



'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list


class   SCREAMING_SNAKE_CASE__				(   unittest.TestCase					):
	"""simple docstring"""


	def      a__ (		self			) ->							Union[str, Any]:
				A:      str														=   """<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z""".split(""" """			)
				A:      Optional[int]														=   dict(zip(A					,   range(len(A			)			)			)			)

				A:      Optional[int]														=   {
				    """pad_token""": """<pad>""",
				    """unk_token""": """<unk>""",
				    """bos_token""": """<s>""",
				    """eos_token""": """</s>""",
				}
				A:      Union[str, Any]														=   {
				    """feature_size""": 1,
				    """padding_value""": 0.0,
				    """sampling_rate""": 1_60_00,
				    """return_attention_mask""": False,
				    """do_normalize""": True,
				}

				A:      Optional[int]														=   tempfile.mkdtemp()
				A:      Optional[int]														=   os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES["""vocab_file"""]			)
				A:      Optional[Any]														=   os.path.join(self.tmpdirname					,   A			)
				with open(self.vocab_file					,   """w"""					,   encoding="""utf-8"""			) as fp:
							fp.write(json.dumps(A			) + """\n"""			)

				with open(self.feature_extraction_file					,   """w"""					,   encoding="""utf-8"""			) as fp:
							fp.write(json.dumps(A			) + """\n"""			)


	def      a__ (		self					,   **A			) ->							Tuple:
				A:      Optional[Any]														=   self.add_kwargs_tokens_map.copy()
				kwargs.update(A			)
				return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname					,   **A			)


	def      a__ (		self					,   **A			) ->							Any:
				return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname					,   **A			)


	def      a__ (		self			) ->							Optional[Any]:
				shutil.rmtree(self.tmpdirname			)


	def      a__ (		self			) ->							List[Any]:
				A:      List[str]														=   self.get_tokenizer()
				A:      Optional[Any]														=   self.get_feature_extractor()

				A:      List[Any]														=   WavaVecaProcessor(tokenizer=A					,   feature_extractor=A			)

				processor.save_pretrained(self.tmpdirname			)
				A:      Any														=   WavaVecaProcessor.from_pretrained(self.tmpdirname			)

				self.assertEqual(processor.tokenizer.get_vocab()					,   tokenizer.get_vocab()			)
				self.assertIsInstance(processor.tokenizer					,   A			)

				self.assertEqual(processor.feature_extractor.to_json_string()					,   feature_extractor.to_json_string()			)
				self.assertIsInstance(processor.feature_extractor					,   A			)


	def      a__ (		self			) ->							Union[str, Any]:
				A:      Union[str, Any]														=   WavaVecaProcessor(tokenizer=self.get_tokenizer()					,   feature_extractor=self.get_feature_extractor()			)
				processor.save_pretrained(self.tmpdirname			)

				A:      Tuple														=   self.get_tokenizer(bos_token="""(BOS)"""					,   eos_token="""(EOS)"""			)
				A:      List[str]														=   self.get_feature_extractor(do_normalize=A					,   padding_value=1.0			)

				A:      Optional[Any]														=   WavaVecaProcessor.from_pretrained(
				    self.tmpdirname					,   bos_token="""(BOS)"""					,   eos_token="""(EOS)"""					,   do_normalize=A					,   padding_value=1.0			)

				self.assertEqual(processor.tokenizer.get_vocab()					,   tokenizer_add_kwargs.get_vocab()			)
				self.assertIsInstance(processor.tokenizer					,   A			)

				self.assertEqual(processor.feature_extractor.to_json_string()					,   feature_extractor_add_kwargs.to_json_string()			)
				self.assertIsInstance(processor.feature_extractor					,   A			)


	def      a__ (		self			) ->							Dict:
				A:      List[Any]														=   self.get_feature_extractor()
				A:      Union[str, Any]														=   self.get_tokenizer()

				A:      Dict														=   WavaVecaProcessor(tokenizer=A					,   feature_extractor=A			)

				A:      Union[str, Any]														=   floats_list((3, 10_00)			)

				A:      Tuple														=   feature_extractor(A					,   return_tensors="""np"""			)
				A:      str														=   processor(A					,   return_tensors="""np"""			)

				for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum()					,   input_processor[key].sum()					,   delta=1e-2			)


	def      a__ (		self			) ->							Tuple:
				A:      Any														=   self.get_feature_extractor()
				A:      List[Any]														=   self.get_tokenizer()

