






from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
 from transformers import (
     AutoConfig,
     BertConfig,
     GPTaConfig,
     TaConfig,
     TFAutoModel,
     TFAutoModelForCausalLM,
     TFAutoModelForMaskedLM,
     TFAutoModelForPreTraining,
     TFAutoModelForQuestionAnswering,
     TFAutoModelForSeqaSeqLM,
     TFAutoModelForSequenceClassification,
     TFAutoModelWithLMHead,
     TFBertForMaskedLM,
     TFBertForPreTraining,
     TFBertForQuestionAnswering,
     TFBertForSequenceClassification,
     TFBertModel,
     TFGPTaLMHeadModel,
     TFRobertaForMaskedLM,
     TFTaForConditionalGeneration,
 )
 from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
 from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
 from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
 from transformers import (
     AutoModel,
     AutoModelForCausalLM,
     AutoModelForMaskedLM,
     AutoModelForPreTraining,
     AutoModelForQuestionAnswering,
     AutoModelForSeqaSeqLM,
     AutoModelForSequenceClassification,
     AutoModelWithLMHead,
     BertForMaskedLM,
     BertForPreTraining,
     BertForQuestionAnswering,
     BertForSequenceClassification,
     BertModel,
     GPTaLMHeadModel,
     RobertaForMaskedLM,
     TaForConditionalGeneration,
 )


@is_pt_tf_cross_test
class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  @slow
  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     for model_name in ["bert-base-uncased"]:
        UpperCamelCase_									=     AutoConfig.from_pretrained(_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )

        UpperCamelCase_									=     TFAutoModel.from_pretrained(_UpperCAmelCase		,		from_pt=_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )

        UpperCamelCase_									=     AutoModel.from_pretrained(_UpperCAmelCase		,		from_tf=_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )




  @slow
  def    _UpperCAmelCase     (     self )			-> Tuple:
     # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     for model_name in ["bert-base-uncased"]:
        UpperCamelCase_									=     AutoConfig.from_pretrained(_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )

        UpperCamelCase_									=     TFAutoModelForPreTraining.from_pretrained(_UpperCAmelCase		,		from_pt=_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )

        UpperCamelCase_									=     AutoModelForPreTraining.from_pretrained(_UpperCAmelCase		,		from_tf=_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )




  @slow
  def    _UpperCAmelCase     (     self )			-> List[Any]:
     for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase_									=     AutoConfig.from_pretrained(_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )

        UpperCamelCase_									=     TFAutoModelForCausalLM.from_pretrained(_UpperCAmelCase		,		from_pt=_UpperCAmelCase )
        UpperCamelCase_		,      UpperCamelCase_									=     TFAutoModelForCausalLM.from_pretrained(
            _UpperCAmelCase		,		output_loading_info=_UpperCAmelCase		,		from_pt=_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )

        UpperCamelCase_									=     AutoModelForCausalLM.from_pretrained(_UpperCAmelCase		,		from_tf=_UpperCAmelCase )
        UpperCamelCase_		,      UpperCamelCase_									=     AutoModelForCausalLM.from_pretrained(
            _UpperCAmelCase		,		output_loading_info=_UpperCAmelCase		,		from_tf=_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )




  @slow
  def    _UpperCAmelCase     (     self )			-> List[str]:
     for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase_									=     AutoConfig.from_pretrained(_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )

        UpperCamelCase_									=     TFAutoModelWithLMHead.from_pretrained(_UpperCAmelCase		,		from_pt=_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )

        UpperCamelCase_									=     AutoModelWithLMHead.from_pretrained(_UpperCAmelCase		,		from_tf=_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )




  @slow
  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase_									=     AutoConfig.from_pretrained(_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )

        UpperCamelCase_									=     TFAutoModelForMaskedLM.from_pretrained(_UpperCAmelCase		,		from_pt=_UpperCAmelCase )
        UpperCamelCase_		,      UpperCamelCase_									=     TFAutoModelForMaskedLM.from_pretrained(
            _UpperCAmelCase		,		output_loading_info=_UpperCAmelCase		,		from_pt=_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )

        UpperCamelCase_									=     AutoModelForMaskedLM.from_pretrained(_UpperCAmelCase		,		from_tf=_UpperCAmelCase )
        UpperCamelCase_		,      UpperCamelCase_									=     AutoModelForMaskedLM.from_pretrained(
            _UpperCAmelCase		,		output_loading_info=_UpperCAmelCase		,		from_tf=_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )




  @slow
  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase_									=     AutoConfig.from_pretrained(_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )

        UpperCamelCase_									=     TFAutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase		,		from_pt=_UpperCAmelCase )
        UpperCamelCase_		,      UpperCamelCase_									=     TFAutoModelForSeqaSeqLM.from_pretrained(
            _UpperCAmelCase		,		output_loading_info=_UpperCAmelCase		,		from_pt=_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )

        UpperCamelCase_									=     AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase		,		from_tf=_UpperCAmelCase )
        UpperCamelCase_		,      UpperCamelCase_									=     AutoModelForSeqaSeqLM.from_pretrained(
            _UpperCAmelCase		,		output_loading_info=_UpperCAmelCase		,		from_tf=_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )




  @slow
  def    _UpperCAmelCase     (     self )			-> str:
     # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     for model_name in ["bert-base-uncased"]:
        UpperCamelCase_									=     AutoConfig.from_pretrained(_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )

        UpperCamelCase_									=     TFAutoModelForSequenceClassification.from_pretrained(_UpperCAmelCase		,		from_pt=_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )

        UpperCamelCase_									=     AutoModelForSequenceClassification.from_pretrained(_UpperCAmelCase		,		from_tf=_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )




  @slow
  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     for model_name in ["bert-base-uncased"]:
        UpperCamelCase_									=     AutoConfig.from_pretrained(_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )

        UpperCamelCase_									=     TFAutoModelForQuestionAnswering.from_pretrained(_UpperCAmelCase		,		from_pt=_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )

        UpperCamelCase_									=     AutoModelForQuestionAnswering.from_pretrained(_UpperCAmelCase		,		from_tf=_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     TFAutoModelWithLMHead.from_pretrained(_UpperCAmelCase		,		from_pt=_UpperCAmelCase )
     self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )
     self.assertEqual(model.num_parameters()		,		14410 )
     self.assertEqual(model.num_parameters(only_trainable=_UpperCAmelCase )		,		14410 )

     UpperCamelCase_									=     AutoModelWithLMHead.from_pretrained(_UpperCAmelCase		,		from_tf=_UpperCAmelCase )
     self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )
     self.assertEqual(model.num_parameters()		,		14410 )
     self.assertEqual(model.num_parameters(only_trainable=_UpperCAmelCase )		,		14410 )




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_									=     TFAutoModelWithLMHead.from_pretrained(_UpperCAmelCase		,		from_pt=_UpperCAmelCase )
     self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )
     self.assertEqual(model.num_parameters()		,		14410 )
     self.assertEqual(model.num_parameters(only_trainable=_UpperCAmelCase )		,		14410 )

     UpperCamelCase_									=     AutoModelWithLMHead.from_pretrained(_UpperCAmelCase		,		from_tf=_UpperCAmelCase )
     self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )
     self.assertEqual(model.num_parameters()		,		14410 )
     self.assertEqual(model.num_parameters(only_trainable=_UpperCAmelCase )		,		14410 )













import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


snake_case__      :	Optional[int]       	=      pytest.mark.integration






@pytest.mark.parametrize('path'    ,      ['paws', 'csv'])
def     _snake_case    (__lowercase    ,      __lowercase):
   inspect_dataset(__lowercase    ,      __lowercase)
   UpperCamelCase_									=     path + '.py'
   assert script_name in os.listdir(__lowercase)
   assert "__pycache__" not in os.listdir(__lowercase)






@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning')
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning')
@pytest.mark.parametrize('path'    ,      ['accuracy'])
def     _snake_case    (__lowercase    ,      __lowercase):
   inspect_metric(__lowercase    ,      __lowercase)
   UpperCamelCase_									=     path + '.py'
   assert script_name in os.listdir(__lowercase)
   assert "__pycache__" not in os.listdir(__lowercase)






@pytest.mark.parametrize(
    'path, config_name, expected_splits'    ,      [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_config_info(__lowercase    ,      config_name=__lowercase)
   assert info.config_name == config_name
   assert list(info.splits.keys()) == expected_splits






@pytest.mark.parametrize(
    'path, config_name, expected_exception'    ,      [
        ('paws', None, ValueError),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   with pytest.raises(__lowercase):
      get_dataset_config_info(__lowercase    ,      config_name=__lowercase)






@pytest.mark.parametrize(
    'path, expected'    ,      [
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_config_names(__lowercase)
   assert expected in config_names






@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'    ,      [
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_infos(__lowercase)
   assert list(infos.keys()) == expected_configs
   UpperCamelCase_									=     expected_configs[0]
   assert expected_config in infos
   UpperCamelCase_									=     infos[expected_config]
   assert info.config_name == expected_config
   assert list(info.splits.keys()) == expected_splits_in_first_config






@pytest.mark.parametrize(
    'path, expected_config, expected_splits'    ,      [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_infos(__lowercase)
   assert expected_config in infos
   UpperCamelCase_									=     infos[expected_config]
   assert info.config_name == expected_config
   assert list(info.splits.keys()) == expected_splits






@pytest.mark.parametrize(
    'path, config_name, expected_exception'    ,      [
        ('paws', None, ValueError),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   with pytest.raises(__lowercase):
      get_dataset_split_names(__lowercase    ,      config_name=__lowercase)





