




'''simple docstring'''







import warnings

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


A     =logging.get_logger(__name__)

A     ={
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}


class 			_a  (	__a ):
       __a				:     Optional[int]   				=					"""mvp"""
       __a				:     Dict   				=					["""past_key_values"""]
       __a				:     int   				=					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
       def __init__(	self						:	List[str]   ,				lowercase						:	List[str]=50_267   ,				lowercase						:	List[str]=1_024   ,				lowercase						:	Any=12   ,				lowercase						:	Optional[int]=4_096   ,				lowercase						:	Tuple=16   ,				lowercase						:	Dict=12   ,				lowercase						:	List[str]=4_096   ,				lowercase						:	Union[str, Any]=16   ,				lowercase						:	str=0.0   ,				lowercase						:	List[str]=0.0   ,				lowercase						:	Union[str, Any]="gelu"   ,				lowercase						:	int=1_024   ,				lowercase						:	Union[str, Any]=0.1   ,				lowercase						:	Union[str, Any]=0.0   ,				lowercase						:	Optional[Any]=0.0   ,				lowercase						:	Union[str, Any]=0.02   ,				lowercase						:	str=0.0   ,				lowercase						:	Optional[Any]=False   ,				lowercase						:	Tuple=True   ,				lowercase						:	Tuple=1   ,				lowercase						:	Optional[Any]=0   ,				lowercase						:	List[Any]=2   ,				lowercase						:	Dict=True   ,				lowercase						:	int=2   ,				lowercase						:	Optional[int]=2   ,				lowercase						:	Any=False   ,				lowercase						:	Tuple=100   ,				lowercase						:	int=800   ,				**lowercase						:	str   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		vocab_size
           UpperCAmelCase			       =		max_position_embeddings
           UpperCAmelCase			       =		d_model
           UpperCAmelCase			       =		encoder_ffn_dim
           UpperCAmelCase			       =		encoder_layers
           UpperCAmelCase			       =		encoder_attention_heads
           UpperCAmelCase			       =		decoder_ffn_dim
           UpperCAmelCase			       =		decoder_layers
           UpperCAmelCase			       =		decoder_attention_heads
           UpperCAmelCase			       =		dropout
           UpperCAmelCase			       =		attention_dropout
           UpperCAmelCase			       =		activation_dropout
           UpperCAmelCase			       =		activation_function
           UpperCAmelCase			       =		init_std
           UpperCAmelCase			       =		encoder_layerdrop
           UpperCAmelCase			       =		decoder_layerdrop
           UpperCAmelCase			       =		classifier_dropout
           UpperCAmelCase			       =		use_cache
           UpperCAmelCase			       =		encoder_layers
           UpperCAmelCase			       =		scale_embedding  # scale factor will be sqrt(d_model) if True
           UpperCAmelCase			       =		use_prompt
           UpperCAmelCase			       =		prompt_length
           UpperCAmelCase			       =		prompt_mid_dim

           super().__init__(
               pad_token_id=lowercase   ,				bos_token_id=lowercase   ,				eos_token_id=lowercase   ,				is_encoder_decoder=lowercase   ,				decoder_start_token_id=lowercase   ,				forced_eos_token_id=lowercase   ,				**lowercase   ,				)

           if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''   ,				lowercase				):
               UpperCAmelCase			       =		self.bos_token_id
               warnings.warn(
                   f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
                   '''The config can simply be saved and uploaded again to be fixed.'''				)





'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import tensorflow as tf

  from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       @slow
       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    List[str]  =					TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' )
        lowercase__				:    Dict  =					AutoTokenizer.from_pretrained('''google/mt5-small''' )

        lowercase__				:    Any  =					tokenizer('''Hello there'''	,   return_tensors='''tf''' ).input_ids
        lowercase__				:    List[Any]  =					tokenizer('''Hi I am'''	,   return_tensors='''tf''' ).input_ids

        lowercase__				:    str  =					model(__lowerCAmelCase	,   labels=__lowerCAmelCase ).loss
        lowercase__				:    List[str]  =					-tf.math.reduce_mean(__lowerCAmelCase ).numpy()

        lowercase__				:    str  =					-2_1.2_2_8_1_6_8
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2E-4 )

