



import unittest

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

from ...generation.test_utils import GenerationTesterMixin
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 BertGenerationDecoder, BertGenerationEncoder

class   _A   :


						'''simple docstring'''





						def __init__(	self	:							Any							,      lowerCamelCase	:							Optional[Any]							,      lowerCamelCase	:							Union[str, Any]=13							,      lowerCamelCase	:							Optional[int]=7							,      lowerCamelCase	:							int=True							,      lowerCamelCase	:							Dict=True							,      lowerCamelCase	:							List[Any]=99							,      lowerCamelCase	:							Any=32							,      lowerCamelCase	:							Dict=5							,      lowerCamelCase	:							Optional[int]=4							,      lowerCamelCase	:							str=37							,      lowerCamelCase	:							Any="gelu"							,      lowerCamelCase	:							Dict=0.1							,      lowerCamelCase	:							Union[str, Any]=0.1							,      lowerCamelCase	:							List[str]=50							,      lowerCamelCase	:							str=0.02							,      lowerCamelCase	:							List[str]=True							,      lowerCamelCase	:							Any=None							,      ):






							'''simple docstring'''



							__lowercase		       =			parent
							__lowercase		       =			batch_size
							__lowercase		       =			seq_length
							__lowercase		       =			is_training
							__lowercase		       =			use_input_mask
							__lowercase		       =			vocab_size
							__lowercase		       =			hidden_size
							__lowercase		       =			num_hidden_layers
							__lowercase		       =			num_attention_heads
							__lowercase		       =			intermediate_size
							__lowercase		       =			hidden_act
							__lowercase		       =			hidden_dropout_prob
							__lowercase		       =			attention_probs_dropout_prob
							__lowercase		       =			max_position_embeddings
							__lowercase		       =			initializer_range
							__lowercase		       =			use_labels
							__lowercase		       =			scope



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							__lowercase		       =			ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size	)

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

							if self.use_labels:
								__lowercase		       =			ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size	)

							__lowercase		       =			self.get_config()

							return config, input_ids, input_mask, token_labels



						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							return BertGenerationConfig(
							    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							,      is_decoder=lowerCamelCase							,      initializer_range=self.initializer_range							,      )



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							(
							    (
							    __lowercase
							)       ,    (
							    __lowercase
							)       ,    (
							    __lowercase
							)       ,    (
							    __lowercase
							)       ,    
							)		       =			self.prepare_config_and_inputs()

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

							return (
							    config,
							    input_ids,
							    input_mask,
							    token_labels,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)



						def     _snake_case	(	self	:							Dict							,      lowerCamelCase	:							str							,      lowerCamelCase	:							Dict							,      lowerCamelCase	:							Union[str, Any]							,      lowerCamelCase	:							Any							,      **lowerCamelCase	:							Union[str, Any]							,      ):






							'''simple docstring'''



							__lowercase		       =			BertGenerationEncoder(config=lowerCamelCase	)
							model.to(lowerCamelCase	)
							model.eval()
							__lowercase		       =			model(lowerCamelCase							,      attention_mask=lowerCamelCase	)
							__lowercase		       =			model(lowerCamelCase	)
							self.parent.assertEqual(result.last_hidden_state.shape							,      (self.batch_size, self.seq_length, self.hidden_size)	)



						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							str							,      lowerCamelCase	:							int							,      lowerCamelCase	:							List[Any]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							int							,      **lowerCamelCase	:							Optional[int]							,      ):






							'''simple docstring'''



							__lowercase		       =			True
							__lowercase		       =			BertGenerationEncoder(config=lowerCamelCase	)
							model.to(lowerCamelCase	)
							model.eval()
							__lowercase		       =			model(
							    lowerCamelCase							,      attention_mask=lowerCamelCase							,      encoder_hidden_states=lowerCamelCase							,      encoder_attention_mask=lowerCamelCase							,      )
							__lowercase		       =			model(
							    lowerCamelCase							,      attention_mask=lowerCamelCase							,      encoder_hidden_states=lowerCamelCase							,      )
							self.parent.assertEqual(result.last_hidden_state.shape							,      (self.batch_size, self.seq_length, self.hidden_size)	)



