






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


snake_case    :						Optional[Any]  =						logging.get_logger(__name__)

snake_case    :						Optional[Any]  =						{
    'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
        'https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'
    ),
}




class 							lowerCAmelCase__  (	lowercase__						):
					__A     :    str           =						'gptsan-japanese'
					__A     :    str           =						[
					    'past_key_values',
					]
					__A     :    str           =						{
					    'hidden_size': 'd_model',
					    'num_attention_heads': 'num_heads',
					    'num_hidden_layers': 'num_layers',
					}







					def __init__(       self      :     Any			,		_A      :     Optional[int]=3_6000			,		_A      :     int=1280			,		_A      :     int=1024			,		_A      :     int=8192			,		_A      :     Any=4096			,		_A      :     str=128			,		_A      :     Optional[Any]=10			,		_A      :     Optional[Any]=0			,		_A      :     Any=16			,		_A      :     List[Any]=16			,		_A      :     int=128			,		_A      :     List[Any]=0.0			,		_A      :     int=1e-5			,		_A      :     List[str]=False			,		_A      :     Optional[Any]=0.0			,		_A      :     Optional[Any]="float32"			,		_A      :     str=False			,		_A      :     List[Any]=False			,		_A      :     Dict=False			,		_A      :     Dict=0.0_02			,		_A      :     Optional[Any]=False			,		_A      :     List[str]=True			,		_A      :     Any=3_5998			,		_A      :     Any=3_5995			,		_A      :     Any=3_5999			,		**_A      :     Optional[Any]			,		):
											A__	:							List[str]						    = vocab_size
											A__	:							List[Any]						    = max_position_embeddings
											A__	:							List[Any]						    = d_model
											A__	:							Union[str, Any]						    = d_ff
											A__	:							int						    = d_ext
											A__	:							Optional[int]						    = d_spout
											A__	:							int						    = num_switch_layers
											A__	:							Union[str, Any]						    = num_ext_layers
											A__	:							str						    = num_switch_layers + num_ext_layers
											A__	:							Any						    = num_heads
											A__	:							Union[str, Any]						    = num_experts
											A__	:							Any						    = expert_capacity
											A__	:							str						    = dropout_rate
											A__	:							Optional[Any]						    = layer_norm_epsilon
											A__	:							List[str]						    = router_bias
											A__	:							int						    = router_jitter_noise
											A__	:							Any						    = router_dtype
											A__	:							Union[str, Any]						    = router_ignore_padding_tokens
											A__	:							Any						    = output_hidden_states
											A__	:							Optional[int]						    = output_attentions
											A__	:							List[Any]						    = initializer_factor
											A__	:							Any						    = output_router_logits
											A__	:							Dict						    = use_cache

											super().__init__(
											    separator_token_id=UpperCAmelCase__			,		pad_token_id=UpperCAmelCase__			,		eos_token_id=UpperCAmelCase__			,		**UpperCAmelCase__			,		)




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  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[int]						    = AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"			,		return_dict=_A).to(_A)
											A__	:							Any						    = AutoTokenizer.from_pretrained("google/mt5-small")

											A__	:							int						    = tokenizer("Hello there"			,		return_tensors="pt").input_ids
											A__	:							List[str]						    = tokenizer("Hi I am"			,		return_tensors="pt").input_ids

											A__	:							int						    = model(input_ids.to(_A)			,		labels=labels.to(_A)).loss
											A__	:							Optional[int]						    = -(labels.shape[-1] * loss.item())

											A__	:							List[str]						    = -84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)