
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 __snake_case		(      unittest.TestCase     ):
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      )
           lowercase							:     Optional[Any]       	=      AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      ).to(lowerCAmelCase__      )
           lowercase							:     int       	=      -1

           lowercase							:     Optional[Any]       	=      ids_tensor((1, 5)		,vocab_size=model.config.vocab_size      ).to(lowerCAmelCase__      )
           lowercase							:     List[str]       	=      model.generate(lowerCAmelCase__		,max_new_tokens=10		,do_sample=lowerCAmelCase__      )
           lowercase							:     int       	=      tokenizer.decode(greedy_ids[0]      )

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

           self.assertEqual(lowerCAmelCase__		,lowerCAmelCase__      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      )
           lowercase							:     List[str]       	=      AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      ).to(lowerCAmelCase__      )
           lowercase							:     int       	=      -1

           lowercase							:     Dict       	=      ids_tensor((1, 5)		,vocab_size=model.config.vocab_size      ).to(lowerCAmelCase__      )
           lowercase							:     Optional[Any]       	=      model.generate(lowerCAmelCase__		,max_new_tokens=10		,do_sample=lowerCAmelCase__      )
           lowercase							:     Any       	=      tokenizer.decode(greedy_ids[0]      )

           lowercase							:     int       	=      TextIteratorStreamer(lowerCAmelCase__      )
           lowercase							:     str       	=      {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
           lowercase							:     Optional[int]       	=      Thread(target=model.generate		,kwargs=lowerCAmelCase__      )
           thread.start()
           lowercase							:     int       	=      ""
           for new_text in streamer:
                  streamer_text += new_text

           self.assertEqual(lowerCAmelCase__		,lowerCAmelCase__      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      )
           lowercase							:     Dict       	=      AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      ).to(lowerCAmelCase__      )
           lowercase							:     Optional[int]       	=      -1

           lowercase							:     Dict       	=      ids_tensor((1, 5)		,vocab_size=model.config.vocab_size      ).to(lowerCAmelCase__      )
           lowercase							:     Optional[Any]       	=      model.generate(lowerCAmelCase__		,max_new_tokens=10		,do_sample=lowerCAmelCase__      )
           lowercase							:     Dict       	=      greedy_ids[:, input_ids.shape[1] :]
           lowercase							:     str       	=      tokenizer.decode(new_greedy_ids[0]      )

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

           self.assertEqual(lowerCAmelCase__		,lowerCAmelCase__      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      AutoTokenizer.from_pretrained("""distilgpt2"""      )
           lowercase							:     str       	=      AutoModelForCausalLM.from_pretrained("""distilgpt2"""      ).to(lowerCAmelCase__      )
           lowercase							:     List[str]       	=      -1

           lowercase							:     Tuple       	=      torch.ones((1, 5)		,device=lowerCAmelCase__      ).long() * model.config.bos_token_id
           with CaptureStdout() as cs:
                  lowercase							:     Optional[Any]       	=      TextStreamer(lowerCAmelCase__		,skip_special_tokens=lowerCAmelCase__      )
                  model.generate(lowerCAmelCase__		,max_new_tokens=1		,do_sample=lowerCAmelCase__		,streamer=lowerCAmelCase__      )

           # 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
           lowercase							:     List[str]       	=      cs.out[:-1]  # Remove the final "\n"
           lowercase							:     Any       	=      tokenizer(lowerCAmelCase__		,return_tensors="""pt"""      )
           self.assertEqual(streamer_text_tokenized.input_ids.shape		,(1, 1)      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      )
           lowercase							:     Union[str, Any]       	=      AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      ).to(lowerCAmelCase__      )
           lowercase							:     Union[str, Any]       	=      -1

           lowercase							:     List[Any]       	=      ids_tensor((1, 5)		,vocab_size=model.config.vocab_size      ).to(lowerCAmelCase__      )
           lowercase							:     int       	=      TextIteratorStreamer(lowerCAmelCase__		,timeout=0.001      )
           lowercase							:     Optional[int]       	=      {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
           lowercase							:     List[Any]       	=      Thread(target=model.generate		,kwargs=lowerCAmelCase__      )
           thread.start()

           # The streamer will timeout after 0.001 seconds, so an exception will be raised
           with self.assertRaises(lowerCAmelCase__      ):
                  lowercase							:     List[Any]       	=      ""
                  for new_text in streamer:
                         streamer_text += new_text



import math
import sys







def 			A_      (    _UpperCAmelCase    ):
						if number != int(_UpperCAmelCase    ):
												raise ValueError("the value of input must be a natural number"    )
						if number < 0:
												raise ValueError("the value of input must not be a negative number"    )
						if number == 0:
												return 1
						SCREAMING_SNAKE_CASE_: List[str]  		=       [-1] * (number + 1)
						SCREAMING_SNAKE_CASE_: str  		=       0
						for i in range(1				,	number + 1    ):
												SCREAMING_SNAKE_CASE_: str  		=       sys.maxsize
												SCREAMING_SNAKE_CASE_: List[Any]  		=       int(math.sqrt(_UpperCAmelCase    )    )
												for j in range(1				,	root + 1    ):
																		SCREAMING_SNAKE_CASE_: List[str]  		=       1 + answers[i - (j**2)]
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       min(_UpperCAmelCase				,	_UpperCAmelCase    )
												SCREAMING_SNAKE_CASE_: Dict  		=       answer
						return answers[number]


if __name__ == "__main__":
						import doctest

						doctest.testmod()






