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    ):
  """simple docstring"""







  @slow
  def       __A   (							self       :				Optional[int]      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"     ,   return_dict=__magic_name__      ).to(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     AutoTokenizer.from_pretrained("google/mt5-small"      )

        SCREAMING_SNAKE_CASE_      =     tokenizer("Hello there"     ,   return_tensors="pt"      ).input_ids
        SCREAMING_SNAKE_CASE_      =     tokenizer("Hi I am"     ,   return_tensors="pt"      ).input_ids

        SCREAMING_SNAKE_CASE_      =     model(input_ids.to(__magic_name__      )     ,   labels=labels.to(__magic_name__      )      ).loss
        SCREAMING_SNAKE_CASE_      =     -(labels.shape[-1] * loss.item())

        SCREAMING_SNAKE_CASE_      =     -84.9127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )



from ....configuration_utils import PretrainedConfig
from ....utils import logging


A   :     str       =				logging.get_logger(__name__)

# TODO: upload to AWS
A   :     Dict       =				{
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  '''retribert'''







  def __init__(							self       :				Optional[int]     ,   __magic_name__       :				Optional[Any]=30_522     ,   __magic_name__       :				int=768     ,   __magic_name__       :				Dict=8     ,   __magic_name__       :				List[Any]=12     ,   __magic_name__       :				Tuple=3_072     ,   __magic_name__       :				List[Any]="gelu"     ,   __magic_name__       :				Any=0.1     ,   __magic_name__       :				Any=0.1     ,   __magic_name__       :				Tuple=512     ,   __magic_name__       :				Dict=2     ,   __magic_name__       :				int=0.02     ,   __magic_name__       :				List[Any]=1e-12     ,   __magic_name__       :				List[str]=True     ,   __magic_name__       :				Dict=128     ,   __magic_name__       :				Union[str, Any]=0     ,   **__magic_name__       :				List[Any]     ,   )     ->		Dict:
        super().__init__(pad_token_id=__magic_name__     ,   **__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     vocab_size
        SCREAMING_SNAKE_CASE_      =     hidden_size
        SCREAMING_SNAKE_CASE_      =     num_hidden_layers
        SCREAMING_SNAKE_CASE_      =     num_attention_heads
        SCREAMING_SNAKE_CASE_      =     hidden_act
        SCREAMING_SNAKE_CASE_      =     intermediate_size
        SCREAMING_SNAKE_CASE_      =     hidden_dropout_prob
        SCREAMING_SNAKE_CASE_      =     attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_      =     max_position_embeddings
        SCREAMING_SNAKE_CASE_      =     type_vocab_size
        SCREAMING_SNAKE_CASE_      =     initializer_range
        SCREAMING_SNAKE_CASE_      =     layer_norm_eps
        SCREAMING_SNAKE_CASE_      =     share_encoders
        SCREAMING_SNAKE_CASE_      =     projection_dim