				A:      Union[str, Any]														=   WavaVecaProcessor(tokenizer=A					,   feature_extractor=A			)

				A:      Optional[Any]														=   """This is a test string"""

				A:      Tuple														=   processor(text=A			)

				A:      List[str]														=   tokenizer(A			)

				for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]					,   encoded_processor[key]			)


	def      a__ (		self			) ->							List[str]:
				A:      List[Any]														=   self.get_feature_extractor()
				A:      Union[str, Any]														=   self.get_tokenizer()

				A:      Optional[Any]														=   WavaVecaProcessor(tokenizer=A					,   feature_extractor=A			)

				A:      List[str]														=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				A:      Dict														=   processor.batch_decode(A			)
				A:      int														=   tokenizer.batch_decode(A			)

				self.assertListEqual(A					,   A			)





	def      a__ (		self			) ->							Dict:
				A:      List[str]														=   self.get_feature_extractor()
				A:      Optional[int]														=   self.get_tokenizer()

				A:      List[str]														=   WavaVecaProcessor(tokenizer=A					,   feature_extractor=A			)

				self.assertListEqual(
				    processor.model_input_names					,   feature_extractor.model_input_names					,   msg="""`processor` and `feature_extractor` model input names do not match"""					,   )










'''simple docstring'''
import json
import os
from typing import List, Optional, Tuple

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


__SCREAMING_SNAKE_CASE :      Tuple             =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE :      Any             ={
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__SCREAMING_SNAKE_CASE :      List[str]             ={
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

__SCREAMING_SNAKE_CASE :      Optional[int]             ={
    'microsoft/biogpt': 1024,
}



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      int				):


			'''simple docstring'''







			A:      str														=   set()
			A:      Any														=   word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)				)
						A:      Union[str, Any]														=   char
			return pairs


class   SCREAMING_SNAKE_CASE__				(   snake_case_					):
	"""simple docstring"""


	A__    :    Dict						=				VOCAB_FILES_NAMES
	A__    :    Dict						=				PRETRAINED_VOCAB_FILES_MAP
	A__    :    Any						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A__    :    Tuple						=				['''input_ids''', '''attention_mask''']


	def __init__(		self					,   A					,   A					,   A="<unk>"					,   A="<s>"					,   A="</s>"					,   A="</s>"					,   A="<pad>"					,   **A					,   ) ->							Dict:
				super().__init__(
				    bos_token=A					,   eos_token=A					,   sep_token=A					,   unk_token=A					,   pad_token=A					,   **A					,   )

				try:
							import sacremoses
				except ImportError:
							raise ImportError(
							    """You need to install sacremoses to use BioGptTokenizer. """
							    """See https://pypi.org/project/sacremoses/ for installation."""			)

				A:      int														=   """en"""
				A:      List[Any]														=   sacremoses
				# cache of sm.MosesTokenizer instance
				A:      str														=   {}
				A:      Any														=   {}
				with open(A					,   encoding="""utf-8"""			) as vocab_handle:
							A:      int														=   json.load(A			)
				A:      Any														=   {v: k for k, v in self.encoder.items()}
				with open(A					,   encoding="""utf-8"""			) as merges_handle:
							A:      str														=   merges_handle.read().split("""\n"""			)[:-1]
				A:      List[str]														=   [tuple(merge.split()[:2]			) for merge in merges]
				A:      Optional[Any]														=   dict(zip(A					,   range(len(A			)			)			)			)
				A:      int														=   {}


	@property
	def      a__ (		self			) ->							Optional[Any]:
				return len(self.encoder			)


	def      a__ (		self			) ->							Optional[Any]:
				return dict(self.encoder					,   **self.added_tokens_encoder			)


	def      a__ (		self					,   A					,   A			) ->							List[Any]:
				if lang not in self.cache_moses_tokenizer:
							A:      Union[str, Any]														=   self.sm.MosesTokenizer(lang=A			)
							A:      List[str]														=   moses_tokenizer
				return self.cache_moses_tokenizer[lang].tokenize(
				    A					,   aggressive_dash_splits=A					,   return_str=A					,   escape=A			)


