



import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

SCREAMING_SNAKE_CASE_				= {
    """vocab_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"""},
    """merges_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"""},
}

SCREAMING_SNAKE_CASE_				= {
    """ctrl""": 2_56,
}

SCREAMING_SNAKE_CASE_				= {
    """Pregnancy""": 16_86_29,
    """Christianity""": 76_75,
    """Explain""": 10_64_23,
    """Fitness""": 6_34_40,
    """Saving""": 6_31_63,
    """Ask""": 2_71_71,
    """Ass""": 9_59_85,
    """Joke""": 16_35_09,
    """Questions""": 4_56_22,
    """Thoughts""": 4_96_05,
    """Retail""": 5_23_42,
    """Feminism""": 16_43_38,
    """Writing""": 1_19_92,
    """Atheism""": 19_22_63,
    """Netflix""": 4_86_16,
    """Computing""": 3_96_39,
    """Opinion""": 4_32_13,
    """Alone""": 4_49_67,
    """Funny""": 5_89_17,
    """Gaming""": 4_03_58,
    """Human""": 40_88,
    """India""": 13_31,
    """Joker""": 7_71_38,
    """Diet""": 3_62_06,
    """Legal""": 1_18_59,
    """Norman""": 49_39,
    """Tip""": 7_26_89,
    """Weight""": 5_23_43,
    """Movies""": 4_62_73,
    """Running""": 2_34_25,
    """Science""": 20_90,
    """Horror""": 3_77_93,
    """Confession""": 6_05_72,
    """Finance""": 1_22_50,
    """Politics""": 1_63_60,
    """Scary""": 19_19_85,
    """Support""": 1_26_54,
    """Technologies""": 3_25_16,
    """Teenage""": 6_61_60,
    """Event""": 3_27_69,
    """Learned""": 6_74_60,
    """Notion""": 18_27_70,
    """Wikipedia""": 3_75_83,
    """Books""": 66_65,
    """Extract""": 7_60_50,
    """Confessions""": 10_27_01,
    """Conspiracy""": 7_59_32,
    """Links""": 6_36_74,
    """Narcissus""": 15_04_25,
    """Relationship""": 5_47_66,
    """Relationships""": 13_47_96,
    """Reviews""": 4_16_71,
    """News""": 42_56,
    """Translation""": 2_68_20,
    """multilingual""": 12_84_06,
}



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Optional[int]:
		a_     :      List[Any] 						=     set()
		a_     :      Any 						=     word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)	)
				a_     :      Optional[int] 						=     char

		a_     :      int 						=     set(SCREAMING_SNAKE_CASE__	)
		return pairs




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					VOCAB_FILES_NAMES
							__lowerCAmelCase    					=					PRETRAINED_VOCAB_FILES_MAP
							__lowerCAmelCase    					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowerCAmelCase    					=					CONTROL_CODES






							def __init__(   self			,     a_			,     a_			,     a_="<unk>"			,     **a_      ):
									super().__init__(unk_token=a_			,     **a_      )

									with open(a_			,     encoding="utf-8"      ) as vocab_handle:
											a_     :      Optional[int] 						=     json.load(a_      )
									a_     :      List[Any] 						=     {v: k for k, v in self.encoder.items()}
									with open(a_			,     encoding="utf-8"      ) as merges_handle:
											a_     :      List[str] 						=     merges_handle.read().split("\n"      )[1:-1]
									a_     :      Tuple 						=     [tuple(merge.split()      ) for merge in merges]
									a_     :      Union[str, Any] 						=     dict(zip(a_			,     range(len(a_      )      )      )      )
									a_     :      int 						=     {}






							@property
							def snake_case_  (   self      ):
									return len(self.encoder      )






							def snake_case_  (   self      ):
									return dict(self.encoder			,     **self.added_tokens_encoder      )






							def snake_case_  (   self			,     a_      ):
									if token in self.cache:
											return self.cache[token]
									a_     :      Optional[Any] 						=     tuple(a_      )
									a_     :      Tuple 						=     tuple(list(word[:-1]      ) + [word[-1] + "</w>"]      )
									a_     :      int 						=     get_pairs(a_      )

