"""simple docstring"""




import warnings

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


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class 			UpperCamelCase__(	lowercase__       ):
						lowerCAmelCase__     :  str  							=						'mvp'
						lowerCAmelCase__     :  Optional[Any]  							=						['past_key_values']
						lowerCAmelCase__     :  int  							=						{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





						def __init__(				self						,__UpperCAmelCase=5_02_67						,__UpperCAmelCase=10_24						,__UpperCAmelCase=12						,__UpperCAmelCase=40_96						,__UpperCAmelCase=16						,__UpperCAmelCase=12						,__UpperCAmelCase=40_96						,__UpperCAmelCase=16						,__UpperCAmelCase=0.0						,__UpperCAmelCase=0.0						,__UpperCAmelCase="gelu"						,__UpperCAmelCase=10_24						,__UpperCAmelCase=0.1						,__UpperCAmelCase=0.0						,__UpperCAmelCase=0.0						,__UpperCAmelCase=0.0_2						,__UpperCAmelCase=0.0						,__UpperCAmelCase=False						,__UpperCAmelCase=True						,__UpperCAmelCase=1						,__UpperCAmelCase=0						,__UpperCAmelCase=2						,__UpperCAmelCase=True						,__UpperCAmelCase=2						,__UpperCAmelCase=2						,__UpperCAmelCase=False						,__UpperCAmelCase=1_00						,__UpperCAmelCase=8_00						,**__UpperCAmelCase						,)    -> Any:
													A__		   =							vocab_size
													A__		   =							max_position_embeddings
													A__		   =							d_model
													A__		   =							encoder_ffn_dim
													A__		   =							encoder_layers
													A__		   =							encoder_attention_heads
													A__		   =							decoder_ffn_dim
													A__		   =							decoder_layers
													A__		   =							decoder_attention_heads
													A__		   =							dropout
													A__		   =							attention_dropout
													A__		   =							activation_dropout
													A__		   =							activation_function
													A__		   =							init_std
													A__		   =							encoder_layerdrop
													A__		   =							decoder_layerdrop
													A__		   =							classifier_dropout
													A__		   =							use_cache
													A__		   =							encoder_layers
													A__		   =							scale_embedding  # scale factor will be sqrt(d_model) if True
													A__		   =							use_prompt
													A__		   =							prompt_length
													A__		   =							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   ):
																				A__		   =							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.'   )













# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a		:       Union[str, Any]			     = {"configuration_timm_backbone": ["TimmBackboneConfig"]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:       Tuple			     = ["TimmBackbone"]


if TYPE_CHECKING:
       from .configuration_timm_backbone import TimmBackboneConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_timm_backbone import TimmBackbone

else:
       import sys

       a		:       List[Any]			     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


