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      :				List[str]     			={
    "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__	(       __A						):







    '''simple docstring'''
    _lowerCamelCase  :		Dict			    =    task_cls()
    _lowerCamelCase  :		int			    =    asdict(__A						)
    _lowerCamelCase  :		int			    =    task_template_from_dict(__A						)
    assert task == reloaded




class 						__snake_case						:





      '''simple docstring'''






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


          """simple docstring"""


          _lowerCamelCase  :		Optional[int]			    =    LanguageModeling(text_column="""input_text""")
          assert {"input_text": "text"} == task.column_mapping




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


          """simple docstring"""


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





class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






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


          """simple docstring"""


          _lowerCamelCase  :		Optional[int]			    =    sorted(["""pos""", """neg"""])



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


          """simple docstring"""


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



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


          """simple docstring"""


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




      def 						_SCREAMING_SNAKE_CASE     (       self  :					int)					->Tuple:


          """simple docstring"""


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





class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






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


          """simple docstring"""


          _lowerCamelCase  :		Tuple			    =    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 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->Dict:


          """simple docstring"""


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





class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






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


          """simple docstring"""


          _lowerCamelCase  :		Optional[int]			    =    Summarization(text_column="""input_text"""     ,	summary_column="""input_summary""")
          self.assertDictEqual({"""input_text""": """text""", """input_summary""": """summary"""}     ,	task.column_mapping)




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[int])					->Optional[int]:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    Features({"""text""": Value("""string""")})
          _lowerCamelCase  :		List[str]			    =    Features({"""summary""": Value("""string""")})
          _lowerCamelCase  :		Any			    =    {"""text_column""": """input_text""", """summary_column""": """input_summary"""}
          _lowerCamelCase  :		Dict			    =    Summarization.from_dict(_UpperCamelCase)
          self.assertEqual("""summarization"""     ,	task.task)
          self.assertEqual(_UpperCamelCase     ,	task.input_schema)
          self.assertEqual(_UpperCamelCase     ,	task.label_schema)





class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






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


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    AutomaticSpeechRecognition(audio_column="""input_audio"""     ,	transcription_column="""input_transcription""")
          self.assertDictEqual({"""input_audio""": """audio""", """input_transcription""": """transcription"""}     ,	task.column_mapping)




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


          """simple docstring"""


          _lowerCamelCase  :		List[str]			    =    Features({"""audio""": Audio()})
          _lowerCamelCase  :		List[Any]			    =    Features({"""transcription""": Value("""string""")})
          _lowerCamelCase  :		Optional[Any]			    =    {
              """audio_column""": """input_audio""",
              """transcription_column""": """input_transcription""",
          }
          _lowerCamelCase  :		Optional[int]			    =    AutomaticSpeechRecognition.from_dict(_UpperCamelCase)
          self.assertEqual("""automatic-speech-recognition"""     ,	task.task)
          self.assertEqual(_UpperCamelCase     ,	task.input_schema)
          self.assertEqual(_UpperCamelCase     ,	task.label_schema)





class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






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


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    sorted(["""pos""", """neg"""])



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


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    AudioClassification(audio_column="""input_audio"""     ,	label_column="""input_label""")
          self.assertDictEqual({"""input_audio""": """audio""", """input_label""": """labels"""}     ,	task.column_mapping)



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


          """simple docstring"""


          _lowerCamelCase  :		str			    =    Features({"""audio""": Audio()})
          _lowerCamelCase  :		int			    =    Features({"""labels""": ClassLabel})
          _lowerCamelCase  :		Any			    =    {
              """audio_column""": """input_image""",
              """label_column""": """input_label""",
          }
          _lowerCamelCase  :		Union[str, Any]			    =    AudioClassification.from_dict(_UpperCamelCase)
          self.assertEqual("""audio-classification"""     ,	task.task)
          self.assertEqual(_UpperCamelCase     ,	task.input_schema)
          self.assertEqual(_UpperCamelCase     ,	task.label_schema)




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[int])					->Optional[int]:


          """simple docstring"""


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





class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






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


          """simple docstring"""


