


import tempfile
import unittest

from transformers import DPRConfig, 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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer
			from transformers.models.dpr.modeling_dpr import (
			    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
			    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
			    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
			)

class 			lowerCamelCase_					:





							def __init__(							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=13			,       _SCREAMING_SNAKE_CASE=7			,       _SCREAMING_SNAKE_CASE=False			,       _SCREAMING_SNAKE_CASE=True			,       _SCREAMING_SNAKE_CASE=True			,       _SCREAMING_SNAKE_CASE=True			,       _SCREAMING_SNAKE_CASE=99			,       _SCREAMING_SNAKE_CASE=32			,       _SCREAMING_SNAKE_CASE=5			,       _SCREAMING_SNAKE_CASE=4			,       _SCREAMING_SNAKE_CASE=37			,       _SCREAMING_SNAKE_CASE="gelu"			,       _SCREAMING_SNAKE_CASE=0.1			,       _SCREAMING_SNAKE_CASE=0.1			,       _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			,       _SCREAMING_SNAKE_CASE=0			,       ):
								a_     			=   parent
								a_     			=   batch_size
								a_     			=   seq_length
								a_     			=   is_training
								a_     			=   use_input_mask
								a_     			=   use_token_type_ids
								a_     			=   use_labels
								a_     			=   vocab_size
								a_     			=   hidden_size
								a_     			=   num_hidden_layers
								a_     			=   num_attention_heads
								a_     			=   intermediate_size
								a_     			=   hidden_act
								a_     			=   hidden_dropout_prob
								a_     			=   attention_probs_dropout_prob
								a_     			=   max_position_embeddings
								a_     			=   type_vocab_size
								a_     			=   type_sequence_label_size
								a_     			=   initializer_range
								a_     			=   num_labels
								a_     			=   num_choices
								a_     			=   scope
								a_     			=   projection_dim





							def   __magic_name__      (							self    ):
								a_     			=   ids_tensor([self.batch_size, self.seq_length]			,       self.vocab_size    )

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

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

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

								a_     			=   self.get_config()

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





							def   __magic_name__      (							self    ):
								return DPRConfig(
								    projection_dim=self.projection_dim			,       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			,       initializer_range=self.initializer_range			,       )





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   DPRContextEncoder(config=_SCREAMING_SNAKE_CASE    )
								model.to(_SCREAMING_SNAKE_CASE    )
								model.eval()
								a_     			=   model(_SCREAMING_SNAKE_CASE			,       attention_mask=_SCREAMING_SNAKE_CASE			,       token_type_ids=_SCREAMING_SNAKE_CASE    )
								a_     			=   model(_SCREAMING_SNAKE_CASE			,       token_type_ids=_SCREAMING_SNAKE_CASE    )
								a_     			=   model(_SCREAMING_SNAKE_CASE    )
								self.parent.assertEqual(result.pooler_output.shape			,       (self.batch_size, self.projection_dim or self.hidden_size)    )





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   DPRQuestionEncoder(config=_SCREAMING_SNAKE_CASE    )
								model.to(_SCREAMING_SNAKE_CASE    )
								model.eval()
								a_     			=   model(_SCREAMING_SNAKE_CASE			,       attention_mask=_SCREAMING_SNAKE_CASE			,       token_type_ids=_SCREAMING_SNAKE_CASE    )
								a_     			=   model(_SCREAMING_SNAKE_CASE			,       token_type_ids=_SCREAMING_SNAKE_CASE    )
								a_     			=   model(_SCREAMING_SNAKE_CASE    )
								self.parent.assertEqual(result.pooler_output.shape			,       (self.batch_size, self.projection_dim or self.hidden_size)    )





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   DPRReader(config=_SCREAMING_SNAKE_CASE    )
								model.to(_SCREAMING_SNAKE_CASE    )
								model.eval()
								a_     			=   model(
								    _SCREAMING_SNAKE_CASE			,       attention_mask=_SCREAMING_SNAKE_CASE			,       )

