"""simple docstring"""





class   UpperCAmelCase_			:


						def __init__(     self	,    a = ""	,    a = False							) ->      None:
													# Mapping from the first character of the prefix of the node
													lowercase__    :	dict[str, RadixNode]     					=			{}

													# A node will be a leaf if the tree contains its word
													lowercase__    :	Tuple     					=			is_leaf

													lowercase__    :	List[Any]     					=			prefix


						def 	_UpperCAmelCase (     self	,    a							) ->      tuple[str, str, str]:
													lowercase__    :	Union[str, Any]     					=			0
													for q, w in zip(self.prefix	,    a							):
																				if q != w:
																											break

																				x += 1

													return self.prefix[:x], self.prefix[x:], word[x:]


						def 	_UpperCAmelCase (     self	,    a							) ->      None:
													for word in words:
																				self.insert(a							)


						def 	_UpperCAmelCase (     self	,    a							) ->      None:
													# Case 1: If the word is the prefix of the node
													# Solution: We set the current node as leaf
													if self.prefix == word:
																				lowercase__    :	Any     					=			True

													# Case 2: The node has no edges that have a prefix to the word
													# Solution: We create an edge from the current node to a new one
													# containing the word
													elif word[0] not in self.nodes:
																				lowercase__    :	Tuple     					=			RadixNode(prefix=a	,    is_leaf=a							)

													else:
																				lowercase__    :	List[str]     					=			self.nodes[word[0]]
																				lowercase__    :	Optional[int]     					=			incoming_node.match(
																				    a							)

																				# Case 3: The node prefix is equal to the matching
																				# Solution: We insert remaining word on the next node
																				if remaining_prefix == "":
																											self.nodes[matching_string[0]].insert(a							)

																				# Case 4: The word is greater equal to the matching
																				# Solution: Create a node in between both nodes, change
																				# prefixes and add the new node for the remaining word
																				else:
																											lowercase__    :	Optional[int]     					=			remaining_prefix

																											lowercase__    :	List[Any]     					=			self.nodes[matching_string[0]]
																											lowercase__    :	List[str]     					=			RadixNode(a	,    a							)
																											lowercase__    :	Tuple     					=			aux_node

																											if remaining_word == "":
																																		lowercase__    :	Optional[Any]     					=			True
																											else:
																																		self.nodes[matching_string[0]].insert(a							)


						def 	_UpperCAmelCase (     self	,    a							) ->      bool:
													lowercase__    :	Union[str, Any]     					=			self.nodes.get(word[0]	,    a							)
													if not incoming_node:
																				return False
													else:
																				lowercase__    :	Dict     					=			incoming_node.match(
																				    a							)
																				# If there is remaining prefix, the word can't be on the tree
																				if remaining_prefix != "":
																											return False
																				# This applies when the word and the prefix are equal
																				elif remaining_word == "":
																											return incoming_node.is_leaf
																				# We have word remaining so we check the next node
																				else:
																											return incoming_node.find(a							)


						def 	_UpperCAmelCase (     self	,    a							) ->      bool:
													lowercase__    :	str     					=			self.nodes.get(word[0]	,    a							)
													if not incoming_node:
																				return False
													else:
																				lowercase__    :	Optional[int]     					=			incoming_node.match(
																				    a							)
																				# If there is remaining prefix, the word can't be on the tree
																				if remaining_prefix != "":
																											return False
																				# We have word remaining so we check the next node
																				elif remaining_word != "":
																											return incoming_node.delete(a							)
																				else:
																											# If it is not a leaf, we don't have to delete
																											if not incoming_node.is_leaf:
																																		return False
																											else:
																																		# We delete the nodes if no edges go from it
																																		if len(incoming_node.nodes							) == 0:
																																									del self.nodes[word[0]]
																																									# We merge the current node with its only child
																																									if len(self.nodes							) == 1 and not self.is_leaf:
																																																lowercase__    :	Optional[Any]     					=			list(self.nodes.values()							)[0]
																																																lowercase__    :	int     					=			merging_node.is_leaf
																																																self.prefix += merging_node.prefix
																																																lowercase__    :	Optional[int]     					=			merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																																		elif len(incoming_node.nodes							) > 1:
																																									lowercase__    :	Any     					=			False
																																		# If there is 1 edge, we merge it with its child
																																		else:
																																									lowercase__    :	Any     					=			list(incoming_node.nodes.values()							)[0]
																																									lowercase__    :	Tuple     					=			merging_node.is_leaf
																																									incoming_node.prefix += merging_node.prefix
																																									lowercase__    :	Dict     					=			merging_node.nodes

																																		return True


						def 	_UpperCAmelCase (     self	,    a = 0							) ->      None:
													if self.prefix != "":
																				print('-' * height	,    self.prefix	,    '  (leaf)' if self.is_leaf else ''							)

													for value in self.nodes.values():
																				value.print_tree(height + 1							)





def        a_  (   ):





