



import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard

class 						lowerCamelCase_     (			unittest.TestCase    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      int			)   ->					Any:
									A				:       Tuple														=    {
									    "model_details": {
									        "Organization": "testing",
									        "Model date": "today",
									        "Model version": "v2.1, Developed by Test Corp in 2019.",
									        "Architecture": "Convolutional Neural Network.",
									    },
									    "metrics": "BLEU and ROUGE-1",
									    "evaluation_data": {
									        "Datasets": {"BLEU": "My-great-dataset-v1", "ROUGE-1": "My-short-dataset-v2.1"},
									        "Preprocessing": "See details on https://arxiv.org/pdf/1810.03993.pdf",
									    },
									    "training_data": {
									        "Dataset": "English Wikipedia dump dated 2018-12-01",
									        "Preprocessing": (
									            "Using SentencePiece vocabulary of size 52k tokens. See details on"
									            " https://arxiv.org/pdf/1810.03993.pdf"
									        ),
									    },
									    "quantitative_analyses": {"BLEU": 55.1, "ROUGE-1": 76},
									}







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					List[Any]:
									A				:       Any														=    ModelCard.from_dict(self.inputs_dict			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"model_details"			)			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"intended_use"			)			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"factors"			)			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"metrics"			)			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"evaluation_data"			)			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"training_data"			)			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"quantitative_analyses"			)			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"ethical_considerations"			)			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"caveats_and_recommendations"			)			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Dict			)   ->					int:
									A				:       List[str]														=    ModelCard.from_dict(self.inputs_dict			)
									A				:       Union[str, Any]														=    json.loads(modelcard.to_json_string()			)
									for key, value in self.inputs_dict.items():
												self.assertEqual(obj[key]						,						__lowerCamelCase			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Tuple:
									A				:       List[Any]														=    ModelCard.from_dict(self.inputs_dict			)

									with tempfile.TemporaryDirectory() as tmpdirname:
												A				:       Tuple														=    os.path.join(__lowerCamelCase						,						"modelcard.json"			)
												model_card_first.to_json_file(__lowerCamelCase			)
												A				:       List[str]														=    ModelCard.from_json_file(__lowerCamelCase			)

									self.assertEqual(model_card_second.to_dict()						,						model_card_first.to_dict()			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Optional[Any]:
									A				:       Optional[int]														=    ModelCard.from_dict(self.inputs_dict			)

									with tempfile.TemporaryDirectory() as tmpdirname:
												model_card_first.save_pretrained(__lowerCamelCase			)
												A				:       Any														=    ModelCard.from_pretrained(__lowerCamelCase			)

									self.assertEqual(model_card_second.to_dict()						,						model_card_first.to_dict()			)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE     =	{"""configuration_yoso""": ["""YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YosoConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE     =	[
							    """YOSO_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """YosoForMaskedLM""",
							    """YosoForMultipleChoice""",
							    """YosoForQuestionAnswering""",
							    """YosoForSequenceClassification""",
							    """YosoForTokenClassification""",
							    """YosoLayer""",
							    """YosoModel""",
							    """YosoPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_yoso import (
														    YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    YosoForMaskedLM,
														    YosoForMultipleChoice,
														    YosoForQuestionAnswering,
														    YosoForSequenceClassification,
														    YosoForTokenClassification,
														    YosoLayer,
														    YosoModel,
														    YosoPreTrainedModel,
														)


else:
							import sys

							__SCREAMING_SNAKE_CASE     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)