import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


lowerCAmelCase      :				Optional[Any]     			=get_logger(__name__)


if TYPE_CHECKING:
    from .builder import DatasetBuilder







def     A__	(       __A ,			__A = None						):







    '''simple docstring'''
    _lowerCamelCase  :		Any			    =    importlib.import_module(__A						)

    # TODO(QL): always update the module to add subsequent new authentication
    if hasattr(__A ,			"""_patched_for_streaming"""						) and module._patched_for_streaming:
        return

    def wrap_auth(__A						):
        @wraps(__A						)
        def wrapper(*__A ,			**__A						):
            return function(*__A ,			use_auth_token=__A ,			**__A						)

        _lowerCamelCase  :		List[str]			    =    """wrap_auth"""
        return wrapper

    # open files in a streaming fashion
    patch_submodule(__A ,			"""open""" ,			wrap_auth(__A						)						).start()
    patch_submodule(__A ,			"""os.listdir""" ,			wrap_auth(__A						)						).start()
    patch_submodule(__A ,			"""os.walk""" ,			wrap_auth(__A						)						).start()
    patch_submodule(__A ,			"""glob.glob""" ,			wrap_auth(__A						)						).start()
    # allow to navigate in remote zip files
    patch_submodule(__A ,			"""os.path.join""" ,			__A						).start()
    patch_submodule(__A ,			"""os.path.dirname""" ,			__A						).start()
    patch_submodule(__A ,			"""os.path.basename""" ,			__A						).start()
    patch_submodule(__A ,			"""os.path.relpath""" ,			__A						).start()
    patch_submodule(__A ,			"""os.path.split""" ,			__A						).start()
    patch_submodule(__A ,			"""os.path.splitext""" ,			__A						).start()
    # allow checks on paths
    patch_submodule(__A ,			"""os.path.exists""" ,			wrap_auth(__A						)						).start()
    patch_submodule(__A ,			"""os.path.isdir""" ,			wrap_auth(__A						)						).start()
    patch_submodule(__A ,			"""os.path.isfile""" ,			wrap_auth(__A						)						).start()
    patch_submodule(__A ,			"""os.path.getsize""" ,			wrap_auth(__A						)						).start()
    patch_submodule(__A ,			"""pathlib.Path""" ,			__A						).start()
    # file readers
    patch_submodule(__A ,			"""gzip.open""" ,			wrap_auth(__A						)						).start()
    patch_submodule(__A ,			"""numpy.load""" ,			wrap_auth(__A						)						).start()
    patch_submodule(__A ,			"""pandas.read_csv""" ,			wrap_auth(__A						) ,			attrs=["""__version__"""]						).start()
    patch_submodule(__A ,			"""pandas.read_excel""" ,			wrap_auth(__A						) ,			attrs=["""__version__"""]						).start()
    patch_submodule(__A ,			"""scipy.io.loadmat""" ,			wrap_auth(__A						) ,			attrs=["""__version__"""]						).start()
    patch_submodule(__A ,			"""xml.etree.ElementTree.parse""" ,			wrap_auth(__A						)						).start()
    patch_submodule(__A ,			"""xml.dom.minidom.parse""" ,			wrap_auth(__A						)						).start()
    _lowerCamelCase  :		Optional[int]			    =    True







def     A__	(       __A						):







    '''simple docstring'''
    extend_module_for_streaming(builder.__module__ ,			use_auth_token=builder.use_auth_token						)
    # if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
    if not builder.__module__.startswith("""datasets."""						):  # check that it's not a packaged builder like csv
        for imports in get_imports(inspect.getfile(builder.__class__						)						):
            if imports[0] == "internal":
                _lowerCamelCase  :		Optional[Any]			    =    imports[1]
                _lowerCamelCase  :		List[str]			    =    """.""".join(builder.__module__.split("""."""						)[:-1] + [internal_import_name]						)
                extend_module_for_streaming(__A ,			use_auth_token=builder.use_auth_token						)

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
    from .builder import DatasetBuilder

