

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 _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(a	)
				SCREAMING_SNAKE_CASE   :      str				     =       -1

				SCREAMING_SNAKE_CASE   :      int				     =       ids_tensor((1, 5)      ,      vocab_size=model.config.vocab_size	).to(a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       model.generate(a      ,      max_new_tokens=10      ,      do_sample=a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       tokenizer.decode(greedy_ids[0]	)

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

				self.assertEqual(a      ,      a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
				SCREAMING_SNAKE_CASE   :      str				     =       AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(a	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       -1

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

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

				self.assertEqual(a      ,      a	)
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       List[str]:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      int				     =       ids_tensor((1, 5)      ,      vocab_size=model.config.vocab_size	).to(a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       model.generate(a      ,      max_new_tokens=10      ,      do_sample=a	)
				SCREAMING_SNAKE_CASE   :      Any				     =       greedy_ids[:, input_ids.shape[1] :]
				SCREAMING_SNAKE_CASE   :      Any				     =       tokenizer.decode(new_greedy_ids[0]	)

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

				self.assertEqual(a      ,      a	)
			def 							__UpperCamelCase (			self							: Any	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       AutoTokenizer.from_pretrained("distilgpt2"	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       AutoModelForCausalLM.from_pretrained("distilgpt2"	).to(a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       -1

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.ones((1, 5)      ,      device=a	).long() * model.config.bos_token_id
				with CaptureStdout() as cs:
					SCREAMING_SNAKE_CASE   :      Dict				     =       TextStreamer(a      ,      skip_special_tokens=a	)
					model.generate(a      ,      max_new_tokens=1      ,      do_sample=a      ,      streamer=a	)

				# 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
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       cs.out[:-1]  # Remove the final "\n"
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       tokenizer(a      ,      return_tensors="pt"	)
				self.assertEqual(streamer_text_tokenized.input_ids.shape      ,      (1, 1)	)






			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Dict:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      Tuple				     =       ids_tensor((1, 5)      ,      vocab_size=model.config.vocab_size	).to(a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       TextIteratorStreamer(a      ,      timeout=0.001	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
				SCREAMING_SNAKE_CASE   :      Any				     =       Thread(target=model.generate      ,      kwargs=a	)
				thread.start()

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


import numpy as np






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Dict	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       (0, 0)
				SCREAMING_SNAKE_CASE   :      Any				     =       None
				SCREAMING_SNAKE_CASE   :      Any				     =       0
				SCREAMING_SNAKE_CASE   :      List[str]				     =       0
				SCREAMING_SNAKE_CASE   :      Any				     =       0
			def __eq__(			self							: List[str]      ,      a							: Any	)							->       Tuple:

				"""simple docstring"""

				return self.position == cell.position






			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       str:

				"""simple docstring"""

				print(self.position	)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: List[Any]      ,      a							: List[Any]=(5, 5)	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.zeros(a	)
				SCREAMING_SNAKE_CASE   :      str				     =       world_size[0]
				SCREAMING_SNAKE_CASE   :      int				     =       world_size[1]
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Optional[int]:

				"""simple docstring"""

				print(self.w	)






			def 							__UpperCamelCase (			self							: Dict      ,      a							: Dict	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [
				    (-1, -1),
				    (-1, 0),
				    (-1, 1),
				    (0, -1),
				    (0, 1),
				    (1, -1),
				    (1, 0),
				    (1, 1),
				]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       cell.position[0]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       cell.position[1]
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       []
				for n in neughbour_cord:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       current_x + n[0]
					SCREAMING_SNAKE_CASE   :      int				     =       current_y + n[1]
					if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
						SCREAMING_SNAKE_CASE   :      int				     =       Cell()
						SCREAMING_SNAKE_CASE   :      str				     =       (x, y)
						SCREAMING_SNAKE_CASE   :      Any				     =       cell
						neighbours.append(a	)
				return neighbours

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	_open.append(_a)

	while _open:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.argmin([n.f for n in _open])
		SCREAMING_SNAKE_CASE   :      List[str]				     =       _open[min_f]
		_closed.append(_open.pop(_a))
		if current == goal:
			break
		for n in world.get_neigbours(_a):
			for c in _closed:
				if c == n:
					continue
			SCREAMING_SNAKE_CASE   :      Tuple				     =       current.g + 1
			SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       n.position
			SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      str				     =       goal.position
			SCREAMING_SNAKE_CASE   :      Any				     =       (ya - ya) ** 2 + (xa - xa) ** 2
			SCREAMING_SNAKE_CASE   :      List[str]				     =       n.h + n.g

			for c in _open:
				if c == n and c.f < n.f:
					continue
			_open.append(_a)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	while current.parent is not None:
		path.append(current.position)
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       current.parent
	path.append(current.position)
	return path[::-1]


if __name__ == "__main__":
			a_						     =    Gridworld()
			# Start position and goal
			a_						     =    Cell()
			a_						     =    (0, 0)
			a_						     =    Cell()
			a_						     =    (4, 4)
			print(F'''path from {start.position} to {goal.position}''')
			a_						     =    astar(world, start, goal)
			#   Just for visual reasons.
			for i in s:
						a_						     =    1
			print(world.w)