



'''simple docstring'''







import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin




class 				__lowerCAmelCase(							lowerCAmelCase__		,			unittest.TestCase      ):
			__snake_case  :		str		=      SpeechaTextaTokenizer
			__snake_case  :		str		=      False






			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					super().setUp()

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      '<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'.split(' '	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      ['he re</w> 123', 'here a 1456']
					SCREAMING_SNAKE_CASE_						:str										=      dict(zip(SCREAMING_SNAKE_CASE      ,       range(len(SCREAMING_SNAKE_CASE	)	)	)	)

					SCREAMING_SNAKE_CASE_						:List[str]										=      {'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

					SCREAMING_SNAKE_CASE_						:List[str]										=      tempfile.mkdtemp()
					SCREAMING_SNAKE_CASE_						:Tuple										=      os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['vocab_file']	)
					SCREAMING_SNAKE_CASE_						:int										=      os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['merges_file']	)

					with open(self.vocab_file      ,       'w'      ,       encoding='utf-8'	) as fp:
							fp.write(json.dumps(SCREAMING_SNAKE_CASE	) + '\n'	)

					with open(self.merges_file      ,       'w'	) as fp:
							fp.write('\n'.join(SCREAMING_SNAKE_CASE	)	)






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      list(self.get_tokenizer().get_vocab().keys()	)

					self.assertEqual(vocab_keys[0]      ,       '<s>'	)
					self.assertEqual(vocab_keys[1]      ,       '<pad>'	)
					self.assertEqual(vocab_keys[-1]      ,       'vocab'	)
					self.assertEqual(len(SCREAMING_SNAKE_CASE	)      ,       14	)






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					self.assertEqual(self.get_tokenizer().vocab_size      ,       14	)






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      SpeechaTextaTokenizer.from_pretrained(self.tmpdirname	)

					# make sure @@ is correctly concatenated
					SCREAMING_SNAKE_CASE_						:Tuple										=      [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
					SCREAMING_SNAKE_CASE_						:str										=      tokenizer.decode(SCREAMING_SNAKE_CASE	)

					self.assertTrue(output_string == 'herecouple words ofthe'	)






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      SpeechaTextaTokenizer.from_pretrained(self.tmpdirname	)

					with tempfile.TemporaryDirectory() as tmp_dirname:
							tokenizer.save_pretrained(SCREAMING_SNAKE_CASE	)
							os.remove(os.path.join(SCREAMING_SNAKE_CASE      ,       'merges.txt'	)	)

							# load tokenizer without merges file should not throw an error
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      SpeechaTextaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)

					with tempfile.TemporaryDirectory() as tmp_dirname:
							# save tokenizer and load again
							tokenizer.save_pretrained(SCREAMING_SNAKE_CASE	)
							SCREAMING_SNAKE_CASE_						:Any										=      SpeechaTextaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)

					self.assertIsNotNone(SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					if not self.test_slow_tokenizer:
							return

					SCREAMING_SNAKE_CASE_						:int										=      inspect.signature(self.tokenizer_class.__init__	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      self.get_tokenizer()

					for parameter_name, parameter in signature.parameters.items():
							if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
									self.assertIn(SCREAMING_SNAKE_CASE      ,       tokenizer.init_kwargs	)




import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Optional[Any]				      =     copy.deepcopy(__UpperCamelCase			)
     a_     :  Dict				      =     sorted(__UpperCamelCase			)
     a_     :  Dict				      =     [bisect.bisect_right(__UpperCamelCase	,	__UpperCamelCase			) for y in x]
     return quantized



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase=0			):
     a_     :  str				      =     np.arange(start=3	,	stop=__UpperCamelCase	,	step=4			).tolist() if k > 0 else [1_0]
     a_     :  Any				      =     _quantize(__UpperCamelCase	,	__UpperCamelCase			)
     # count number of elements per group
     a_     :  Optional[int]				      =     np.unique(__UpperCamelCase	,	return_counts=__UpperCamelCase			)[1]
     a_     :  List[str]				      =     [0] + bins + [np.inf]
     logger.info("""Using {} as bins for aspect lengths quantization""".format(__UpperCamelCase			)			)
     logger.info("""Count of instances per bin: {}""".format(__UpperCamelCase			)			)
     return groups


class 	a__						(    lowerCAmelCase_     ):

       def __init__( self   :  int   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  str     ):
            if not isinstance(lowerCamelCase_   ,				lowerCamelCase_     ):
                 raise ValueError(
                     """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase_     )     )
            a_     :  Optional[Any]				      =     sampler
            a_     :  List[Any]				      =     group_ids
            a_     :  Tuple				      =     batch_size

       def __iter__( self   :  Dict     ):
            a_     :  Tuple				      =     defaultdict(lowerCamelCase_     )
            a_     :  Any				      =     defaultdict(lowerCamelCase_     )

            a_     :  Any				      =     0
            for idx in self.sampler:
                 a_     :  str				      =     self.group_ids[idx]
                 buffer_per_group[group_id].append(lowerCamelCase_     )
                 samples_per_group[group_id].append(lowerCamelCase_     )
                 if len(buffer_per_group[group_id]     ) == self.batch_size:
                      yield buffer_per_group[group_id]  # TODO
                      num_batches += 1
                      del buffer_per_group[group_id]
                 assert len(buffer_per_group[group_id]     ) < self.batch_size

            # now we have run out of elements that satisfy
            # the group criteria, let's return the remaining
            # elements so that the size of the sampler is
            # deterministic
            a_     :  Optional[Any]				      =     len(self     )
            a_     :  Any				      =     expected_num_batches - num_batches
            if num_remaining > 0:
                 # for the remaining batches, group the batches by similar lengths
                 a_     :  str				      =     []
                 for group_id, idxs in sorted(buffer_per_group.items()   ,				key=lambda lowerCamelCase_     : x[0]     ):
                      batch_idx.extend(lowerCamelCase_     )
                      if len(lowerCamelCase_     ) >= self.batch_size:
                           yield batch_idx[: self.batch_size]
                           a_     :  Union[str, Any]				      =     batch_idx[self.batch_size :]
                           num_remaining -= 1
                 if len(lowerCamelCase_     ) > 0:
                      yield batch_idx
                      num_remaining -= 1
            assert num_remaining == 0

       def __len__( self   :  List[Any]     ):
            return (len(self.sampler     ) + self.batch_size - 1) // self.batch_size