    _lowerCamelCase  :		Union[str, Any]			    =    [
        cls.__module__
        for cls in type(__A						).__mro__[1:]  # make sure it's not the same module we've already patched
        if issubclass(cls ,			__A						) and cls.__module__ != DatasetBuilder.__module__
    ]  # check it's not a standard builder from datasets.builder
    for module in parent_builder_modules:
        extend_module_for_streaming(__A ,			use_auth_token=builder.use_auth_token						)





import unittest

from transformers import YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        YosoForMaskedLM,
        YosoForMultipleChoice,
        YosoForQuestionAnswering,
        YosoForSequenceClassification,
        YosoForTokenClassification,
        YosoModel,
    )
    from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST




class 						__snake_case						:





      '''simple docstring'''






      def __init__(       self  :					List[Any]     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					Tuple=13     ,	_UpperCamelCase  :					Union[str, Any]=7     ,	_UpperCamelCase  :					Union[str, Any]=True     ,	_UpperCamelCase  :					Dict=True     ,	_UpperCamelCase  :					List[Any]=True     ,	_UpperCamelCase  :					Optional[Any]=True     ,	_UpperCamelCase  :					Optional[int]=99     ,	_UpperCamelCase  :					List[Any]=32     ,	_UpperCamelCase  :					Dict=5     ,	_UpperCamelCase  :					Dict=4     ,	_UpperCamelCase  :					Optional[int]=37     ,	_UpperCamelCase  :					List[Any]="gelu"     ,	_UpperCamelCase  :					int=0.1     ,	_UpperCamelCase  :					str=0.1     ,	_UpperCamelCase  :					int=512     ,	_UpperCamelCase  :					Optional[Any]=16     ,	_UpperCamelCase  :					Tuple=2     ,	_UpperCamelCase  :					Any=0.0_2     ,	_UpperCamelCase  :					Any=3     ,	_UpperCamelCase  :					Union[str, Any]=4     ,	_UpperCamelCase  :					List[Any]=None     ,	)					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		List[str]			    =    parent
          _lowerCamelCase  :		List[Any]			    =    batch_size
          _lowerCamelCase  :		int			    =    seq_length
          _lowerCamelCase  :		List[Any]			    =    is_training
          _lowerCamelCase  :		List[Any]			    =    use_input_mask
          _lowerCamelCase  :		Tuple			    =    use_token_type_ids
          _lowerCamelCase  :		int			    =    use_labels
          _lowerCamelCase  :		int			    =    vocab_size
          _lowerCamelCase  :		List[str]			    =    hidden_size
          _lowerCamelCase  :		List[Any]			    =    num_hidden_layers
          _lowerCamelCase  :		str			    =    num_attention_heads
          _lowerCamelCase  :		List[str]			    =    intermediate_size
          _lowerCamelCase  :		List[Any]			    =    hidden_act
          _lowerCamelCase  :		Optional[int]			    =    hidden_dropout_prob
          _lowerCamelCase  :		Optional[int]			    =    attention_probs_dropout_prob
          _lowerCamelCase  :		Tuple			    =    max_position_embeddings
          _lowerCamelCase  :		List[str]			    =    type_vocab_size
          _lowerCamelCase  :		Dict			    =    type_sequence_label_size
          _lowerCamelCase  :		str			    =    initializer_range
          _lowerCamelCase  :		Tuple			    =    num_labels
          _lowerCamelCase  :		Union[str, Any]			    =    num_choices
          _lowerCamelCase  :		str			    =    scope



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


          """simple docstring"""


          _lowerCamelCase  :		Optional[int]			    =    ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size)

          _lowerCamelCase  :		Tuple			    =    None
          if self.use_input_mask:
              _lowerCamelCase  :		Dict			    =    random_attention_mask([self.batch_size, self.seq_length])

          _lowerCamelCase  :		str			    =    None
          if self.use_token_type_ids:
              _lowerCamelCase  :		Optional[Any]			    =    ids_tensor([self.batch_size, self.seq_length]     ,	self.type_vocab_size)

