
from __future__ import annotations

class     _SCREAMING_SNAKE_CASE							:





				def __init__(		self :		List[Any]					,      snake_case_ :		int							):


								"""simple docstring"""

								A     :  Union[str, Any]   =				value
								A     :  Node | None   =				None
								A     :  Node | None   =				None



class     _SCREAMING_SNAKE_CASE							:
				lowerCamelCase_       =     42





				def __init__(		self :		Any							):


								"""simple docstring"""

								A     :  Optional[int]   =				0





				def        _UpperCAmelCase						(		self :		Dict					,      snake_case_ :		Node | None					,      snake_case_ :		int							):


								"""simple docstring"""

								if node is None:
												return

								if path_sum == self.target:
												self.paths += 1

								if node.left:
												self.depth_first_search(node.left					,      path_sum + node.left.value							)
								if node.right:
												self.depth_first_search(node.right					,      path_sum + node.right.value							)






				def        _UpperCAmelCase						(		self :		Dict					,      snake_case_ :		Node | None					,      snake_case_ :		int | None = None							):


								"""simple docstring"""

								if node is None:
												return 0
								if target is not None:
												A     :  Any   =				target

								self.depth_first_search(snake_case_					,      node.value							)
								self.path_sum(node.left							)
								self.path_sum(node.right							)

								return self.paths


if __name__ == "__main__":
					import doctest

					doctest.testmod()

import unittest

from transformers import 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    RemBertConfig,
					    RemBertForCausalLM,
					    RemBertForMaskedLM,
					    RemBertForMultipleChoice,
					    RemBertForQuestionAnswering,
					    RemBertForSequenceClassification,
					    RemBertForTokenClassification,
					    RemBertModel,
					)
					from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class     _SCREAMING_SNAKE_CASE							:





				def __init__(		self :		Union[str, Any]					,      snake_case_ :		Union[str, Any]					,      snake_case_ :		Any=13					,      snake_case_ :		Optional[int]=7					,      snake_case_ :		Any=True					,      snake_case_ :		Optional[Any]=True					,      snake_case_ :		Tuple=True					,      snake_case_ :		int=True					,      snake_case_ :		Optional[int]=99					,      snake_case_ :		Optional[int]=32					,      snake_case_ :		Optional[Any]=18					,      snake_case_ :		List[str]=43					,      snake_case_ :		Optional[int]=5					,      snake_case_ :		int=4					,      snake_case_ :		List[str]=37					,      snake_case_ :		Any="gelu"					,      snake_case_ :		List[Any]=0.1					,      snake_case_ :		Union[str, Any]=0.1					,      snake_case_ :		List[Any]=512					,      snake_case_ :		int=16					,      snake_case_ :		int=2					,      snake_case_ :		Optional[int]=0.02					,      snake_case_ :		Optional[int]=3					,      snake_case_ :		Union[str, Any]=4					,      snake_case_ :		str=None					,      ):


								"""simple docstring"""

								A     :  int   =				parent
								A     :  Union[str, Any]   =				batch_size
								A     :  int   =				seq_length
								A     :  Union[str, Any]   =				is_training
								A     :  Tuple   =				use_input_mask
								A     :  Union[str, Any]   =				use_token_type_ids
								A     :  str   =				use_labels
								A     :  Dict   =				vocab_size
								A     :  int   =				hidden_size
								A     :  str   =				input_embedding_size
								A     :  Tuple   =				output_embedding_size
								A     :  str   =				num_hidden_layers
								A     :  Dict   =				num_attention_heads
								A     :  str   =				intermediate_size
								A     :  List[Any]   =				hidden_act
								A     :  Optional[Any]   =				hidden_dropout_prob
								A     :  Optional[Any]   =				attention_probs_dropout_prob
								A     :  Union[str, Any]   =				max_position_embeddings
								A     :  Dict   =				type_vocab_size
								A     :  Any   =				type_sequence_label_size
								A     :  Optional[Any]   =				initializer_range
								A     :  List[Any]   =				num_labels
								A     :  Any   =				num_choices
								A     :  Any   =				scope





				def        _UpperCAmelCase						(		self :		int							):


								"""simple docstring"""

								A     :  Tuple   =				ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size							)

								A     :  Optional[Any]   =				None
								if self.use_input_mask:
												A     :  Union[str, Any]   =				random_attention_mask([self.batch_size, self.seq_length]							)

								A     :  Optional[int]   =				None
								if self.use_token_type_ids:
												A     :  Union[str, Any]   =				ids_tensor([self.batch_size, self.seq_length]					,      self.type_vocab_size							)

