





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


A_		:		Dict		     =			logging.get_logger(__name__)

A_		:		List[str]		     =			{
    'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}




class     _lowerCAmelCase(	__lowerCamelCase ):





       """simple docstring"""







       a    :		Any									='gpt_neox_japanese'





       def __init__( self  ,      _lowerCamelCase=3_2_0_0_0  ,      _lowerCamelCase=2_5_6_0  ,      _lowerCamelCase=3_2  ,      _lowerCamelCase=3_2  ,      _lowerCamelCase=4  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=1.0_0  ,      _lowerCamelCase=1_0_0_0_0  ,      _lowerCamelCase=2_0_4_8  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=1e-5  ,      _lowerCamelCase=True  ,      _lowerCamelCase=3_1_9_9_6  ,      _lowerCamelCase=3_1_9_9_9  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.0  ,      **_lowerCamelCase  ,      ):
         super().__init__(bos_token_id=UpperCamelCase_  ,      eos_token_id=UpperCamelCase_  ,      **UpperCamelCase_							)
         UpperCamelCase_: List[Any]   				=    vocab_size
         UpperCamelCase_: Optional[Any]   				=    max_position_embeddings
         UpperCamelCase_: Union[str, Any]   				=    hidden_size
         UpperCamelCase_: Union[str, Any]   				=    num_hidden_layers
         UpperCamelCase_: List[Any]   				=    num_attention_heads
         UpperCamelCase_: List[Any]   				=    intermediate_multiple_size
         UpperCamelCase_: str   				=    hidden_act
         UpperCamelCase_: int   				=    rotary_pct
         UpperCamelCase_: Dict   				=    rotary_emb_base
         UpperCamelCase_: Optional[int]   				=    initializer_range
         UpperCamelCase_: Dict   				=    layer_norm_eps
         UpperCamelCase_: Optional[Any]   				=    use_cache
         UpperCamelCase_: Tuple   				=    attention_dropout
         UpperCamelCase_: Union[str, Any]   				=    hidden_dropout







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							):
         UpperCamelCase_: Optional[int]   				=    AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'  ,      return_dict=_lowerCamelCase							).to(_lowerCamelCase							)
         UpperCamelCase_: Dict   				=    AutoTokenizer.from_pretrained('google/mt5-small'							)

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

         UpperCamelCase_: int   				=    model(input_ids.to(_lowerCamelCase							)  ,      labels=labels.to(_lowerCamelCase							)							).loss
         UpperCamelCase_: Tuple   				=    -(labels.shape[-1] * loss.item())

         UpperCamelCase_: Any   				=    -8_4.9_1_2_7
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1e-4							)