							'''simple docstring'''

							lowercase__    :	Optional[Any]     					=			'banana bananas bandana band apple all beast'.split()
							lowercase__    :	Optional[int]     					=			RadixNode()
							root.insert_many(_lowerCAmelCase  )

							assert all(root.find(_lowerCAmelCase  ) for word in words  )
							assert not root.find('bandanas'  )
							assert not root.find('apps'  )
							root.delete('all'  )
							assert not root.find('all'  )
							root.delete('banana'  )
							assert not root.find('banana'  )
							assert root.find('bananas'  )

							return True





def        a_  (   ):





							'''simple docstring'''

							assert test_trie()





def        a_  (   ):





							'''simple docstring'''

							lowercase__    :	Any     					=			RadixNode()
							lowercase__    :	Union[str, Any]     					=			'banana bananas bandanas bandana band apple all beast'.split()
							root.insert_many(_lowerCAmelCase  )

							print('Words:'     ,				_lowerCAmelCase  )
							print('Tree:'  )
							root.print_tree()


if __name__ == "__main__":
							main()


"""simple docstring"""





import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
       import tensorflow as tf

       from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments


@require_tf
class   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self	,    a							) ->      str:
             for model_result in results.values():
                    for batch_size, sequence_length in zip(model_result['bs']	,    model_result['ss']							):
                           lowercase__    :	str     					=			model_result['result'][batch_size][sequence_length]
                           self.assertIsNotNone(a							)


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Dict     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	int     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    eager_mode=a	,    multi_process=a	,    )
             lowercase__    :	str     					=			TensorFlowBenchmark(a							)
             lowercase__    :	Optional[int]     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	List[str]     					=			'sgugger/tiny-distilbert-classification'
             lowercase__    :	int     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    only_pretrain_model=a	,    )
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmark(a							)
             lowercase__    :	Optional[int]     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	Optional[int]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Union[str, Any]     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmark(a							)
             lowercase__    :	List[str]     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	Any     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	List[Any]     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	Any     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    eager_mode=a	,    multi_process=a	,    )
             lowercase__    :	Tuple     					=			TensorFlowBenchmark(a	,    [config]							)
             lowercase__    :	Dict     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Optional[Any]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	List[str]     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	Any     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	List[str]     					=			TensorFlowBenchmark(a	,    [config]							)
             lowercase__    :	Any     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Optional[Any]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Any     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmark(a							)
             lowercase__    :	Tuple     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_train_result							)
             self.check_results_dict_not_empty(results.memory_train_result							)


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Optional[Any]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Optional[int]     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	str     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	str     					=			TensorFlowBenchmark(a	,    [config]							)
             lowercase__    :	Optional[int]     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_train_result							)
             self.check_results_dict_not_empty(results.memory_train_result							)


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	List[str]     					=			'patrickvonplaten/t5-tiny-random'
             lowercase__    :	Any     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	List[str]     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	int     					=			TensorFlowBenchmark(a	,    configs=[config]							)
             lowercase__    :	Union[str, Any]     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU'							)							) == 0	,    'Cannot do xla on CPU.'							)
      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Any     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    use_xla=a	,    multi_process=a	,    )
             lowercase__    :	Any     					=			TensorFlowBenchmark(a							)
             lowercase__    :	Dict     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Any     					=			'sshleifer/tiny-gpt2'
             with tempfile.TemporaryDirectory() as tmp_dir:
                    lowercase__    :	List[Any]     					=			TensorFlowBenchmarkArguments(
                        models=[MODEL_ID]	,    inference=a	,    save_to_csv=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    inference_time_csv_file=os.path.join(a	,    'inf_time.csv'							)	,    inference_memory_csv_file=os.path.join(a	,    'inf_mem.csv'							)	,    env_info_csv_file=os.path.join(a	,    'env.csv'							)	,    multi_process=a	,    )
                    lowercase__    :	Union[str, Any]     					=			TensorFlowBenchmark(a							)
                    benchmark.run()
                    self.assertTrue(Path(os.path.join(a	,    'inf_time.csv'							)							).exists()							)
                    self.assertTrue(Path(os.path.join(a	,    'inf_mem.csv'							)							).exists()							)
                    self.assertTrue(Path(os.path.join(a	,    'env.csv'							)							).exists()							)


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	Tuple     					=			'sshleifer/tiny-gpt2'

             def _check_summary_is_not_empty(a							):
                    self.assertTrue(hasattr(a	,    'sequential'							)							)
                    self.assertTrue(hasattr(a	,    'cumulative'							)							)
                    self.assertTrue(hasattr(a	,    'current'							)							)
                    self.assertTrue(hasattr(a	,    'total'							)							)

             with tempfile.TemporaryDirectory() as tmp_dir:
                    lowercase__    :	Optional[Any]     					=			TensorFlowBenchmarkArguments(
                        models=[MODEL_ID]	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    log_filename=os.path.join(a	,    'log.txt'							)	,    log_print=a	,    trace_memory_line_by_line=a	,    eager_mode=a	,    multi_process=a	,    )
                    lowercase__    :	Optional[int]     					=			TensorFlowBenchmark(a							)
                    lowercase__    :	Optional[Any]     					=			benchmark.run()
                    _check_summary_is_not_empty(result.inference_summary							)
                    self.assertTrue(Path(os.path.join(a	,    'log.txt'							)							).exists()							)

