


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 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[Any]	)     ->	Optional[Any]:



	"""simple docstring"""






	a_     			=   task_cls()
	a_     			=   asdict(a__	)
	a_     			=   task_template_from_dict(a__	)
	assert task == reloaded

class 			lowerCamelCase_					:





							def   __magic_name__      (							self    ):
								a_     			=   LanguageModeling(text_column="""input_text"""    )
								assert {"input_text": "text"} == task.column_mapping





							def   __magic_name__      (							self    ):
								a_     			=   Features({"""text""": Value("""string"""    )}    )
								a_     			=   {"""text_column""": """input_text"""}
								a_     			=   LanguageModeling.from_dict(UpperCAmelCase_    )
								assert "language-modeling" == task.task
								assert input_schema == task.input_schema







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							def   __magic_name__      (							self    ):
								a_     			=   sorted(["""pos""", """neg"""]    )





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





							def   __magic_name__      (							self    ):
								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(UpperCAmelCase_    )
								self.assertEqual("""text-classification"""			,       task.task    )
								self.assertEqual(UpperCAmelCase_			,       task.input_schema    )
								self.assertEqual(UpperCAmelCase_			,       task.label_schema    )





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







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							def   __magic_name__      (							self    ):
								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   __magic_name__      (							self    ):
								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(UpperCAmelCase_    )
								self.assertEqual("""question-answering-extractive"""			,       task.task    )
								self.assertEqual(UpperCAmelCase_			,       task.input_schema    )
								self.assertEqual(UpperCAmelCase_			,       task.label_schema    )







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





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





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







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





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





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







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							def   __magic_name__      (							self    ):
								a_     			=   sorted(["""pos""", """neg"""]    )





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





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





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







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							def   __magic_name__      (							self    ):
								a_     			=   sorted(["""pos""", """neg"""]    )





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





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





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







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							def   __magic_name__      (							self    ):
								a_     			=   DatasetInfo(task_templates=QuestionAnsweringExtractive()    )
								a_     			=   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(_SCREAMING_SNAKE_CASE    ):
									return x

								def dont_keep_task(_SCREAMING_SNAKE_CASE    ):
									a_     			=   deepcopy(UpperCAmelCase_    )
									a_     			=   0
									return out

								a_     			=   dataset.map(UpperCAmelCase_    )
								assert mapped_dataset.info.task_templates == dataset.info.task_templates
								# reload from cache
								a_     			=   dataset.map(UpperCAmelCase_    )
								assert mapped_dataset.info.task_templates == dataset.info.task_templates

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





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

								def process(_SCREAMING_SNAKE_CASE    ):
									return example

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


"""simple docstring"""

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a__			:						Dict							=			logging.get_logger(__name__)

a__			:						Any							=			{
    """google/long-t5-local-base""": """https://huggingface.co/google/long-t5-local-base/blob/main/config.json""",
    """google/long-t5-local-large""": """https://huggingface.co/google/long-t5-local-large/blob/main/config.json""",
    """google/long-t5-tglobal-base""": """https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json""",
    """google/long-t5-tglobal-large""": """https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json""",
}


class       _snake_case  (    UpperCamelCase							):
						__snake_case			      =  'longt5'
						__snake_case			      =  ['past_key_values']
						__snake_case			      =  {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}







						def __init__(self  ,				UpperCAmelCase_=3_2_1_2_8  ,				UpperCAmelCase_=5_1_2  ,				UpperCAmelCase_=6_4  ,				UpperCAmelCase_=2_0_4_8  ,				UpperCAmelCase_=6  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=8  ,				UpperCAmelCase_=1_2_7  ,				UpperCAmelCase_=1_6  ,				UpperCAmelCase_=3_2  ,				UpperCAmelCase_=1_2_8  ,				UpperCAmelCase_=0.1  ,				UpperCAmelCase_=1e-6  ,				UpperCAmelCase_=1.0  ,				UpperCAmelCase_="relu"  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_="local"  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=0  ,				UpperCAmelCase_=1  ,				**UpperCAmelCase_  ,				)      ->    int:
											__lowercase        =      vocab_size
											__lowercase        =      d_model
											__lowercase        =      d_kv
											__lowercase        =      d_ff
											__lowercase        =      num_layers
											# default = symmetry
											__lowercase        =      num_decoder_layers if num_decoder_layers is not None else self.num_layers
											__lowercase        =      num_heads
											__lowercase        =      local_radius
											__lowercase        =      global_block_size
											__lowercase        =      relative_attention_num_buckets
											__lowercase        =      relative_attention_max_distance
											__lowercase        =      dropout_rate
											__lowercase        =      layer_norm_epsilon
											__lowercase        =      initializer_factor
											__lowercase        =      feed_forward_proj
											__lowercase        =      encoder_attention_type
											__lowercase        =      use_cache

											__lowercase        =      self.feed_forward_proj.split('''-'''				)
											__lowercase        =      act_info[-1]
											__lowercase        =      act_info[0] == '''gated'''

											if len(UpperCAmelCase_				) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_				) > 2:
																raise ValueError(
																    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
																    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
																    '''\'gated-gelu\' or \'relu\''''				)

											# for backwards compatibility
											if feed_forward_proj == "gated-gelu":
																__lowercase        =      '''gelu_new'''

											super().__init__(
											    pad_token_id=UpperCAmelCase_  ,				eos_token_id=UpperCAmelCase_  ,				is_encoder_decoder=UpperCAmelCase_  ,				**UpperCAmelCase_  ,				)



class       _snake_case  (    UpperCamelCase							):







						@property
						def 							_UpperCamelCase     (self				)      ->    Mapping[str, Mapping[int, str]]:
											__lowercase        =      {
											    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
											    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
											}
											if self.use_past:
																__lowercase        =      '''past_encoder_sequence + sequence'''
																__lowercase        =      {0: '''batch'''}
																__lowercase        =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
											else:
																__lowercase        =      {0: '''batch''', 1: '''decoder_sequence'''}
																__lowercase        =      {0: '''batch''', 1: '''decoder_sequence'''}

											if self.use_past:
																self.fill_with_past_key_values_(UpperCAmelCase_  ,				direction='''inputs'''				)

											return common_inputs







						@property
						def 							_UpperCamelCase     (self				)      ->    int:
											return 1_3