          _lowerCamelCase  :		List[str]			    =    None
          _lowerCamelCase  :		Optional[int]			    =    None
          _lowerCamelCase  :		Dict			    =    None
          if self.use_labels:
              _lowerCamelCase  :		List[str]			    =    ids_tensor([self.batch_size]     ,	self.type_sequence_label_size)
              _lowerCamelCase  :		List[str]			    =    ids_tensor([self.batch_size, self.seq_length]     ,	self.num_labels)
              _lowerCamelCase  :		Any			    =    ids_tensor([self.batch_size]     ,	self.num_choices)

          _lowerCamelCase  :		Optional[Any]			    =    self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



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


          """simple docstring"""


          return YosoConfig(
              vocab_size=self.vocab_size     ,	hidden_size=self.hidden_size     ,	num_hidden_layers=self.num_hidden_layers     ,	num_attention_heads=self.num_attention_heads     ,	intermediate_size=self.intermediate_size     ,	hidden_act=self.hidden_act     ,	hidden_dropout_prob=self.hidden_dropout_prob     ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,	max_position_embeddings=self.max_position_embeddings     ,	type_vocab_size=self.type_vocab_size     ,	is_decoder=_UpperCamelCase     ,	initializer_range=self.initializer_range     ,	)



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


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    self.get_config()
          _lowerCamelCase  :		List[str]			    =    300
          return config



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


          """simple docstring"""


          (
              (
              _lowerCamelCase
          )    ,				(
              _lowerCamelCase
          )    ,				(
              _lowerCamelCase
          )    ,				(
              _lowerCamelCase
          )    ,				(
              _lowerCamelCase
          )    ,				(
              _lowerCamelCase
          )    ,				(
              _lowerCamelCase
          )    ,				
          )  :		Dict			    =    self.prepare_config_and_inputs()

          _lowerCamelCase  :		int			    =    True
          _lowerCamelCase  :		List[Any]			    =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          _lowerCamelCase  :		Any			    =    ids_tensor([self.batch_size, self.seq_length]     ,	vocab_size=2)

          return (
              config,
              input_ids,
              token_type_ids,
              input_mask,
              sequence_labels,
              token_labels,
              choice_labels,
              encoder_hidden_states,
              encoder_attention_mask,
          )



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any     ,	_UpperCamelCase  :					Tuple     ,	_UpperCamelCase  :					Optional[Any]     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					Dict     ,	_UpperCamelCase  :					Optional[int]     ,	_UpperCamelCase  :					Any     ,	_UpperCamelCase  :					Optional[int])					->str:


          """simple docstring"""


          _lowerCamelCase  :		str			    =    YosoModel(config=_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase  :		Union[str, Any]			    =    model(_UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	token_type_ids=_UpperCamelCase)
          _lowerCamelCase  :		Optional[Any]			    =    model(_UpperCamelCase     ,	token_type_ids=_UpperCamelCase)
          _lowerCamelCase  :		Optional[int]			    =    model(_UpperCamelCase)
          self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size))



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


          """simple docstring"""


          _lowerCamelCase  :		Optional[Any]			    =    True
          _lowerCamelCase  :		str			    =    YosoModel(_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase  :		Optional[int]			    =    model(
              _UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	token_type_ids=_UpperCamelCase     ,	encoder_hidden_states=_UpperCamelCase     ,	encoder_attention_mask=_UpperCamelCase     ,	)
          _lowerCamelCase  :		Union[str, Any]			    =    model(
              _UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	token_type_ids=_UpperCamelCase     ,	encoder_hidden_states=_UpperCamelCase     ,	)
          _lowerCamelCase  :		List[str]			    =    model(_UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	token_type_ids=_UpperCamelCase)
          self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size))



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					Optional[int]     ,	_UpperCamelCase  :					Dict     ,	_UpperCamelCase  :					Tuple     ,	_UpperCamelCase  :					Optional[Any]     ,	_UpperCamelCase  :					Optional[Any]     ,	_UpperCamelCase  :					str)					->Tuple:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    YosoForMaskedLM(config=_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase  :		Tuple			    =    model(_UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	token_type_ids=_UpperCamelCase     ,	labels=_UpperCamelCase)
          self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.vocab_size))



