
import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class        _UpperCamelCase				(					lowercase__     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						Tuple					      = None
			__UpperCamelCase      :						Optional[int]					      = BloomTokenizerFast
			__UpperCamelCase      :						Optional[int]					      = BloomTokenizerFast
			__UpperCamelCase      :						List[str]					      = True
			__UpperCamelCase      :						int					      = False
			__UpperCamelCase      :						List[str]					      = """tokenizer_file"""
			__UpperCamelCase      :						Optional[Any]					      = {"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}

			def       lowerCAmelCase__	(       self :       Tuple      ):
							super().setUp()
							UpperCamelCase_:							Union[str, Any]	=						BloomTokenizerFast.from_pretrained("""bigscience/tokenizer"""      )
							tokenizer.save_pretrained(self.tmpdirname      )

			def       lowerCAmelCase__	(       self :       str						,	**snake_case_ :       Dict      ):
							kwargs.update(self.special_tokens_map      )
							return BloomTokenizerFast.from_pretrained(self.tmpdirname						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							int	=						self.get_rust_tokenizer()

							UpperCamelCase_:							List[str]	=						["The quick brown fox</s>", "jumps over the lazy dog</s>"]
							UpperCamelCase_:							Optional[int]	=						[[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

							UpperCamelCase_:							Optional[int]	=						tokenizer.batch_encode_plus(snake_case_      )["input_ids"]
							self.assertListEqual(snake_case_						,	snake_case_      )

							UpperCamelCase_:							List[Any]	=						tokenizer.batch_decode(snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       List[str]=6      ):
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
															UpperCamelCase_:							List[Any]	=						self.rust_tokenizer_class.from_pretrained(snake_case_						,	**snake_case_      )
															# tokenizer_r.pad_token = None # Hotfixing padding = None
															# Simple input
															UpperCamelCase_:							Dict	=						"This is a simple input"
															UpperCamelCase_:							Optional[Any]	=						["This is a simple input 1", "This is a simple input 2"]
															UpperCamelCase_:							List[str]	=						("This is a simple input", "This is a pair")
															UpperCamelCase_:							Any	=						[
															    ("This is a simple input 1", "This is a simple input 2"),
															    ("This is a simple pair 1", "This is a simple pair 2"),
															]

															# Simple input tests
															try:
																			tokenizer_r.encode(snake_case_						,	max_length=snake_case_      )
																			tokenizer_r.encode_plus(snake_case_						,	max_length=snake_case_      )

																			tokenizer_r.batch_encode_plus(snake_case_						,	max_length=snake_case_      )
																			tokenizer_r.encode(snake_case_						,	max_length=snake_case_      )
																			tokenizer_r.batch_encode_plus(snake_case_						,	max_length=snake_case_      )
															except ValueError:
																			self.fail("""Bloom Tokenizer should be able to deal with padding"""      )

															UpperCamelCase_:							str	=						None  # Hotfixing padding = None
															self.assertRaises(snake_case_						,	tokenizer_r.encode						,	snake_case_						,	max_length=snake_case_						,	padding="""max_length"""      )

															# Simple input
															self.assertRaises(snake_case_						,	tokenizer_r.encode_plus						,	snake_case_						,	max_length=snake_case_						,	padding="""max_length"""      )

															# Simple input
															self.assertRaises(
															    snake_case_						,	tokenizer_r.batch_encode_plus						,	snake_case_						,	max_length=snake_case_						,	padding="""max_length"""						,	)

															# Pair input
															self.assertRaises(snake_case_						,	tokenizer_r.encode						,	snake_case_						,	max_length=snake_case_						,	padding="""max_length"""      )

															# Pair input
															self.assertRaises(snake_case_						,	tokenizer_r.encode_plus						,	snake_case_						,	max_length=snake_case_						,	padding="""max_length"""      )

															# Pair input
															self.assertRaises(
															    snake_case_						,	tokenizer_r.batch_encode_plus						,	snake_case_						,	max_length=snake_case_						,	padding="""max_length"""						,	)

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							Optional[Any]	=						self.get_rust_tokenizer()
							UpperCamelCase_:							List[str]	=						load_dataset("""xnli"""						,	"""all_languages"""						,	split="""test"""						,	streaming=snake_case_      )

							UpperCamelCase_:							Optional[Any]	=						next(iter(snake_case_      )      )["premise"]  # pick up one data
							UpperCamelCase_:							Any	=						list(sample_data.values()      )

							UpperCamelCase_:							List[str]	=						list(map(tokenizer.encode						,	snake_case_      )      )
							UpperCamelCase_:							Optional[Any]	=						[tokenizer.decode(snake_case_						,	clean_up_tokenization_spaces=snake_case_      ) for x in output_tokens]
							self.assertListEqual(snake_case_						,	snake_case_      )




			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
							# any sequence length constraints. This test of the parent class will fail since it relies on the
							# maximum sequence length of the positoonal embeddings.
							self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map      )						,	1      )
							self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()      )[0]      )						,	1      )








import random







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = False						)  ->	dict:
				UpperCamelCase_:							dict	=						{i: [] for i in range(lowerCamelCase						)}

				# if probability is greater or equal than 1, then generate a complete graph
				if probability >= 1:
								return complete_graph(lowerCamelCase						)
				# if probability is lower or equal than 0, then return a graph without edges
				if probability <= 0:
								return graph

				# for each couple of nodes, add an edge from u to v
				# if the number randomly generated is greater than probability probability
				for i in range(lowerCamelCase						):
								for j in range(i + 1     ,    lowerCamelCase						):
												if random.random() < probability:
																graph[i].append(lowerCamelCase						)
																if not directed:
																				# if the graph is undirected, add an edge in from j to i, either
																				graph[j].append(lowerCamelCase						)
				return graph







def        A__					(						lowerCamelCase						)  ->	dict:
				return {
				    i: [j for j in range(lowerCamelCase						) if i != j] for i in range(lowerCamelCase						)
				}


if __name__ == "__main__":
							import doctest

							doctest.testmod()






