



"""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 numpy as np
       import tensorflow as tf

       from transformers import TFXLMRobertaModel




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







      @slow
      def  _a   (					self		:     int			)							->		List[Any]:






            """simple docstring"""

            __SCREAMING_SNAKE_CASE       		=   TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base'''			)

            __SCREAMING_SNAKE_CASE       		=   {
                '''input_ids''': tf.convert_to_tensor([[0, 26_46, 1_02_69, 83, 9_99_42, 2]]				,				dtype=tf.intaa			),  # "My dog is cute"
                '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]				,				dtype=tf.intaa			),
            }

            __SCREAMING_SNAKE_CASE       		=   model(__SCREAMING_SNAKE_CASE			)['''last_hidden_state''']
            __SCREAMING_SNAKE_CASE       		=   tf.TensorShape((1, 6, 7_68)			)
            self.assertEqual(output.shape				,				__SCREAMING_SNAKE_CASE			)
            # compare the actual values for a slice.
            __SCREAMING_SNAKE_CASE       		=   tf.convert_to_tensor(
                [
                    [
                        [0.0_68_17_62, 0.10_89_44_51, 0.06_77_25_04],
                        [-0.06_42_36_68, 0.02_36_66_15, 0.04_32_93_44],
                        [-0.06_05_72_95, 0.09_97_41_35, -0.00_07_05_84],
                    ]
                ]				,				dtype=tf.floataa				,				)

            self.assertTrue(np.allclose(output[:, :3, :3].numpy()				,				expected_slice.numpy()				,				atol=1E-4			)			)








import warnings

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


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

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






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		List[str]					='''mvp'''
       __lowerCAmelCase    :		List[Any]					=['''past_key_values''']
       __lowerCAmelCase    :		Optional[Any]					={'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





       def __init__(				self	:Any,      snake_case	:Any=5_0267,      snake_case	:List[str]=1024,      snake_case	:List[str]=12,      snake_case	:List[Any]=4096,      snake_case	:List[Any]=16,      snake_case	:Optional[int]=12,      snake_case	:Optional[int]=4096,      snake_case	:List[str]=16,      snake_case	:Optional[Any]=0.0,      snake_case	:Tuple=0.0,      snake_case	:str="gelu",      snake_case	:int=1024,      snake_case	:Any=0.1,      snake_case	:Dict=0.0,      snake_case	:Optional[Any]=0.0,      snake_case	:int=0.0_2,      snake_case	:Union[str, Any]=0.0,      snake_case	:Tuple=False,      snake_case	:Optional[int]=True,      snake_case	:Any=1,      snake_case	:Union[str, Any]=0,      snake_case	:Optional[Any]=2,      snake_case	:Union[str, Any]=True,      snake_case	:Tuple=2,      snake_case	:Any=2,      snake_case	:List[Any]=False,      snake_case	:Optional[int]=100,      snake_case	:Union[str, Any]=800,      **snake_case	:Tuple,      ):



          """simple docstring"""

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

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

          if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated',      snake_case):
             _lowercase              =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.')