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


          """simple docstring"""


          _lowerCamelCase  :		Tuple			    =    YosoForQuestionAnswering(config=_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase  :		int			    =    model(
              _UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	token_type_ids=_UpperCamelCase     ,	start_positions=_UpperCamelCase     ,	end_positions=_UpperCamelCase     ,	)
          self.parent.assertEqual(result.start_logits.shape     ,	(self.batch_size, self.seq_length))
          self.parent.assertEqual(result.end_logits.shape     ,	(self.batch_size, self.seq_length))



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


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    self.num_labels
          _lowerCamelCase  :		Tuple			    =    YosoForSequenceClassification(_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase  :		Optional[Any]			    =    model(_UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	token_type_ids=_UpperCamelCase     ,	labels=_UpperCamelCase)
          self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_labels))



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


          """simple docstring"""


          _lowerCamelCase  :		Tuple			    =    self.num_labels
          _lowerCamelCase  :		Union[str, Any]			    =    YosoForTokenClassification(config=_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase  :		str			    =    model(_UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	token_type_ids=_UpperCamelCase     ,	labels=_UpperCamelCase)
          self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.num_labels))



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any]     ,	_UpperCamelCase  :					Tuple     ,	_UpperCamelCase  :					List[str]     ,	_UpperCamelCase  :					Optional[Any]     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					Dict     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					Tuple)					->str:


          """simple docstring"""


          _lowerCamelCase  :		Any			    =    self.num_choices
          _lowerCamelCase  :		Tuple			    =    YosoForMultipleChoice(config=_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase  :		Optional[int]			    =    input_ids.unsqueeze(1).expand(-1     ,	self.num_choices     ,	-1).contiguous()
          _lowerCamelCase  :		Union[str, Any]			    =    token_type_ids.unsqueeze(1).expand(-1     ,	self.num_choices     ,	-1).contiguous()
          _lowerCamelCase  :		Optional[Any]			    =    input_mask.unsqueeze(1).expand(-1     ,	self.num_choices     ,	-1).contiguous()
          _lowerCamelCase  :		str			    =    model(
              _UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	token_type_ids=_UpperCamelCase     ,	labels=_UpperCamelCase     ,	)
          self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_choices))




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


          """simple docstring"""


          _lowerCamelCase  :		Dict			    =    self.prepare_config_and_inputs()
          (
              (
              _lowerCamelCase
          )    ,				(
              _lowerCamelCase
          )    ,				(
              _lowerCamelCase
          )    ,				(
              _lowerCamelCase
          )    ,				(
              _lowerCamelCase
          )    ,				(
              _lowerCamelCase
          )    ,				(
              _lowerCamelCase
          )    ,				
          )  :		Tuple			    =    config_and_inputs
          _lowerCamelCase  :		Union[str, Any]			    =    {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
          return config, inputs_dict





@require_torch
class 						__snake_case						(			__lowerCAmelCase			,	__lowerCAmelCase			,	unittest.TestCase		):





      '''simple docstring'''






      _snake_case     		=							(
          (
              YosoModel,
              YosoForMaskedLM,
              YosoForMultipleChoice,
              YosoForQuestionAnswering,
              YosoForSequenceClassification,
              YosoForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      _snake_case     		=							False
      _snake_case     		=							False
      _snake_case     		=							False

      _snake_case     		=							()
      _snake_case     		=							(
          {
              'feature-extraction': YosoModel,
              'fill-mask': YosoForMaskedLM,
              'question-answering': YosoForQuestionAnswering,
              'text-classification': YosoForSequenceClassification,
              'token-classification': YosoForTokenClassification,
              'zero-shot': YosoForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )



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


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    YosoModelTester(self)
          _lowerCamelCase  :		Optional[int]			    =    ConfigTester(self     ,	config_class=_UpperCamelCase     ,	hidden_size=37)



