

import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
	import torch

	from transformers import (
	    MraForMaskedLM,
	    MraForMultipleChoice,
	    MraForQuestionAnswering,
	    MraForSequenceClassification,
	    MraForTokenClassification,
	    MraModel,
	)
	from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST


class        _SCREAMING_SNAKE_CASE	:







						def __init__(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE=2			,		_SCREAMING_SNAKE_CASE=8			,		_SCREAMING_SNAKE_CASE=True			,		_SCREAMING_SNAKE_CASE=True			,		_SCREAMING_SNAKE_CASE=True			,		_SCREAMING_SNAKE_CASE=True			,		_SCREAMING_SNAKE_CASE=99			,		_SCREAMING_SNAKE_CASE=16			,		_SCREAMING_SNAKE_CASE=5			,		_SCREAMING_SNAKE_CASE=2			,		_SCREAMING_SNAKE_CASE=36			,		_SCREAMING_SNAKE_CASE="gelu"			,		_SCREAMING_SNAKE_CASE=0.0			,		_SCREAMING_SNAKE_CASE=0.0			,		_SCREAMING_SNAKE_CASE=512			,		_SCREAMING_SNAKE_CASE=16			,		_SCREAMING_SNAKE_CASE=2			,		_SCREAMING_SNAKE_CASE=0.0_2			,		_SCREAMING_SNAKE_CASE=3			,		_SCREAMING_SNAKE_CASE=4			,		_SCREAMING_SNAKE_CASE=None			,		)->				Tuple:
													lowerCamelCase_				     =parent
													lowerCamelCase_				     =batch_size
													lowerCamelCase_				     =seq_length
													lowerCamelCase_				     =is_training
													lowerCamelCase_				     =use_input_mask
													lowerCamelCase_				     =use_token_type_ids
													lowerCamelCase_				     =use_labels
													lowerCamelCase_				     =vocab_size
													lowerCamelCase_				     =hidden_size
													lowerCamelCase_				     =num_hidden_layers
													lowerCamelCase_				     =num_attention_heads
													lowerCamelCase_				     =intermediate_size
													lowerCamelCase_				     =hidden_act
													lowerCamelCase_				     =hidden_dropout_prob
													lowerCamelCase_				     =attention_probs_dropout_prob
													lowerCamelCase_				     =max_position_embeddings
													lowerCamelCase_				     =type_vocab_size
													lowerCamelCase_				     =type_sequence_label_size
													lowerCamelCase_				     =initializer_range
													lowerCamelCase_				     =num_labels
													lowerCamelCase_				     =num_choices
													lowerCamelCase_				     =scope







						def     _snake_case							(       self				)->				Optional[Any]:
													lowerCamelCase_				     =ids_tensor([self.batch_size, self.seq_length]			,		self.vocab_size				)

													lowerCamelCase_				     =None
													if self.use_input_mask:
																				lowerCamelCase_				     =random_attention_mask([self.batch_size, self.seq_length]				)

													lowerCamelCase_				     =None
													if self.use_token_type_ids:
																				lowerCamelCase_				     =ids_tensor([self.batch_size, self.seq_length]			,		self.type_vocab_size				)

													lowerCamelCase_				     =None
													lowerCamelCase_				     =None
													lowerCamelCase_				     =None
													if self.use_labels:
																				lowerCamelCase_				     =ids_tensor([self.batch_size]			,		self.type_sequence_label_size				)
																				lowerCamelCase_				     =ids_tensor([self.batch_size, self.seq_length]			,		self.num_labels				)
																				lowerCamelCase_				     =ids_tensor([self.batch_size]			,		self.num_choices				)

													lowerCamelCase_				     =self.get_config()

													return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







						def     _snake_case							(       self				)->				List[Any]:
													return MraConfig(
													    vocab_size=self.vocab_size			,		hidden_size=self.hidden_size			,		num_hidden_layers=self.num_hidden_layers			,		num_attention_heads=self.num_attention_heads			,		intermediate_size=self.intermediate_size			,		hidden_act=self.hidden_act			,		hidden_dropout_prob=self.hidden_dropout_prob			,		attention_probs_dropout_prob=self.attention_probs_dropout_prob			,		max_position_embeddings=self.max_position_embeddings			,		type_vocab_size=self.type_vocab_size			,		is_decoder=a_			,		initializer_range=self.initializer_range			,		)







						def     _snake_case							(       self				)->				Optional[int]:
													lowerCamelCase_				     =self.get_config()
													lowerCamelCase_				     =300
													return config







						def     _snake_case							(       self				)->				Tuple:
													(
													    lowerCamelCase_					
													)				     =self.prepare_config_and_inputs()

													lowerCamelCase_				     =True
													lowerCamelCase_				     =floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
													lowerCamelCase_				     =ids_tensor([self.batch_size, self.seq_length]			,		vocab_size=2				)

													return (
													    config,
													    input_ids,
													    token_type_ids,
													    input_mask,
													    sequence_labels,
													    token_labels,
													    choice_labels,
													    encoder_hidden_states,
													    encoder_attention_mask,
													)







