





from __future__ import annotations

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    list[tuple[int, int]]

SCREAMING_SNAKE_CASE__							:      str         =    [
    [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__							:      Dict         =    ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right







class 	__lowerCAmelCase      :





   def __init__(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      )					->	Any:
      """simple docstring"""





      a__						:    Optional[int]   		=	pos_x
      a__						:    List[str]   		=	pos_y
      a__						:    List[Any]   		=	(pos_y, pos_x)
      a__						:    str   		=	goal_x
      a__						:    int   		=	goal_y
      a__						:    Optional[int]   		=	g_cost
      a__						:    Any   		=	parent
      a__						:    Any   		=	self.calculate_heuristic()





   def 						_snake_case			(			self   )					->	float:
      """simple docstring"""





      a__						:    Dict   		=	abs(self.pos_x - self.goal_x   )
      a__						:    List[Any]   		=	abs(self.pos_y - self.goal_y   )
      return dx + dy





   def __lt__(			self       ,      snake_case   )					->	bool:
      """simple docstring"""





      return self.f_cost < other.f_cost







class 	__lowerCAmelCase      :





   def __init__(			self       ,      snake_case       ,      snake_case   )					->	List[Any]:
      """simple docstring"""





      a__						:    Tuple   		=	Node(start[1]       ,      start[0]       ,      goal[1]       ,      goal[0]       ,      0       ,      snake_case   )
      a__						:    Optional[int]   		=	Node(goal[1]       ,      goal[0]       ,      goal[1]       ,      goal[0]       ,      99_999       ,      snake_case   )

      a__						:    Dict   		=	[self.start]
      a__						:    list[Node]   		=	[]

      a__						:    int   		=	False





   def 						_snake_case			(			self   )					->	Path | None:
      """simple docstring"""





      while self.open_nodes:
         # Open Nodes are sorted using __lt__
         self.open_nodes.sort()
         a__						:    Optional[Any]   		=	self.open_nodes.pop(0   )

         if current_node.pos == self.target.pos:
            a__						:    Tuple   		=	True
            return self.retrace_path(snake_case   )

         self.closed_nodes.append(snake_case   )
         a__						:    Any   		=	self.get_successors(snake_case   )

         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(snake_case   )
            else:
               # retrieve the best current path
               a__						:    Tuple   		=	self.open_nodes.pop(self.open_nodes.index(snake_case   )   )

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

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





   def 						_snake_case			(			self       ,      snake_case   )					->	list[Node]:
      """simple docstring"""





      a__						:    Dict   		=	[]
      for action in delta:
         a__						:    Optional[int]   		=	parent.pos_x + action[1]
         a__						:    Any   		=	parent.pos_y + action[0]

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

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

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





   def 						_snake_case			(			self       ,      snake_case   )					->	Path:
      """simple docstring"""





      a__						:    Any   		=	node
      a__						:    Union[str, Any]   		=	[]
      while current_node is not None:
         path.append((current_node.pos_y, current_node.pos_x)   )
         a__						:    Optional[Any]   		=	current_node.parent
      path.reverse()
      return path


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Tuple         =    (0, 0)
  SCREAMING_SNAKE_CASE__							:      Optional[int]         =    (len(grid) - 1, len(grid[0]) - 1)
  for elem in grid:
    print(elem)

  print("""------""")

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

    for elem in grid:
      print(elem)








# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


SCREAMING_SNAKE_CASE__							:      List[str]         =    Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(4_2)

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    {"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

SCREAMING_SNAKE_CASE__							:      Any         =    """zero2"""
SCREAMING_SNAKE_CASE__							:      Dict         =    """zero3"""
SCREAMING_SNAKE_CASE__							:      str         =    [ZEROa, ZEROa]






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   # customize the test name generator function as we want both params to appear in the sub-test
   # name, as by default it shows only the first param
   a__						:    Optional[Any]   		=	parameterized.to_safe_name("_".join(str(lowerCamelCase ) for x in param.args ) )
   return F"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 	__lowerCAmelCase      (       _UpperCamelCase	):





   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	Dict:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   @require_torch_multi_gpu
   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	str:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   @require_torch_multi_gpu
   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   def 						_snake_case			(			self       ,      snake_case   )					->	str:
      """simple docstring"""





      pass





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = 10       ,      snake_case = True       ,      snake_case = True       ,      snake_case = True       ,      )					->	str:
      """simple docstring"""





      a__						:    Tuple   		=	models[model]

      a__						:    int   		=	self.run_trainer(
          stage=snake_case       ,      model_name=snake_case       ,      eval_steps=snake_case       ,      num_train_epochs=1       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )

      self.do_checks(snake_case   )

      return output_dir





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = 10       ,      snake_case = 1       ,      snake_case = True       ,      snake_case = True       ,      )					->	Optional[Any]:
      """simple docstring"""





      a__						:    str   		=	self.get_auto_remove_tmp_dir("./xxx"       ,      after=snake_case   )
      a__						:    List[Any]   		=	F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(snake_case   )}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

      if fpaa:
         args.extend(["--fp16"]   )

      # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
      # hence the separate config files
      a__						:    str   		=	F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
      a__						:    Dict   		=	[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
      a__						:    Optional[int]   		=	self.get_launcher(snake_case   )

      a__						:    Optional[int]   		=	launcher + script + args + ds_args
      # keep for quick debug
      # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
      execute_subprocess_async(snake_case       ,      env=self.get_env()   )

      return output_dir





   def 						_snake_case			(			self       ,      snake_case=False   )					->	List[Any]:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	min(2       ,      get_gpu_count()   ) if distributed else 1
      return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()

