


'''simple docstring'''

import gc
import unittest

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


if is_torch_available():
							import torch

							from transformers import (
							    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
							    CTRLForSequenceClassification,
							    CTRLLMHeadModel,
							    CTRLModel,
							)
class _lowerCamelCase	:



		'''simple docstring'''







		def __init__( self		:						Dict  ,					_A		:						Optional[Any]  ,					_A		:						Optional[Any]=14  ,					_A		:						int=7  ,					_A		:						Optional[Any]=True  ,					_A		:						Any=True  ,					_A		:						str=True  ,					_A		:						List[str]=True  ,					_A		:						Union[str, Any]=True  ,					_A		:						Optional[Any]=99  ,					_A		:						List[str]=32  ,					_A		:						int=5  ,					_A		:						Union[str, Any]=4  ,					_A		:						Optional[Any]=37  ,					_A		:						List[str]="gelu"  ,					_A		:						Optional[Any]=0.1  ,					_A		:						Dict=0.1  ,					_A		:						Any=512  ,					_A		:						Optional[Any]=16  ,					_A		:						int=2  ,					_A		:						Union[str, Any]=0.02  ,					_A		:						Tuple=3  ,					_A		:						Any=4  ,					_A		:						Dict=None  ,					)  ->			List[str]:
			__magic_name__	:   Optional[int]					     =					parent
			__magic_name__	:   str					     =					batch_size
			__magic_name__	:   Dict					     =					seq_length
			__magic_name__	:   Optional[Any]					     =					is_training
			__magic_name__	:   List[str]					     =					use_token_type_ids
			__magic_name__	:   Any					     =					use_input_mask
			__magic_name__	:   str					     =					use_labels
			__magic_name__	:   List[str]					     =					use_mc_token_ids
			__magic_name__	:   Dict					     =					vocab_size
			__magic_name__	:   Tuple					     =					hidden_size
			__magic_name__	:   Optional[Any]					     =					num_hidden_layers
			__magic_name__	:   List[str]					     =					num_attention_heads
			__magic_name__	:   List[str]					     =					intermediate_size
			__magic_name__	:   Tuple					     =					hidden_act
			__magic_name__	:   Optional[Any]					     =					hidden_dropout_prob
			__magic_name__	:   Tuple					     =					attention_probs_dropout_prob
			__magic_name__	:   str					     =					max_position_embeddings
			__magic_name__	:   Dict					     =					type_vocab_size
			__magic_name__	:   str					     =					type_sequence_label_size
			__magic_name__	:   Tuple					     =					initializer_range
			__magic_name__	:   Union[str, Any]					     =					num_labels
			__magic_name__	:   str					     =					num_choices
			__magic_name__	:   List[Any]					     =					scope
			__magic_name__	:   Union[str, Any]					     =					self.vocab_size - 1







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			List[Any]:
			__magic_name__	:   Any					     =					ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size      )

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

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

			__magic_name__	:   str					     =					None
			if self.use_mc_token_ids:
				__magic_name__	:   Union[str, Any]					     =					ids_tensor([self.batch_size, self.num_choices]  ,					self.seq_length      )

			__magic_name__	:   Tuple					     =					None
			__magic_name__	:   Any					     =					None
			__magic_name__	:   str					     =					None
			if self.use_labels:
				__magic_name__	:   Tuple					     =					ids_tensor([self.batch_size]  ,					self.type_sequence_label_size      )
				__magic_name__	:   Union[str, Any]					     =					ids_tensor([self.batch_size, self.seq_length]  ,					self.num_labels      )
				__magic_name__	:   Tuple					     =					ids_tensor([self.batch_size]  ,					self.num_choices      )

			__magic_name__	:   int					     =					self.get_config()

			__magic_name__	:   Optional[int]					     =					ids_tensor([self.num_hidden_layers, self.num_attention_heads]  ,					2      )

			return (
			    config,
			    input_ids,
			    input_mask,
			    head_mask,
			    token_type_ids,
			    mc_token_ids,
			    sequence_labels,
			    token_labels,
			    choice_labels,
			)







		def        __lowerCAmelCase		( self		:						str      )  ->			Any:
			return CTRLConfig(
			    vocab_size=self.vocab_size  ,					n_embd=self.hidden_size  ,					n_layer=self.num_hidden_layers  ,					n_head=self.num_attention_heads  ,					n_positions=self.max_position_embeddings  ,					pad_token_id=self.pad_token_id  ,					)







