


'''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

lowerCAmelCase						=					logging.get_logger(__name__)






class        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					='vision-encoder-decoder'
       __UpperCAmelCase					=True






       def __init__( self			,     **_UpperCamelCase   )->      Dict:
            super().__init__(**_UpperCamelCase   )
            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				=							kwargs.pop('encoder'   )
            _A				=							encoder_config.pop('model_type'   )
            _A				=							kwargs.pop('decoder'   )
            _A				=							decoder_config.pop('model_type'   )

            _A				=							AutoConfig.for_model(_UpperCamelCase			,     **_UpperCamelCase   )
            _A				=							AutoConfig.for_model(_UpperCamelCase			,     **_UpperCamelCase   )
            _A				=							True






       @classmethod
       def   UpperCamelCase						( cls			,     _UpperCamelCase			,     _UpperCamelCase			,     **_UpperCamelCase   )->      PretrainedConfig:
            logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'   )
            _A				=							True
            _A				=							True

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






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							copy.deepcopy(self.__dict__   )
            _A				=							self.encoder.to_dict()
            _A				=							self.decoder.to_dict()
            _A				=							self.__class__.model_type
            return output






class        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					=version.parse('1.11'	)






       @property
       def   UpperCamelCase						( self   )->      Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                ]   )






       @property
       def   UpperCamelCase						( self   )->      float:
            return 1e-4






       @property
       def   UpperCamelCase						( self   )->      Mapping[str, Mapping[int, str]]:
            return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}   )






class        lowerCAmelCase_  (     UpperCAmelCase	):






       @property
       def   UpperCamelCase						( self   )->      Mapping[str, Mapping[int, str]]:
            _A				=							OrderedDict()
            _A				=							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
            _A				=							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
            _A				=							{0: 'batch', 1: 'encoder_sequence'}

            return common_inputs






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase = -1			,     _UpperCamelCase = -1			,     _UpperCamelCase = False			,     _UpperCamelCase = None			,     )->      Mapping[str, Any]:
            import torch

            _A				=							OrderedDict()

            _A				=							super().generate_dummy_inputs(
                _UpperCamelCase			,     batch_size=_UpperCamelCase			,     seq_length=_UpperCamelCase			,     is_pair=_UpperCamelCase			,     framework=_UpperCamelCase   )

            _A		,				_A				=							dummy_input['input_ids'].shape
            _A				=							(batch, encoder_sequence, self._config.encoder_hidden_size)
            _A				=							dummy_input.pop('input_ids'   )
            _A				=							dummy_input.pop('attention_mask'   )
            _A				=							torch.zeros(_UpperCamelCase   )

            return common_inputs






class        lowerCAmelCase_  (     UpperCAmelCase	):






       @property
       def   UpperCamelCase						( self   )->      None:
            pass






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      OnnxConfig:
            return VisionEncoderDecoderEncoderOnnxConfig(_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase = "default"   )->      OnnxConfig:
            _A				=							encoder_config.hidden_size
            return VisionEncoderDecoderDecoderOnnxConfig(_UpperCamelCase			,     _UpperCamelCase   )









'''simple docstring'''
from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip





