




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


lowerCamelCase_  						=       get_tests_dir("fixtures/test_sentencepiece.model")
@require_torch
@require_torchaudio
@require_sentencepiece
class 				__a (			unittest.TestCase					):


					"""simple docstring"""


					def   __A    (     self      :			str						)		->       Any:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =tempfile.mkdtemp()

									SCREAMING_SNAKE_CASE__       =["""<s>""", """<pad>""", """</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est"""]
									SCREAMING_SNAKE_CASE__       =dict(zip(_UpperCamelCase    ,range(len(_UpperCamelCase						)						)						)						)
									SCREAMING_SNAKE_CASE__       =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"""]						)

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

									SCREAMING_SNAKE_CASE__       ={
									    """feature_size""": 2_4,
									    """num_mel_bins""": 2_4,
									    """padding_value""": 0.0,
									    """sampling_rate""": 1_6_0_0_0,
									    """return_attention_mask""": False,
									    """do_normalize""": True,
									}
									save_json(_UpperCamelCase    ,save_dir / FEATURE_EXTRACTOR_NAME						)




					def   __A    (     self      :			List[Any]    ,**_UpperCamelCase      :			int						)		->       Union[str, Any]:


									'''simple docstring'''





									return SpeechaTextTokenizer.from_pretrained(self.tmpdirname    ,**_UpperCamelCase						)




					def   __A    (     self      :			Union[str, Any]    ,**_UpperCamelCase      :			List[str]						)		->       Dict:


									'''simple docstring'''





									return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname    ,**_UpperCamelCase						)




					def   __A    (     self      :			Any						)		->       List[str]:


									'''simple docstring'''





									shutil.rmtree(self.tmpdirname						)




					def   __A    (     self      :			Optional[int]						)		->       Optional[Any]:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self.get_tokenizer()
									SCREAMING_SNAKE_CASE__       =self.get_feature_extractor()

									SCREAMING_SNAKE_CASE__       =SpeechaTextProcessor(tokenizer=_UpperCamelCase    ,feature_extractor=_UpperCamelCase						)

									processor.save_pretrained(self.tmpdirname						)
									SCREAMING_SNAKE_CASE__       =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   __A    (     self      :			Dict						)		->       Tuple:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =SpeechaTextProcessor(
									    tokenizer=self.get_tokenizer()    ,feature_extractor=self.get_feature_extractor()						)
									processor.save_pretrained(self.tmpdirname						)

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

									SCREAMING_SNAKE_CASE__       =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   __A    (     self      :			List[Any]						)		->       Tuple:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self.get_feature_extractor()
									SCREAMING_SNAKE_CASE__       =self.get_tokenizer()

									SCREAMING_SNAKE_CASE__       =SpeechaTextProcessor(tokenizer=_UpperCamelCase    ,feature_extractor=_UpperCamelCase						)

									SCREAMING_SNAKE_CASE__       =floats_list((3, 1_0_0_0)						)

									SCREAMING_SNAKE_CASE__       =feature_extractor(_UpperCamelCase    ,return_tensors="""np"""						)
									SCREAMING_SNAKE_CASE__       =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   __A    (     self      :			str						)		->       str:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self.get_feature_extractor()
									SCREAMING_SNAKE_CASE__       =self.get_tokenizer()

									SCREAMING_SNAKE_CASE__       =SpeechaTextProcessor(tokenizer=_UpperCamelCase    ,feature_extractor=_UpperCamelCase						)

									SCREAMING_SNAKE_CASE__       ="""This is a test string"""

									SCREAMING_SNAKE_CASE__       =processor(text=_UpperCamelCase						)

									SCREAMING_SNAKE_CASE__       =tokenizer(_UpperCamelCase						)

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




					def   __A    (     self      :			Optional[int]						)		->       Tuple:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self.get_feature_extractor()
									SCREAMING_SNAKE_CASE__       =self.get_tokenizer()

									SCREAMING_SNAKE_CASE__       =SpeechaTextProcessor(tokenizer=_UpperCamelCase    ,feature_extractor=_UpperCamelCase						)

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

									SCREAMING_SNAKE_CASE__       =processor.batch_decode(_UpperCamelCase						)
									SCREAMING_SNAKE_CASE__       =tokenizer.batch_decode(_UpperCamelCase						)

									self.assertListEqual(_UpperCamelCase    ,_UpperCamelCase						)




					def   __A    (     self      :			List[Any]						)		->       Optional[int]:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self.get_feature_extractor()
									SCREAMING_SNAKE_CASE__       =self.get_tokenizer()

									SCREAMING_SNAKE_CASE__       =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"""    ,)












def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =len(__UpperCamelCase     )
    # Create a stack and push index of fist element to it
    SCREAMING_SNAKE_CASE__       =[]
    st.append(0     )

    # Span value of first element is always 1
    SCREAMING_SNAKE_CASE__       =1

    # Calculate span values for rest of the elements
    for i in range(1,    __UpperCamelCase     ):
        # Pop elements from stack while stack is not
        # empty and top of stack is smaller than price[i]
        while len(__UpperCamelCase     ) > 0 and price[st[0]] <= price[i]:
            st.pop()

        # If stack becomes empty, then price[i] is greater
        # than all elements on left of it, i.e. price[0],
        # price[1], ..price[i-1]. Else the price[i]  is
        # greater than elements after top of stack
        SCREAMING_SNAKE_CASE__       =i + 1 if len(__UpperCamelCase     ) <= 0 else (i - st[0])

        # Push this element to stack
        st.append(__UpperCamelCase     )



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    for i in range(0,    __UpperCamelCase     ):
        print(arr[i],    end=""" """     )


# Driver program to test above function
lowerCamelCase_  						=       [10, 4, 5, 90, 120, 80]
lowerCamelCase_  						=       [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)




print_array(S, len(price))






