




from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


lowerCamelCase_  						=       {
    "id": "5733be284776f41900661182",
    "title": "University_of_Notre_Dame",
    "context": "Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.",
    "question": "To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?",
    "answers": {"text": ["Saint Bernadette Soubirous"], "answer_start": [515]},
}



@pytest.mark.parametrize(
    """task_cls""",    [
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ],    )
def      UpperCAmelCase_   (     __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =task_cls()
    SCREAMING_SNAKE_CASE__       =asdict(__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__       =task_template_from_dict(__UpperCamelCase     )
    assert task == reloaded
class 				__a :


     """simple docstring"""


     def   __A    (     self      :			int						)		->       Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =LanguageModeling(text_column="""input_text"""						)
         assert {"input_text": "text"} == task.column_mapping




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =Features({"""text""": Value("""string"""						)}						)
         SCREAMING_SNAKE_CASE__       ={"""text_column""": """input_text"""}
         SCREAMING_SNAKE_CASE__       =LanguageModeling.from_dict(_UpperCamelCase						)
         assert "language-modeling" == task.task
         assert input_schema == task.input_schema






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =sorted(["""pos""", """neg"""]						)




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =TextClassification(text_column="""input_text"""    ,label_column="""input_label"""						)
         self.assertDictEqual({"""input_text""": """text""", """input_label""": """labels"""}    ,task.column_mapping						)




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =Features({"""text""": Value("""string"""						)}						)
         # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
         SCREAMING_SNAKE_CASE__       =Features({"""labels""": ClassLabel}						)
         SCREAMING_SNAKE_CASE__       ={"""text_column""": """input_text""", """label_column""": """input_labels"""}
         SCREAMING_SNAKE_CASE__       =TextClassification.from_dict(_UpperCamelCase						)
         self.assertEqual("""text-classification"""    ,task.task						)
         self.assertEqual(_UpperCamelCase    ,task.input_schema						)
         self.assertEqual(_UpperCamelCase    ,task.label_schema						)




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =TextClassification(text_column="""input_text"""    ,label_column="""input_label"""						)
         self.assertEqual(task.label_schema["""labels"""]    ,_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels						)}						)						)
         self.assertEqual(task.label_schema["""labels"""]    ,ClassLabel(names=self.labels						)						)






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =QuestionAnsweringExtractive(
             context_column="""input_context"""    ,question_column="""input_question"""    ,answers_column="""input_answers"""						)
         self.assertDictEqual(
             {"""input_context""": """context""", """input_question""": """question""", """input_answers""": """answers"""}    ,task.column_mapping						)




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =Features({"""question""": Value("""string"""						), """context""": Value("""string"""						)}						)
         SCREAMING_SNAKE_CASE__       =Features(
             {
                 """answers""": Sequence(
                     {
                         """text""": Value("""string"""						),
                         """answer_start""": Value("""int32"""						),
                     }						)
             }						)
         SCREAMING_SNAKE_CASE__       ={
             """context_column""": """input_input_context""",
             """question_column""": """input_question""",
             """answers_column""": """input_answers""",
         }
         SCREAMING_SNAKE_CASE__       =QuestionAnsweringExtractive.from_dict(_UpperCamelCase						)
         self.assertEqual("""question-answering-extractive"""    ,task.task						)
         self.assertEqual(_UpperCamelCase    ,task.input_schema						)
         self.assertEqual(_UpperCamelCase    ,task.label_schema						)






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =Summarization(text_column="""input_text"""    ,summary_column="""input_summary"""						)
         self.assertDictEqual({"""input_text""": """text""", """input_summary""": """summary"""}    ,task.column_mapping						)




     def   __A    (     self      :			str						)		->       Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =Features({"""text""": Value("""string"""						)}						)
         SCREAMING_SNAKE_CASE__       =Features({"""summary""": Value("""string"""						)}						)
         SCREAMING_SNAKE_CASE__       ={"""text_column""": """input_text""", """summary_column""": """input_summary"""}
         SCREAMING_SNAKE_CASE__       =Summarization.from_dict(_UpperCamelCase						)
         self.assertEqual("""summarization"""    ,task.task						)
         self.assertEqual(_UpperCamelCase    ,task.input_schema						)
         self.assertEqual(_UpperCamelCase    ,task.label_schema						)






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =AutomaticSpeechRecognition(audio_column="""input_audio"""    ,transcription_column="""input_transcription"""						)
         self.assertDictEqual({"""input_audio""": """audio""", """input_transcription""": """transcription"""}    ,task.column_mapping						)