								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)    )
								self.parent.assertEqual(result.relevance_logits.shape			,       (self.batch_size,)    )





							def   __magic_name__      (							self    ):
								a_     			=   self.prepare_config_and_inputs()
								(
								    (
								    a_
								)			, (
								    a_
								)			, (
								    a_
								)			, (
								    a_
								)			, (
								    a_
								)			, (
								    a_
								)			, (
								    a_
								)			, 
								)     			=   config_and_inputs
								a_     			=   {"""input_ids""": input_ids}
								return config, inputs_dict







@require_torch
class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE   ,		_SCREAMING_SNAKE_CASE   ,		unittest.TestCase    ):
							_lowerCamelCase				: Optional[Any]       =					(
							    (
							        DPRContextEncoder,
							        DPRQuestionEncoder,
							        DPRReader,
							    )
							    if is_torch_available()
							    else ()
							)
							_lowerCamelCase				: Optional[int]       =					{"""feature-extraction""": DPRQuestionEncoder} if is_torch_available() else {}

							_lowerCamelCase				: Union[str, Any]       =					False
							_lowerCamelCase				: Union[str, Any]       =					False  # why?
							_lowerCamelCase				: str       =					False
							_lowerCamelCase				: Optional[int]       =					False





							def   __magic_name__      (							self    ):
								a_     			=   DPRModelTester(self    )
								a_     			=   ConfigTester(self			,       config_class=_SCREAMING_SNAKE_CASE			,       hidden_size=37    )





							def   __magic_name__      (							self    ):
								self.config_tester.run_common_tests()





							def   __magic_name__      (							self    ):
								a_     			=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_context_encoder(*_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								a_     			=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_question_encoder(*_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								a_     			=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_reader(*_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								a_     			=   self.model_tester.prepare_config_and_inputs()[0]

								a_     			=   DPRQuestionEncoder(config=_SCREAMING_SNAKE_CASE    )
								model.to(_SCREAMING_SNAKE_CASE    )
								model.eval()

								with tempfile.TemporaryDirectory() as tmp_dirname:
									model.save_pretrained(_SCREAMING_SNAKE_CASE    )
									a_     			=   DPRQuestionEncoder.from_pretrained(_SCREAMING_SNAKE_CASE			,       projection_dim=512    )

								self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )





							@slow
							def   __magic_name__      (							self    ):
								for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a_     			=   DPRContextEncoder.from_pretrained(_SCREAMING_SNAKE_CASE    )
									self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )

								for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a_     			=   DPRContextEncoder.from_pretrained(_SCREAMING_SNAKE_CASE    )
									self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )

								for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a_     			=   DPRQuestionEncoder.from_pretrained(_SCREAMING_SNAKE_CASE    )
									self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )

								for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a_     			=   DPRReader.from_pretrained(_SCREAMING_SNAKE_CASE    )
									self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )







@require_torch
class 			lowerCamelCase_					(    unittest.TestCase    ):





							@slow
							def   __magic_name__      (							self    ):
								a_     			=   DPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base"""			,       return_dict=_SCREAMING_SNAKE_CASE    )
								model.to(_SCREAMING_SNAKE_CASE    )

								a_     			=   torch.tensor(
								    [[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]]			,       dtype=torch.long			,       device=_SCREAMING_SNAKE_CASE    )  # [CLS] hello, is my dog cute? [SEP]
								a_     			=   model(_SCREAMING_SNAKE_CASE    )[0]  # embedding shape = (1, 768)
								# compare the actual values for a slice.
								a_     			=   torch.tensor(
								    [
								        [
								            0.0_3_2_3_6_2_5_3,
								            0.1_2_7_5_3_3_3_5,
								            0.1_6_8_1_8_5_0_9,
								            0.0_0_2_7_9_7_8_6,
								            0.3_8_9_6_9_3_3,
								            0.2_4_2_6_4_9_4_5,
								            0.2_1_7_8_9_7_1,
								            -0.0_2_3_3_5_2_2_7,
								            -0.0_8_4_8_1_9_5_9,
								            -0.1_4_3_2_4_1_1_7,
								        ]
								    ]			,       dtype=torch.float			,       device=_SCREAMING_SNAKE_CASE			,       )
								self.assertTrue(torch.allclose(output[:, :10]			,       _SCREAMING_SNAKE_CASE			,       atol=1E-4    )    )