	def      a__ (		self					,   A					,   A			) ->							str:
				if lang not in self.cache_moses_detokenizer:
							A:      Optional[int]														=   self.sm.MosesDetokenizer(lang=A			)
							A:      Dict														=   moses_detokenizer
				return self.cache_moses_detokenizer[lang].detokenize(A			)


	def      a__ (		self					,   A			) ->							Optional[int]:
				A:      Union[str, Any]														=   tuple(token[:-1]			) + (token[-1] + """</w>""",)
				if token in self.cache:
							return self.cache[token]
				A:      List[str]														=   get_pairs(A			)

				if not pairs:
							return token + "</w>"

				while True:
							A:      List[Any]														=   min(A					,   key=lambda A			: self.bpe_ranks.get(A					,   float("""inf"""			)			)			)
							if bigram not in self.bpe_ranks:
										break
							A							,		A:      Dict														=   bigram
							A:      Optional[Any]														=   []
							A:      str														=   0
							while i < len(A			):
										try:
													A:      Optional[Any]														=   word.index(A					,   A			)
										except ValueError:
													new_word.extend(word[i:]			)
													break
										else:
													new_word.extend(word[i:j]			)
													A:      Optional[Any]														=   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:      Dict														=   tuple(A			)
							A:      Any														=   new_word
							if len(A			) == 1:
										break
							else:
										A:      Union[str, Any]														=   get_pairs(A			)
				A:      Dict														=   """ """.join(A			)
				if word == "\n  </w>":
							A:      List[Any]														=   """\n</w>"""
				A:      Tuple														=   word
				return word


	def      a__ (		self					,   A					,   A=False			) ->							Any:
				if bypass_tokenizer:
							A:      str														=   text.split()
				else:
							A:      str														=   self.moses_tokenize(A					,   self.lang			)

				A:      Tuple														=   []
				for token in text:
							if token:
										split_tokens.extend(list(self.bpe(A			).split(""" """			)			)			)

				return split_tokens


	def      a__ (		self					,   A			) ->							Dict:
				return self.encoder.get(A					,   self.encoder.get(self.unk_token			)			)


	def      a__ (		self					,   A			) ->							Any:
				return self.decoder.get(A					,   self.unk_token			)


	def      a__ (		self					,   A			) ->							Tuple:
				A:      Any														=   [t.replace(""" """					,   """"""			).replace("""</w>"""					,   """ """			) for t in tokens]
				A:      Union[str, Any]														=   """""".join(A			).split()
				# detokenize
				A:      List[str]														=   self.moses_detokenize(A					,   self.lang			)
				return text


	def      a__ (		self					,   A					,   A = None			) ->							List[int]:
				if token_ids_a is None:
							return [self.sep_token_id] + token_ids_a
				A:      Tuple														=   [self.sep_token_id]
				return sep + token_ids_a + sep + token_ids_a


	def      a__ (		self					,   A					,   A = None					,   A = False			) ->							List[int]:
				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=A					,   token_ids_a=A					,   already_has_special_tokens=A			)
				# no bos used in fairseq
				if token_ids_a is not None:
							return [1] + ([0] * len(A			)) + [1] + ([0] * len(A			))
				return [1] + ([0] * len(A			))


	def      a__ (		self					,   A					,   A = None			) ->							List[int]:
				A:      Any														=   [self.sep_token_id]

				# no bos used in fairseq
				if token_ids_a is None:
							return len(token_ids_a + sep			) * [0]
				return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]


	def      a__ (		self					,   A					,   A = None			) ->							Tuple[str]:
				if not os.path.isdir(A			):
							logger.error(f'Vocabulary path ({save_directory}) should be a directory'			)
							return
				A:      Dict														=   os.path.join(
				    A					,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)
				A:      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:      Tuple														=   0
				with open(A					,   """w"""					,   encoding="""utf-8"""			) as writer:
							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:      Optional[Any]														=   token_index
										writer.write(""" """.join(A			) + """\n"""			)
										index += 1

				return vocab_file, merge_file


	def __getstate__(		self			) ->							List[str]:
				A:      List[str]														=   self.__dict__.copy()
				A:      Any														=   None
				return state





	def __setstate__(		self					,   A			) ->							Optional[int]:
				A:      List[str]														=   d

				try:
							import sacremoses
				except ImportError:
							raise ImportError(
							    """You need to install sacremoses to use XLMTokenizer. """
							    """See https://pypi.org/project/sacremoses/ for installation."""			)

				A:      Dict														=   sacremoses





