






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


__A          = {
    '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    __A    (   _lowercase       ):

					'''simple docstring'''







					_A									=       task_cls()
					_A									=       asdict(_lowercase       )
					_A									=       task_template_from_dict(_lowercase       )
					assert task == reloaded





class   SCREAMING_SNAKE_CASE :





							"""simple docstring"""

							def 					__A       (     self:      List[str]      )			-> Any:
												_A									=       LanguageModeling(text_column='''input_text'''      )
												assert {"input_text": "text"} == task.column_mapping



							def 					__A       (     self:      int      )			-> Union[str, Any]:
												_A									=       Features({'''text''': Value('''string'''      )}      )
												_A									=       {'''text_column''': '''input_text'''}
												_A									=       LanguageModeling.from_dict(__A      )
												assert "language-modeling" == task.task
												assert input_schema == task.input_schema





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							def 					__A       (     self:      Dict      )			-> Union[str, Any]:
												_A									=       sorted(['''pos''', '''neg''']      )

							def 					__A       (     self:      int      )			-> Optional[Any]:
												_A									=       TextClassification(text_column='''input_text'''						,   label_column='''input_label'''      )
												self.assertDictEqual({'''input_text''': '''text''', '''input_label''': '''labels'''}						,   task.column_mapping      )

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



							def 					__A       (     self:      str      )			-> List[Any]:
												_A									=       TextClassification(text_column='''input_text'''						,   label_column='''input_label'''      )
												self.assertEqual(task.label_schema['''labels''']						,   __A      )
												_A									=       task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels      )}      )      )
												self.assertEqual(task.label_schema['''labels''']						,   ClassLabel(names=self.labels      )      )





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							def 					__A       (     self:      Optional[int]      )			-> List[str]:
												_A									=       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]      )			-> Any:
												_A									=       Features({'''question''': Value('''string'''      ), '''context''': Value('''string'''      )}      )
												_A									=       Features(
												    {
												        '''answers''': Sequence(
												            {
												                '''text''': Value('''string'''      ),
												                '''answer_start''': Value('''int32'''      ),
												            }      )
												    }      )
												_A									=       {
												    '''context_column''': '''input_input_context''',
												    '''question_column''': '''input_question''',
												    '''answers_column''': '''input_answers''',
												}
												_A									=       QuestionAnsweringExtractive.from_dict(__A      )
												self.assertEqual('''question-answering-extractive'''						,   task.task      )
												self.assertEqual(__A						,   task.input_schema      )
												self.assertEqual(__A						,   task.label_schema      )





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							def 					__A       (     self:      Tuple      )			-> Optional[int]:
												_A									=       Summarization(text_column='''input_text'''						,   summary_column='''input_summary'''      )
												self.assertDictEqual({'''input_text''': '''text''', '''input_summary''': '''summary'''}						,   task.column_mapping      )



							def 					__A       (     self:      Optional[Any]      )			-> List[str]:
												_A									=       Features({'''text''': Value('''string'''      )}      )
												_A									=       Features({'''summary''': Value('''string'''      )}      )
												_A									=       {'''text_column''': '''input_text''', '''summary_column''': '''input_summary'''}
												_A									=       Summarization.from_dict(__A      )
												self.assertEqual('''summarization'''						,   task.task      )
												self.assertEqual(__A						,   task.input_schema      )
												self.assertEqual(__A						,   task.label_schema      )





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							def 					__A       (     self:      Optional[int]      )			-> Union[str, Any]:
												_A									=       AutomaticSpeechRecognition(audio_column='''input_audio'''						,   transcription_column='''input_transcription'''      )
												self.assertDictEqual({'''input_audio''': '''audio''', '''input_transcription''': '''transcription'''}						,   task.column_mapping      )



							def 					__A       (     self:      List[Any]      )			-> Tuple:
												_A									=       Features({'''audio''': Audio()}      )
												_A									=       Features({'''transcription''': Value('''string'''      )}      )
												_A									=       {
												    '''audio_column''': '''input_audio''',
												    '''transcription_column''': '''input_transcription''',
												}
												_A									=       AutomaticSpeechRecognition.from_dict(__A      )
												self.assertEqual('''automatic-speech-recognition'''						,   task.task      )
												self.assertEqual(__A						,   task.input_schema      )
												self.assertEqual(__A						,   task.label_schema      )





