






import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow


class    _lowerCAmelCase			:



    """simple docstring"""



    def 					__magic_name__							(      self    :    Any			,		UpperCamelCase__    :    Dict			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    Union[str, Any]):






           '''simple docstring'''







           return None


class    _lowerCAmelCase			:



    """simple docstring"""



    def 					__magic_name__							(      self    :    Dict			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    Dict			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    Union[str, Any]):






           '''simple docstring'''







           return None


class    _lowerCAmelCase			(		unittest.TestCase      ):



    """simple docstring"""



    _lowercase				:   str      =  [
        # (model_name, model_kwargs)
        ('''bert-base-cased''', {}),
        ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
    ]



    @require_tf
    @slow
    def 					__magic_name__							(      self    :    Dict):






           '''simple docstring'''







           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                  self._test_export(UpperCamelCase__			,		"""tf"""			,		1_2			,		**UpperCamelCase__)



    @require_torch
    @slow
    def 					__magic_name__							(      self    :    Any):






           '''simple docstring'''







           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                  self._test_export(UpperCamelCase__			,		"""pt"""			,		1_2			,		**UpperCamelCase__)



    @require_torch
    @slow
    def 					__magic_name__							(      self    :    Any):






           '''simple docstring'''







           from transformers import BertModel

           snake_case__	  =       ["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
           with NamedTemporaryFile(mode="""w+t""") as vocab_file:
                  vocab_file.write("""\n""".join(UpperCamelCase__))
                  vocab_file.flush()
                  snake_case__	  =       BertTokenizerFast(vocab_file.name)

           with TemporaryDirectory() as bert_save_dir:
                  snake_case__	  =       BertModel(BertConfig(vocab_size=len(UpperCamelCase__)))
                  model.save_pretrained(UpperCamelCase__)
                  self._test_export(UpperCamelCase__			,		"""pt"""			,		1_2			,		UpperCamelCase__)



    @require_tf
    @slow
    def 					__magic_name__							(      self    :    int):






           '''simple docstring'''







           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                  snake_case__	  =       self._test_export(UpperCamelCase__			,		"""tf"""			,		1_2			,		**UpperCamelCase__)
                  snake_case__	  =       quantize(Path(UpperCamelCase__))

                  # Ensure the actual quantized model is not bigger than the original one
                  if quantized_path.stat().st_size >= Path(UpperCamelCase__).stat().st_size:
                         self.fail("""Quantized model is bigger than initial ONNX model""")



    @require_torch
    @slow
    def 					__magic_name__							(      self    :    Any):






           '''simple docstring'''







           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                  snake_case__	  =       self._test_export(UpperCamelCase__			,		"""pt"""			,		1_2			,		**UpperCamelCase__)
                  snake_case__	  =       quantize(UpperCamelCase__)

                  # Ensure the actual quantized model is not bigger than the original one
                  if quantized_path.stat().st_size >= Path(UpperCamelCase__).stat().st_size:
                         self.fail("""Quantized model is bigger than initial ONNX model""")



    def 					__magic_name__							(      self    :    Optional[int]			,		UpperCamelCase__    :    Dict			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Tuple			,		UpperCamelCase__    :    str=None			,		**UpperCamelCase__    :    List[str]):






           '''simple docstring'''







           try:
                  # Compute path
                  with TemporaryDirectory() as tempdir:
                         snake_case__	  =       Path(UpperCamelCase__).joinpath("""model.onnx""")

                  # Remove folder if exists
                  if path.parent.exists():
                         path.parent.rmdir()

                  # Export
                  convert(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		**UpperCamelCase__)

                  return path
           except Exception as e:
                  self.fail(UpperCamelCase__)



    @require_torch
    @require_tokenizers
    @slow
    def 					__magic_name__							(      self    :    Optional[Any]):






           '''simple docstring'''







           from transformers import BertModel

           snake_case__	  =       BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""))
           snake_case__	  =       BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""")
           self._test_infer_dynamic_axis(UpperCamelCase__			,		UpperCamelCase__			,		"""pt""")



    @require_tf
    @require_tokenizers
    @slow
    def 					__magic_name__							(      self    :    Tuple):






           '''simple docstring'''







           from transformers import TFBertModel

           snake_case__	  =       TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""))
           snake_case__	  =       BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""")
           self._test_infer_dynamic_axis(UpperCamelCase__			,		UpperCamelCase__			,		"""tf""")



    def 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Dict):






           '''simple docstring'''







           snake_case__	  =       FeatureExtractionPipeline(UpperCamelCase__			,		UpperCamelCase__)

           snake_case__	  =       ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
           snake_case__  , snake_case__  , snake_case__  , snake_case__	  =       infer_shapes(UpperCamelCase__			,		UpperCamelCase__)

           # Assert all variables are present
           self.assertEqual(len(UpperCamelCase__)			,		len(UpperCamelCase__))
           self.assertTrue(all(var_name in shapes for var_name in variable_names))
           self.assertSequenceEqual(variable_names[:3]			,		UpperCamelCase__)
           self.assertSequenceEqual(variable_names[3:]			,		UpperCamelCase__)

           # Assert inputs are {0: batch, 1: sequence}
           for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
                  self.assertDictEqual(shapes[var_name]			,		{0: """batch""", 1: """sequence"""})

           # Assert outputs are {0: batch, 1: sequence} and {0: batch}
           self.assertDictEqual(shapes["""output_0"""]			,		{0: """batch""", 1: """sequence"""})
           self.assertDictEqual(shapes["""output_1"""]			,		{0: """batch"""})



    def 					__magic_name__							(      self    :    Dict):






