"""simple docstring"""
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


lowercase__   			=      {
    '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  (      _SCREAMING_SNAKE_CASE  )      ->Any:
  a__:	str     				=      task_cls()
  a__:	Union[str, Any]     				=      asdict(SCREAMING_SNAKE_CASE__  )
  a__:	int     				=      task_template_from_dict(SCREAMING_SNAKE_CASE__  )
  assert task == reloaded




class   __snake_case			:



      def     lowerCamelCase_  (		self)      ->     Tuple:





        '''simple docstring'''





        a__:	Optional[Any]     				=      LanguageModeling(text_column='input_text')
        assert {"input_text": "text"} == task.column_mapping



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        a__:	Dict     				=      Features({'text': Value('string')})
        a__:	int     				=      {'text_column': 'input_text'}
        a__:	Optional[Any]     				=      LanguageModeling.from_dict(A_)
        assert "language-modeling" == task.task
        assert input_schema == task.input_schema







class   __snake_case			(	A_							):



      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        a__:	List[Any]     				=      sorted(['pos', 'neg'])



      def     lowerCamelCase_  (		self)      ->     Tuple:





        '''simple docstring'''





        a__:	Optional[int]     				=      TextClassification(text_column='input_text'       ,     label_column='input_label')
        self.assertDictEqual({'input_text': 'text', 'input_label': 'labels'}       ,     task.column_mapping)



      def     lowerCamelCase_  (		self)      ->     Any:





        '''simple docstring'''





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



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





        '''simple docstring'''





        a__:	Any     				=      TextClassification(text_column='input_text'       ,     label_column='input_label')
        self.assertEqual(task.label_schema['labels']       ,     A_)
        a__:	Dict     				=      task.align_with_features(Features({'input_label': ClassLabel(names=self.labels)}))
        self.assertEqual(task.label_schema['labels']       ,     ClassLabel(names=self.labels))







class   __snake_case			(	A_							):



      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        a__:	List[Any]     				=      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     lowerCamelCase_  (		self)      ->     Any:





        '''simple docstring'''





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







class   __snake_case			(	A_							):



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





        '''simple docstring'''





        a__:	Union[str, Any]     				=      Summarization(text_column='input_text'       ,     summary_column='input_summary')
        self.assertDictEqual({'input_text': 'text', 'input_summary': 'summary'}       ,     task.column_mapping)



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





        '''simple docstring'''





        a__:	List[str]     				=      Features({'text': Value('string')})
        a__:	Tuple     				=      Features({'summary': Value('string')})
        a__:	str     				=      {'text_column': 'input_text', 'summary_column': 'input_summary'}
        a__:	Any     				=      Summarization.from_dict(A_)
        self.assertEqual('summarization'       ,     task.task)
        self.assertEqual(A_       ,     task.input_schema)
        self.assertEqual(A_       ,     task.label_schema)







class   __snake_case			(	A_							):



      def     lowerCamelCase_  (		self)      ->     Tuple:





        '''simple docstring'''





        a__:	Optional[int]     				=      AutomaticSpeechRecognition(audio_column='input_audio'       ,     transcription_column='input_transcription')
        self.assertDictEqual({'input_audio': 'audio', 'input_transcription': 'transcription'}       ,     task.column_mapping)



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





        '''simple docstring'''





        a__:	Optional[Any]     				=      Features({'audio': Audio()})
        a__:	Union[str, Any]     				=      Features({'transcription': Value('string')})
        a__:	Optional[Any]     				=      {
            'audio_column': 'input_audio',
            'transcription_column': 'input_transcription',
        }
        a__:	Union[str, Any]     				=      AutomaticSpeechRecognition.from_dict(A_)
        self.assertEqual('automatic-speech-recognition'       ,     task.task)
        self.assertEqual(A_       ,     task.input_schema)
        self.assertEqual(A_       ,     task.label_schema)







class   __snake_case			(	A_							):



      def     lowerCamelCase_  (		self)      ->     List[Any]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      sorted(['pos', 'neg'])



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





        '''simple docstring'''





        a__:	List[str]     				=      AudioClassification(audio_column='input_audio'       ,     label_column='input_label')
        self.assertDictEqual({'input_audio': 'audio', 'input_label': 'labels'}       ,     task.column_mapping)



      def     lowerCamelCase_  (		self)      ->     Tuple:





        '''simple docstring'''





        a__:	List[Any]     				=      Features({'audio': Audio()})
        a__:	Any     				=      Features({'labels': ClassLabel})
        a__:	Any     				=      {
            'audio_column': 'input_image',
            'label_column': 'input_label',
        }
        a__:	List[Any]     				=      AudioClassification.from_dict(A_)
        self.assertEqual('audio-classification'       ,     task.task)
        self.assertEqual(A_       ,     task.input_schema)
        self.assertEqual(A_       ,     task.label_schema)



      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        a__:	Tuple     				=      AudioClassification(audio_column='input_audio'       ,     label_column='input_label')
        self.assertEqual(task.label_schema['labels']       ,     A_)
        a__:	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			(	A_							):



      def     lowerCamelCase_  (		self)      ->     Tuple:





        '''simple docstring'''





        a__:	int     				=      sorted(['pos', 'neg'])



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





        '''simple docstring'''





        a__:	Optional[int]     				=      ImageClassification(image_column='input_image'       ,     label_column='input_label')
        self.assertDictEqual({'input_image': 'image', 'input_label': 'labels'}       ,     task.column_mapping)



      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        a__:	Dict     				=      Features({'image': Image()})
        a__:	int     				=      Features({'labels': ClassLabel})
        a__:	List[Any]     				=      {
            'image_column': 'input_image',
            'label_column': 'input_label',
        }
        a__:	str     				=      ImageClassification.from_dict(A_)
        self.assertEqual('image-classification'       ,     task.task)
        self.assertEqual(A_       ,     task.input_schema)
        self.assertEqual(A_       ,     task.label_schema)



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        a__:	List[str]     				=      ImageClassification(image_column='input_image'       ,     label_column='input_label')
        self.assertEqual(task.label_schema['labels']       ,     A_)
        a__:	Dict     				=      task.align_with_features(Features({'input_label': ClassLabel(names=self.labels)}))
        self.assertEqual(task.label_schema['labels']       ,     ClassLabel(names=self.labels))







class   __snake_case			(	A_							):



      def     lowerCamelCase_  (		self)      ->     Any:





        '''simple docstring'''





        a__:	int     				=      DatasetInfo(task_templates=QuestionAnsweringExtractive())
        a__:	Optional[Any]     				=      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(lowercase):
          return x

        def dont_keep_task(lowercase):
          a__:	Dict     				=      deepcopy(A_)
          a__:	Optional[int]     				=      0
          return out

        a__:	Union[str, Any]     				=      dataset.map(A_)
        assert mapped_dataset.info.task_templates == dataset.info.task_templates
        # reload from cache
        a__:	Tuple     				=      dataset.map(A_)
        assert mapped_dataset.info.task_templates == dataset.info.task_templates

        a__:	List[Any]     				=      dataset.map(A_)
        assert mapped_dataset.info.task_templates == []
        # reload from cache
        a__:	List[Any]     				=      dataset.map(A_)
        assert mapped_dataset.info.task_templates == []



      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        a__:	str     				=      Features({'text': Value('string'), 'label': ClassLabel(names=('pos', 'neg'))})
        a__:	Tuple     				=      TextClassification(text_column='text'       ,     label_column='label')
        a__:	Union[str, Any]     				=      DatasetInfo(features=A_       ,     task_templates=A_)
        a__:	Optional[Any]     				=      Dataset.from_dict({'text': ['A sentence.'], 'label': ['pos']}       ,     info=A_)

        def process(lowercase):
          return example

        a__:	Optional[int]     				=      dataset.remove_columns('label')
        a__:	Any     				=      modified_dataset.map(A_)
        assert mapped_dataset.info.task_templates == []






import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   Any							):
     # Initialise PyTorch model
     __UpperCamelCase       				=json.load(open(SCREAMING_SNAKE_CASE__     ,	'r'							)							)
     __UpperCamelCase       				=GPTNeoConfig(
         hidden_size=config_json['n_embd']     ,	num_layers=config_json['n_layer']     ,	num_heads=config_json['n_head']     ,	attention_types=config_json['attention_types']     ,	max_position_embeddings=config_json['n_positions']     ,	resid_dropout=config_json['res_dropout']     ,	embed_dropout=config_json['embed_dropout']     ,	attention_dropout=config_json['attn_dropout']     ,	)
     print(F'Building PyTorch model from configuration: {config}'							)
     __UpperCamelCase       				=GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__							)

     # Load weights from tf checkpoint
     load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   _A									=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help=(
           'The config json file corresponding to the pre-trained mesh-tf model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _A									=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