          _lowerCamelCase  :		Optional[Any]			    =    sorted(["""pos""", """neg"""])



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[int])					->Union[str, Any]:


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any])					->Tuple:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    Features({"""image""": Image()})
          _lowerCamelCase  :		str			    =    Features({"""labels""": ClassLabel})
          _lowerCamelCase  :		Union[str, Any]			    =    {
              """image_column""": """input_image""",
              """label_column""": """input_label""",
          }
          _lowerCamelCase  :		Optional[int]			    =    ImageClassification.from_dict(_UpperCamelCase)
          self.assertEqual("""image-classification"""     ,	task.task)
          self.assertEqual(_UpperCamelCase     ,	task.input_schema)
          self.assertEqual(_UpperCamelCase     ,	task.label_schema)




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


          """simple docstring"""


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





class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      def 						_SCREAMING_SNAKE_CASE     (       self  :					int)					->Any:


          """simple docstring"""


          _lowerCamelCase  :		str			    =    DatasetInfo(task_templates=QuestionAnsweringExtractive())
          _lowerCamelCase  :		Union[str, Any]			    =    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  :					List[Any]):
              return x

          def dont_keep_task(_UpperCamelCase  :					List[str]):
              _lowerCamelCase  :		List[Any]			    =    deepcopy(_UpperCamelCase)
              _lowerCamelCase  :		Dict			    =    0
              return out

          _lowerCamelCase  :		Optional[int]			    =    dataset.map(_UpperCamelCase)
          assert mapped_dataset.info.task_templates == dataset.info.task_templates
          # reload from cache
          _lowerCamelCase  :		Union[str, Any]			    =    dataset.map(_UpperCamelCase)
          assert mapped_dataset.info.task_templates == dataset.info.task_templates

          _lowerCamelCase  :		int			    =    dataset.map(_UpperCamelCase)
          assert mapped_dataset.info.task_templates == []
          # reload from cache
          _lowerCamelCase  :		Any			    =    dataset.map(_UpperCamelCase)
          assert mapped_dataset.info.task_templates == []




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->Optional[Any]:


          """simple docstring"""


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

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

          _lowerCamelCase  :		Tuple			    =    dataset.remove_columns("""label""")
          _lowerCamelCase  :		str			    =    modified_dataset.map(_UpperCamelCase)
          assert mapped_dataset.info.task_templates == []





import torch

from accelerate import Accelerator, DistributedType




class 						__snake_case						:





      '''simple docstring'''






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


          """simple docstring"""


          if self.enabled:
              _lowerCamelCase  :		str			    =    self.model.no_sync()
              self.model_sync_obj.__enter__()

          return self



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


          """simple docstring"""


          if self.enabled:
              # Average all models on exit
              self._sync_and_avg_model_params()
              self.model_sync_obj.__exit__(_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase)



      def __init__(       self  :					Union[str, Any]     ,	_UpperCamelCase  :					Accelerator     ,	_UpperCamelCase  :					torch.nn.Module     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					bool = True)					->int:


          """simple docstring"""


          if accelerator.distributed_type not in [
              DistributedType.NO,
              DistributedType.MULTI_CPU,
              DistributedType.MULTI_GPU,
          ]:
              raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""")
          _lowerCamelCase  :		str			    =    enabled and accelerator.distributed_type != DistributedType.NO
          _lowerCamelCase  :		Optional[int]			    =    0
          if self.enabled:
              _lowerCamelCase  :		Union[str, Any]			    =    accelerator
              _lowerCamelCase  :		int			    =    model
              _lowerCamelCase  :		int			    =    local_sgd_steps



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


          """simple docstring"""


          self.num_steps += 1
          if not self.enabled:
              return

          if self.num_steps % self.local_sgd_steps == 0:
              self._sync_and_avg_model_params()




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


          """simple docstring"""


          self.accelerator.wait_for_everyone()
          with self.accelerator.autocast():
              for param in self.model.parameters():
                  _lowerCamelCase  :		Union[str, Any]			    =    self.accelerator.reduce(param.data     ,	reduction="""mean""")