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


          """simple docstring"""


          self.config_tester.run_common_tests()



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


          """simple docstring"""


          _lowerCamelCase  :		Optional[Any]			    =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any)					->Tuple:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
              _lowerCamelCase  :		List[str]			    =    type
              self.model_tester.create_and_check_model(*_UpperCamelCase)



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


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase)



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


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*_UpperCamelCase)



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


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase)



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


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					str)					->Any:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase)



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


          """simple docstring"""


          for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _lowerCamelCase  :		Tuple			    =    YosoModel.from_pretrained(_UpperCamelCase)
              self.assertIsNotNone(_UpperCamelCase)




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


          """simple docstring"""


          return





@require_torch
class 						__snake_case						(			unittest.TestCase		):





      '''simple docstring'''






      @slow
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict)					->Dict:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    YosoModel.from_pretrained("""uw-madison/yoso-4096""")
          _lowerCamelCase  :		int			    =    torch.tensor([[0, 1, 2, 3, 4, 5]])

          with torch.no_grad():
              _lowerCamelCase  :		Any			    =    model(_UpperCamelCase)[0]

          _lowerCamelCase  :		List[Any]			    =    torch.Size((1, 6, 768))
          self.assertEqual(output.shape     ,	_UpperCamelCase)

          _lowerCamelCase  :		int			    =    torch.tensor(
              [[[-0.0_6_1_1, 0.1_2_4_2, 0.0_8_4_0], [0.0_2_8_0, -0.0_0_4_8, 0.1_1_2_5], [0.0_1_0_6, 0.0_2_2_6, 0.0_7_5_1]]])

          self.assertTrue(torch.allclose(output[:, :3, :3]     ,	_UpperCamelCase     ,	atol=1E-4))



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


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    YosoForMaskedLM.from_pretrained("""uw-madison/yoso-4096""")
          _lowerCamelCase  :		Tuple			    =    torch.tensor([[0, 1, 2, 3, 4, 5]])

          with torch.no_grad():
              _lowerCamelCase  :		Optional[Any]			    =    model(_UpperCamelCase)[0]

          _lowerCamelCase  :		Union[str, Any]			    =    5_0265

          _lowerCamelCase  :		Any			    =    torch.Size((1, 6, vocab_size))
          self.assertEqual(output.shape     ,	_UpperCamelCase)

          _lowerCamelCase  :		List[str]			    =    torch.tensor(
              [[[-2.1_3_1_3, -3.7_2_8_5, -2.2_4_0_7], [-2.7_0_4_7, -3.3_3_1_4, -2.6_4_0_8], [0.0_6_2_9, -2.5_1_6_6, -0.3_3_5_6]]])

          self.assertTrue(torch.allclose(output[:, :3, :3]     ,	_UpperCamelCase     ,	atol=1E-4))




      @slow
      def 						_SCREAMING_SNAKE_CASE     (       self  :					str)					->Tuple:


          """simple docstring"""


          _lowerCamelCase  :		Tuple			    =    YosoForMaskedLM.from_pretrained("""uw-madison/yoso-4096""")
          _lowerCamelCase  :		Any			    =    torch.arange(4096).unsqueeze(0)

          with torch.no_grad():
              _lowerCamelCase  :		Optional[Any]			    =    model(_UpperCamelCase)[0]

          _lowerCamelCase  :		Dict			    =    5_0265

          _lowerCamelCase  :		str			    =    torch.Size((1, 4096, vocab_size))
          self.assertEqual(output.shape     ,	_UpperCamelCase)

          _lowerCamelCase  :		int			    =    torch.tensor(
              [[[-2.3_9_1_4, -4.3_7_4_2, -5.0_9_5_6], [-4.0_9_8_8, -4.2_3_8_4, -7.0_4_0_6], [-3.1_4_2_7, -3.7_1_9_2, -6.6_8_0_0]]])

          self.assertTrue(torch.allclose(output[:, :3, :3]     ,	_UpperCamelCase     ,	atol=1E-4))




