



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

SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Union[str, Any]									=  'vision-encoder-decoder'
					lowerCAmelCase_				:    List[str]									=  True



					def __init__(			self    ,	**lowerCAmelCase     ):
							super().__init__(**lowerCAmelCase     )
							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(lowerCAmelCase    ,	**lowerCAmelCase     )
							UpperCAmelCase_						=				AutoConfig.for_model(lowerCAmelCase    ,	**lowerCAmelCase     )
							UpperCAmelCase_						=				True



					@classmethod
					def    A__		(			cls    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	**lowerCAmelCase     ):
							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()    ,	**lowerCAmelCase     )



					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 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Any									=  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 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					@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    ,	lowerCAmelCase    ,	lowerCAmelCase = -1    ,	lowerCAmelCase = -1    ,	lowerCAmelCase = False    ,	lowerCAmelCase = None    ,	):
							import torch

							UpperCAmelCase_						=				OrderedDict()

							UpperCAmelCase_						=				super().generate_dummy_inputs(
							    lowerCAmelCase    ,	batch_size=lowerCAmelCase    ,	seq_length=lowerCAmelCase    ,	is_pair=lowerCAmelCase    ,	framework=lowerCAmelCase     )

							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(lowerCAmelCase     )

							return common_inputs





class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					@property
					def    A__		(			self     ):
							pass



					def    A__		(			self    ,	lowerCAmelCase     ):
							return VisionEncoderDecoderEncoderOnnxConfig(lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = "default"     ):
							UpperCAmelCase_						=				encoder_config.hidden_size
							return VisionEncoderDecoderDecoderOnnxConfig(lowerCAmelCase    ,	lowerCAmelCase     )






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


SCREAMING_SNAKE_CASE     				=						{"tokenization_herbert": ["HerbertTokenizer"]}

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


if TYPE_CHECKING:
			from .tokenization_herbert import HerbertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_herbert_fast import HerbertTokenizerFast

else:
			import sys

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

