



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					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    int         =			AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _lowerCamelCase						:    int         =			AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__lowerCAmelCase		)
         _lowerCamelCase						:    str         =			-1

         _lowerCamelCase						:    Tuple         =			ids_tensor((1, 5)						, vocab_size=model.config.vocab_size		).to(__lowerCAmelCase		)
         _lowerCamelCase						:    List[str]         =			model.generate(__lowerCAmelCase						, max_new_tokens=1_0						, do_sample=__lowerCAmelCase		)
         _lowerCamelCase						:    int         =			tokenizer.decode(greedy_ids[0]		)

         with CaptureStdout() as cs:
                _lowerCamelCase						:    str         =			TextStreamer(__lowerCAmelCase		)
                model.generate(__lowerCAmelCase						, max_new_tokens=1_0						, do_sample=__lowerCAmelCase						, streamer=__lowerCAmelCase		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _lowerCamelCase						:    Union[str, Any]         =			cs.out[:-1]

         self.assertEqual(__lowerCAmelCase						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






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

         _lowerCamelCase						:    str         =			ids_tensor((1, 5)						, vocab_size=model.config.vocab_size		).to(__lowerCAmelCase		)
         _lowerCamelCase						:    Tuple         =			model.generate(__lowerCAmelCase						, max_new_tokens=1_0						, do_sample=__lowerCAmelCase		)
         _lowerCamelCase						:    Optional[int]         =			tokenizer.decode(greedy_ids[0]		)

         _lowerCamelCase						:    List[str]         =			TextIteratorStreamer(__lowerCAmelCase		)
         _lowerCamelCase						:    int         =			{'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _lowerCamelCase						:    List[Any]         =			Thread(target=model.generate						, kwargs=__lowerCAmelCase		)
         thread.start()
         _lowerCamelCase						:    int         =			''''''
         for new_text in streamer:
                streamer_text += new_text

         self.assertEqual(__lowerCAmelCase						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _lowerCamelCase						:    Tuple         =			AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__lowerCAmelCase		)
         _lowerCamelCase						:    Tuple         =			-1

         _lowerCamelCase						:    str         =			ids_tensor((1, 5)						, vocab_size=model.config.vocab_size		).to(__lowerCAmelCase		)
         _lowerCamelCase						:    List[Any]         =			model.generate(__lowerCAmelCase						, max_new_tokens=1_0						, do_sample=__lowerCAmelCase		)
         _lowerCamelCase						:    List[str]         =			greedy_ids[:, input_ids.shape[1] :]
         _lowerCamelCase						:    str         =			tokenizer.decode(new_greedy_ids[0]		)

         with CaptureStdout() as cs:
                _lowerCamelCase						:    int         =			TextStreamer(__lowerCAmelCase						, skip_prompt=__lowerCAmelCase		)
                model.generate(__lowerCAmelCase						, max_new_tokens=1_0						, do_sample=__lowerCAmelCase						, streamer=__lowerCAmelCase		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _lowerCamelCase						:    Optional[int]         =			cs.out[:-1]

         self.assertEqual(__lowerCAmelCase						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			AutoTokenizer.from_pretrained('''distilgpt2'''		)
         _lowerCamelCase						:    List[Any]         =			AutoModelForCausalLM.from_pretrained('''distilgpt2'''		).to(__lowerCAmelCase		)
         _lowerCamelCase						:    Any         =			-1

         _lowerCamelCase						:    Any         =			torch.ones((1, 5)						, device=__lowerCAmelCase		).long() * model.config.bos_token_id
         with CaptureStdout() as cs:
                _lowerCamelCase						:    Union[str, 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
         _lowerCamelCase						:    List[str]         =			cs.out[:-1]  # Remove the final "\n"
         _lowerCamelCase						:    Union[str, Any]         =			tokenizer(__lowerCAmelCase						, return_tensors='''pt'''		)
         self.assertEqual(streamer_text_tokenized.input_ids.shape						, (1, 1)		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						str		):






         """simple docstring"""






         _lowerCamelCase						:    Any         =			AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _lowerCamelCase						:    Any         =			AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__lowerCAmelCase		)
         _lowerCamelCase						:    List[str]         =			-1

         _lowerCamelCase						:    str         =			ids_tensor((1, 5)						, vocab_size=model.config.vocab_size		).to(__lowerCAmelCase		)
         _lowerCamelCase						:    Dict         =			TextIteratorStreamer(__lowerCAmelCase						, timeout=0.0_01		)
         _lowerCamelCase						:    Dict         =			{'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _lowerCamelCase						:    int         =			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		):
                _lowerCamelCase						:    str         =			''''''
                for new_text in streamer:
                       streamer_text += new_text







"""simple docstring"""






import math






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       return math.sqrt(A_						) * math.sqrt(A_						) == num






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       _lowerCamelCase						:    Dict         =			0
       _lowerCamelCase						:    Tuple         =			n
       while left <= right:
              _lowerCamelCase						:    List[str]         =			(left + right) // 2
              if mid**2 == n:
                     return True
              elif mid**2 > n:
                     _lowerCamelCase						:    int         =			mid - 1
              else:
                     _lowerCamelCase						:    Optional[Any]         =			mid + 1
       return False


if __name__ == "__main__":
   import doctest

   doctest.testmod()


