


'''simple docstring'''
import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY






@is_pipeline_test
@require_torch_or_tf
class 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Any   						=MODEL_FOR_CAUSAL_LM_MAPPING
						_UpperCAmelCase		:       Any   						=TF_MODEL_FOR_CAUSAL_LM_MAPPING






						@require_torch
						def 				_UpperCAmelCase       (  self							:      Any	):
													A_			    =		pipeline(task="text-generation" ,  model="sshleifer/tiny-ctrl" ,  framework="pt"	)
													# Using `do_sample=False` to force deterministic output
													A_			    =		text_generator("This is a test" ,  do_sample=lowerCAmelCase	)
													self.assertEqual(
													    lowerCAmelCase ,  [
													        {
													            "generated_text": (
													                "This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."
													                " oscope. FiliFili@@"
													            )
													        }
													    ] ,  )

													A_			    =		text_generator(["This is a test", "This is a second test"]	)
													self.assertEqual(
													    lowerCAmelCase ,  [
													        [
													            {
													                "generated_text": (
													                    "This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."
													                    " oscope. FiliFili@@"
													                )
													            }
													        ],
													        [
													            {
													                "generated_text": (
													                    "This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"
													                    " oscope. oscope. FiliFili@@"
													                )
													            }
													        ],
													    ] ,  )

													A_			    =		text_generator("This is a test" ,  do_sample=lowerCAmelCase ,  num_return_sequences=2 ,  return_tensors=lowerCAmelCase	)
													self.assertEqual(
													    lowerCAmelCase ,  [
													        {"generated_token_ids": ANY(lowerCAmelCase	)},
													        {"generated_token_ids": ANY(lowerCAmelCase	)},
													    ] ,  )
													A_			    =		text_generator.model.config.eos_token_id
													A_			    =		"<pad>"
													A_			    =		text_generator(
													    ["This is a test", "This is a second test"] ,  do_sample=lowerCAmelCase ,  num_return_sequences=2 ,  batch_size=2 ,  return_tensors=lowerCAmelCase ,  )
													self.assertEqual(
													    lowerCAmelCase ,  [
													        [
													            {"generated_token_ids": ANY(lowerCAmelCase	)},
													            {"generated_token_ids": ANY(lowerCAmelCase	)},
													        ],
													        [
													            {"generated_token_ids": ANY(lowerCAmelCase	)},
													            {"generated_token_ids": ANY(lowerCAmelCase	)},
													        ],
													    ] ,  )






						@require_tf
						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													A_			    =		pipeline(task="text-generation" ,  model="sshleifer/tiny-ctrl" ,  framework="tf"	)

													# Using `do_sample=False` to force deterministic output
													A_			    =		text_generator("This is a test" ,  do_sample=lowerCAmelCase	)
													self.assertEqual(
													    lowerCAmelCase ,  [
													        {
													            "generated_text": (
													                "This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"
													                " please,"
													            )
													        }
													    ] ,  )

													A_			    =		text_generator(["This is a test", "This is a second test"] ,  do_sample=lowerCAmelCase	)
													self.assertEqual(
													    lowerCAmelCase ,  [
													        [
													            {
													                "generated_text": (
													                    "This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"
													                    " please,"
													                )
													            }
													        ],
													        [
													            {
													                "generated_text": (
													                    "This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"
													                    " Cannes 閲閲Cannes Cannes Cannes 攵 please,"
													                )
													            }
													        ],
													    ] ,  )






						def 				_UpperCAmelCase       (  self							:      str ,  lowerCAmelCase							:      List[Any] ,  lowerCAmelCase							:      Any ,  lowerCAmelCase							:      Optional[Any]	):
													A_			    =		TextGenerationPipeline(model=lowerCAmelCase ,  tokenizer=lowerCAmelCase	)
													return text_generator, ["This is a test", "Another test"]






						def 				_UpperCAmelCase       (  self							:      Optional[int]	):
													A_			    =		"Hello I believe in"
													A_			    =		pipeline("text-generation" ,  model="hf-internal-testing/tiny-random-gpt2"	)
													A_			    =		text_generator(lowerCAmelCase	)
													self.assertEqual(
													    lowerCAmelCase ,  [{"generated_text": "Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"}] ,  )

