

from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput






@dataclass
class       __A (							lowerCAmelCase       ):
    lowerCAmelCase_							:						List[str]						 =							None
    lowerCAmelCase_							:						Optional[Any]						 =							None
    lowerCAmelCase_							:						int						 =							None
    lowerCAmelCase_							:						Union[str, Any]						 =							None






class       __A (							lowerCAmelCase       ):






    def __init__(		self	:			Any			, UpperCAmelCase_	:			Dict=1			, UpperCAmelCase_	:			int=0			, UpperCAmelCase_	:			Optional[Any]=2			, UpperCAmelCase_	:			Optional[int]=512			, UpperCAmelCase_	:			int="cls"			, UpperCAmelCase_	:			int=False			, UpperCAmelCase_	:			Any=True			, **UpperCAmelCase_	:			Optional[int]			, ):
        super().__init__(pad_token_id=_snake_case			, bos_token_id=_snake_case			, eos_token_id=_snake_case			, **_snake_case			)
        lowerCAmelCase :	Optional[int]				=					project_dim
        lowerCAmelCase :	Union[str, Any]				=					pooler_fn
        lowerCAmelCase :	List[Any]				=					learn_encoder
        lowerCAmelCase :	Dict				=					use_attention_mask






class       __A (							lowerCAmelCase       ):
    lowerCAmelCase_							:						Tuple						 =							[R"pooler", R"logit_scale"]
    lowerCAmelCase_							:						List[str]						 =							[R"position_ids", R"predictions.decoder.bias"]
    lowerCAmelCase_							:						Tuple						 =							"roberta"
    lowerCAmelCase_							:						Optional[Any]						 =							RobertaSeriesConfig






    def __init__(		self	:			int			, UpperCAmelCase_	:			Optional[Any]			):
        super().__init__(_snake_case			)
        lowerCAmelCase :	List[str]				=					XLMRobertaModel(_snake_case			)
        lowerCAmelCase :	List[str]				=					nn.Linear(config.hidden_size			, config.project_dim			)
        lowerCAmelCase :	str				=					getattr(_snake_case			, 'has_pre_transformation'			, _snake_case			)
        if self.has_pre_transformation:
            lowerCAmelCase :	int				=					nn.Linear(config.hidden_size			, config.project_dim			)
            lowerCAmelCase :	Any				=					nn.LayerNorm(config.hidden_size			, eps=config.layer_norm_eps			)
        self.post_init()






    def 		lowercase__       (		self	:			Tuple			, UpperCAmelCase_	:			Dict = None			, UpperCAmelCase_	:			Optional[int] = None			, UpperCAmelCase_	:			Optional[Any] = None			, UpperCAmelCase_	:			Optional[Any] = None			, UpperCAmelCase_	:			Any = None			, UpperCAmelCase_	:			Union[str, Any] = None			, UpperCAmelCase_	:			str = None			, UpperCAmelCase_	:			Union[str, Any] = None			, UpperCAmelCase_	:			Union[str, Any] = None			, UpperCAmelCase_	:			List[str] = None			, UpperCAmelCase_	:			Tuple = None			, ):
        lowerCAmelCase :	int				=					return_dict if return_dict is not None else self.config.use_return_dict

        lowerCAmelCase :	Union[str, Any]				=					self.base_model(
            input_ids=_snake_case			, attention_mask=_snake_case			, token_type_ids=_snake_case			, position_ids=_snake_case			, head_mask=_snake_case			, inputs_embeds=_snake_case			, encoder_hidden_states=_snake_case			, encoder_attention_mask=_snake_case			, output_attentions=_snake_case			, output_hidden_states=True if self.has_pre_transformation else output_hidden_states			, return_dict=_snake_case			, )

        if self.has_pre_transformation:
            lowerCAmelCase :	Optional[int]				=					outputs['hidden_states'][-2]
            lowerCAmelCase :	Tuple				=					self.pre_LN(_snake_case			)
            lowerCAmelCase :	Optional[Any]				=					self.transformation_pre(_snake_case			)

            return TransformationModelOutput(
                projection_state=_snake_case			, last_hidden_state=outputs.last_hidden_state			, hidden_states=outputs.hidden_states			, attentions=outputs.attentions			, )
        else:
            lowerCAmelCase :	Any				=					self.transformation(outputs.last_hidden_state			)
            return TransformationModelOutput(
                projection_state=_snake_case			, last_hidden_state=outputs.last_hidden_state			, hidden_states=outputs.hidden_states			, attentions=outputs.attentions			, )







from typing import TYPE_CHECKING

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


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

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

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


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


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Dict    = [
					    '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

					A     :	List[Any]    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)