								A     :  Any   =				None
								A     :  int   =				None
								A     :  Dict   =				None
								if self.use_labels:
												A     :  Union[str, Any]   =				ids_tensor([self.batch_size]					,      self.type_sequence_label_size							)
												A     :  Optional[Any]   =				ids_tensor([self.batch_size, self.seq_length]					,      self.num_labels							)
												A     :  List[str]   =				ids_tensor([self.batch_size]					,      self.num_choices							)

								A     :  str   =				RemBertConfig(
								    vocab_size=self.vocab_size					,      hidden_size=self.hidden_size					,      input_embedding_size=self.input_embedding_size					,      output_embedding_size=self.output_embedding_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=snake_case_					,      initializer_range=self.initializer_range					,      )

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





				def        _UpperCAmelCase						(		self :		List[Any]							):


								"""simple docstring"""

								(
								    (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       
								)     :  Optional[int]   =				self.prepare_config_and_inputs()

								A     :  Optional[Any]   =				True
								A     :  Optional[Any]   =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
								A     :  Any   =				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        _UpperCAmelCase						(		self :		int					,      snake_case_ :		Dict					,      snake_case_ :		Any					,      snake_case_ :		str					,      snake_case_ :		str					,      snake_case_ :		Dict					,      snake_case_ :		List[Any]					,      snake_case_ :		int							):


								"""simple docstring"""

								A     :  Union[str, Any]   =				RemBertModel(config=snake_case_							)
								model.to(snake_case_							)
								model.eval()
								A     :  List[Any]   =				model(snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_							)
								A     :  int   =				model(snake_case_					,      token_type_ids=snake_case_							)
								A     :  List[Any]   =				model(snake_case_							)
								self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)							)





				def        _UpperCAmelCase						(		self :		Union[str, Any]					,      snake_case_ :		int					,      snake_case_ :		Tuple					,      snake_case_ :		List[str]					,      snake_case_ :		List[str]					,      snake_case_ :		List[Any]					,      snake_case_ :		Optional[Any]					,      snake_case_ :		Optional[Any]					,      snake_case_ :		List[Any]					,      snake_case_ :		List[Any]					,      ):


								"""simple docstring"""

								A     :  Any   =				True
								A     :  List[Any]   =				RemBertModel(snake_case_							)
								model.to(snake_case_							)
								model.eval()
								A     :  Dict   =				model(
								    snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      encoder_hidden_states=snake_case_					,      encoder_attention_mask=snake_case_					,      )
								A     :  List[Any]   =				model(
								    snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      encoder_hidden_states=snake_case_					,      )
								A     :  Optional[Any]   =				model(snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_							)
								self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)							)





				def        _UpperCAmelCase						(		self :		Any					,      snake_case_ :		List[Any]					,      snake_case_ :		Optional[Any]					,      snake_case_ :		Tuple					,      snake_case_ :		List[str]					,      snake_case_ :		Dict					,      snake_case_ :		Dict					,      snake_case_ :		Any					,      snake_case_ :		Union[str, Any]					,      snake_case_ :		Dict					,      ):


								"""simple docstring"""

								A     :  Any   =				RemBertForCausalLM(config=snake_case_							)
								model.to(snake_case_							)
								model.eval()
								A     :  Optional[Any]   =				model(snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      labels=snake_case_							)
								self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)							)





				def        _UpperCAmelCase						(		self :		Optional[int]					,      snake_case_ :		Dict					,      snake_case_ :		str					,      snake_case_ :		Dict					,      snake_case_ :		str					,      snake_case_ :		Union[str, Any]					,      snake_case_ :		str					,      snake_case_ :		Dict							):


								"""simple docstring"""

								A     :  Union[str, Any]   =				RemBertForMaskedLM(config=snake_case_							)
								model.to(snake_case_							)
								model.eval()
								A     :  str   =				model(snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      labels=snake_case_							)
								self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)							)





				def        _UpperCAmelCase						(		self :		Tuple					,      snake_case_ :		List[str]					,      snake_case_ :		Tuple					,      snake_case_ :		int					,      snake_case_ :		int					,      snake_case_ :		int					,      snake_case_ :		Dict					,      snake_case_ :		Any					,      snake_case_ :		Any					,      snake_case_ :		Any					,      ):


								"""simple docstring"""

								A     :  Dict   =				True
								A     :  Any   =				True
								A     :  str   =				RemBertForCausalLM(config=snake_case_							)
								model.to(snake_case_							)
								model.eval()

								# first forward pass
								A     :  Any   =				model(
								    snake_case_					,      attention_mask=snake_case_					,      encoder_hidden_states=snake_case_					,      encoder_attention_mask=snake_case_					,      use_cache=snake_case_					,      )
								A     :  List[Any]   =				outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								A     :  Tuple   =				ids_tensor((self.batch_size, 3)					,      config.vocab_size							)
								A     :  Optional[Any]   =				ids_tensor((self.batch_size, 3)					,      vocab_size=2							)

