





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


UpperCAmelCase_ 							=       500000

UpperCAmelCase_							, UpperCAmelCase_ 							=       os.path.split(__file__)
UpperCAmelCase_ 							=       os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))





@get_duration
def   lowerCamelCase__       (       UpperCamelCase__     :    datasets.Dataset    ,      **UpperCamelCase__     :    str  )     ->	Any:



					'''simple docstring'''




					_snake_case													=			dataset.map(**UpperCamelCase__  )





@get_duration
def   lowerCamelCase__       (       UpperCamelCase__     :    datasets.Dataset    ,      **UpperCamelCase__     :    Dict  )     ->	Any:



					'''simple docstring'''




					_snake_case													=			dataset.filter(**UpperCamelCase__  )





def   lowerCamelCase__       (       )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			{'num examples': SPEED_TEST_N_EXAMPLES}
					with tempfile.TemporaryDirectory() as tmp_dir:
										_snake_case													=			datasets.Features({'text': datasets.Value('string'  ), 'numbers': datasets.Value('float32'  )}  )
										_snake_case													=			generate_example_dataset(
										    os.path.join(UpperCamelCase__    ,      'dataset.arrow'  )    ,      UpperCamelCase__    ,      num_examples=UpperCamelCase__  )

										_snake_case													=			transformers.AutoTokenizer.from_pretrained('bert-base-cased'    ,      use_fast=UpperCamelCase__  )

										def tokenize(UpperCamelCase__     :    Any  ):
															return tokenizer(examples['text']  )

										_snake_case													=			map(UpperCamelCase__  )

										_snake_case													=			map(UpperCamelCase__    ,      batched=UpperCamelCase__  )

										_snake_case													=			map(UpperCamelCase__    ,      function=lambda UpperCamelCase__  : None    ,      batched=UpperCamelCase__  )

										with dataset.formatted_as(type='numpy'  ):
															_snake_case													=			map(UpperCamelCase__    ,      function=lambda UpperCamelCase__  : None    ,      batched=UpperCamelCase__  )

										with dataset.formatted_as(type='pandas'  ):
															_snake_case													=			map(UpperCamelCase__    ,      function=lambda UpperCamelCase__  : None    ,      batched=UpperCamelCase__  )

										with dataset.formatted_as(type='torch'    ,      columns='numbers'  ):
															_snake_case													=			map(UpperCamelCase__    ,      function=lambda UpperCamelCase__  : None    ,      batched=UpperCamelCase__  )

										with dataset.formatted_as(type='tensorflow'    ,      columns='numbers'  ):
															_snake_case													=			map(UpperCamelCase__    ,      function=lambda UpperCamelCase__  : None    ,      batched=UpperCamelCase__  )

										_snake_case													=			map(UpperCamelCase__    ,      function=UpperCamelCase__    ,      batched=UpperCamelCase__  )

										_snake_case													=			filter(UpperCamelCase__  )

										# Activate later when tokenizer support batched inputs
										# with dataset.formatted_as(type='numpy'):
										#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

					with open(UpperCamelCase__    ,      'wb'  ) as f:
										f.write(json.dumps(UpperCamelCase__  ).encode('utf-8'  )  )


if __name__ == "__main__":  # useful to run the profiler
					benchmark_map_filter()










def   lowerCamelCase__       (       UpperCamelCase__     :    list[list[int]]    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    list[int]  )     ->	bool:



					'''simple docstring'''




					if graph[path[curr_ind - 1]][next_ver] == 0:
										return False

					# 2. Validate that next vertex is not already in path
					return not any(vertex == next_ver for vertex in path  )





def   lowerCamelCase__       (       UpperCamelCase__     :    list[list[int]]    ,      UpperCamelCase__     :    list[int]    ,      UpperCamelCase__     :    int  )     ->	bool:



					'''simple docstring'''




					if curr_ind == len(UpperCamelCase__  ):
										# return whether path exists between current and starting vertices
										return graph[path[curr_ind - 1]][path[0]] == 1

					# Recursive Step
					for next_ver in range(0    ,      len(UpperCamelCase__  )  ):
										if valid_connection(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__  ):
															# Insert current vertex  into path as next transition
															_snake_case													=			next_ver
															# Validate created path
															if util_hamilton_cycle(UpperCamelCase__    ,      UpperCamelCase__    ,      curr_ind + 1  ):
																				return True
															# Backtrack
															_snake_case													=			-1
					return False





def   lowerCamelCase__       (       UpperCamelCase__     :    list[list[int]]    ,      UpperCamelCase__     :    int = 0  )     ->	list[int]:



					'''simple docstring'''




					_snake_case													=			[-1] * (len(UpperCamelCase__  ) + 1)
					# initialize start and end of path with starting index
					_snake_case													=			_snake_case													=			start_index
					# evaluate and if we find answer return path either return empty array
					return path if util_hamilton_cycle(UpperCamelCase__    ,      UpperCamelCase__    ,      1  ) else []