													A_			    =		text_generator(lowerCAmelCase ,  stop_sequence=" fe"	)
													self.assertEqual(lowerCAmelCase ,  [{"generated_text": "Hello I believe in fe"}]	)






						def 				_UpperCAmelCase       (  self							:      str ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      str	):
													A_			    =		text_generator.model
													A_			    =		text_generator.tokenizer

													A_			    =		text_generator("This is a test"	)
													self.assertEqual(lowerCAmelCase ,  [{"generated_text": ANY(lowerCAmelCase	)}]	)
													self.assertTrue(outputs[0]["generated_text"].startswith("This is a test"	)	)

													A_			    =		text_generator("This is a test" ,  return_full_text=lowerCAmelCase	)
													self.assertEqual(lowerCAmelCase ,  [{"generated_text": ANY(lowerCAmelCase	)}]	)
													self.assertNotIn("This is a test" ,  outputs[0]["generated_text"]	)

													A_			    =		pipeline(task="text-generation" ,  model=lowerCAmelCase ,  tokenizer=lowerCAmelCase ,  return_full_text=lowerCAmelCase	)
													A_			    =		text_generator("This is a test"	)
													self.assertEqual(lowerCAmelCase ,  [{"generated_text": ANY(lowerCAmelCase	)}]	)
													self.assertNotIn("This is a test" ,  outputs[0]["generated_text"]	)

													A_			    =		text_generator("This is a test" ,  return_full_text=lowerCAmelCase	)
													self.assertEqual(lowerCAmelCase ,  [{"generated_text": ANY(lowerCAmelCase	)}]	)
													self.assertTrue(outputs[0]["generated_text"].startswith("This is a test"	)	)

													A_			    =		text_generator(["This is great !", "Something else"] ,  num_return_sequences=2 ,  do_sample=lowerCAmelCase	)
													self.assertEqual(
													    lowerCAmelCase ,  [
													        [{"generated_text": ANY(lowerCAmelCase	)}, {"generated_text": ANY(lowerCAmelCase	)}],
													        [{"generated_text": ANY(lowerCAmelCase	)}, {"generated_text": ANY(lowerCAmelCase	)}],
													    ] ,  )

													if text_generator.tokenizer.pad_token is not None:
																				A_			    =		text_generator(
																				    ["This is great !", "Something else"] ,  num_return_sequences=2 ,  batch_size=2 ,  do_sample=lowerCAmelCase	)
																				self.assertEqual(
																				    lowerCAmelCase ,  [
																				        [{"generated_text": ANY(lowerCAmelCase	)}, {"generated_text": ANY(lowerCAmelCase	)}],
																				        [{"generated_text": ANY(lowerCAmelCase	)}, {"generated_text": ANY(lowerCAmelCase	)}],
																				    ] ,  )

													with self.assertRaises(lowerCAmelCase	):
																				A_			    =		text_generator("test" ,  return_full_text=lowerCAmelCase ,  return_text=lowerCAmelCase	)
													with self.assertRaises(lowerCAmelCase	):
																				A_			    =		text_generator("test" ,  return_full_text=lowerCAmelCase ,  return_tensors=lowerCAmelCase	)
													with self.assertRaises(lowerCAmelCase	):
																				A_			    =		text_generator("test" ,  return_text=lowerCAmelCase ,  return_tensors=lowerCAmelCase	)

													# Empty prompt is slighly special
													# it requires BOS token to exist.
													# Special case for Pegasus which will always append EOS so will
													# work even without BOS.
													if (
													    text_generator.tokenizer.bos_token_id is not None
													    or "Pegasus" in tokenizer.__class__.__name__
													    or "Git" in model.__class__.__name__
													):
																				A_			    =		text_generator(""	)
																				self.assertEqual(lowerCAmelCase ,  [{"generated_text": ANY(lowerCAmelCase	)}]	)
													else:
																				with self.assertRaises((ValueError, AssertionError)	):
																											A_			    =		text_generator(""	)

													if text_generator.framework == "tf":
																				# TF generation does not support max_new_tokens, and it's impossible
																				# to control long generation with only max_length without
																				# fancy calculation, dismissing tests for now.
																				return
													# We don't care about infinite range models.
													# They already work.
													# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
													A_			    =		["RwkvForCausalLM", "XGLMForCausalLM", "GPTNeoXForCausalLM"]
													if (
													    tokenizer.model_max_length < 1_00_00
													    and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
													):
																				# Handling of large generations
																				with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)	):
																											text_generator("This is a test" * 5_00 ,  max_new_tokens=20	)