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE				)->				Dict:
													lowerCamelCase_				     =MraModel(config=a_				)
													model.to(a_				)
													model.eval()
													lowerCamelCase_				     =model(a_			,		attention_mask=a_			,		token_type_ids=a_				)
													lowerCamelCase_				     =model(a_			,		token_type_ids=a_				)
													lowerCamelCase_				     =model(a_				)
													self.parent.assertEqual(result.last_hidden_state.shape			,		(self.batch_size, self.seq_length, self.hidden_size)				)







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		)->				List[str]:
													lowerCamelCase_				     =True
													lowerCamelCase_				     =MraModel(a_				)
													model.to(a_				)
													model.eval()
													lowerCamelCase_				     =model(
													    a_			,		attention_mask=a_			,		token_type_ids=a_			,		encoder_hidden_states=a_			,		encoder_attention_mask=a_			,		)
													lowerCamelCase_				     =model(
													    a_			,		attention_mask=a_			,		token_type_ids=a_			,		encoder_hidden_states=a_			,		)
													lowerCamelCase_				     =model(a_			,		attention_mask=a_			,		token_type_ids=a_				)
													self.parent.assertEqual(result.last_hidden_state.shape			,		(self.batch_size, self.seq_length, self.hidden_size)				)







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE				)->				Dict:
													lowerCamelCase_				     =MraForMaskedLM(config=a_				)
													model.to(a_				)
													model.eval()
													lowerCamelCase_				     =model(a_			,		attention_mask=a_			,		token_type_ids=a_			,		labels=a_				)
													self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.seq_length, self.vocab_size)				)







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE				)->				List[str]:
													lowerCamelCase_				     =MraForQuestionAnswering(config=a_				)
													model.to(a_				)
													model.eval()
													lowerCamelCase_				     =model(
													    a_			,		attention_mask=a_			,		token_type_ids=a_			,		start_positions=a_			,		end_positions=a_			,		)
													self.parent.assertEqual(result.start_logits.shape			,		(self.batch_size, self.seq_length)				)
													self.parent.assertEqual(result.end_logits.shape			,		(self.batch_size, self.seq_length)				)







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE				)->				List[str]:
													lowerCamelCase_				     =self.num_labels
													lowerCamelCase_				     =MraForSequenceClassification(a_				)
													model.to(a_				)
													model.eval()
													lowerCamelCase_				     =model(a_			,		attention_mask=a_			,		token_type_ids=a_			,		labels=a_				)
													self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_labels)				)







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE				)->				Optional[Any]:
													lowerCamelCase_				     =self.num_labels
													lowerCamelCase_				     =MraForTokenClassification(config=a_				)
													model.to(a_				)
													model.eval()
													lowerCamelCase_				     =model(a_			,		attention_mask=a_			,		token_type_ids=a_			,		labels=a_				)
													self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.seq_length, self.num_labels)				)







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE				)->				Dict:
													lowerCamelCase_				     =self.num_choices
													lowerCamelCase_				     =MraForMultipleChoice(config=a_				)
													model.to(a_				)
													model.eval()
													lowerCamelCase_				     =input_ids.unsqueeze(1				).expand(-1			,		self.num_choices			,		-1				).contiguous()
													lowerCamelCase_				     =token_type_ids.unsqueeze(1				).expand(-1			,		self.num_choices			,		-1				).contiguous()
													lowerCamelCase_				     =input_mask.unsqueeze(1				).expand(-1			,		self.num_choices			,		-1				).contiguous()
													lowerCamelCase_				     =model(
													    a_			,		attention_mask=a_			,		token_type_ids=a_			,		labels=a_			,		)
													self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_choices)				)







						def     _snake_case							(       self				)->				Optional[Any]:
													lowerCamelCase_				     =self.prepare_config_and_inputs()
													(
													    lowerCamelCase_					
													)				     =config_and_inputs
													lowerCamelCase_				     ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
													return config, inputs_dict


@require_torch
class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase      ,  unittest.TestCase):
						_UpperCamelCase:Optional[Any]     =						(
						    (
						        MraModel,
						        MraForMaskedLM,
						        MraForMultipleChoice,
						        MraForQuestionAnswering,
						        MraForSequenceClassification,
						        MraForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						_UpperCamelCase:Union[str, Any]     =						False
						_UpperCamelCase:List[str]     =						False
						_UpperCamelCase:str     =						False
						_UpperCamelCase:Union[str, Any]     =						False

