

'''simple docstring'''
import warnings

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


lowerCamelCase			:				List[str] 			=					logging.get_logger(__name__)

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







class 							A__   (	A__  ):
  A__					=    'mvp'
  A__					=    ['past_key_values']
  A__					=    {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





  def __init__(		self				:     str							, _a				:     List[str]=5_0267							, _a				:     int=1024							, _a				:     str=12							, _a				:     Tuple=4096							, _a				:     List[Any]=16							, _a				:     str=12							, _a				:     Union[str, Any]=4096							, _a				:     Dict=16							, _a				:     Any=0.0							, _a				:     Union[str, Any]=0.0							, _a				:     str="gelu"							, _a				:     List[Any]=1024							, _a				:     List[str]=0.1							, _a				:     List[Any]=0.0							, _a				:     Any=0.0							, _a				:     Any=0.02							, _a				:     Any=0.0							, _a				:     Any=False							, _a				:     Tuple=True							, _a				:     List[Any]=1							, _a				:     Tuple=0							, _a				:     Union[str, Any]=2							, _a				:     Union[str, Any]=True							, _a				:     str=2							, _a				:     str=2							, _a				:     Optional[int]=False							, _a				:     List[Any]=100							, _a				:     List[Any]=800							, **_a				:     Union[str, Any]							, )     ->   List[Any]:





       '''simple docstring'''






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

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

       if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'							, _a			):
            _SCREAMING_SNAKE_CASE							   =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'''
import os
import sys
import unittest


lowerCamelCase			:				Optional[Any] 			=					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCamelCase			:				Optional[int] 			=					os.path.join(git_repo_path, "src", "transformers")

lowerCamelCase			:				Union[str, Any] 			=					"\n{0} = None\n"

lowerCamelCase			:				Optional[Any] 			=					"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


lowerCamelCase			:				List[Any] 			=					"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"







class 							A__   (	unittest.TestCase  ):





  def 						A  (		self				:     Optional[int]			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'			)
       self.assertIsNone(_a			)

       _SCREAMING_SNAKE_CASE							   =find_backend('    if not is_tokenizers_available():'			)
       self.assertEqual(_a							, 'tokenizers'			)

       _SCREAMING_SNAKE_CASE							   =find_backend('    if not is_tensorflow_text_available():'			)
       self.assertEqual(_a							, 'tensorflow_text'			)

       _SCREAMING_SNAKE_CASE							   =find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'			)
       self.assertEqual(_a							, 'sentencepiece_and_tokenizers'			)

       _SCREAMING_SNAKE_CASE							   =find_backend(
           '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'			)
       self.assertEqual(_a							, 'sentencepiece_and_tensorflow_text'			)

       _SCREAMING_SNAKE_CASE							   =find_backend(
           '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'			)
       self.assertEqual(_a							, 'sentencepiece_and_tokenizers_and_vision'			)





  def 						A  (		self				:     Optional[int]			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =read_init()
       # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
       self.assertIn('torch'							, _a			)
       self.assertIn('tensorflow_text'							, _a			)
       self.assertIn('sentencepiece_and_tokenizers'							, _a			)

       # Likewise, we can't assert on the exact content of a key
       self.assertIn('BertModel'							, objects['torch']			)
       self.assertIn('TFBertModel'							, objects['tf']			)
       self.assertIn('FlaxBertModel'							, objects['flax']			)
       self.assertIn('BertModel'							, objects['torch']			)
       self.assertIn('TFBertTokenizer'							, objects['tensorflow_text']			)
       self.assertIn('convert_slow_tokenizer'							, objects['sentencepiece_and_tokenizers']			)





  def 						A  (		self				:     Optional[int]			)     ->   List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =create_dummy_object('CONSTANT'							, '\'torch\''			)
       self.assertEqual(_a							, '\nCONSTANT = None\n'			)

       _SCREAMING_SNAKE_CASE							   =create_dummy_object('function'							, '\'torch\''			)
       self.assertEqual(
           _a							, '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'			)

       _SCREAMING_SNAKE_CASE							   ='\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
       _SCREAMING_SNAKE_CASE							   =create_dummy_object('FakeClass'							, '\'torch\''			)
       self.assertEqual(_a							, _a			)





  def 						A  (		self				:     Optional[int]			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   ='# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
       _SCREAMING_SNAKE_CASE							   =create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}			)
       self.assertEqual(dummy_files['torch']							, _a			)