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =Features({"""audio""": Audio()}						)
         SCREAMING_SNAKE_CASE__       =Features({"""transcription""": Value("""string"""						)}						)
         SCREAMING_SNAKE_CASE__       ={
             """audio_column""": """input_audio""",
             """transcription_column""": """input_transcription""",
         }
         SCREAMING_SNAKE_CASE__       =AutomaticSpeechRecognition.from_dict(_UpperCamelCase						)
         self.assertEqual("""automatic-speech-recognition"""    ,task.task						)
         self.assertEqual(_UpperCamelCase    ,task.input_schema						)
         self.assertEqual(_UpperCamelCase    ,task.label_schema						)






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =sorted(["""pos""", """neg"""]						)




     def   __A    (     self      :			int						)		->       Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =AudioClassification(audio_column="""input_audio"""    ,label_column="""input_label"""						)
         self.assertDictEqual({"""input_audio""": """audio""", """input_label""": """labels"""}    ,task.column_mapping						)




     def   __A    (     self      :			Any						)		->       Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =Features({"""audio""": Audio()}						)
         SCREAMING_SNAKE_CASE__       =Features({"""labels""": ClassLabel}						)
         SCREAMING_SNAKE_CASE__       ={
             """audio_column""": """input_image""",
             """label_column""": """input_label""",
         }
         SCREAMING_SNAKE_CASE__       =AudioClassification.from_dict(_UpperCamelCase						)
         self.assertEqual("""audio-classification"""    ,task.task						)
         self.assertEqual(_UpperCamelCase    ,task.input_schema						)
         self.assertEqual(_UpperCamelCase    ,task.label_schema						)




     def   __A    (     self      :			str						)		->       int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =AudioClassification(audio_column="""input_audio"""    ,label_column="""input_label"""						)
         self.assertEqual(task.label_schema["""labels"""]    ,_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels						)}						)						)
         self.assertEqual(task.label_schema["""labels"""]    ,ClassLabel(names=self.labels						)						)






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =sorted(["""pos""", """neg"""]						)




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =ImageClassification(image_column="""input_image"""    ,label_column="""input_label"""						)
         self.assertDictEqual({"""input_image""": """image""", """input_label""": """labels"""}    ,task.column_mapping						)




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =Features({"""image""": Image()}						)
         SCREAMING_SNAKE_CASE__       =Features({"""labels""": ClassLabel}						)
         SCREAMING_SNAKE_CASE__       ={
             """image_column""": """input_image""",
             """label_column""": """input_label""",
         }
         SCREAMING_SNAKE_CASE__       =ImageClassification.from_dict(_UpperCamelCase						)
         self.assertEqual("""image-classification"""    ,task.task						)
         self.assertEqual(_UpperCamelCase    ,task.input_schema						)
         self.assertEqual(_UpperCamelCase    ,task.label_schema						)




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =ImageClassification(image_column="""input_image"""    ,label_column="""input_label"""						)
         self.assertEqual(task.label_schema["""labels"""]    ,_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels						)}						)						)
         self.assertEqual(task.label_schema["""labels"""]    ,ClassLabel(names=self.labels						)						)






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =DatasetInfo(task_templates=QuestionAnsweringExtractive()						)
         SCREAMING_SNAKE_CASE__       =Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}    ,info=_UpperCamelCase						)
         assert isinstance(dataset.info.task_templates    ,_UpperCamelCase						)
         assert len(dataset.info.task_templates						) == 1

         def keep_task(_UpperCamelCase      :			Optional[int]						):
             return x

         def dont_keep_task(_UpperCamelCase      :			Optional[int]						):
             SCREAMING_SNAKE_CASE__       =deepcopy(_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__       =0
             return out

         SCREAMING_SNAKE_CASE__       =dataset.map(_UpperCamelCase						)
         assert mapped_dataset.info.task_templates == dataset.info.task_templates
         # reload from cache
         SCREAMING_SNAKE_CASE__       =dataset.map(_UpperCamelCase						)
         assert mapped_dataset.info.task_templates == dataset.info.task_templates

         SCREAMING_SNAKE_CASE__       =dataset.map(_UpperCamelCase						)
         assert mapped_dataset.info.task_templates == []
         # reload from cache
         SCREAMING_SNAKE_CASE__       =dataset.map(_UpperCamelCase						)
         assert mapped_dataset.info.task_templates == []




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =Features({"""text""": Value("""string"""						), """label""": ClassLabel(names=("""pos""", """neg""")						)}						)
         SCREAMING_SNAKE_CASE__       =TextClassification(text_column="""text"""    ,label_column="""label"""						)
         SCREAMING_SNAKE_CASE__       =DatasetInfo(features=_UpperCamelCase    ,task_templates=_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =Dataset.from_dict({"""text""": ["""A sentence."""], """label""": ["""pos"""]}    ,info=_UpperCamelCase						)

         def process(_UpperCamelCase      :			List[str]						):
             return example

         SCREAMING_SNAKE_CASE__       =dataset.remove_columns("""label"""						)
         SCREAMING_SNAKE_CASE__       =modified_dataset.map(_UpperCamelCase						)
         assert mapped_dataset.info.task_templates == []












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))






