"""simple docstring"""



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class SCREAMING_SNAKE_CASE     (    A_   ,      A_   ,      A_	):







	"""simple docstring"""


	@register_to_config
	def __init__(   self		:      Dict					,   _snake_case		:      int					,   _snake_case		:      int					,   _snake_case		:      int					,   _snake_case		:      float					,   _snake_case		:      int					,   _snake_case		:      int					,   _snake_case		:      int					,   _snake_case		:      int					,   _snake_case		:      str					,   _snake_case		:      bool = False					,   )				->			Optional[int]:




				'''simple docstring'''






				super().__init__()

				a__     				=		nn.Linear(_snake_case					,   _snake_case					,   bias=_snake_case				)

				a__     				=		nn.Embedding(_snake_case					,   _snake_case				)
				a__     				=		False

				a__     				=		nn.Dropout(p=_snake_case				)

				a__     				=		TaConfig(
				    d_model=_snake_case					,   num_heads=_snake_case					,   d_kv=_snake_case					,   d_ff=_snake_case					,   feed_forward_proj=_snake_case					,   dropout_rate=_snake_case					,   is_decoder=_snake_case					,   is_encoder_decoder=_snake_case					,   )
				a__     				=		nn.ModuleList()
				for lyr_num in range(_snake_case				):
							a__     				=		TaBlock(_snake_case				)
							self.encoders.append(_snake_case				)

				a__     				=		TaLayerNorm(_snake_case				)
				a__     				=		nn.Dropout(p=_snake_case				)



	def        _lowerCAmelCase				(   self		:      int					,   _snake_case		:      Optional[int]					,   _snake_case		:      List[Any]				)				->			Dict:




				'''simple docstring'''






				a__     				=		self.input_proj(_snake_case				)

				# terminal relative positional encodings
				a__     				=		encoder_inputs.shape[1]
				a__     				=		torch.arange(_snake_case					,   device=encoder_inputs.device				)

				a__     				=		encoder_inputs_mask.sum(-1				)
				a__     				=		torch.roll(input_positions.unsqueeze(0				)					,   tuple(seq_lens.tolist()				)					,   dims=0				)
				x += self.position_encoding(_snake_case				)

				a__     				=		self.dropout_pre(_snake_case				)

				# inverted the attention mask
				a__     				=		encoder_inputs.size()
				a__     				=		self.get_extended_attention_mask(_snake_case					,   _snake_case				)

				for lyr in self.encoders:
							a__     				=		lyr(_snake_case					,   _snake_case				)[0]
				a__     				=		self.layer_norm(_snake_case				)

				return self.dropout_post(_snake_case				), encoder_inputs_mask



"""simple docstring"""



import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata





def   _lowerCamelCase ( UpperCAmelCase__					)							->     str:


   '''simple docstring'''


   a__     				=		min(re.search('^ +',UpperCAmelCase__					).end() if t.startswith(' '					) else 0 for t in string.splitlines()					)
   return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(UpperCAmelCase__					)]					)


__magic_name__    				=					"\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

__magic_name__    				=					"\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


__magic_name__    				=					"\\n# Begin of markdown\n\nSome cool dataset card\n"




class SCREAMING_SNAKE_CASE     (    unittest.TestCase	):







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      Tuple				)				->			Dict:




    '''simple docstring'''






    with tempfile.TemporaryDirectory() as tmp_dir:
       a__     				=		Path(_snake_case				) / 'README.md'
       with open(_snake_case					,   'w+'				) as readme_file:
          readme_file.write(_snake_case				)
       a__     				=		DatasetMetadata.from_readme(_snake_case				)
       self.assertDictEqual(_snake_case					,   {'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}				)

       with open(_snake_case					,   'w+'				) as readme_file:
          readme_file.write(_snake_case				)
       a__     				=		DatasetMetadata.from_readme(_snake_case				)
       self.assertDictEqual(_snake_case					,   {}				)

       with open(_snake_case					,   'w+'				) as readme_file:
          readme_file.write(_snake_case				)
       a__     				=		DatasetMetadata.from_readme(_snake_case				)
       self.assertEqual(_snake_case					,   {}				)



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




    '''simple docstring'''






    a__     				=		_dedent(
        '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '				)
    assert DatasetMetadata.from_yaml_string(_snake_case				)

    a__     				=		_dedent(
        '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '				)
    with self.assertRaises(_snake_case				):
       DatasetMetadata.from_yaml_string(_snake_case				)

    a__     				=		_dedent(
        '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '				)
    assert DatasetMetadata.from_yaml_string(_snake_case				)


