


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       __magic_name__	(     unittest.TestCase):


     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''			)
      UpperCamelCase__					:    Optional[Any]          =   AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''			).to(lowerCamelCase__			)
      UpperCamelCase__					:    int          =   -1

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

      with CaptureStdout() as cs:
       UpperCamelCase__					:    Union[str, 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
      UpperCamelCase__					:    Union[str, Any]          =   cs.out[:-1]

      self.assertEqual(lowerCamelCase__							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Optional[int]:





      '''simple docstring'''






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

      UpperCamelCase__					:    Dict          =   ids_tensor((1, 5)							,	vocab_size=model.config.vocab_size			).to(lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   model.generate(lowerCamelCase__							,	max_new_tokens=10							,	do_sample=lowerCamelCase__			)
      UpperCamelCase__					:    str          =   tokenizer.decode(greedy_ids[0]			)

      UpperCamelCase__					:    Optional[Any]          =   TextIteratorStreamer(lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
      UpperCamelCase__					:    Optional[Any]          =   Thread(target=model.generate							,	kwargs=lowerCamelCase__			)
      thread.start()
      UpperCamelCase__					:    List[Any]          =   ''''''
      for new_text in streamer:
       streamer_text += new_text

      self.assertEqual(lowerCamelCase__							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Any			)  ->      Optional[int]:





      '''simple docstring'''






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

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

      with CaptureStdout() as cs:
       UpperCamelCase__					:    Optional[Any]          =   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
      UpperCamelCase__					:    List[Any]          =   cs.out[:-1]

      self.assertEqual(lowerCamelCase__							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Any:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   AutoTokenizer.from_pretrained('''distilgpt2'''			)
      UpperCamelCase__					:    Any          =   AutoModelForCausalLM.from_pretrained('''distilgpt2'''			).to(lowerCamelCase__			)
      UpperCamelCase__					:    List[str]          =   -1

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


     def      UpperCAmelCase__	(      self							:     int			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''			)
      UpperCamelCase__					:    Optional[int]          =   AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''			).to(lowerCamelCase__			)
      UpperCamelCase__					:    Dict          =   -1

      UpperCamelCase__					:    Optional[Any]          =   ids_tensor((1, 5)							,	vocab_size=model.config.vocab_size			).to(lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   TextIteratorStreamer(lowerCamelCase__							,	timeout=0.001			)
      UpperCamelCase__					:    Tuple          =   {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
      UpperCamelCase__					:    Union[str, 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__			):
       UpperCamelCase__					:    Optional[int]          =   ''''''
       for new_text in streamer:
        streamer_text += new_text






import functools

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    str          =   len(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Optional[Any]          =   len(SCREAMING_SNAKE_CASE     )

 @functools.cache
 def min_distance(SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				int     ) -> int:
  # if first word index is overflow - delete all from the second word
  if indexa >= len_worda:
   return len_worda - indexa
  # if second word index is overflow - delete all from the first word
  if indexa >= len_worda:
   return len_worda - indexa
  UpperCamelCase__					:    Tuple          =   int(worda[indexa] != worda[indexa]     )  # current letters not identical
  return min(
      1 + min_distance(indexa + 1    , SCREAMING_SNAKE_CASE     )    , 1 + min_distance(SCREAMING_SNAKE_CASE    , indexa + 1     )    , diff + min_distance(indexa + 1    , indexa + 1     )    , )

 return min_distance(0    , 0     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