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						Tuple  ,					_A		:						str  ,					_A		:						Tuple  ,					_A		:						str  ,					_A		:						Optional[int]  ,					*_A		:						int      )  ->			List[Any]:
			__magic_name__	:   Any					     =					CTRLModel(config=_A      )
			model.to(_A      )
			model.eval()

			model(_A  ,					token_type_ids=_A  ,					head_mask=_A      )
			model(_A  ,					token_type_ids=_A      )
			__magic_name__	:   Union[str, Any]					     =					model(_A      )
			self.parent.assertEqual(result.last_hidden_state.shape  ,					(self.batch_size, self.seq_length, self.hidden_size)      )
			self.parent.assertEqual(len(result.past_key_values      )  ,					config.n_layer      )







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						Union[str, Any]  ,					_A		:						Union[str, Any]  ,					_A		:						Dict  ,					_A		:						Optional[Any]  ,					_A		:						Optional[int]  ,					*_A		:						str      )  ->			List[str]:
			__magic_name__	:   Any					     =					CTRLLMHeadModel(_A      )
			model.to(_A      )
			model.eval()

			__magic_name__	:   List[Any]					     =					model(_A  ,					token_type_ids=_A  ,					labels=_A      )
			self.parent.assertEqual(result.loss.shape  ,					()      )
			self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.seq_length, self.vocab_size)      )







		def        __lowerCAmelCase		( self		:						Tuple      )  ->			int:
			__magic_name__	:   List[str]					     =					self.prepare_config_and_inputs()

			(
			    __magic_name__    
			)	:   List[Any]					     =					config_and_inputs

			__magic_name__	:   Any					     =					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'head_mask': head_mask}

			return config, inputs_dict







		def        __lowerCAmelCase		( self		:						Optional[int]  ,					_A		:						Any  ,					_A		:						Optional[int]  ,					_A		:						Tuple  ,					_A		:						Dict  ,					*_A		:						List[Any]      )  ->			Any:
			__magic_name__	:   str					     =					self.num_labels
			__magic_name__	:   Union[str, Any]					     =					CTRLForSequenceClassification(_A      )
			model.to(_A      )
			model.eval()
			__magic_name__	:   int					     =					ids_tensor([self.batch_size]  ,					self.type_sequence_label_size      )
			__magic_name__	:   Union[str, Any]					     =					model(_A  ,					token_type_ids=_A  ,					labels=_A      )
			self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.num_labels)      )







@require_torch
class _lowerCamelCase	(						lowercase__		,					lowercase__		,					lowercase__		,					unittest.TestCase							):



		'''simple docstring'''







		A_     :      List[str]				   =    (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
		A_     :      Union[str, Any]				   =    (CTRLLMHeadModel,) if is_torch_available() else ()
		A_     :      Union[str, Any]				   =    (
		    {
		        """feature-extraction""": CTRLModel,
		        """text-classification""": CTRLForSequenceClassification,
		        """text-generation""": CTRLLMHeadModel,
		        """zero-shot""": CTRLForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		A_     :      Dict				   =    True
		A_     :      Optional[Any]				   =    False
		A_     :      Optional[int]				   =    False







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						Optional[Any]  ,					_A		:						Dict  ,					_A		:						Tuple  ,					_A		:						Any  ,					_A		:						Optional[Any]      )  ->			Optional[Any]:
			if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
				# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
				# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
				# config could not be created.
				return True

			return False







		def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			List[Any]:
			__magic_name__	:   List[str]					     =					CTRLModelTester(self      )
			__magic_name__	:   List[str]					     =					ConfigTester(self  ,					config_class=_A  ,					n_embd=37      )







		def        __lowerCAmelCase		( self		:						Dict      )  ->			Any:
			super().tearDown()
			# clean-up as much as possible GPU memory occupied by PyTorch
			gc.collect()
			torch.cuda.empty_cache()







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			int:
			self.config_tester.run_common_tests()







		def        __lowerCAmelCase		( self		:						Tuple      )  ->			Optional[Any]:
			__magic_name__	:   Optional[Any]					     =					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_ctrl_model(*_A      )







		def        __lowerCAmelCase		( self		:						Any      )  ->			int:
			__magic_name__	:   Any					     =					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_lm_head_model(*_A      )