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							def 					__A       (     self:      List[str]      )			-> Optional[int]:
												_A									=       sorted(['''pos''', '''neg''']      )

							def 					__A       (     self:      List[Any]      )			-> str:
												_A									=       AudioClassification(audio_column='''input_audio'''						,   label_column='''input_label'''      )
												self.assertDictEqual({'''input_audio''': '''audio''', '''input_label''': '''labels'''}						,   task.column_mapping      )

							def 					__A       (     self:      Union[str, Any]      )			-> Union[str, Any]:
												_A									=       Features({'''audio''': Audio()}      )
												_A									=       Features({'''labels''': ClassLabel}      )
												_A									=       {
												    '''audio_column''': '''input_image''',
												    '''label_column''': '''input_label''',
												}
												_A									=       AudioClassification.from_dict(__A      )
												self.assertEqual('''audio-classification'''						,   task.task      )
												self.assertEqual(__A						,   task.input_schema      )
												self.assertEqual(__A						,   task.label_schema      )



							def 					__A       (     self:      Optional[Any]      )			-> List[str]:
												_A									=       AudioClassification(audio_column='''input_audio'''						,   label_column='''input_label'''      )
												self.assertEqual(task.label_schema['''labels''']						,   __A      )
												_A									=       task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels      )}      )      )
												self.assertEqual(task.label_schema['''labels''']						,   ClassLabel(names=self.labels      )      )





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							def 					__A       (     self:      int      )			-> str:
												_A									=       sorted(['''pos''', '''neg''']      )

							def 					__A       (     self:      Dict      )			-> int:
												_A									=       ImageClassification(image_column='''input_image'''						,   label_column='''input_label'''      )
												self.assertDictEqual({'''input_image''': '''image''', '''input_label''': '''labels'''}						,   task.column_mapping      )

							def 					__A       (     self:      Optional[Any]      )			-> Optional[Any]:
												_A									=       Features({'''image''': Image()}      )
												_A									=       Features({'''labels''': ClassLabel}      )
												_A									=       {
												    '''image_column''': '''input_image''',
												    '''label_column''': '''input_label''',
												}
												_A									=       ImageClassification.from_dict(__A      )
												self.assertEqual('''image-classification'''						,   task.task      )
												self.assertEqual(__A						,   task.input_schema      )
												self.assertEqual(__A						,   task.label_schema      )



							def 					__A       (     self:      int      )			-> int:
												_A									=       ImageClassification(image_column='''input_image'''						,   label_column='''input_label'''      )
												self.assertEqual(task.label_schema['''labels''']						,   __A      )
												_A									=       task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels      )}      )      )
												self.assertEqual(task.label_schema['''labels''']						,   ClassLabel(names=self.labels      )      )





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							def 					__A       (     self:      Optional[Any]      )			-> Optional[Any]:
												_A									=       DatasetInfo(task_templates=QuestionAnsweringExtractive()      )
												_A									=       Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}						,   info=__A      )
												assert isinstance(dataset.info.task_templates						,   __A      )
												assert len(dataset.info.task_templates      ) == 1

												def keep_task(__A:      Union[str, Any]      ):
																	return x

												def dont_keep_task(__A:      Optional[Any]      ):
																	_A									=       deepcopy(__A      )
																	_A									=       0
																	return out

												_A									=       dataset.map(__A      )
												assert mapped_dataset.info.task_templates == dataset.info.task_templates
												# reload from cache
												_A									=       dataset.map(__A      )
												assert mapped_dataset.info.task_templates == dataset.info.task_templates

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



							def 					__A       (     self:      List[Any]      )			-> Optional[int]:
												_A									=       Features({'''text''': Value('''string'''      ), '''label''': ClassLabel(names=('''pos''', '''neg''')      )}      )
												_A									=       TextClassification(text_column='''text'''						,   label_column='''label'''      )
												_A									=       DatasetInfo(features=__A						,   task_templates=__A      )
												_A									=       Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']}						,   info=__A      )

												def process(__A:      Optional[Any]      ):
																	return example

												_A									=       dataset.remove_columns('''label'''      )
												_A									=       modified_dataset.map(__A      )
												assert mapped_dataset.info.task_templates == []




