreamer(__UpperCAmelCase     ,					timeout=0.0_0_1				)
      __UpperCamelCase      =	{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
      __UpperCamelCase      =	Thread(target=model.generate     ,					kwargs=__UpperCAmelCase				)
      thread.start()

      # The streamer will timeout after 0.001 seconds, so an exception will be raised
      with self.assertRaises(__UpperCAmelCase				):
       __UpperCamelCase      =	''
       for new_text in streamer:
        streamer_text += new_text





"""simple docstring"""

def 							A						(    snake_case				:int		)				->     bool:
 return sum(i for i in range(1    , number // 2 + 1		) if number % i == 0		) == number


if __name__ == "__main__":
      print("Program to check whether a number is a Perfect number or not...")
      UpperCamelCase     :    Union[str, Any]       =			int(input("Enter number: ").strip())
      print(f'''{number} is {"" if perfect(number) else "not "}a Perfect Number.''')



