






"""simple docstring"""
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


lowerCamelCase_       			=   [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        '''text-classification''',
        '''language-modeling''',
        '''summarization''',
        '''token-classification''',
        '''question-answering''',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
			import run_clm_flax
			import run_flax_glue
			import run_flax_ner
			import run_mlm_flax
			import run_qa
			import run_summarization_flax
			import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase_       			=   logging.getLogger()

def        snake_case   (     ):
			UpperCAmelCase_  :				Tuple      =      argparse.ArgumentParser()
			parser.add_argument("-f"		)
			UpperCAmelCase_  :				str      =      parser.parse_args()
			return args.f

def        snake_case   (     A__			,A__="eval"		):
			UpperCAmelCase_  :				List[str]      =      os.path.join(A__			,F"""{split}_results.json"""		)
			if os.path.exists(A__		):
						with open(A__			,"r"		) as f:
									return json.load(A__		)
			raise ValueError(F"""can't find {path}"""		)


lowerCamelCase_       			=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class     UpperCamelCase_ (__A  ):





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    List[Any]:
				UpperCAmelCase_  :				int      =      self.get_auto_remove_tmp_dir()
				UpperCAmelCase_  :				Tuple      =      f"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

				with patch.object(lowerCAmelCase_			,			"argv"			,			lowerCAmelCase_		):
							run_flax_glue.main()
							UpperCAmelCase_  :				Optional[Any]      =      get_results(lowerCAmelCase_		)
							self.assertGreaterEqual(result["eval_accuracy"]			,			0.7_5		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    List[str]:
				UpperCAmelCase_  :				Any      =      self.get_auto_remove_tmp_dir()
				UpperCAmelCase_  :				Optional[Any]      =      f"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

				with patch.object(lowerCAmelCase_			,			"argv"			,			lowerCAmelCase_		):
							run_clm_flax.main()
							UpperCAmelCase_  :				Tuple      =      get_results(lowerCAmelCase_		)
							self.assertLess(result["eval_perplexity"]			,			100		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Any:
				UpperCAmelCase_  :				Union[str, Any]      =      self.get_auto_remove_tmp_dir()
				UpperCAmelCase_  :				List[str]      =      f"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

				with patch.object(lowerCAmelCase_			,			"argv"			,			lowerCAmelCase_		):
							run_summarization_flax.main()
							UpperCAmelCase_  :				Dict      =      get_results(lowerCAmelCase_			,			split="test"		)
							self.assertGreaterEqual(result["test_rouge1"]			,			10		)
							self.assertGreaterEqual(result["test_rouge2"]			,			2		)
							self.assertGreaterEqual(result["test_rougeL"]			,			7		)
							self.assertGreaterEqual(result["test_rougeLsum"]			,			7		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Dict:
				UpperCAmelCase_  :				Optional[Any]      =      self.get_auto_remove_tmp_dir()
				UpperCAmelCase_  :				int      =      f"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

				with patch.object(lowerCAmelCase_			,			"argv"			,			lowerCAmelCase_		):
							run_mlm_flax.main()
							UpperCAmelCase_  :				Optional[int]      =      get_results(lowerCAmelCase_		)
							self.assertLess(result["eval_perplexity"]			,			42		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Optional[int]:
				UpperCAmelCase_  :				Any      =      self.get_auto_remove_tmp_dir()
				UpperCAmelCase_  :				Union[str, Any]      =      f"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

				with patch.object(lowerCAmelCase_			,			"argv"			,			lowerCAmelCase_		):
							run_ta_mlm_flax.main()
							UpperCAmelCase_  :				str      =      get_results(lowerCAmelCase_		)
							self.assertGreaterEqual(result["eval_accuracy"]			,			0.4_2		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Any:
				# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
				UpperCAmelCase_  :				Union[str, Any]      =      7 if get_gpu_count() > 1 else 2

				UpperCAmelCase_  :				Optional[int]      =      self.get_auto_remove_tmp_dir()
				UpperCAmelCase_  :				List[Any]      =      f"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

				with patch.object(lowerCAmelCase_			,			"argv"			,			lowerCAmelCase_		):
							run_flax_ner.main()
							UpperCAmelCase_  :				Optional[Any]      =      get_results(lowerCAmelCase_		)
							self.assertGreaterEqual(result["eval_accuracy"]			,			0.7_5		)
							self.assertGreaterEqual(result["eval_f1"]			,			0.3		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Optional[Any]:
				UpperCAmelCase_  :				Optional[Any]      =      self.get_auto_remove_tmp_dir()
				UpperCAmelCase_  :				Optional[Any]      =      f"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

				with patch.object(lowerCAmelCase_			,			"argv"			,			lowerCAmelCase_		):
							run_qa.main()
							UpperCAmelCase_  :				Tuple      =      get_results(lowerCAmelCase_		)
							self.assertGreaterEqual(result["eval_f1"]			,			30		)
							self.assertGreaterEqual(result["eval_exact"]			,			30		)