						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							List[str]							,      lowerCamelCase	:							List[str]							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							str							,      lowerCamelCase	:							List[str]							,      lowerCamelCase	:							Optional[int]							,      **lowerCamelCase	:							Optional[Any]							,      ):






							'''simple docstring'''



							__lowercase		       =			True
							__lowercase		       =			True
							__lowercase		       =			BertGenerationDecoder(config=lowerCamelCase	).to(lowerCamelCase	).eval()

							# first forward pass
							__lowercase		       =			model(
							    lowerCamelCase							,      attention_mask=lowerCamelCase							,      encoder_hidden_states=lowerCamelCase							,      encoder_attention_mask=lowerCamelCase							,      use_cache=lowerCamelCase							,      )
							__lowercase		       =			outputs.past_key_values

							# create hypothetical multiple next token and extent to next_input_ids
							__lowercase		       =			ids_tensor((self.batch_size, 3)							,      config.vocab_size	)
							__lowercase		       =			ids_tensor((self.batch_size, 3)							,      vocab_size=2	)

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

							__lowercase		       =			model(
							    lowerCamelCase							,      attention_mask=lowerCamelCase							,      encoder_hidden_states=lowerCamelCase							,      encoder_attention_mask=lowerCamelCase							,      output_hidden_states=lowerCamelCase							,      )["hidden_states"][0]
							__lowercase		       =			model(
							    lowerCamelCase							,      attention_mask=lowerCamelCase							,      encoder_hidden_states=lowerCamelCase							,      encoder_attention_mask=lowerCamelCase							,      past_key_values=lowerCamelCase							,      output_hidden_states=lowerCamelCase							,      )["hidden_states"][0]

							# select random slice
							__lowercase		       =			ids_tensor((1,)							,      output_from_past.shape[-1]	).item()
							__lowercase		       =			output_from_no_past[:, -3:, random_slice_idx].detach()
							__lowercase		       =			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(lowerCamelCase							,      lowerCamelCase							,      atol=1e-3	)	)



						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							Optional[int]							,      *lowerCamelCase	:							Tuple							,      ):






							'''simple docstring'''



							__lowercase		       =			BertGenerationDecoder(lowerCamelCase	)
							model.to(lowerCamelCase	)
							model.eval()
							__lowercase		       =			model(lowerCamelCase							,      attention_mask=lowerCamelCase							,      labels=lowerCamelCase	)
							self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.seq_length, self.vocab_size)	)



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							__lowercase       ,    __lowercase       ,    __lowercase       ,    __lowercase		       =			self.prepare_config_and_inputs()
							__lowercase		       =			{"input_ids": input_ids, "attention_mask": input_mask}
							return config, inputs_dict

@require_torch
class   _A   ( _lowercase  ,						_lowercase  ,						_lowercase  ,						unittest.TestCase	):


						'''simple docstring'''





						_snake_case			:     Any      							=      (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
						_snake_case			:     Dict      							=      (BertGenerationDecoder,) if is_torch_available() else ()
						_snake_case			:     str      							=      (
						    {"""feature-extraction""": BertGenerationEncoder, """text-generation""": BertGenerationDecoder}
						    if is_torch_available()
						    else {}
						)



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							__lowercase		       =			BertGenerationEncoderTester(self	)
							__lowercase		       =			ConfigTester(self							,      config_class=lowerCamelCase							,      hidden_size=37	)



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							self.config_tester.run_common_tests()



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							__lowercase		       =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase	)



						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase       ,    __lowercase       ,    __lowercase       ,    __lowercase		       =			self.model_tester.prepare_config_and_inputs()
							__lowercase		       =			"bert"
							self.model_tester.create_and_check_model(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)



