


import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard


class 	a__						(    unittest.TestCase     ):

       def     UpperCAmelCase( self   :  List[str]     ):
            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""": 5_5.1, """ROUGE-1""": 7_6},
            }

       def     UpperCAmelCase( self   :  int     ):
            a_     :  Tuple				      =     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     UpperCAmelCase( self   :  Any     ):
            a_     :  Optional[int]				      =     ModelCard.from_dict(self.inputs_dict     )
            a_     :  Optional[int]				      =     json.loads(modelcard.to_json_string()     )
            for key, value in self.inputs_dict.items():
                 self.assertEqual(obj[key]   ,				lowerCamelCase_     )

       def     UpperCAmelCase( self   :  List[Any]     ):
            a_     :  List[str]				      =     ModelCard.from_dict(self.inputs_dict     )

            with tempfile.TemporaryDirectory() as tmpdirname:
                 a_     :  List[Any]				      =     os.path.join(lowerCamelCase_   ,				"""modelcard.json"""     )
                 model_card_first.to_json_file(lowerCamelCase_     )
                 a_     :  Union[str, Any]				      =     ModelCard.from_json_file(lowerCamelCase_     )

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

       def     UpperCAmelCase( self   :  List[str]     ):
            a_     :  List[Any]				      =     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()     )









'''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    A						(					SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__						):




				'''simple docstring'''





				@register_to_config
				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = False					,    )      ->			Tuple:
							super().__init__()

							__UpperCamelCase :     Optional[int]										=  nn.Linear(_UpperCAmelCase					,    _UpperCAmelCase					,    bias=_UpperCAmelCase     )

							__UpperCamelCase :     str										=  nn.Embedding(_UpperCAmelCase					,    _UpperCAmelCase     )
							__UpperCamelCase :     Any										=  False

							__UpperCamelCase :     Union[str, Any]										=  nn.Dropout(p=_UpperCAmelCase     )

							__UpperCamelCase :     Dict										=  TaConfig(
							    d_model=_UpperCAmelCase					,    num_heads=_UpperCAmelCase					,    d_kv=_UpperCAmelCase					,    d_ff=_UpperCAmelCase					,    feed_forward_proj=_UpperCAmelCase					,    dropout_rate=_UpperCAmelCase					,    is_decoder=_UpperCAmelCase					,    is_encoder_decoder=_UpperCAmelCase					,    )
							__UpperCamelCase :     Tuple										=  nn.ModuleList()
							for lyr_num in range(_UpperCAmelCase     ):
										__UpperCamelCase :     List[str]										=  TaBlock(_UpperCAmelCase     )
										self.encoders.append(_UpperCAmelCase     )

							__UpperCamelCase :     Tuple										=  TaLayerNorm(_UpperCAmelCase     )
							__UpperCamelCase :     Any										=  nn.Dropout(p=_UpperCAmelCase     )





				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			int:
							__UpperCamelCase :     Any										=  self.input_proj(_UpperCAmelCase     )

							# terminal relative positional encodings
							__UpperCamelCase :     Optional[Any]										=  encoder_inputs.shape[1]
							__UpperCamelCase :     Optional[int]										=  torch.arange(_UpperCAmelCase					,    device=encoder_inputs.device     )

							__UpperCamelCase :     Tuple										=  encoder_inputs_mask.sum(-1     )
							__UpperCamelCase :     Optional[Any]										=  torch.roll(input_positions.unsqueeze(0     )					,    tuple(seq_lens.tolist()     )					,    dims=0     )
							x += self.position_encoding(_UpperCAmelCase     )

							__UpperCamelCase :     Any										=  self.dropout_pre(_UpperCAmelCase     )

							# inverted the attention mask
							__UpperCamelCase :     Union[str, Any]										=  encoder_inputs.size()
							__UpperCamelCase :     Optional[Any]										=  self.get_extended_attention_mask(_UpperCAmelCase					,    _UpperCAmelCase     )

							for lyr in self.encoders:
										__UpperCamelCase :     List[Any]										=  lyr(_UpperCAmelCase					,    _UpperCAmelCase     )[0]
							__UpperCamelCase :     Union[str, Any]										=  self.layer_norm(_UpperCAmelCase     )

							return self.dropout_post(_UpperCAmelCase     ), encoder_inputs_mask