									if not pairs:
											return token

									while True:
											a_     :      Any 						=     min(a_			,     key=lambda a_      : self.bpe_ranks.get(a_			,     float("inf"      )      )      )
											if bigram not in self.bpe_ranks:
													break
											a_     :      Tuple 						=     bigram
											a_     :      Tuple 						=     []
											a_     :      Tuple 						=     0
											while i < len(a_      ):
													try:
															a_     :      Optional[int] 						=     word.index(a_			,     a_      )
													except ValueError:
															new_word.extend(word[i:]      )
															break
													else:
															new_word.extend(word[i:j]      )
															a_     :      int 						=     j

													if word[i] == first and i < len(a_      ) - 1 and word[i + 1] == second:
															new_word.append(first + second      )
															i += 2
													else:
															new_word.append(word[i]      )
															i += 1
											a_     :      Optional[Any] 						=     tuple(a_      )
											a_     :      Tuple 						=     new_word
											if len(a_      ) == 1:
													break
											else:
													a_     :      int 						=     get_pairs(a_      )
									a_     :      str 						=     "@@ ".join(a_      )
									a_     :      Tuple 						=     word[:-4]
									a_     :      Union[str, Any] 						=     word
									return word






							def snake_case_  (   self			,     a_      ):
									a_     :      Tuple 						=     []

									a_     :      Tuple 						=     re.findall(R"\S+\n?"			,     a_      )

									for token in words:
											split_tokens.extend(list(self.bpe(a_      ).split(" "      )      )      )
									return split_tokens






							def snake_case_  (   self			,     a_      ):
									return self.encoder.get(a_			,     self.encoder.get(self.unk_token      )      )






							def snake_case_  (   self			,     a_      ):
									return self.decoder.get(a_			,     self.unk_token      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Optional[int] 						=     " ".join(a_      ).replace("@@ "			,     ""      ).strip()
									return out_string






							def snake_case_  (   self			,     a_			,     a_ = None      ):
									if not os.path.isdir(a_      ):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
											return
									a_     :      Optional[Any] 						=     os.path.join(
									    a_			,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )
									a_     :      List[Any] 						=     os.path.join(
									    a_			,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]      )

									with open(a_			,     "w"			,     encoding="utf-8"      ) as f:
											f.write(json.dumps(self.encoder			,     indent=2			,     sort_keys=a_			,     ensure_ascii=a_      ) + "\n"      )

									a_     :      Any 						=     0
									with open(a_			,     "w"			,     encoding="utf-8"      ) as writer:
											writer.write("#version: 0.2\n"      )
											for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,     key=lambda a_      : kv[1]      ):
													if index != token_index:
															logger.warning(
															    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
															    " Please check that the tokenizer is not corrupted!"      )
															a_     :      int 						=     token_index
													writer.write(" ".join(a_      ) + "\n"      )
													index += 1

									return vocab_file, merge_file

							# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
							#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
							#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
							#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
							#     return ''.join(tokens_generated_so_far)




"""simple docstring"""
import unittest

from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_				= get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")




@require_sentencepiece
@require_tokenizers
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					SpeechTaTokenizer
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					True






							def snake_case_  (   self      ):
									super().setUp()

									# We have a SentencePiece fixture for testing
									a_     :      Any 						=     SpeechTaTokenizer(a_      )

									a_     :      Optional[int] 						=     AddedToken("<mask>"			,     lstrip=a_			,     rstrip=a_      )
									a_     :      Any 						=     mask_token
									tokenizer.add_special_tokens({"mask_token": mask_token}      )
									tokenizer.add_tokens(["<ctc_blank>"]      )

									tokenizer.save_pretrained(self.tmpdirname      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Tuple 						=     "this is a test"
									a_     :      Any 						=     "this is a test"
									return input_text, output_text






							def snake_case_  (   self			,     a_			,     a_=False			,     a_=2_0			,     a_=5      ):
									a_						,				a_     :      Optional[Any] 						=     self.get_input_output_texts(a_      )
									a_     :      Optional[Any] 						=     tokenizer.encode(a_			,     add_special_tokens=a_      )
									a_     :      Dict 						=     tokenizer.decode(a_			,     clean_up_tokenization_spaces=a_      )
									return text, ids






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     "<pad>"
									a_     :      Optional[int] 						=     1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_      )			,     a_      )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_      )			,     a_      )






