"""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     :		Optional[Any]											=logging.get_logger(__name__)


class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					str										=						"vision-encoder-decoder"
      lowerCamelCase__	:					Tuple										=						True


      def __init__(     self	,    **a							) ->      Any:
             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}"""							)

             lowercase__    :	int     					=			kwargs.pop('encoder'							)
             lowercase__    :	Optional[Any]     					=			encoder_config.pop('model_type'							)
             lowercase__    :	Dict     					=			kwargs.pop('decoder'							)
             lowercase__    :	Dict     					=			decoder_config.pop('model_type'							)

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


      @classmethod
      def 	_UpperCAmelCase (     cls	,    a	,    a	,    **a							) ->      PretrainedConfig:
             logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'							)
             lowercase__    :	Optional[Any]     					=			True
             lowercase__    :	str     					=			True

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


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	Optional[Any]     					=			copy.deepcopy(self.__dict__							)
             lowercase__    :	List[Any]     					=			self.encoder.to_dict()
             lowercase__    :	Optional[int]     					=			self.decoder.to_dict()
             lowercase__    :	List[Any]     					=			self.__class__.model_type
             return output





class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					int										=						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   UpperCAmelCase_			(					_a):


      @property
      def 	_UpperCAmelCase (     self							) ->      Mapping[str, Mapping[int, str]]:
             lowercase__    :	Tuple     					=			OrderedDict()
             lowercase__    :	Dict     					=			{0: 'batch', 1: 'past_decoder_sequence + sequence'}
             lowercase__    :	Union[str, Any]     					=			{0: 'batch', 1: 'past_decoder_sequence + sequence'}
             lowercase__    :	Optional[int]     					=			{0: 'batch', 1: 'encoder_sequence'}

             return common_inputs


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

             lowercase__    :	List[Any]     					=			OrderedDict()

             lowercase__    :	Dict     					=			super().generate_dummy_inputs(
                 a	,    batch_size=a	,    seq_length=a	,    is_pair=a	,    framework=a							)

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

             return common_inputs





class   UpperCAmelCase_			(					_a):


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


      def 	_UpperCAmelCase (     self	,    a							) ->      OnnxConfig:
             return VisionEncoderDecoderEncoderOnnxConfig(a							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a = "default"							) ->      OnnxConfig:
             lowercase__    :	Dict     					=			encoder_config.hidden_size
             return VisionEncoderDecoderDecoderOnnxConfig(a	,    a							)


"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_UpperCamelCase     :		str											=					{
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Tuple											=					[
           "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTNeoForCausalLM",
           "GPTNeoForQuestionAnswering",
           "GPTNeoForSequenceClassification",
           "GPTNeoForTokenClassification",
           "GPTNeoModel",
           "GPTNeoPreTrainedModel",
           "load_tf_weights_in_gpt_neo",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Dict											=					[
           "FlaxGPTNeoForCausalLM",
           "FlaxGPTNeoModel",
           "FlaxGPTNeoPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neo import (
                  GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoForCausalLM,
                  GPTNeoForQuestionAnswering,
                  GPTNeoForSequenceClassification,
                  GPTNeoForTokenClassification,
                  GPTNeoModel,
                  GPTNeoPreTrainedModel,
                  load_tf_weights_in_gpt_neo,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
       import sys

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