		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
		def        __lowerCAmelCase		( self		:						Any      )  ->			Any:
			pass







		@slow
		def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			Dict:
			for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__magic_name__	:   str					     =					CTRLModel.from_pretrained(_A      )
				self.assertIsNotNone(_A      )







		@unittest.skip('The model doesn\'t support left padding'      )  # and it's not used enough to be worth fixing :)
		def        __lowerCAmelCase		( self		:						int      )  ->			str:
			pass







@require_torch
class _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Any:
			super().tearDown()
			# clean-up as much as possible GPU memory occupied by PyTorch
			gc.collect()
			torch.cuda.empty_cache()







		@slow
		def        __lowerCAmelCase		( self		:						str      )  ->			Optional[Any]:
			__magic_name__	:   Tuple					     =					CTRLLMHeadModel.from_pretrained('ctrl'      )
			model.to(_A      )
			__magic_name__	:   Dict					     =					torch.tensor(
			    [[11859, 0, 1611, 8]]  ,					dtype=torch.long  ,					device=_A      )  # Legal the president is
			__magic_name__	:   str					     =					[
			    11859,
			    0,
			    1611,
			    8,
			    5,
			    150,
			    26449,
			    2,
			    19,
			    348,
			    469,
			    3,
			    2595,
			    48,
			    20740,
			    246533,
			    246533,
			    19,
			    30,
			    5,
			]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

			__magic_name__	:   List[str]					     =					model.generate(_A  ,					do_sample=_A      )
			self.assertListEqual(output_ids[0].tolist()  ,					_A      )



'''simple docstring'''

from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowerCAmelCase				:Any        =      TypeVar('''T''')





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return (position - 1) // 2





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return (2 * position) + 1





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return (2 * position) + 2
class _lowerCamelCase	(						Generic[T]							):



		'''simple docstring'''







		def __init__( self		:						Union[str, Any]      )  ->			None:
			__magic_name__	:   list[tuple[T, int]]					     =					[]
			__magic_name__	:   dict[T, int]					     =					{}
			__magic_name__	:   int					     =					0







		def __len__( self		:						Optional[int]      )  ->			int:
			return self.elements







		def __repr__( self		:						Optional[Any]      )  ->			str:
			return str(self.heap      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			bool:
			# Check if the priority queue is empty
			return self.elements == 0







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						T  ,					_A		:						int      )  ->			None:
			# Add an element with given priority to the queue
			self.heap.append((elem, weight)      )
			__magic_name__	:   List[str]					     =					self.elements
			self.elements += 1
			self._bubble_up(_A      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			T:
			# Remove and return the element with lowest weight (highest priority)
			if self.elements > 1:
				self._swap_nodes(0  ,					self.elements - 1      )
			__magic_name__     ,    __magic_name__	:   Optional[Any]					     =					self.heap.pop()
			del self.position_map[elem]
			self.elements -= 1
			if self.elements > 0:
				__magic_name__     ,    __magic_name__	:   Tuple					     =					self.heap[0]
				self._bubble_down(_A      )
			return elem







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						T  ,					_A		:						int      )  ->			None:
			# Update the weight of the given key
			__magic_name__	:   Optional[Any]					     =					self.position_map[elem]
			__magic_name__	:   Any					     =					(elem, weight)
			if position > 0:
				__magic_name__	:   Optional[int]					     =					get_parent_position(_A      )
				__magic_name__     ,    __magic_name__	:   Any					     =					self.heap[parent_position]
				if parent_weight > weight:
					self._bubble_up(_A      )
				else:
					self._bubble_down(_A      )
			else:
				self._bubble_down(_A      )







		def        __lowerCAmelCase		( self		:						Optional[int]  ,					_A		:						T      )  ->			None:
			# Place a node at the proper position (upward movement) [to be used internally
			# only]
			__magic_name__	:   Union[str, Any]					     =					self.position_map[elem]
			if curr_pos == 0:
				return None
			__magic_name__	:   Optional[Any]					     =					get_parent_position(_A      )
			__magic_name__     ,    __magic_name__	:   Any					     =					self.heap[curr_pos]
			__magic_name__     ,    __magic_name__	:   int					     =					self.heap[parent_position]
			if parent_weight > weight:
				self._swap_nodes(_A  ,					_A      )
				return self._bubble_up(_A      )
			return None







