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 							lowercase						(						unittest.TestCase ):


   def __UpperCAmelCase       (   self				:	str)      ->					Any:
         lowercase_     =      AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
         lowercase_     =      AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(__snake_case)
         lowercase_     =      -1

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

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

         self.assertEqual(__snake_case   ,		__snake_case)


   def __UpperCAmelCase       (   self				:	Dict)      ->					Dict:
         lowercase_     =      AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
         lowercase_     =      AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(__snake_case)
         lowercase_     =      -1

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

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

         self.assertEqual(__snake_case   ,		__snake_case)


   def __UpperCAmelCase       (   self				:	str)      ->					Optional[Any]:
         lowercase_     =      AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
         lowercase_     =      AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(__snake_case)
         lowercase_     =      -1

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

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

         self.assertEqual(__snake_case   ,		__snake_case)


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					Dict:
         # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
         # with actual models -- the dummy models' tokenizers are not aligned with their models, and
         # `skip_special_tokens=True` has no effect on them
         lowercase_     =      AutoTokenizer.from_pretrained("distilgpt2")
         lowercase_     =      AutoModelForCausalLM.from_pretrained("distilgpt2").to(__snake_case)
         lowercase_     =      -1

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

         # 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_     =      cs.out[:-1]  # Remove the final "\n"
         lowercase_     =      tokenizer(__snake_case   ,		return_tensors="pt")
         self.assertEqual(streamer_text_tokenized.input_ids.shape   ,		(1, 1))


   def __UpperCAmelCase       (   self				:	List[Any])      ->					Any:
         lowercase_     =      AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
         lowercase_     =      AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(__snake_case)
         lowercase_     =      -1

         lowercase_     =      ids_tensor((1, 5)   ,		vocab_size=model.config.vocab_size).to(__snake_case)
         lowercase_     =      TextIteratorStreamer(__snake_case   ,		timeout=0.001)
         lowercase_     =      {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
         lowercase_     =      Thread(target=model.generate   ,		kwargs=__snake_case)
         thread.start()

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







'''simple docstring'''

from __future__ import annotations


def __a (	__lowerCamelCase			:						list[list[int]]				)	->							bool:
      '''simple docstring'''


      lowercase_     =      len(__lowerCamelCase				)
      # We need to create solution object to save path.
      lowercase_     =      [[0 for _ in range(__lowerCamelCase				)] for _ in range(__lowerCamelCase				)]
      lowercase_     =      run_maze(__lowerCamelCase ,		0 ,		0 ,		__lowerCamelCase				)
      if solved:
            print("\n".join(str(__lowerCamelCase				) for row in solutions				)				)
      else:
            print("No solution exists!"				)
      return solved


def __a (	__lowerCamelCase			:						list[list[int]] ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						list[list[int]]				)	->							bool:
      '''simple docstring'''


      lowercase_     =      len(__lowerCamelCase				)
      # Final check point.
      if i == j == (size - 1):
            lowercase_     =      1
            return True

      lowercase_     =      (not i < 0) and (not j < 0)  # Check lower bounds
      lowercase_     =      (i < size) and (j < size)  # Check upper bounds

      if lower_flag and upper_flag:
            # check for already visited and block points.
            lowercase_     =      (not solutions[i][j]) and (not maze[i][j])
            if block_flag:
                  # check visited
                  lowercase_     =      1

                  # check for directions
                  if (
                      run_maze(__lowerCamelCase ,		i + 1 ,		__lowerCamelCase ,		__lowerCamelCase				)
                      or run_maze(__lowerCamelCase ,		__lowerCamelCase ,		j + 1 ,		__lowerCamelCase				)
                      or run_maze(__lowerCamelCase ,		i - 1 ,		__lowerCamelCase ,		__lowerCamelCase				)
                      or run_maze(__lowerCamelCase ,		__lowerCamelCase ,		j - 1 ,		__lowerCamelCase				)
                  ):
                        return True

                  lowercase_     =      0
                  return False
      return False


if __name__ == "__main__":
       import doctest

       doctest.testmod()






