


'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	int		=     '''roberta'''





     def __init__( self       :      Optional[Any]    ,      UpperCAmelCase__       :      Tuple=5_0_2_6_5    ,      UpperCAmelCase__       :      str=7_6_8    ,      UpperCAmelCase__       :      Optional[Any]=1_2    ,      UpperCAmelCase__       :      Optional[int]=1_2    ,      UpperCAmelCase__       :      int=3_0_7_2    ,      UpperCAmelCase__       :      Any="gelu"    ,      UpperCAmelCase__       :      Union[str, Any]=0.1    ,      UpperCAmelCase__       :      List[Any]=0.1    ,      UpperCAmelCase__       :      Any=5_1_2    ,      UpperCAmelCase__       :      List[Any]=2    ,      UpperCAmelCase__       :      List[str]=0.02    ,      UpperCAmelCase__       :      Optional[Any]=1E-12    ,      UpperCAmelCase__       :      str=1    ,      UpperCAmelCase__       :      Optional[int]=0    ,      UpperCAmelCase__       :      str=2    ,      UpperCAmelCase__       :      List[str]="absolute"    ,      UpperCAmelCase__       :      Optional[int]=True    ,      UpperCAmelCase__       :      List[str]=None    ,      **UpperCAmelCase__       :      List[Any]    ,      )				->  Any:
        super().__init__(pad_token_id=UpperCAmelCase__    ,      bos_token_id=UpperCAmelCase__    ,      eos_token_id=UpperCAmelCase__    ,      **UpperCAmelCase__    )

        lowerCAmelCase     =			vocab_size
        lowerCAmelCase     =			hidden_size
        lowerCAmelCase     =			num_hidden_layers
        lowerCAmelCase     =			num_attention_heads
        lowerCAmelCase     =			hidden_act
        lowerCAmelCase     =			intermediate_size
        lowerCAmelCase     =			hidden_dropout_prob
        lowerCAmelCase     =			attention_probs_dropout_prob
        lowerCAmelCase     =			max_position_embeddings
        lowerCAmelCase     =			type_vocab_size
        lowerCAmelCase     =			initializer_range
        lowerCAmelCase     =			layer_norm_eps
        lowerCAmelCase     =			position_embedding_type
        lowerCAmelCase     =			use_cache
        lowerCAmelCase     =			classifier_dropout




class 		UpperCAmelCase_      (  __lowercase		):





     @property
     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
           lowerCAmelCase     =			{0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
           lowerCAmelCase     =			{0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]    )







'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
							from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase__   (  unittest.TestCase     ):






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''				,							return_dict=__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       AutoTokenizer.from_pretrained('''google/mt5-small'''		)

											lowercase_	:     int													=       tokenizer('''Hello there'''				,							return_tensors='''pt'''		).input_ids
											lowercase_	:     Union[str, Any]													=       tokenizer('''Hi I am'''				,							return_tensors='''pt'''		).input_ids

											lowercase_	:     Union[str, Any]													=       model(input_ids.to(__SCREAMING_SNAKE_CASE		)				,							labels=labels.to(__SCREAMING_SNAKE_CASE		)		).loss
											lowercase_	:     int													=       -(labels.shape[-1] * loss.item())

											lowercase_	:     Any													=       -84.9_127
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)