																				A_			    =		text_generator("This is a test" * 5_00 ,  handle_long_generation="hole" ,  max_new_tokens=20	)
																				# Hole strategy cannot work
																				with self.assertRaises(lowerCAmelCase	):
																											text_generator(
																											    "This is a test" * 5_00 ,  handle_long_generation="hole" ,  max_new_tokens=tokenizer.model_max_length + 10 ,  )






						@require_torch
						@require_accelerate
						@require_torch_gpu
						def 				_UpperCAmelCase       (  self							:      int	):
													import torch

													# Classic `model_kwargs`
													A_			    =		pipeline(
													    model="hf-internal-testing/tiny-random-bloom" ,  model_kwargs={"device_map": "auto", "torch_dtype": torch.bfloataa} ,  )
													self.assertEqual(pipe.model.device ,  torch.device(0	)	)
													self.assertEqual(pipe.model.lm_head.weight.dtype ,  torch.bfloataa	)
													A_			    =		pipe("This is a test"	)
													self.assertEqual(
													    lowerCAmelCase ,  [
													        {
													            "generated_text": (
													                "This is a test test test test test test test test test test test test test test test test"
													                " test"
													            )
													        }
													    ] ,  )

													# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
													A_			    =		pipeline(model="hf-internal-testing/tiny-random-bloom" ,  device_map="auto" ,  torch_dtype=torch.bfloataa	)
													self.assertEqual(pipe.model.device ,  torch.device(0	)	)
													self.assertEqual(pipe.model.lm_head.weight.dtype ,  torch.bfloataa	)
													A_			    =		pipe("This is a test"	)
													self.assertEqual(
													    lowerCAmelCase ,  [
													        {
													            "generated_text": (
													                "This is a test test test test test test test test test test test test test test test test"
													                " test"
													            )
													        }
													    ] ,  )

													# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
													A_			    =		pipeline(model="hf-internal-testing/tiny-random-bloom" ,  device_map="auto"	)
													self.assertEqual(pipe.model.device ,  torch.device(0	)	)
													self.assertEqual(pipe.model.lm_head.weight.dtype ,  torch.floataa	)
													A_			    =		pipe("This is a test"	)
													self.assertEqual(
													    lowerCAmelCase ,  [
													        {
													            "generated_text": (
													                "This is a test test test test test test test test test test test test test test test test"
													                " test"
													            )
													        }
													    ] ,  )






						@require_torch
						@require_torch_gpu
						def 				_UpperCAmelCase       (  self							:      Any	):
													import torch

													A_			    =		pipeline(model="hf-internal-testing/tiny-random-bloom" ,  device=0 ,  torch_dtype=torch.floataa	)
													pipe("This is a test"	)






						@require_torch
						@require_accelerate
						@require_torch_gpu
						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													import torch

													A_			    =		pipeline(model="hf-internal-testing/tiny-random-bloom" ,  device_map="auto" ,  torch_dtype=torch.floataa	)
													pipe("This is a test" ,  do_sample=lowerCAmelCase ,  top_p=0.5	)






						def 				_UpperCAmelCase       (  self							:      Any	):
													A_			    =		"Hello world"
													A_			    =		pipeline("text-generation" ,  model="hf-internal-testing/tiny-random-gpt2"	)
													if text_generator.model.framework == "tf":
																				A_			    =		logging.get_logger("transformers.generation.tf_utils"	)
													else:
																				A_			    =		logging.get_logger("transformers.generation.utils"	)
													A_			    =		"Both `max_new_tokens`"  # The beggining of the message to be checked in this test

													# Both are set by the user -> log warning
													with CaptureLogger(lowerCAmelCase	) as cl:
																				A_			    =		text_generator(lowerCAmelCase ,  max_length=10 ,  max_new_tokens=1	)
													self.assertIn(lowerCAmelCase ,  cl.out	)