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import ast
import difflib
from collections.abc import Mapping
from typing import Any, Callable, Dict
class 				a_ (						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 pass






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__=None	,  lowerCAmelCase__=False	):
       try:
              lowerCAmelCase__     	=					ast.parse(lowerCAmelCase__	)
       except SyntaxError as e:
              print('The code generated by the agent is not valid.\n'	,  lowerCAmelCase__	)
              return
       if state is None:
              lowerCAmelCase__     	=					{}
       lowerCAmelCase__     	=					None
       for idx, node in enumerate(expression.body	):
              try:
                     lowerCAmelCase__     	=					evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
              except InterpretorError as e:
                     lowerCAmelCase__     	=					F"""Evaluation of the code stopped at line {idx} before the end because of the following error"""
                     if chat_mode:
                            msg += (
                                F""". Copy paste the following error message and send it back to the agent:\nI get an error: '{e}'"""
                            )
                     else:
                            msg += F""":\n{e}"""
                     print(lowerCAmelCase__	)
                     break
              if line_result is not None:
                     lowerCAmelCase__     	=					line_result

       return result






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       if isinstance(lowerCAmelCase__	,  ast.Assign	):
              # Assignement -> we evaluate the assignement which should update the state
              # We return the variable assigned as it may be used to determine the final result.
              return evaluate_assign(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       elif isinstance(lowerCAmelCase__	,  ast.Call	):
              # Function call -> we return the value of the function call
              return evaluate_call(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       elif isinstance(lowerCAmelCase__	,  ast.Constant	):
              # Constant -> just return the value
              return expression.value
       elif isinstance(lowerCAmelCase__	,  ast.Dict	):
              # Dict -> evaluate all keys and values
              lowerCAmelCase__     	=					[evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	) for k in expression.keys]
              lowerCAmelCase__     	=					[evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	) for v in expression.values]
              return dict(zip(lowerCAmelCase__	,  lowerCAmelCase__	)	)
       elif isinstance(lowerCAmelCase__	,  ast.Expr	):
              # Expression -> evaluate the content
              return evaluate_ast(expression.value	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       elif isinstance(lowerCAmelCase__	,  ast.For	):
              # For loop -> execute the loop
              return evaluate_for(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       elif isinstance(lowerCAmelCase__	,  ast.FormattedValue	):
              # Formatted value (part of f-string) -> evaluate the content and return
              return evaluate_ast(expression.value	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       elif isinstance(lowerCAmelCase__	,  ast.If	):
              # If -> execute the right branch
              return evaluate_if(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       elif hasattr(lowerCAmelCase__	,  'Index'	) and isinstance(lowerCAmelCase__	,  ast.Index	):
              return evaluate_ast(expression.value	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       elif isinstance(lowerCAmelCase__	,  ast.JoinedStr	):
              return "".join([str(evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)	) for v in expression.values]	)
       elif isinstance(lowerCAmelCase__	,  ast.List	):
              # List -> evaluate all elements
              return [evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	) for elt in expression.elts]
       elif isinstance(lowerCAmelCase__	,  ast.Name	):
              # Name -> pick up the value in the state
              return evaluate_name(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       elif isinstance(lowerCAmelCase__	,  ast.Subscript	):
              # Subscript -> return the value of the indexing
              return evaluate_subscript(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       else:
              # For now we refuse anything else. Let's add things as we need them.
              raise InterpretorError(F"""{expression.__class__.__name__} is not supported."""	)






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       lowerCAmelCase__     	=					assign.targets
       lowerCAmelCase__     	=					evaluate_ast(assign.value	,  lowerCAmelCase__	,  lowerCAmelCase__	)

       if len(lowerCAmelCase__	) == 1:
              lowerCAmelCase__     	=					result
       else:
              if len(lowerCAmelCase__	) != len(lowerCAmelCase__	):
                     raise InterpretorError(F"""Expected {len(lowerCAmelCase__	)} values but got {len(lowerCAmelCase__	)}."""	)
              for var_name, r in zip(lowerCAmelCase__	,  lowerCAmelCase__	):
                     lowerCAmelCase__     	=					r
       return result