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						T      )  ->			None:
			# Place a node at the proper position (downward movement) [to be used
			# internally only]
			__magic_name__	:   Dict					     =					self.position_map[elem]
			__magic_name__     ,    __magic_name__	:   List[str]					     =					self.heap[curr_pos]
			__magic_name__	:   str					     =					get_child_left_position(_A      )
			__magic_name__	:   List[str]					     =					get_child_right_position(_A      )
			if child_left_position < self.elements and child_right_position < self.elements:
				__magic_name__     ,    __magic_name__	:   int					     =					self.heap[child_left_position]
				__magic_name__     ,    __magic_name__	:   int					     =					self.heap[child_right_position]
				if child_right_weight < child_left_weight and child_right_weight < weight:
					self._swap_nodes(_A  ,					_A      )
					return self._bubble_down(_A      )
			if child_left_position < self.elements:
				__magic_name__     ,    __magic_name__	:   Any					     =					self.heap[child_left_position]
				if child_left_weight < weight:
					self._swap_nodes(_A  ,					_A      )
					return self._bubble_down(_A      )
			else:
				return None
			if child_right_position < self.elements:
				__magic_name__     ,    __magic_name__	:   str					     =					self.heap[child_right_position]
				if child_right_weight < weight:
					self._swap_nodes(_A  ,					_A      )
					return self._bubble_down(_A      )
			return None







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						int  ,					_A		:						int      )  ->			None:
			# Swap the nodes at the given positions
			__magic_name__	:   List[Any]					     =					self.heap[nodea_pos][0]
			__magic_name__	:   Union[str, Any]					     =					self.heap[nodea_pos][0]
			__magic_name__     ,    __magic_name__	:   Optional[Any]					     =					(
			    self.heap[nodea_pos],
			    self.heap[nodea_pos],
			)
			__magic_name__	:   Optional[Any]					     =					nodea_pos
			__magic_name__	:   Optional[Any]					     =					nodea_pos







class _lowerCamelCase	(						Generic[T]							):



		'''simple docstring'''







		def __init__( self		:						Union[str, Any]      )  ->			None:
			__magic_name__	:   dict[T, dict[T, int]]					     =					{}
			__magic_name__	:   int					     =					0







		def __repr__( self		:						str      )  ->			str:
			return str(self.connections      )







		def __len__( self		:						Union[str, Any]      )  ->			int:
			return self.nodes







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						T      )  ->			None:
			# Add a node in the graph if it is not in the graph
			if node not in self.connections:
				__magic_name__	:   str					     =					{}
				self.nodes += 1







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						T  ,					_A		:						T  ,					_A		:						int      )  ->			None:
			# Add an edge between 2 nodes in the graph
			self.add_node(_A      )
			self.add_node(_A      )
			__magic_name__	:   Any					     =					weight
			__magic_name__	:   Dict					     =					weight







def lowerCamelCase      (      lowerCAmelCase    :     GraphUndirectedWeighted[T]		,		):





	"""simple docstring"""







	__magic_name__	:   dict[T, int]					     =					{node: maxsize for node in graph.connections}
	__magic_name__	:   dict[T, T | None]					     =					{node: None for node in graph.connections}

	__magic_name__	:   MinPriorityQueue[T]					     =					MinPriorityQueue()
	for node, weight in dist.items():
		priority_queue.push(lowerCAmelCase		,		lowerCAmelCase		)

	if priority_queue.is_empty():
		return dist, parent

	# initialization
	__magic_name__	:   Any					     =					priority_queue.extract_min()
	__magic_name__	:   List[str]					     =					0
	for neighbour in graph.connections[node]:
		if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
			__magic_name__	:   Any					     =					dist[node] + graph.connections[node][neighbour]
			priority_queue.update_key(lowerCAmelCase		,		dist[neighbour]		)
			__magic_name__	:   Tuple					     =					node

    # running prim's algorithm
	while not priority_queue.is_empty():
		__magic_name__	:   Tuple					     =					priority_queue.extract_min()
		for neighbour in graph.connections[node]:
			if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
				__magic_name__	:   int					     =					dist[node] + graph.connections[node][neighbour]
				priority_queue.update_key(lowerCAmelCase		,		dist[neighbour]		)
				__magic_name__	:   Union[str, Any]					     =					node
	return dist, parent