													# The user only sets one -> no warning
													with CaptureLogger(lowerCAmelCase	) as cl:
																				A_			    =		text_generator(lowerCAmelCase ,  max_new_tokens=1	)
													self.assertNotIn(lowerCAmelCase ,  cl.out	)

													with CaptureLogger(lowerCAmelCase	) as cl:
																				A_			    =		text_generator(lowerCAmelCase ,  max_length=10	)
													self.assertNotIn(lowerCAmelCase ,  cl.out	)









'''simple docstring'''
from __future__ import annotations

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		list[tuple[int, int]]

__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      List[str] ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      float ,  lowerCAmelCase							:      Node | None ,  ):
													A_			    =		pos_x
													A_			    =		pos_y
													A_			    =		(pos_y, pos_x)
													A_			    =		goal_x
													A_			    =		goal_y
													A_			    =		g_cost
													A_			    =		parent
													A_			    =		self.calculate_heuristic()






						def 				_UpperCAmelCase       (  self							:      Tuple	):
													A_			    =		abs(self.pos_x - self.goal_x	)
													A_			    =		abs(self.pos_y - self.goal_y	)
													return dx + dy






						def __lt__(  self							:      Tuple ,  lowerCAmelCase							:      Any	):
													return self.f_cost < other.f_cost






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      Union[str, Any] ,  lowerCAmelCase							:      tuple[int, int] ,  lowerCAmelCase							:      tuple[int, int]	):
													A_			    =		Node(start[1] ,  start[0] ,  goal[1] ,  goal[0] ,  0 ,  lowerCAmelCase	)
													A_			    =		Node(goal[1] ,  goal[0] ,  goal[1] ,  goal[0] ,  9_99_99 ,  lowerCAmelCase	)

													A_			    =		[self.start]
													A_			    =		[]

													A_			    =		False






						def 				_UpperCAmelCase       (  self							:      str	):
													while self.open_nodes:
																				# Open Nodes are sorted using __lt__
																				self.open_nodes.sort()
																				A_			    =		self.open_nodes.pop(0	)

																				if current_node.pos == self.target.pos:
																											A_			    =		True
																											return self.retrace_path(lowerCAmelCase	)

																				self.closed_nodes.append(lowerCAmelCase	)
																				A_			    =		self.get_successors(lowerCAmelCase	)

																				for child_node in successors:
																											if child_node in self.closed_nodes:
																																		continue

																											if child_node not in self.open_nodes:
																																		self.open_nodes.append(lowerCAmelCase	)
																											else:
																																		# retrieve the best current path
																																		A_			    =		self.open_nodes.pop(self.open_nodes.index(lowerCAmelCase	)	)

																																		if child_node.g_cost < better_node.g_cost:
																																									self.open_nodes.append(lowerCAmelCase	)
																																		else:
																																									self.open_nodes.append(lowerCAmelCase	)

													if not self.reached:
																				return [self.start.pos]
													return None






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      Node	):
													A_			    =		[]
													for action in delta:
																				A_			    =		parent.pos_x + action[1]
																				A_			    =		parent.pos_y + action[0]

																				if not (0 <= pos_x <= len(grid[0]	) - 1 and 0 <= pos_y <= len(lowerCAmelCase	) - 1):
																											continue

																				if grid[pos_y][pos_x] != 0:
																											continue

																				successors.append(
																				    Node(
																				        lowerCAmelCase ,  lowerCAmelCase ,  self.target.pos_y ,  self.target.pos_x ,  parent.g_cost + 1 ,  lowerCAmelCase ,  )	)
													return successors






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      Node | None	):
													A_			    =		node
													A_			    =		[]
													while current_node is not None:
																				path.append((current_node.pos_y, current_node.pos_x)	)
																				A_			    =		current_node.parent
													path.reverse()
													return path


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		(0, 0)
							__SCREAMING_SNAKE_CASE					:				Dict									=		(len(grid) - 1, len(grid[0]) - 1)
							for elem in grid:
														print(elem)

							print('''------''')

							__SCREAMING_SNAKE_CASE					:				int									=		GreedyBestFirst(init, goal)
							__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		greedy_bf.search()
							if path:
														for pos_x, pos_y in path:
																					__SCREAMING_SNAKE_CASE					:				List[Any]									=		2

														for elem in grid:
																					print(elem)





