






"""simple docstring"""


import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
   from ... import PreTrainedTokenizerBase, TensorType

_UpperCamelCase    :   Union[str, Any]   			=						logging.get_logger(__name__)






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					'''vision-encoder-decoder'''
    __magic_name__   					=					True





    def __init__(							self     : Union[str, Any]    ,		**A     : Optional[Any]      ):


      '''simple docstring'''




      super().__init__(**A      )
      if "encoder" not in kwargs or "decoder" not in kwargs:
        raise ValueError(
            F'''A configuraton of type {self.model_type} cannot be instantiated because '''
            F'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''      )

      a					:      int    =       kwargs.pop('encoder'      )
      a					:      List[str]    =       encoder_config.pop('model_type'      )
      a					:      Any    =       kwargs.pop('decoder'      )
      a					:      Dict    =       decoder_config.pop('model_type'      )

      a					:      Optional[Any]    =       AutoConfig.for_model(A    ,		**A      )
      a					:      List[str]    =       AutoConfig.for_model(A    ,		**A      )
      a					:      Dict    =       True





    @classmethod
    def     lowerCamelCase__				(							cls     : Optional[Any]    ,		A     : PretrainedConfig    ,		A     : PretrainedConfig    ,		**A     : Any      ):


      '''simple docstring'''




      logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'      )
      a					:      str    =       True
      a					:      Optional[int]    =       True

      return cls(encoder=encoder_config.to_dict()    ,		decoder=decoder_config.to_dict()    ,		**A      )





    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      a					:      List[str]    =       copy.deepcopy(self.__dict__      )
      a					:      Optional[Any]    =       self.encoder.to_dict()
      a					:      List[Any]    =       self.decoder.to_dict()
      a					:      Any    =       self.__class__.model_type
      return output






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					version.parse('''1.11'''	)





    @property
    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      return OrderedDict(
          [
              ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
          ]      )





    @property
    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




      return 1E-4





    @property
    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}      )






class 						snake_case  (  UpperCAmelCase	):





    @property
    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      a					:      Union[str, Any]    =       OrderedDict()
      a					:      Any    =       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
      a					:      List[str]    =       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
      a					:      Tuple    =       {0: 'batch', 1: 'encoder_sequence'}

      return common_inputs





    def     lowerCamelCase__				(							self     : Optional[Any]    ,		A     : "PreTrainedTokenizerBase"    ,		A     : int = -1    ,		A     : int = -1    ,		A     : bool = False    ,		A     : Optional["TensorType"] = None    ,		):


      '''simple docstring'''




      import torch

      a					:      List[Any]    =       OrderedDict()

      a					:      Union[str, Any]    =       super().generate_dummy_inputs(
          A    ,		batch_size=A    ,		seq_length=A    ,		is_pair=A    ,		framework=A      )

      a,					a					:      Dict    =       dummy_input['input_ids'].shape
      a					:      Union[str, Any]    =       (batch, encoder_sequence, self._config.encoder_hidden_size)
      a					:      Tuple    =       dummy_input.pop('input_ids'      )
      a					:      str    =       dummy_input.pop('attention_mask'      )
      a					:      Optional[Any]    =       torch.zeros(A      )

      return common_inputs






class 						snake_case  (  UpperCAmelCase	):





    @property
    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      pass





    def     lowerCamelCase__				(							self     : Dict    ,		A     : PretrainedConfig      ):


      '''simple docstring'''




      return VisionEncoderDecoderEncoderOnnxConfig(A      )





    def     lowerCamelCase__				(							self     : List[Any]    ,		A     : PretrainedConfig    ,		A     : PretrainedConfig    ,		A     : str = "default"      ):


      '''simple docstring'''




      a					:      str    =       encoder_config.hidden_size
      return VisionEncoderDecoderDecoderOnnxConfig(A    ,		A      )












"""simple docstring"""


import os
import sys

_UpperCamelCase    :   str   			=						os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_UpperCamelCase    :   int   			=						[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]




@add_start_docstrings(AutoConfig.__doc__				)
def       snake_case  (*A_				:Optional[int]     ,      **A_				:int				):







  '''simple docstring'''

  return AutoConfig.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoTokenizer.__doc__				)
def       snake_case  (*A_				:Optional[int]     ,      **A_				:List[Any]				):







  '''simple docstring'''

  return AutoTokenizer.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModel.__doc__				)
def       snake_case  (*A_				:Optional[Any]     ,      **A_				:Tuple				):







  '''simple docstring'''

  return AutoModel.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModelForCausalLM.__doc__				)
def       snake_case  (*A_				:str     ,      **A_				:Optional[int]				):







  '''simple docstring'''

  return AutoModelForCausalLM.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModelForMaskedLM.__doc__				)
def       snake_case  (*A_				:Optional[Any]     ,      **A_				:Dict				):







  '''simple docstring'''

  return AutoModelForMaskedLM.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModelForSequenceClassification.__doc__				)
def       snake_case  (*A_				:Dict     ,      **A_				:str				):







  '''simple docstring'''

  return AutoModelForSequenceClassification.from_pretrained(*A_     ,      **A_				)






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__				)
def       snake_case  (*A_				:Dict     ,      **A_				:List[Any]				):







  '''simple docstring'''

  return AutoModelForQuestionAnswering.from_pretrained(*A_     ,      **A_				)