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     list(self.get_tokenizer().get_vocab().keys()      )

									self.assertEqual(vocab_keys[0]			,     "<s>"      )
									self.assertEqual(vocab_keys[1]			,     "<pad>"      )
									self.assertEqual(vocab_keys[-4]			,     "œ"      )
									self.assertEqual(vocab_keys[-2]			,     "<mask>"      )
									self.assertEqual(vocab_keys[-1]			,     "<ctc_blank>"      )
									self.assertEqual(len(a_      )			,     8_1      )






							def snake_case_  (   self      ):
									self.assertEqual(self.get_tokenizer().vocab_size			,     7_9      )






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.get_tokenizers(do_lower_case=a_      )
									for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
													a_     :      Dict 						=     tokenizer.vocab_size
													a_     :      List[str] 						=     len(a_      )

													self.assertNotEqual(a_			,     0      )

													# We usually have added tokens from the start in tests because our vocab fixtures are
													# smaller than the original vocabs - let's not assert this
													# self.assertEqual(vocab_size, all_size)

													a_     :      Optional[int] 						=     ["aaaaa bbbbbb", "cccccccccdddddddd"]
													a_     :      int 						=     tokenizer.add_tokens(a_      )
													a_     :      List[Any] 						=     tokenizer.vocab_size
													a_     :      Tuple 						=     len(a_      )

													self.assertNotEqual(a_			,     0      )
													self.assertEqual(a_			,     a_      )
													self.assertEqual(a_			,     len(a_      )      )
													self.assertEqual(a_			,     all_size + len(a_      )      )

													a_     :      str 						=     tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"			,     add_special_tokens=a_      )

													self.assertGreaterEqual(len(a_      )			,     4      )
													self.assertGreater(tokens[0]			,     tokenizer.vocab_size - 1      )
													self.assertGreater(tokens[-3]			,     tokenizer.vocab_size - 1      )

													a_     :      Tuple 						=     {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
													a_     :      Dict 						=     tokenizer.add_special_tokens(a_      )
													a_     :      Optional[Any] 						=     tokenizer.vocab_size
													a_     :      Any 						=     len(a_      )

													self.assertNotEqual(a_			,     0      )
													self.assertEqual(a_			,     a_      )
													self.assertEqual(a_			,     len(a_      )      )
													self.assertEqual(a_			,     all_size_a + len(a_      )      )

													a_     :      Any 						=     tokenizer.encode(
													    ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"			,     add_special_tokens=a_      )

													self.assertGreaterEqual(len(a_      )			,     6      )
													self.assertGreater(tokens[0]			,     tokenizer.vocab_size - 1      )
													self.assertGreater(tokens[0]			,     tokens[1]      )
													self.assertGreater(tokens[-3]			,     tokenizer.vocab_size - 1      )
													self.assertGreater(tokens[-3]			,     tokens[-4]      )
													self.assertEqual(tokens[0]			,     tokenizer.eos_token_id      )
													self.assertEqual(tokens[-3]			,     tokenizer.pad_token_id      )






							def snake_case_  (   self      ):
									pass






							def snake_case_  (   self      ):
									pass






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     self.get_tokenizer()

									a_     :      Any 						=     tokenizer.tokenize("This is a test"      )
									# fmt: off
									self.assertListEqual(a_			,     [SPIECE_UNDERLINE, "T", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "a", SPIECE_UNDERLINE, "t", "e", "s", "t"]      )
									# fmt: on

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(a_      )			,     [4, 3_2, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 7, 4, 6, 5, 1_2, 6]			,     )

									a_     :      Optional[int] 						=     tokenizer.tokenize("I was born in 92000, and this is falsé."      )
									self.assertListEqual(
									    a_			,     [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "92000", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."]      )

									a_     :      Tuple 						=     tokenizer.convert_tokens_to_ids(a_      )
									# fmt: off
									self.assertListEqual(a_			,     [4, 3_0, 4, 2_0, 7, 1_2, 4, 2_5, 8, 1_3, 9, 4, 1_0, 9, 4, 3, 2_3, 4, 7, 9, 1_4, 4, 6, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 1_9, 7, 1_5, 1_2, 7_3, 2_6]      )
									# fmt: on

									a_     :      Tuple 						=     tokenizer.convert_ids_to_tokens(a_      )
									self.assertListEqual(
									    a_			,     [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "<unk>", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."]      )