						_UpperCamelCase:List[str]     =						()







						def     _snake_case							(       self				)->				Tuple:
													lowerCamelCase_				     =MraModelTester(self				)
													lowerCamelCase_				     =ConfigTester(self			,		config_class=a_			,		hidden_size=37				)







						def     _snake_case							(       self				)->				Tuple:
													self.config_tester.run_common_tests()







						def     _snake_case							(       self				)->				Union[str, Any]:
													lowerCamelCase_				     =self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*a_				)







						def     _snake_case							(       self				)->				str:
													lowerCamelCase_				     =self.model_tester.prepare_config_and_inputs()
													for type in ["absolute", "relative_key", "relative_key_query"]:
																				lowerCamelCase_				     =type
																				self.model_tester.create_and_check_model(*a_				)







						def     _snake_case							(       self				)->				List[Any]:
													lowerCamelCase_				     =self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*a_				)







						def     _snake_case							(       self				)->				int:
													lowerCamelCase_				     =self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_multiple_choice(*a_				)







						def     _snake_case							(       self				)->				List[Any]:
													lowerCamelCase_				     =self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*a_				)







						def     _snake_case							(       self				)->				Tuple:
													lowerCamelCase_				     =self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*a_				)







						def     _snake_case							(       self				)->				str:
													lowerCamelCase_				     =self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*a_				)







						@slow
						def     _snake_case							(       self				)->				Optional[int]:





													for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				lowerCamelCase_				     =MraModel.from_pretrained(a_				)
																				self.assertIsNotNone(a_				)







						@unittest.skip(reason="""MRA does not output attentions"""				)
						def     _snake_case							(       self				)->				List[str]:
													return


@require_torch
class        _SCREAMING_SNAKE_CASE	(   unittest.TestCase):







						@slow
						def     _snake_case							(       self				)->				Any:
													lowerCamelCase_				     =MraModel.from_pretrained("""uw-madison/mra-base-512-4"""				)
													lowerCamelCase_				     =torch.arange(256				).unsqueeze(0				)

													with torch.no_grad():
																				lowerCamelCase_				     =model(a_				)[0]

													lowerCamelCase_				     =torch.Size((1, 256, 768)				)
													self.assertEqual(output.shape			,		a_				)

													lowerCamelCase_				     =torch.tensor(
													    [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]]				)

													self.assertTrue(torch.allclose(output[:, :3, :3]			,		a_			,		atol=1E-4				)				)







						@slow
						def     _snake_case							(       self				)->				List[Any]:
													lowerCamelCase_				     =MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4"""				)
													lowerCamelCase_				     =torch.arange(256				).unsqueeze(0				)

													with torch.no_grad():
																				lowerCamelCase_				     =model(a_				)[0]

													lowerCamelCase_				     =5_0265

													lowerCamelCase_				     =torch.Size((1, 256, vocab_size)				)
													self.assertEqual(output.shape			,		a_				)

													lowerCamelCase_				     =torch.tensor(
													    [[[9.2_5_9_5, -3.6_0_3_8, 11.8819], [9.3_8_6_9, -3.2_6_9_3, 11.0956], [11.8524, -3.4_9_3_8, 13.1210]]]				)

													self.assertTrue(torch.allclose(output[:, :3, :3]			,		a_			,		atol=1E-4				)				)







						@slow
						def     _snake_case							(       self				)->				Optional[Any]:
													lowerCamelCase_				     =MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3"""				)
													lowerCamelCase_				     =torch.arange(4096				).unsqueeze(0				)

													with torch.no_grad():
																				lowerCamelCase_				     =model(a_				)[0]

													lowerCamelCase_				     =5_0265

													lowerCamelCase_				     =torch.Size((1, 4096, vocab_size)				)
													self.assertEqual(output.shape			,		a_				)

													lowerCamelCase_				     =torch.tensor(
													    [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]]				)

													self.assertTrue(torch.allclose(output[:, :3, :3]			,		a_			,		atol=1E-4				)				)





import numpy as np

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	(0, 0)
					__UpperCAmelCase    :     List[str]													=	None
					__UpperCAmelCase    :     int													=	0
					__UpperCAmelCase    :     Union[str, Any]													=	0
					__UpperCAmelCase    :     List[Any]													=	0




			def __eq__(   self							: Optional[int]       ,       a_							: List[Any] ):
					'''simple docstring'''





					return self.position == cell.position




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					print(self.position )

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: str       ,       a_							: Dict=(5, 5) ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	np.zeros(a_ )
					__UpperCAmelCase    :     List[Any]													=	world_size[0]
					__UpperCAmelCase    :     List[str]													=	world_size[1]




			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					print(self.w )




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: Tuple ):
					'''simple docstring'''





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

def        a						(      _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	[]
		__UpperCAmelCase    :     Optional[Any]													=	[]
		_open.append(_UpperCAmelCase						)

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

						for c in _open:
								if c == n and c.f < n.f:
										continue
						_open.append(_UpperCAmelCase						)
		__UpperCAmelCase    :     str													=	[]
		while current.parent is not None:
				path.append(current.position						)
				__UpperCAmelCase    :     int													=	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)





