


"""simple docstring"""




import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
   from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


__SCREAMING_SNAKE_CASE					=						get_tests_dir("""fixtures/test_sentencepiece.model""")





@require_torch
@require_torchaudio
@require_sentencepiece
class __snake_case		(					unittest.TestCase      ):







   """simple docstring"""




   def SCREAMING_SNAKE_CASE_      (		self							:str       ):
          _a          =				tempfile.mkdtemp()

          _a          =				['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
          _a          =				dict(zip(UpperCamelCase__				,  range(len(UpperCamelCase__       )       )       )       )
          _a          =				Path(self.tmpdirname       )
          save_json(UpperCamelCase__				,  save_dir / VOCAB_FILES_NAMES["vocab_file"]       )
          if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                 copyfile(UpperCamelCase__				,  save_dir / VOCAB_FILES_NAMES["spm_file"]       )

          _a          =				SpeechaTextTokenizer.from_pretrained(self.tmpdirname       )
          tokenizer.save_pretrained(self.tmpdirname       )

          _a          =				{
              'feature_size': 24,
              'num_mel_bins': 24,
              'padding_value': 0.0,
              'sampling_rate': 16_000,
              'return_attention_mask': False,
              'do_normalize': True,
          }
          save_json(UpperCamelCase__				,  save_dir / FEATURE_EXTRACTOR_NAME       )




   def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]				,  **UpperCamelCase__							:Optional[int]       ):
          return SpeechaTextTokenizer.from_pretrained(self.tmpdirname				,  **UpperCamelCase__       )




   def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]				,  **UpperCamelCase__							:Optional[Any]       ):
          return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname				,  **UpperCamelCase__       )




   def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]       ):
          shutil.rmtree(self.tmpdirname       )




   def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]       ):
          _a          =				self.get_tokenizer()
          _a          =				self.get_feature_extractor()

          _a          =				SpeechaTextProcessor(tokenizer=UpperCamelCase__				,  feature_extractor=UpperCamelCase__       )

          processor.save_pretrained(self.tmpdirname       )
          _a          =				SpeechaTextProcessor.from_pretrained(self.tmpdirname       )

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

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




   def SCREAMING_SNAKE_CASE_      (		self							:List[str]       ):
          _a          =				SpeechaTextProcessor(
              tokenizer=self.get_tokenizer()				,  feature_extractor=self.get_feature_extractor()       )
          processor.save_pretrained(self.tmpdirname       )

          _a          =				self.get_tokenizer(bos_token="(BOS)"				,  eos_token="(EOS)"       )
          _a          =				self.get_feature_extractor(do_normalize=UpperCamelCase__				,  padding_value=1.0       )

          _a          =				SpeechaTextProcessor.from_pretrained(
              self.tmpdirname				,  bos_token="(BOS)"				,  eos_token="(EOS)"				,  do_normalize=UpperCamelCase__				,  padding_value=1.0       )

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

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




   def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]       ):
          _a          =				self.get_feature_extractor()
          _a          =				self.get_tokenizer()

          _a          =				SpeechaTextProcessor(tokenizer=UpperCamelCase__				,  feature_extractor=UpperCamelCase__       )

          _a          =				floats_list((3, 1_000)       )

          _a          =				feature_extractor(UpperCamelCase__				,  return_tensors="np"       )
          _a          =				processor(UpperCamelCase__				,  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 SCREAMING_SNAKE_CASE_      (		self							:int       ):
          _a          =				self.get_feature_extractor()
          _a          =				self.get_tokenizer()

          _a          =				SpeechaTextProcessor(tokenizer=UpperCamelCase__				,  feature_extractor=UpperCamelCase__       )

          _a          =				'This is a test string'

          _a          =				processor(text=UpperCamelCase__       )

          _a          =				tokenizer(UpperCamelCase__       )

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




   def SCREAMING_SNAKE_CASE_      (		self							:int       ):
          _a          =				self.get_feature_extractor()
          _a          =				self.get_tokenizer()

