





"""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      (       _lowercase ):
						UpperCAmelCase_ =			"vision-encoder-decoder"
						UpperCAmelCase_ =			True


						def __init__(self   ,							**__a		) ->   Optional[int]:
								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     snake_case_   (cls   ,							__a   ,							__a   ,							**__a		) ->   PretrainedConfig:
								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     snake_case_   (self		) ->   Tuple:
								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 ):
						UpperCAmelCase_ =			version.parse("1.11" )


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


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




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





class        _lowerCamelCase      (       _lowercase ):


						@property
						def     snake_case_   (self		) ->   Mapping[str, Mapping[int, str]]:
								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     snake_case_   (self   ,							__a   ,							__a = -1   ,							__a = -1   ,							__a = False   ,							__a = None   ,							) ->   Mapping[str, Any]:
								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        _lowerCamelCase      (       _lowercase ):


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


						def     snake_case_   (self   ,							__a		) ->   OnnxConfig:
								return VisionEncoderDecoderEncoderOnnxConfig(__a		)




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













"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__     			=			{
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__     			=			['''BlenderbotTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__     			=			[
						    '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BlenderbotForCausalLM''',
						    '''BlenderbotForConditionalGeneration''',
						    '''BlenderbotModel''',
						    '''BlenderbotPreTrainedModel''',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__     			=			[
						    '''TFBlenderbotForConditionalGeneration''',
						    '''TFBlenderbotModel''',
						    '''TFBlenderbotPreTrainedModel''',
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__     			=			[
						    '''FlaxBlenderbotForConditionalGeneration''',
						    '''FlaxBlenderbotModel''',
						    '''FlaxBlenderbotPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_blenderbot import (
						    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    BlenderbotConfig,
						    BlenderbotOnnxConfig,
						)
						from .tokenization_blenderbot import BlenderbotTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_blenderbot import (
												    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BlenderbotForCausalLM,
												    BlenderbotForConditionalGeneration,
												    BlenderbotModel,
												    BlenderbotPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_blenderbot import (
												    TFBlenderbotForConditionalGeneration,
												    TFBlenderbotModel,
												    TFBlenderbotPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_blenderbot import (
												    FlaxBlenderbotForConditionalGeneration,
												    FlaxBlenderbotModel,
												    FlaxBlenderbotPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase__     			=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