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       if not isinstance(call.func	,  ast.Name	):
              raise InterpretorError(
                  F"""It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func} of """
                  F"""type {type(call.func	)}."""	)
       lowerCAmelCase__     	=					call.func.id
       if func_name not in tools:
              raise InterpretorError(
                  F"""It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func.id})."""	)

       lowerCAmelCase__     	=					tools[func_name]
       # Todo deal with args
       lowerCAmelCase__     	=					[evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	) for arg in call.args]
       lowerCAmelCase__     	=					{keyword.arg: evaluate_ast(keyword.value	,  lowerCAmelCase__	,  lowerCAmelCase__	) for keyword in call.keywords}
       return func(*lowerCAmelCase__	,  **lowerCAmelCase__	)






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       lowerCAmelCase__     	=					evaluate_ast(subscript.slice	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       lowerCAmelCase__     	=					evaluate_ast(subscript.value	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       if isinstance(lowerCAmelCase__	,  (list, tuple)	):
              return value[int(lowerCAmelCase__	)]
       if index in value:
              return value[index]
       if isinstance(lowerCAmelCase__	,  lowerCAmelCase__	) and isinstance(lowerCAmelCase__	,  lowerCAmelCase__	):
              lowerCAmelCase__     	=					difflib.get_close_matches(lowerCAmelCase__	,  list(value.keys()	)	)
              if len(lowerCAmelCase__	) > 0:
                     return value[close_matches[0]]

       raise InterpretorError(F"""Could not index {value} with '{index}'."""	)






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       if name.id in state:
              return state[name.id]
       lowerCAmelCase__     	=					difflib.get_close_matches(name.id	,  list(state.keys()	)	)
       if len(lowerCAmelCase__	) > 0:
              return state[close_matches[0]]
       raise InterpretorError(F"""The variable `{name.id}` is not defined."""	)






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       if len(condition.ops	) > 1:
              raise InterpretorError('Cannot evaluate conditions with multiple operators'	)

       lowerCAmelCase__     	=					evaluate_ast(condition.left	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       lowerCAmelCase__     	=					condition.ops[0]
       lowerCAmelCase__     	=					evaluate_ast(condition.comparators[0]	,  lowerCAmelCase__	,  lowerCAmelCase__	)

       if isinstance(lowerCAmelCase__	,  ast.Eq	):
              return left == right
       elif isinstance(lowerCAmelCase__	,  ast.NotEq	):
              return left != right
       elif isinstance(lowerCAmelCase__	,  ast.Lt	):
              return left < right
       elif isinstance(lowerCAmelCase__	,  ast.LtE	):
              return left <= right
       elif isinstance(lowerCAmelCase__	,  ast.Gt	):
              return left > right
       elif isinstance(lowerCAmelCase__	,  ast.GtE	):
              return left >= right
       elif isinstance(lowerCAmelCase__	,  ast.Is	):
              return left is right
       elif isinstance(lowerCAmelCase__	,  ast.IsNot	):
              return left is not right
       elif isinstance(lowerCAmelCase__	,  ast.In	):
              return left in right
       elif isinstance(lowerCAmelCase__	,  ast.NotIn	):
              return left not in right
       else:
              raise InterpretorError(F"""Operator not supported: {comparator}"""	)






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       lowerCAmelCase__     	=					None
       if evaluate_condition(if_statement.test	,  lowerCAmelCase__	,  lowerCAmelCase__	):
              for line in if_statement.body:
                     lowerCAmelCase__     	=					evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
                     if line_result is not None:
                            lowerCAmelCase__     	=					line_result
       else:
              for line in if_statement.orelse:
                     lowerCAmelCase__     	=					evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
                     if line_result is not None:
                            lowerCAmelCase__     	=					line_result
       return result






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       lowerCAmelCase__     	=					None
       lowerCAmelCase__     	=					evaluate_ast(for_loop.iter	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       for counter in iterator:
              lowerCAmelCase__     	=					counter
              for expression in for_loop.body:
                     lowerCAmelCase__     	=					evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
                     if line_result is not None:
                            lowerCAmelCase__     	=					line_result
       return result