          _a          =				SpeechaTextProcessor(tokenizer=UpperCamelCase__				,  feature_extractor=UpperCamelCase__       )

          _a          =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          _a          =				processor.batch_decode(UpperCamelCase__       )
          _a          =				tokenizer.batch_decode(UpperCamelCase__       )

          self.assertListEqual(UpperCamelCase__				,  UpperCamelCase__       )




   def SCREAMING_SNAKE_CASE_      (		self							:Optional[int]       ):
          _a          =				self.get_feature_extractor()
          _a          =				self.get_tokenizer()

          _a          =				SpeechaTextProcessor(tokenizer=UpperCamelCase__				,  feature_extractor=UpperCamelCase__       )

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




"""simple docstring"""




from __future__ import annotations

__SCREAMING_SNAKE_CASE					=						[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]





class __snake_case		:







   """simple docstring"""




   def __init__(		self							:Optional[Any]				,  UpperCamelCase__							:int       ):
          _a          =				-1
          _a          =				-1
          _a          =				[-1] * length
          _a          =				None
          _a          =				None




   def __repr__(		self							:Optional[Any]       ):
          return f'Node(min_value={self.minn} max_value={self.maxx})'






def 						__a      (  a					):





       """simple docstring"""







       _a          =				Node(len(a					)					)
       _a   , _a          =				min(a					), max(a					)
       # Leaf node case where the node contains only one unique value
       if root.minn == root.maxx:
              return root
       _a          =				(root.minn + root.maxx) // 2

       _a          =				[]
       _a          =				[]

       for index, num in enumerate(a					):
              if num <= pivot:
                     left_arr.append(a					)
              else:
                     right_arr.append(a					)
              _a          =				len(a					)
       _a          =				build_tree(a					)
       _a          =				build_tree(a					)
       return root



def 						__a      (  a,		a,		a					):





       """simple docstring"""







       if index < 0 or node is None:
              return 0
       # Leaf node cases
       if node.minn == node.maxx:
              return index + 1 if node.minn == num else 0
       _a          =				(node.minn + node.maxx) // 2
       if num <= pivot:
              # go the left subtree and map index to the left subtree
              return rank_till_index(node.left,		a,		node.map_left[index] - 1					)
       else:
              # go to the right subtree and map index to the right subtree
              return rank_till_index(node.right,		a,		index - node.map_left[index]					)



def 						__a      (  a,		a,		a,		a					):





       """simple docstring"""







       if start > end:
              return 0
       _a          =				rank_till_index(a,		a,		a					)
       _a          =				rank_till_index(a,		a,		start - 1					)
       return rank_till_end - rank_before_start



def 						__a      (  a,		a,		a,		a					):





       """simple docstring"""







       if index > (end - start) or start > end or node is None:
              return -1
       # Leaf node case
       if node.minn == node.maxx:
              return node.minn
       # Number of elements in the left subtree in interval [start, end]
       _a          =				node.map_left[end] - (
           node.map_left[start - 1] if start else 0
       )
       if num_elements_in_left_tree > index:
              return quantile(
                  node.left,		a,		(node.map_left[start - 1] if start else 0),		node.map_left[end] - 1,		)
       else:
              return quantile(
                  node.right,		index - num_elements_in_left_tree,		start - (node.map_left[start - 1] if start else 0),		end - node.map_left[end],		)



def 						__a      (  a,		a,		a,		a,		a					):





       """simple docstring"""







       if (
           start > end
           or node is None
           or start_num > end_num
           or node.minn > end_num
           or node.maxx < start_num
       ):
              return 0
       if start_num <= node.minn and node.maxx <= end_num:
              return end - start + 1
       _a          =				range_counting(
           node.left,		(node.map_left[start - 1] if start else 0),		node.map_left[end] - 1,		a,		a,		)
       _a          =				range_counting(
           node.right,		start - (node.map_left[start - 1] if start else 0),		end - node.map_left[end],		a,		a,		)
       return left + right


if __name__ == "__main__":
   import doctest

   doctest.testmod()
