



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



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'vision-encoder-decoder'
							_lowerCamelCase						=		True



							def __init__(						self				,    **lowercase_    ):
													super().__init__(**lowercase_    )
													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}"""    )

													_snake_case						:					List[str]    =   kwargs.pop("encoder"    )
													_snake_case						:					Dict    =   encoder_config.pop("model_type"    )
													_snake_case						:					Union[str, Any]    =   kwargs.pop("decoder"    )
													_snake_case						:					List[Any]    =   decoder_config.pop("model_type"    )

													_snake_case						:					int    =   AutoConfig.for_model(lowercase_				,    **lowercase_    )
													_snake_case						:					Optional[Any]    =   AutoConfig.for_model(lowercase_				,    **lowercase_    )
													_snake_case						:					Union[str, Any]    =   True



							@classmethod
							def 	UpperCamelCase	(						cls				,    lowercase_				,    lowercase_				,    **lowercase_    ):
													logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"    )
													_snake_case						:					Dict    =   True
													_snake_case						:					Tuple    =   True

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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   copy.deepcopy(self.__dict__    )
													_snake_case						:					int    =   self.encoder.to_dict()
													_snake_case						:					Optional[Any]    =   self.decoder.to_dict()
													_snake_case						:					Any    =   self.__class__.model_type
													return output




class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		version.parse('1.11'		)



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



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



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




class        lowercase_   (					__snake_case		):



							@property
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Union[str, Any]    =   OrderedDict()
													_snake_case						:					str    =   {0: "batch", 1: "past_decoder_sequence + sequence"}
													_snake_case						:					List[str]    =   {0: "batch", 1: "past_decoder_sequence + sequence"}
													_snake_case						:					Dict    =   {0: "batch", 1: "encoder_sequence"}

													return common_inputs



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = -1				,    lowercase_ = -1				,    lowercase_ = False				,    lowercase_ = None				,    ):
													import torch

													_snake_case						:					List[str]    =   OrderedDict()

													_snake_case						:					Any    =   super().generate_dummy_inputs(
													    lowercase_				,    batch_size=lowercase_				,    seq_length=lowercase_				,    is_pair=lowercase_				,    framework=lowercase_    )

													_snake_case						:					Any    =   dummy_input["input_ids"].shape
													_snake_case						:					Optional[int]    =   (batch, encoder_sequence, self._config.encoder_hidden_size)
													_snake_case						:					Any    =   dummy_input.pop("input_ids"    )
													_snake_case						:					Tuple    =   dummy_input.pop("attention_mask"    )
													_snake_case						:					Tuple    =   torch.zeros(lowercase_    )

													return common_inputs




class        lowercase_   (					__snake_case		):



							@property
							def 	UpperCamelCase	(						self    ):
													pass



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													return VisionEncoderDecoderEncoderOnnxConfig(lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = "default"    ):
													_snake_case						:					List[Any]    =   encoder_config.hidden_size
													return VisionEncoderDecoderDecoderOnnxConfig(lowercase_				,    lowercase_    )
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'M-CLIP'



							def __init__(						self				,    lowercase_=1_024				,    lowercase_=768				,    **lowercase_    ):
													_snake_case						:					str    =   transformerDimSize
													_snake_case						:					Union[str, Any]    =   imageDimSize
													super().__init__(**lowercase_    )




class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		MCLIPConfig



							def __init__(						self				,    lowercase_				,    *lowercase_				,    **lowercase_    ):
													super().__init__(lowercase_				,    *lowercase_				,    **lowercase_    )
													_snake_case						:					List[Any]    =   XLMRobertaModel(lowercase_    )
													_snake_case						:					int    =   torch.nn.Linear(
													    in_features=config.transformerDimensions				,    out_features=config.numDims    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Tuple    =   self.transformer(input_ids=lowercase_				,    attention_mask=lowercase_    )[0]
													_snake_case						:					Tuple    =   (embs * attention_mask.unsqueeze(2    )).sum(dim=1    ) / attention_mask.sum(dim=1    )[:, None]
													return self.LinearTransformation(lowercase_    ), embs