
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__          ={
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}



class 		UpperCamelCase__  (	__UpperCamelCase   ):
   _SCREAMING_SNAKE_CASE				:							Any     	=				"data2vec-text"
   def __init__(self			: Dict ,    snake_case_			: Dict=3_0_5_2_2 ,    snake_case_			: Any=7_6_8 ,    snake_case_			: Dict=1_2 ,    snake_case_			: int=1_2 ,    snake_case_			: int=3_0_7_2 ,    snake_case_			: Optional[int]="gelu" ,    snake_case_			: int=0.1 ,    snake_case_			: int=0.1 ,    snake_case_			: List[Any]=5_1_2 ,    snake_case_			: Any=2 ,    snake_case_			: str=0.02 ,    snake_case_			: Any=1E-12 ,    snake_case_			: Optional[int]=1 ,    snake_case_			: List[Any]=0 ,    snake_case_			: Union[str, Any]=2 ,    snake_case_			: Optional[int]="absolute" ,    snake_case_			: List[str]=True ,    snake_case_			: str=None ,    **snake_case_			: Dict ,    ):
    super().__init__(pad_token_id=snake_case_ ,    bos_token_id=snake_case_ ,    eos_token_id=snake_case_ ,    **snake_case_       )

    __a			:  int		    =  vocab_size
    __a			:  str		    =  hidden_size
    __a			:  str		    =  num_hidden_layers
    __a			:  Optional[int]		    =  num_attention_heads
    __a			:  int		    =  hidden_act
    __a			:  Optional[Any]		    =  intermediate_size
    __a			:  str		    =  hidden_dropout_prob
    __a			:  List[str]		    =  attention_probs_dropout_prob
    __a			:  Tuple		    =  max_position_embeddings
    __a			:  int		    =  type_vocab_size
    __a			:  Optional[Any]		    =  initializer_range
    __a			:  Tuple		    =  layer_norm_eps
    __a			:  Optional[int]		    =  position_embedding_type
    __a			:  Any		    =  use_cache
    __a			:  List[str]		    =  classifier_dropout





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


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 		UpperCamelCase__  (	unittest.TestCase   ):
   @slow
   def 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  List[str]		    =  AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' ,    return_dict=snake_case_       ).to(snake_case_       )
    __a			:  List[Any]		    =  AutoTokenizer.from_pretrained('''google/mt5-small'''       )

    __a			:  Optional[int]		    =  tokenizer('''Hello there''' ,    return_tensors='''pt'''       ).input_ids
    __a			:  Dict		    =  tokenizer('''Hi I am''' ,    return_tensors='''pt'''       ).input_ids

    __a			:  Optional[Any]		    =  model(input_ids.to(snake_case_       ) ,    labels=labels.to(snake_case_       )       ).loss
    __a			:  Tuple		    =  -(labels.shape[-1] * loss.item())

    __a			:  Dict		    =  -84.9127
    self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1E-4       )
