


import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


_lowercase							:   str						=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def __init__(    self					, a__					, a__=768 )						->					Dict:
					super().__init__(a__ )
					A             =					proj_size

					A             =					CLIPVisionModel(a__ )
					A             =					PaintByExampleMapper(a__ )
					A             =					nn.LayerNorm(config.hidden_size )
					A             =					nn.Linear(config.hidden_size					, self.proj_size )

					# uncondition for scaling
					A             =					nn.Parameter(torch.randn((1, 1, self.proj_size) ) )

				def   _UpperCAmelCase       (    self					, a__					, a__=False )						->					Dict:
					A             =					self.model(pixel_values=a__ )
					A             =					clip_output.pooler_output
					A             =					self.mapper(latent_states[:, None] )
					A             =					self.final_layer_norm(a__ )
					A             =					self.proj_out(a__ )
					if return_uncond_vector:
						return latent_states, self.uncond_vector

					return latent_states






class    _UpperCamelCase     (       nn.Module					):

				"""simple docstring"""







				def __init__(    self					, a__ )						->					Tuple:
					super().__init__()
					A             =					(config.num_hidden_layers + 1) // 5
					A             =					config.hidden_size
					A             =					1
					A             =					nn.ModuleList(
					    [
					        BasicTransformerBlock(a__					, a__					, a__					, activation_fn="""gelu"""					, attention_bias=a__ )
					        for _ in range(a__ )
					    ] )

				def   _UpperCAmelCase       (    self					, a__ )						->					Optional[Any]:
					for block in self.blocks:
						A             =					block(a__ )

					return hidden_states







from typing import TYPE_CHECKING

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


_lowercase							:   int						=						{
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   List[Any]						=						[
				    "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RobertaPreLayerNormForCausalLM",
				    "RobertaPreLayerNormForMaskedLM",
				    "RobertaPreLayerNormForMultipleChoice",
				    "RobertaPreLayerNormForQuestionAnswering",
				    "RobertaPreLayerNormForSequenceClassification",
				    "RobertaPreLayerNormForTokenClassification",
				    "RobertaPreLayerNormModel",
				    "RobertaPreLayerNormPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   Optional[int]						=						[
				    "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRobertaPreLayerNormForCausalLM",
				    "TFRobertaPreLayerNormForMaskedLM",
				    "TFRobertaPreLayerNormForMultipleChoice",
				    "TFRobertaPreLayerNormForQuestionAnswering",
				    "TFRobertaPreLayerNormForSequenceClassification",
				    "TFRobertaPreLayerNormForTokenClassification",
				    "TFRobertaPreLayerNormMainLayer",
				    "TFRobertaPreLayerNormModel",
				    "TFRobertaPreLayerNormPreTrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   str						=						[
				    "FlaxRobertaPreLayerNormForCausalLM",
				    "FlaxRobertaPreLayerNormForMaskedLM",
				    "FlaxRobertaPreLayerNormForMultipleChoice",
				    "FlaxRobertaPreLayerNormForQuestionAnswering",
				    "FlaxRobertaPreLayerNormForSequenceClassification",
				    "FlaxRobertaPreLayerNormForTokenClassification",
				    "FlaxRobertaPreLayerNormModel",
				    "FlaxRobertaPreLayerNormPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roberta_prelayernorm import (
				    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    RobertaPreLayerNormConfig,
				    RobertaPreLayerNormOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roberta_prelayernorm import (
								    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RobertaPreLayerNormForCausalLM,
								    RobertaPreLayerNormForMaskedLM,
								    RobertaPreLayerNormForMultipleChoice,
								    RobertaPreLayerNormForQuestionAnswering,
								    RobertaPreLayerNormForSequenceClassification,
								    RobertaPreLayerNormForTokenClassification,
								    RobertaPreLayerNormModel,
								    RobertaPreLayerNormPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roberta_prelayernorm import (
								    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRobertaPreLayerNormForCausalLM,
								    TFRobertaPreLayerNormForMaskedLM,
								    TFRobertaPreLayerNormForMultipleChoice,
								    TFRobertaPreLayerNormForQuestionAnswering,
								    TFRobertaPreLayerNormForSequenceClassification,
								    TFRobertaPreLayerNormForTokenClassification,
								    TFRobertaPreLayerNormMainLayer,
								    TFRobertaPreLayerNormModel,
								    TFRobertaPreLayerNormPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roberta_prelayernorm import (
								    FlaxRobertaPreLayerNormForCausalLM,
								    FlaxRobertaPreLayerNormForMaskedLM,
								    FlaxRobertaPreLayerNormForMultipleChoice,
								    FlaxRobertaPreLayerNormForQuestionAnswering,
								    FlaxRobertaPreLayerNormForSequenceClassification,
								    FlaxRobertaPreLayerNormForTokenClassification,
								    FlaxRobertaPreLayerNormModel,
								    FlaxRobertaPreLayerNormPreTrainedModel,
								)

else:
				import sys

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