           '''simple docstring'''







           snake_case__	  =       ["""input_ids""", """attention_mask""", """token_type_ids"""]
           snake_case__	  =       {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
           snake_case__  , snake_case__	  =       ensure_valid_input(FuncContiguousArgs()			,		UpperCamelCase__			,		UpperCamelCase__)

           # Should have exactly the same number of args (all are valid)
           self.assertEqual(len(UpperCamelCase__)			,		3)

           # Should have exactly the same input names
           self.assertEqual(set(UpperCamelCase__)			,		set(UpperCamelCase__))

           # Parameter should be reordered according to their respective place in the function:
           # (input_ids, token_type_ids, attention_mask)
           self.assertEqual(UpperCamelCase__			,		(tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""]))

           # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
           snake_case__  , snake_case__	  =       ensure_valid_input(FuncNonContiguousArgs()			,		UpperCamelCase__			,		UpperCamelCase__)

           # Should have exactly the one arg (all before the one not provided "some_other_args")
           self.assertEqual(len(UpperCamelCase__)			,		1)
           self.assertEqual(len(UpperCamelCase__)			,		1)

           # Should have only "input_ids"
           self.assertEqual(inputs_args[0]			,		tokens["""input_ids"""])
           self.assertEqual(ordered_input_names[0]			,		"""input_ids""")



    def 					__magic_name__							(      self    :    List[Any]):






           '''simple docstring'''







           snake_case__	  =       generate_identified_filename(Path("""/home/something/my_fake_model.onnx""")			,		"""-test""")
           self.assertEqual("""/home/something/my_fake_model-test.onnx"""			,		generated.as_posix())








from __future__ import annotations

import time

a__       =     list[tuple[int, int]]

a__       =     [
    [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],
]

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


class    _lowerCAmelCase			:



				"""simple docstring"""



				def __init__(      self    :    Dict			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    Node | None):






											'''simple docstring'''







											snake_case__	  =       pos_x
											snake_case__	  =       pos_y
											snake_case__	  =       (pos_y, pos_x)
											snake_case__	  =       goal_x
											snake_case__	  =       goal_y
											snake_case__	  =       parent


class    _lowerCAmelCase			:



				"""simple docstring"""



				def __init__(      self    :    Optional[int]			,		UpperCamelCase__    :    tuple[int, int]			,		UpperCamelCase__    :    tuple[int, int]):






											'''simple docstring'''







											snake_case__	  =       Node(start[1]			,		start[0]			,		goal[1]			,		goal[0]			,		UpperCamelCase__)
											snake_case__	  =       Node(goal[1]			,		goal[0]			,		goal[1]			,		goal[0]			,		UpperCamelCase__)

											snake_case__	  =       [self.start]
											snake_case__	  =       False



				def 					__magic_name__							(      self    :    List[Any]):






											'''simple docstring'''







											while self.node_queue:
																		snake_case__	  =       self.node_queue.pop(0)

																		if current_node.pos == self.target.pos:
																									snake_case__	  =       True
																									return self.retrace_path(UpperCamelCase__)

																		snake_case__	  =       self.get_successors(UpperCamelCase__)

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

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



				def 					__magic_name__							(      self    :    Any			,		UpperCamelCase__    :    Node):






											'''simple docstring'''







											snake_case__	  =       []
											for action in delta:
																		snake_case__	  =       parent.pos_x + action[1]
																		snake_case__	  =       parent.pos_y + action[0]
																		if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(UpperCamelCase__) - 1):
																									continue

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

																		successors.append(
																		    Node(UpperCamelCase__			,		UpperCamelCase__			,		self.target.pos_y			,		self.target.pos_x			,		UpperCamelCase__))
											return successors



				def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    Node | None):






											'''simple docstring'''







											snake_case__	  =       node
											snake_case__	  =       []
											while current_node is not None:
																		path.append((current_node.pos_y, current_node.pos_x))
																		snake_case__	  =       current_node.parent
											path.reverse()
											return path


class    _lowerCAmelCase			:



				"""simple docstring"""



				def __init__(      self    :    str			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    Any):






											'''simple docstring'''







											snake_case__	  =       BreadthFirstSearch(UpperCamelCase__			,		UpperCamelCase__)
											snake_case__	  =       BreadthFirstSearch(UpperCamelCase__			,		UpperCamelCase__)
											snake_case__	  =       False



				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
																		snake_case__	  =       self.fwd_bfs.node_queue.pop(0)
																		snake_case__	  =       self.bwd_bfs.node_queue.pop(0)

																		if current_bwd_node.pos == current_fwd_node.pos:
																									snake_case__	  =       True
																									return self.retrace_bidirectional_path(
																									    UpperCamelCase__			,		UpperCamelCase__)

																		snake_case__	  =       current_bwd_node
																		snake_case__	  =       current_fwd_node

																		snake_case__	  =       {
																		    self.fwd_bfs: self.fwd_bfs.get_successors(UpperCamelCase__),
																		    self.bwd_bfs: self.bwd_bfs.get_successors(UpperCamelCase__),
																		}

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

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



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    Node			,		UpperCamelCase__    :    Node):






											'''simple docstring'''







											snake_case__	  =       self.fwd_bfs.retrace_path(UpperCamelCase__)
											snake_case__	  =       self.bwd_bfs.retrace_path(UpperCamelCase__)
											bwd_path.pop()
											bwd_path.reverse()
											snake_case__	  =       fwd_path + bwd_path
											return path


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

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

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

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

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

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