"""simple docstring"""
from __future__ import annotations

import time

lowerCamelCase_       			=   list[tuple[int, int]]

lowerCamelCase_       			=   [
    [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],
]

lowerCamelCase_       			=   [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right







class     UpperCamelCase_ :





	def __init__(   self      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Node | None		)		->    List[str]:
				UpperCAmelCase_  :				List[Any]      =      pos_x
				UpperCAmelCase_  :				Dict      =      pos_y
				UpperCAmelCase_  :				List[Any]      =      (pos_y, pos_x)
				UpperCAmelCase_  :				int      =      goal_x
				UpperCAmelCase_  :				int      =      goal_y
				UpperCAmelCase_  :				Union[str, Any]      =      parent







class     UpperCamelCase_ :





	def __init__(   self      :				Dict			,			lowerCAmelCase_      :				tuple[int, int]			,			lowerCAmelCase_      :				tuple[int, int]		)		->    int:
				UpperCAmelCase_  :				Dict      =      Node(start[1]			,			start[0]			,			goal[1]			,			goal[0]			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				List[str]      =      Node(goal[1]			,			goal[0]			,			goal[1]			,			goal[0]			,			lowerCAmelCase_		)

				UpperCAmelCase_  :				List[str]      =      [self.start]
				UpperCAmelCase_  :				List[Any]      =      False





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Path | None:
				while self.node_queue:
							UpperCAmelCase_  :				Optional[int]      =      self.node_queue.pop(0		)

							if current_node.pos == self.target.pos:
										UpperCAmelCase_  :				int      =      True
										return self.retrace_path(lowerCAmelCase_		)

							UpperCAmelCase_  :				Union[str, Any]      =      self.get_successors(lowerCAmelCase_		)

							for node in successors:
										self.node_queue.append(lowerCAmelCase_		)

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				Node		)		->    list[Node]:
				UpperCAmelCase_  :				Union[str, Any]      =      []
				for action in delta:
							UpperCAmelCase_  :				Optional[Any]      =      parent.pos_x + action[1]
							UpperCAmelCase_  :				Tuple      =      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			,			lowerCAmelCase_		)		)
				return successors





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				Node | None		)		->    Path:
				UpperCAmelCase_  :				Any      =      node
				UpperCAmelCase_  :				Any      =      []
				while current_node is not None:
							path.append((current_node.pos_y, current_node.pos_x)		)
							UpperCAmelCase_  :				int      =      current_node.parent
				path.reverse()
				return path







class     UpperCamelCase_ :





	def __init__(   self      :				str			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Tuple		)		->    Tuple:
				UpperCAmelCase_  :				Union[str, Any]      =      BreadthFirstSearch(lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      BreadthFirstSearch(lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      False





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Path | None:
				while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
							UpperCAmelCase_  :				List[Any]      =      self.fwd_bfs.node_queue.pop(0		)
							UpperCAmelCase_  :				Tuple      =      self.bwd_bfs.node_queue.pop(0		)

							if current_bwd_node.pos == current_fwd_node.pos:
										UpperCAmelCase_  :				Optional[int]      =      True
										return self.retrace_bidirectional_path(
										    lowerCAmelCase_			,			lowerCAmelCase_		)

							UpperCAmelCase_  :				Any      =      current_bwd_node
							UpperCAmelCase_  :				Optional[int]      =      current_fwd_node

							UpperCAmelCase_  :				Any      =      {
							    self.fwd_bfs: self.fwd_bfs.get_successors(lowerCAmelCase_		),
							    self.bwd_bfs: self.bwd_bfs.get_successors(lowerCAmelCase_		),
							}

							for bfs in [self.fwd_bfs, self.bwd_bfs]:
										for node in successors[bfs]:
													bfs.node_queue.append(lowerCAmelCase_		)

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				Node			,			lowerCAmelCase_      :				Node		)		->    Path:
				UpperCAmelCase_  :				Tuple      =      self.fwd_bfs.retrace_path(lowerCAmelCase_		)
				UpperCAmelCase_  :				str      =      self.bwd_bfs.retrace_path(lowerCAmelCase_		)
				bwd_path.pop()
				bwd_path.reverse()
				UpperCAmelCase_  :				int      =      fwd_path + bwd_path
				return path


if __name__ == "__main__":
			# all coordinates are given in format [y,x]
			import doctest

			doctest.testmod()
			lowerCamelCase_       			=   (0, 0)
			lowerCamelCase_       			=   (len(grid) - 1, len(grid[0]) - 1)
			for elem in grid:
						print(elem)

			lowerCamelCase_       			=   time.time()
			lowerCamelCase_       			=   BreadthFirstSearch(init, goal)
			lowerCamelCase_       			=   bfs.search()
			lowerCamelCase_       			=   time.time() - start_bfs_time

			print('''Unidirectional BFS computation time : ''', bfs_time)

			lowerCamelCase_       			=   time.time()
			lowerCamelCase_       			=   BidirectionalBreadthFirstSearch(init, goal)
			lowerCamelCase_       			=   bd_bfs.search()
			lowerCamelCase_       			=   time.time() - start_bd_bfs_time

			print('''Bidirectional BFS computation time : ''', bd_bfs_time)