							@slow
							def snake_case_  (   self      ):
									# Use custom sequence because this tokenizer does not handle numbers.
									a_     :      List[Any] 						=     [
									    "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
									    "general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
									    "Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
									    "models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
									    "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
									    "conditioning on both left and right context in all layers.",
									    "The quick brown fox jumps over the lazy dog.",
									]

									# fmt: off
									a_     :      Tuple 						=     {
									    "input_ids": [
									        [4, 3_2, 1_3, 7, 9, 1_2, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_2, 4, 6_4, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_5, 2_2, 4, 2_8, 9, 8, 2_0, 9, 4, 7, 1_2, 4, 2_4, 2_2, 6, 8, 1_3, 1_7, 1_1, 3_9, 6, 1_3, 7, 9, 1_2, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_2, 4, 7, 9, 1_4, 4, 2_4, 2_2, 6, 8, 1_3, 1_7, 1_1, 3_9, 2_4, 1_3, 5, 6, 1_3, 7, 1_0, 9, 5, 1_4, 3_9, 2_5, 5, 1_3, 6, 6_3, 4, 2_4, 1_3, 8, 2_7, 1_0, 1_4, 5, 1_2, 4, 2_1, 5, 9, 5, 1_3, 7, 1_5, 3_9, 2_4, 1_6, 1_3, 2_4, 8, 1_2, 5, 4, 7, 1_3, 1_7, 1_1, 1_0, 6, 5, 1_7, 6, 1_6, 1_3, 5, 1_2, 4, 6_4, 4_0, 4_7, 5_4, 3_2, 2_3, 4, 5_3, 4_9, 3_2, 2_3, 4, 5_4, 8, 4_0, 4_7, 5_4, 3_2, 7, 2_3, 4, 6_9, 5_2, 4_3, 2_3, 4, 5_1, 1_0, 1_2, 6, 1_0, 1_5, 4_0, 5, 1_3, 6, 2_3, 4, 6_9, 5_2, 4_8, 5, 6, 2_6, 2_6, 2_6, 6_3, 4, 1_9, 8, 1_3, 4, 4_8, 7, 6, 1_6, 1_3, 7, 1_5, 4, 5_2, 7, 9, 2_1, 1_6, 7, 2_1, 5, 4, 6_1, 9, 1_4, 5, 1_3, 1_2, 6, 7, 9, 1_4, 1_0, 9, 2_1, 4, 6_4, 4_8, 5_2, 6_1, 6_3, 4, 7, 9, 1_4, 4, 4_8, 7, 6, 1_6, 1_3, 7, 1_5, 4, 5_2, 7, 9, 2_1, 1_6, 7, 2_1, 5, 4, 5_3, 5, 9, 5, 1_3, 7, 6, 1_0, 8, 9, 4, 6_4, 4_8, 5_2, 5_3, 6_3, 4, 2_0, 1_0, 6, 1_1, 4, 8, 2_7, 5, 1_3, 4, 6, 1_1, 1_0, 1_3, 6, 2_2, 3_9, 6, 2_0, 8, 4, 2_4, 1_3, 5, 6, 1_3, 7, 1_0, 9, 5, 1_4, 4, 1_8, 8, 1_4, 5, 1_5, 1_2, 4, 1_0, 9, 4, 8, 9, 5, 4, 1_1, 1_6, 9, 1_4, 1_3, 5, 1_4, 4, 2_4, 1_5, 1_6, 1_2, 4, 1_5, 7, 9, 2_1, 1_6, 7, 2_1, 5, 1_2, 4, 7, 9, 1_4, 4, 1_4, 5, 5, 2_4, 4, 1_0, 9, 6, 5, 1_3, 8, 2_4, 5, 1_3, 7, 2_5, 1_0, 1_5, 1_0, 6, 2_2, 4, 2_5, 5, 6, 2_0, 5, 5, 9, 4, 5_8, 7, 3_7, 2_3, 4, 4_9, 2_2, 3_2, 8, 1_3, 1_7, 1_1, 4, 7, 9, 1_4, 4, 3_2, 5, 9, 1_2, 8, 1_3, 5_5, 1_5, 8, 2_0, 2_6, 2],
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									}
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=a_			,     model_name="microsoft/speecht5_asr"			,     revision="c5ef64c71905caeccde0e4462ef3f9077224c524"			,     sequences=a_			,     )