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






							'''simple docstring'''



							__lowercase		       =			self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(*lowerCamelCase	)



						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase		       =			self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCamelCase	)



						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							(
							    (
							    __lowercase
							)       ,    (
							    __lowercase
							)       ,    (
							    __lowercase
							)       ,    (
							    __lowercase
							)       ,    (
							    __lowercase
							)       ,    (
							    __lowercase
							)       ,    
							)		       =			self.model_tester.prepare_config_and_inputs_for_decoder()

							__lowercase		       =			None

							self.model_tester.create_and_check_model_as_decoder(
							    lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase							,      )



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							__lowercase		       =			self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase	)



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






							'''simple docstring'''



							__lowercase		       =			BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"	)
							self.assertIsNotNone(lowerCamelCase	)

@require_torch
class   _A   ( unittest.TestCase	):


						'''simple docstring'''





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






							'''simple docstring'''



							__lowercase		       =			BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"	)
							__lowercase		       =			torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]	)
							with torch.no_grad():
								__lowercase		       =			model(lowerCamelCase	)[0]
							__lowercase		       =			torch.Size([1, 8, 1_024]	)
							self.assertEqual(output.shape							,      lowerCamelCase	)
							__lowercase		       =			torch.tensor(
							    [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]	)
							self.assertTrue(torch.allclose(output[:, :3, :3]							,      lowerCamelCase							,      atol=1e-4	)	)

@require_torch
class   _A   ( unittest.TestCase	):


						'''simple docstring'''





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






							'''simple docstring'''



							__lowercase		       =			BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"	)
							__lowercase		       =			torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]	)
							with torch.no_grad():
								__lowercase		       =			model(lowerCamelCase	)[0]
							__lowercase		       =			torch.Size([1, 8, 50_358]	)
							self.assertEqual(output.shape							,      lowerCamelCase	)
							__lowercase		       =			torch.tensor(
							    [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]	)
							self.assertTrue(torch.allclose(output[:, :3, :3]							,      lowerCamelCase							,      atol=1e-4	)	)








from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class   _A   :


						'''simple docstring'''





						_snake_case			:     int
						_snake_case			:     TreeNode | None      							=      None
						_snake_case			:     TreeNode | None      							=      None

snake_case__					:     Dict    		=   namedtuple("""CoinsDistribResult""", """moves excess""")



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):

	if root is None:
		return 0

	# Validation
	def count_nodes(_SCREAMING_SNAKE_CASE   ) -> int:
		if node is None:
			return 0

		return count_nodes(node.left   ) + count_nodes(node.right   ) + 1

	def count_coins(_SCREAMING_SNAKE_CASE   ) -> int:
		if node is None:
			return 0

		return count_coins(node.left   ) + count_coins(node.right   ) + node.data

	if count_nodes(_SCREAMING_SNAKE_CASE   ) != count_coins(_SCREAMING_SNAKE_CASE   ):
		raise ValueError("The nodes number should be same as the number of coins"   )

	# Main calculation
	def get_distrib(_SCREAMING_SNAKE_CASE   ) -> CoinsDistribResult:

		if node is None:
			return CoinsDistribResult(0  ,	1   )

		__lowercase       ,    __lowercase		       =			get_distrib(node.left   )
		__lowercase       ,    __lowercase		       =			get_distrib(node.right   )

		__lowercase		       =			1 - left_distrib_excess
		__lowercase		       =			1 - right_distrib_excess

		__lowercase		       =			(
		    left_distrib_moves
		    + right_distrib_moves
		    + abs(_SCREAMING_SNAKE_CASE   )
		    + abs(_SCREAMING_SNAKE_CASE   )
		)
		__lowercase		       =			node.data - coins_to_left - coins_to_right

		return CoinsDistribResult(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )

	return get_distrib(_SCREAMING_SNAKE_CASE   )[0]


if __name__ == "__main__":
	import doctest

	doctest.testmod()



