





import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard
class       UpperCamelCase_		(	unittest.TestCase       ):




					def 						lowerCAmelCase      (		self	)		->				Any:
										_snake_case						=					{
										    "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 						lowerCAmelCase      (		self	)		->				Optional[int]:
										_snake_case						=					ModelCard.from_dict(self.inputs_dict	)
										self.assertTrue(hasattr(__UpperCAmelCase					,    'model_details'	)	)
										self.assertTrue(hasattr(__UpperCAmelCase					,    'intended_use'	)	)
										self.assertTrue(hasattr(__UpperCAmelCase					,    'factors'	)	)
										self.assertTrue(hasattr(__UpperCAmelCase					,    'metrics'	)	)
										self.assertTrue(hasattr(__UpperCAmelCase					,    'evaluation_data'	)	)
										self.assertTrue(hasattr(__UpperCAmelCase					,    'training_data'	)	)
										self.assertTrue(hasattr(__UpperCAmelCase					,    'quantitative_analyses'	)	)
										self.assertTrue(hasattr(__UpperCAmelCase					,    'ethical_considerations'	)	)
										self.assertTrue(hasattr(__UpperCAmelCase					,    'caveats_and_recommendations'	)	)




					def 						lowerCAmelCase      (		self	)		->				Dict:
										_snake_case						=					ModelCard.from_dict(self.inputs_dict	)
										_snake_case						=					json.loads(modelcard.to_json_string()	)
										for key, value in self.inputs_dict.items():
															self.assertEqual(obj[key]					,    __UpperCAmelCase	)




					def 						lowerCAmelCase      (		self	)		->				Optional[int]:
										_snake_case						=					ModelCard.from_dict(self.inputs_dict	)

										with tempfile.TemporaryDirectory() as tmpdirname:
															_snake_case						=					os.path.join(__UpperCAmelCase					,    'modelcard.json'	)
															model_card_first.to_json_file(__UpperCAmelCase	)
															_snake_case						=					ModelCard.from_json_file(__UpperCAmelCase	)

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






					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case						=					ModelCard.from_dict(self.inputs_dict	)

										with tempfile.TemporaryDirectory() as tmpdirname:
															model_card_first.save_pretrained(__UpperCAmelCase	)
															_snake_case						=					ModelCard.from_pretrained(__UpperCAmelCase	)

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







"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowercase          = logging.get_logger(__name__)

__lowercase          = {
    """squeezebert/squeezebert-uncased""": (
        """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"""
    ),
    """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json""",
    """squeezebert/squeezebert-mnli-headless""": (
        """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"""
    ),
}






class 		_A    (    _a    ):







    """simple docstring"""




    UpperCAmelCase			:				Tuple      =			SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
    UpperCAmelCase			:				Any      =			"""squeezebert"""






    def __init__(   self							:       List[str]							,					__UpperCAmelCase							:       Tuple=30522							,					__UpperCAmelCase							:       Tuple=768							,					__UpperCAmelCase							:       int=12							,					__UpperCAmelCase							:       str=12							,					__UpperCAmelCase							:       List[Any]=3072							,					__UpperCAmelCase							:       Dict="gelu"							,					__UpperCAmelCase							:       List[Any]=0.1							,					__UpperCAmelCase							:       Optional[int]=0.1							,					__UpperCAmelCase							:       Optional[Any]=512							,					__UpperCAmelCase							:       int=2							,					__UpperCAmelCase							:       Dict=0.02							,					__UpperCAmelCase							:       Optional[int]=1e-12							,					__UpperCAmelCase							:       Optional[Any]=0							,					__UpperCAmelCase							:       Optional[Any]=768							,					__UpperCAmelCase							:       Tuple=4							,					__UpperCAmelCase							:       Dict=4							,					__UpperCAmelCase							:       str=4							,					__UpperCAmelCase							:       List[Any]=1							,					__UpperCAmelCase							:       Optional[int]=4							,					__UpperCAmelCase							:       Optional[int]=4							,					**__UpperCAmelCase							:       Tuple							,					):
      super().__init__(pad_token_id=__UpperCAmelCase							,					**__UpperCAmelCase)

      a		:						List[Any] 				=     vocab_size
      a		:						List[Any] 				=     hidden_size
      a		:						Dict 				=     num_hidden_layers
      a		:						int 				=     num_attention_heads
      a		:						Dict 				=     hidden_act
      a		:						Tuple 				=     intermediate_size
      a		:						int 				=     hidden_dropout_prob
      a		:						List[Any] 				=     attention_probs_dropout_prob
      a		:						Union[str, Any] 				=     max_position_embeddings
      a		:						Union[str, Any] 				=     type_vocab_size
      a		:						Optional[Any] 				=     initializer_range
      a		:						Any 				=     layer_norm_eps
      a		:						Any 				=     embedding_size
      a		:						Tuple 				=     q_groups
      a		:						List[str] 				=     k_groups
      a		:						str 				=     v_groups
      a		:						Optional[Any] 				=     post_attention_groups
      a		:						List[str] 				=     intermediate_groups
      a		:						Optional[int] 				=     output_groups






class 		_A    (    _a    ):







    """simple docstring"""




    @property
    def       __snake_case				(   self							:       Optional[Any]):
      if self.task == "multiple-choice":
        a		:						Optional[int] 				=     {0: "batch", 1: "choice", 2: "sequence"}
      else:
        a		:						Tuple 				=     {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("token_type_ids", dynamic_axis),
          ])