							@slow
							def   __magic_name__      (							self    ):
								a_     			=   DPRReaderTokenizer.from_pretrained("""facebook/dpr-reader-single-nq-base"""    )
								a_     			=   DPRReader.from_pretrained("""facebook/dpr-reader-single-nq-base"""    )
								model.to(_SCREAMING_SNAKE_CASE    )

								a_     			=   tokenizer(
								    questions="""What is love ?"""			,       titles="""Haddaway"""			,       texts="""What Is Love is a song recorded by the artist Haddaway"""			,       padding=_SCREAMING_SNAKE_CASE			,       return_tensors="""pt"""			,       )
								encoded_inputs.to(_SCREAMING_SNAKE_CASE    )

								a_     			=   model(**_SCREAMING_SNAKE_CASE    )

								# compare the actual values for a slice.
								a_     			=   torch.tensor(
								    [[-1_0.3_0_0_5, -1_0.7_7_6_5, -1_1.4_8_7_2, -1_1.6_8_4_1, -1_1.9_3_1_2, -1_0.3_0_0_2, -9.8_5_4_4, -1_1.7_3_7_8, -1_2.0_8_2_1, -1_0.2_9_7_5]]			,       dtype=torch.float			,       device=_SCREAMING_SNAKE_CASE			,       )

								a_     			=   torch.tensor(
								    [[-1_1.0_6_8_4, -1_1.7_0_4_1, -1_1.5_3_9_7, -1_0.3_4_6_5, -1_0.8_7_9_1, -6.8_4_4_3, -1_1.9_9_5_9, -1_1.0_3_6_4, -1_0.0_0_9_6, -6.8_4_0_5]]			,       dtype=torch.float			,       device=_SCREAMING_SNAKE_CASE			,       )
								self.assertTrue(torch.allclose(outputs.start_logits[:, :10]			,       _SCREAMING_SNAKE_CASE			,       atol=1E-4    )    )
								self.assertTrue(torch.allclose(outputs.end_logits[:, :10]			,       _SCREAMING_SNAKE_CASE			,       atol=1E-4    )    )



from .balanced_parentheses import balanced_parentheses
from .stack import Stack

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	int:



	"""simple docstring"""






	return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(UpperCamelCase     ,					-1	)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	str:



	"""simple docstring"""






	if not balanced_parentheses(UpperCamelCase	):
		raise ValueError("""Mismatched parentheses"""	)
	a_     			=   Stack()
	a_     			=   []
	for char in expression_str:
		if char.isalpha() or char.isdigit():
			postfix.append(UpperCamelCase	)
		elif char == "(":
			stack.push(UpperCamelCase	)
		elif char == ")":
			while not stack.is_empty() and stack.peek() != "(":
				postfix.append(stack.pop()	)
			stack.pop()
		else:
			while not stack.is_empty() and precedence(UpperCamelCase	) <= precedence(stack.peek()	):
				postfix.append(stack.pop()	)
			stack.push(UpperCamelCase	)
	while not stack.is_empty():
		postfix.append(stack.pop()	)
	return " ".join(UpperCamelCase	)


if __name__ == "__main__":
			from doctest import testmod

			testmod()
			_A							     =       'a+b*(c^d-e)^(f+g*h)-i'

			print('Infix to Postfix Notation demonstration:\n')
			print('Infix notation: ' + expression)
			print('Postfix notation: ' + infix_to_postfix(expression))