


'''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

a__  :					str        =       logging.get_logger(__name__)
class 					lowercase_     (    a__   ):
    __UpperCAmelCase   			=	'vision-encoder-decoder'
    __UpperCAmelCase   			=	True


    def __init__(     self  ,					**a    ):
          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}'''    )

          UpperCamelCase__										=				kwargs.pop("encoder"    )
          UpperCamelCase__										=				encoder_config.pop("model_type"    )
          UpperCamelCase__										=				kwargs.pop("decoder"    )
          UpperCamelCase__										=				decoder_config.pop("model_type"    )

          UpperCamelCase__										=				AutoConfig.for_model(a  ,					**a    )
          UpperCamelCase__										=				AutoConfig.for_model(a  ,					**a    )
          UpperCamelCase__										=				True


    @classmethod
    def    __a							(     cls  ,					a  ,					a  ,					**a    ):
          logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"    )
          UpperCamelCase__										=				True
          UpperCamelCase__										=				True

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


    def    __a							(     self    ):
          UpperCamelCase__										=				copy.deepcopy(self.__dict__    )
          UpperCamelCase__										=				self.encoder.to_dict()
          UpperCamelCase__										=				self.decoder.to_dict()
          UpperCamelCase__										=				self.__class__.model_type
          return output

class 					lowercase_     (    a__   ):
    __UpperCAmelCase   			=	version.parse('1.11'   )


    @property
    def    __a							(     self    ):
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]    )


    @property
    def    __a							(     self    ):
          return 1e-4


    @property
    def    __a							(     self    ):
          return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}}    )

class 					lowercase_     (    a__   ):


    @property
    def    __a							(     self    ):
          UpperCamelCase__										=				OrderedDict()
          UpperCamelCase__										=				{0: "batch", 1: "past_decoder_sequence + sequence"}
          UpperCamelCase__										=				{0: "batch", 1: "past_decoder_sequence + sequence"}
          UpperCamelCase__										=				{0: "batch", 1: "encoder_sequence"}

          return common_inputs


    def    __a							(     self  ,					a  ,					a = -1  ,					a = -1  ,					a = False  ,					a = None  ,					):
          import torch

          UpperCamelCase__										=				OrderedDict()

          UpperCamelCase__										=				super().generate_dummy_inputs(
              a  ,					batch_size=a  ,					seq_length=a  ,					is_pair=a  ,					framework=a    )

          UpperCamelCase__					,	UpperCamelCase__										=				dummy_input["input_ids"].shape
          UpperCamelCase__										=				(batch, encoder_sequence, self._config.encoder_hidden_size)
          UpperCamelCase__										=				dummy_input.pop("input_ids"    )
          UpperCamelCase__										=				dummy_input.pop("attention_mask"    )
          UpperCamelCase__										=				torch.zeros(a    )

          return common_inputs

class 					lowercase_     (    a__   ):


    @property
    def    __a							(     self    ):
          pass


    def    __a							(     self  ,					a    ):
          return VisionEncoderDecoderEncoderOnnxConfig(a    )


    def    __a							(     self  ,					a  ,					a  ,					a = "default"    ):
          UpperCamelCase__										=				encoder_config.hidden_size
          return VisionEncoderDecoderDecoderOnnxConfig(a  ,					a    )









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE       :      str												= {
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Tuple												= ["LlamaTokenizer"]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= ["LlamaTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      int												= [
     "LlamaForCausalLM",
     "LlamaModel",
     "LlamaPreTrainedModel",
     "LlamaForSequenceClassification",
 ]


if TYPE_CHECKING:
 from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_llama import LlamaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_llama_fast import LlamaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
 import sys

 SCREAMING_SNAKE_CASE       :      Optional[int]												= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