								# append to next input_ids and
								A     :  Tuple   =				torch.cat([input_ids, next_tokens]					,      dim=-1							)
								A     :  str   =				torch.cat([input_mask, next_mask]					,      dim=-1							)

								A     :  List[Any]   =				model(
								    snake_case_					,      attention_mask=snake_case_					,      encoder_hidden_states=snake_case_					,      encoder_attention_mask=snake_case_					,      output_hidden_states=snake_case_					,      )['''hidden_states'''][0]
								A     :  Optional[int]   =				model(
								    snake_case_					,      attention_mask=snake_case_					,      encoder_hidden_states=snake_case_					,      encoder_attention_mask=snake_case_					,      past_key_values=snake_case_					,      output_hidden_states=snake_case_					,      )['''hidden_states'''][0]

								# select random slice
								A     :  Optional[int]   =				ids_tensor((1,)					,      output_from_past.shape[-1]							).item()
								A     :  int   =				output_from_no_past[:, -3:, random_slice_idx].detach()
								A     :  Optional[int]   =				output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(snake_case_					,      snake_case_					,      atol=1E-3							)							)





				def        _UpperCAmelCase						(		self :		Optional[Any]					,      snake_case_ :		Dict					,      snake_case_ :		Any					,      snake_case_ :		Optional[int]					,      snake_case_ :		Tuple					,      snake_case_ :		Union[str, Any]					,      snake_case_ :		List[str]					,      snake_case_ :		List[Any]							):


								"""simple docstring"""

								A     :  Dict   =				RemBertForQuestionAnswering(config=snake_case_							)
								model.to(snake_case_							)
								model.eval()
								A     :  Optional[int]   =				model(
								    snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      start_positions=snake_case_					,      end_positions=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)							)





				def        _UpperCAmelCase						(		self :		Dict					,      snake_case_ :		Any					,      snake_case_ :		Union[str, Any]					,      snake_case_ :		Any					,      snake_case_ :		Union[str, Any]					,      snake_case_ :		Optional[int]					,      snake_case_ :		str					,      snake_case_ :		Optional[int]							):


								"""simple docstring"""

								A     :  Any   =				self.num_labels
								A     :  Union[str, Any]   =				RemBertForSequenceClassification(snake_case_							)
								model.to(snake_case_							)
								model.eval()
								A     :  Optional[Any]   =				model(snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      labels=snake_case_							)
								self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)							)





				def        _UpperCAmelCase						(		self :		str					,      snake_case_ :		List[Any]					,      snake_case_ :		Optional[Any]					,      snake_case_ :		List[Any]					,      snake_case_ :		Any					,      snake_case_ :		int					,      snake_case_ :		Dict					,      snake_case_ :		Optional[int]							):


								"""simple docstring"""

								A     :  Optional[Any]   =				self.num_labels
								A     :  List[Any]   =				RemBertForTokenClassification(config=snake_case_							)
								model.to(snake_case_							)
								model.eval()
								A     :  List[Any]   =				model(snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      labels=snake_case_							)
								self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.num_labels)							)





				def        _UpperCAmelCase						(		self :		int					,      snake_case_ :		List[str]					,      snake_case_ :		Tuple					,      snake_case_ :		Dict					,      snake_case_ :		Optional[int]					,      snake_case_ :		Dict					,      snake_case_ :		str					,      snake_case_ :		Union[str, Any]							):


								"""simple docstring"""

								A     :  Dict   =				self.num_choices
								A     :  Optional[Any]   =				RemBertForMultipleChoice(config=snake_case_							)
								model.to(snake_case_							)
								model.eval()
								A     :  int   =				input_ids.unsqueeze(1							).expand(-1					,      self.num_choices					,      -1							).contiguous()
								A     :  List[str]   =				token_type_ids.unsqueeze(1							).expand(-1					,      self.num_choices					,      -1							).contiguous()
								A     :  str   =				input_mask.unsqueeze(1							).expand(-1					,      self.num_choices					,      -1							).contiguous()
								A     :  List[str]   =				model(
								    snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      labels=snake_case_					,      )
								self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_choices)							)






				def        _UpperCAmelCase						(		self :		str							):


								"""simple docstring"""

								A     :  str   =				self.prepare_config_and_inputs()
								(
								    (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       
								)     :  Tuple   =				config_and_inputs
								A     :  Tuple   =				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
								return config, inputs_dict



@require_torch
class     _SCREAMING_SNAKE_CASE							(		snake_case,    snake_case,    unittest.TestCase							):
				lowerCamelCase_       =     (
				    (
				        RemBertModel,
				        RemBertForMaskedLM,
				        RemBertForCausalLM,
				        RemBertForMultipleChoice,
				        RemBertForQuestionAnswering,
				        RemBertForSequenceClassification,
				        RemBertForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				lowerCamelCase_       =     (RemBertForCausalLM,) if is_torch_available() else ()
				lowerCamelCase_       =     (
				    {
				        'feature-extraction': RemBertModel,
				        'fill-mask': RemBertForMaskedLM,
				        'question-answering': RemBertForQuestionAnswering,
				        'text-classification': RemBertForSequenceClassification,
				        'text-generation': RemBertForCausalLM,
				        'token-classification': RemBertForTokenClassification,
				        'zero-shot': RemBertForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)





				def        _UpperCAmelCase						(		self :		List[str]							):


								"""simple docstring"""

								A     :  str   =				RemBertModelTester(self							)
								A     :  Any   =				ConfigTester(self					,      config_class=snake_case_					,      hidden_size=37							)





				def        _UpperCAmelCase						(		self :		Dict							):


								"""simple docstring"""

								self.config_tester.run_common_tests()





				def        _UpperCAmelCase						(		self :		Any							):


								"""simple docstring"""

								A     :  Any   =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case_							)





				def        _UpperCAmelCase						(		self :		Union[str, Any]							):


								"""simple docstring"""

								A     :  Union[str, Any]   =				self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
												A     :  Dict   =				type
												self.model_tester.create_and_check_model(*snake_case_							)





				def        _UpperCAmelCase						(		self :		List[str]							):


								"""simple docstring"""

								A     :  Tuple   =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*snake_case_							)





				def        _UpperCAmelCase						(		self :		Optional[int]							):


								"""simple docstring"""

								A     :  Dict   =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*snake_case_							)





				def        _UpperCAmelCase						(		self :		int							):


								"""simple docstring"""

								A     :  List[str]   =				self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(*snake_case_							)





				def        _UpperCAmelCase						(		self :		Dict							):


								"""simple docstring"""

								A     :  Optional[Any]   =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*snake_case_							)





				def        _UpperCAmelCase						(		self :		Any							):


								"""simple docstring"""

								A     :  Union[str, Any]   =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*snake_case_							)





				def        _UpperCAmelCase						(		self :		Any							):


								"""simple docstring"""

								A     :  List[Any]   =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*snake_case_							)





				def        _UpperCAmelCase						(		self :		int							):


								"""simple docstring"""

								A     :  List[Any]   =				self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(*snake_case_							)





				def        _UpperCAmelCase						(		self :		int							):


								"""simple docstring"""

								(
								    (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       
								)     :  Optional[int]   =				self.model_tester.prepare_config_and_inputs_for_decoder()

								A     :  str   =				None

								self.model_tester.create_and_check_model_as_decoder(
								    snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      )






				@slow
				def        _UpperCAmelCase						(		self :		Optional[Any]							):


								"""simple docstring"""

								for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A     :  Dict   =				RemBertModel.from_pretrained(snake_case_							)
												self.assertIsNotNone(snake_case_							)



@require_torch
class     _SCREAMING_SNAKE_CASE							(		unittest.TestCase							):





				@slow
				def        _UpperCAmelCase						(		self :		List[str]							):


								"""simple docstring"""

								A     :  Tuple   =				RemBertModel.from_pretrained('''google/rembert'''							)
								A     :  int   =				torch.tensor([[312, 5_6498, 313, 2125, 313]]							)
								A     :  List[str]   =				torch.tensor([[0, 0, 0, 1, 1]]							)
								with torch.no_grad():
												A     :  Any   =				model(snake_case_					,      token_type_ids=snake_case_					,      output_hidden_states=snake_case_							)

								A     :  Any   =				1152

								A     :  Tuple   =				torch.Size((1, 5, hidden_size)							)
								self.assertEqual(output['''last_hidden_state'''].shape					,      snake_case_							)

								A     :  Union[str, Any]   =				torch.tensor(
								    [
								        [
								            [0.07_54, -0.20_22, 0.19_04],
								            [-0.33_54, -0.36_92, -0.47_91],
								            [-0.23_14, -0.67_29, -0.07_49],
								            [-0.03_96, -0.31_05, -0.42_34],
								            [-0.15_71, -0.05_25, 0.53_53],
								        ]
								    ]							)

								# Running on the original tf implementation gives slightly different results here.
								# Not clear why this variations is present
								# TODO: Find reason for discrepancy
								# expected_original_implementation = [[
								#     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
								#     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
								#     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
								#     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
								#     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
								# ]]

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