
"""simple docstring"""





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 __lowerCAmelCase					(       _lowercase  ,					unittest.TestCase   ):




			"""simple docstring"""







			snake_case						   =					None
			snake_case						   =					BloomTokenizerFast
			snake_case						   =					BloomTokenizerFast
			snake_case						   =					True
			snake_case						   =					False
			snake_case						   =					"tokenizer_file"
			snake_case						   =					{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}




			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			List[str]:







								"""simple docstring"""
								super().setUp()
								A_								=    BloomTokenizerFast.from_pretrained("bigscience/tokenizer"						)
								tokenizer.save_pretrained(self.tmpdirname						)




			def   lowerCamelCase__  (					self    :       int							, **_snake_case    :       Optional[int]						)   ->			List[Any]:







								"""simple docstring"""
								kwargs.update(self.special_tokens_map						)
								return BloomTokenizerFast.from_pretrained(self.tmpdirname							, **_snake_case						)




			def   lowerCamelCase__  (					self    :       Any						)   ->			Union[str, Any]:







								"""simple docstring"""
								A_								=    self.get_rust_tokenizer()

								A_								=    ["The quick brown fox</s>", "jumps over the lazy dog</s>"]
								A_								=    [[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

								A_								=    tokenizer.batch_encode_plus(_snake_case						)["input_ids"]
								self.assertListEqual(_snake_case							, _snake_case						)

								A_								=    tokenizer.batch_decode(_snake_case						)
								self.assertListEqual(_snake_case							, _snake_case						)




			def   lowerCamelCase__  (					self    :       int							, _snake_case    :       List[Any]=6						)   ->			int:







								"""simple docstring"""
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'						):
																		A_								=    self.rust_tokenizer_class.from_pretrained(_snake_case							, **_snake_case						)
																		# tokenizer_r.pad_token = None # Hotfixing padding = None
																		# Simple input
																		A_								=    "This is a simple input"
																		A_								=    ["This is a simple input 1", "This is a simple input 2"]
																		A_								=    ("This is a simple input", "This is a pair")
																		A_								=    [
																		    ("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"						)

																		A_								=    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    :       str						)   ->			Optional[int]:







								"""simple docstring"""
								A_								=    self.get_rust_tokenizer()
								A_								=    load_dataset("xnli"							, "all_languages"							, split="test"							, streaming=_snake_case						)

								A_								=    next(iter(_snake_case						)						)["premise"]  # pick up one data
								A_								=    list(sample_data.values()						)

								A_								=    list(map(tokenizer.encode							, _snake_case						)						)
								A_								=    [tokenizer.decode(_snake_case							, clean_up_tokenization_spaces=_snake_case						) for x in output_tokens]
								self.assertListEqual(_snake_case							, _snake_case						)






			def   lowerCamelCase__  (					self    :       int						)   ->			Tuple:







								"""simple docstring"""
								# 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						)






"""simple docstring"""





from __future__ import annotations
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    0.00
					A_								=    0
					for resistor in resistors:
										if resistor <= 0:
															A_								=    f'Resistor at index {index} has a negative or zero value!'
															raise ValueError(__a					)
										first_sum += 1 / float(__a					)
										index += 1
					return 1 / first_sum
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    0.00
					A_								=    0
					for resistor in resistors:
										sum_r += resistor
										if resistor < 0:
															A_								=    f'Resistor at index {index} has a negative value!'
															raise ValueError(__a					)
										index += 1
					return sum_r


if __name__ == "__main__":
		import doctest

		doctest.testmod()




