





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

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


UpperCAmelCase_      							=     logging.get_logger(__name__)  # pylint: disable=invalid-name
class 			UpperCamelCase_		(  _lowerCamelCase					):




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=768  )		->							Dict:
										super().__init__(lowerCAmelCase_  )
										_snake_case									=				proj_size

										_snake_case									=				CLIPVisionModel(lowerCAmelCase_  )
										_snake_case									=				PaintByExampleMapper(lowerCAmelCase_  )
										_snake_case									=				nn.LayerNorm(config.hidden_size  )
										_snake_case									=				nn.Linear(config.hidden_size				,						self.proj_size  )

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






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=False  )		->							Optional[Any]:
										_snake_case									=				self.model(pixel_values=lowerCAmelCase_  )
										_snake_case									=				clip_output.pooler_output
										_snake_case									=				self.mapper(latent_states[:, None]  )
										_snake_case									=				self.final_layer_norm(lowerCAmelCase_  )
										_snake_case									=				self.proj_out(lowerCAmelCase_  )
										if return_uncond_vector:
															return latent_states, self.uncond_vector

										return latent_states





class 			UpperCamelCase_		(  nn.Module					):




					def __init__(		self				,						lowerCAmelCase_  )		->							Optional[int]:
										super().__init__()
										_snake_case									=				(config.num_hidden_layers + 1) // 5
										_snake_case									=				config.hidden_size
										_snake_case									=				1
										_snake_case									=				nn.ModuleList(
										    [
										        BasicTransformerBlock(lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						activation_fn='gelu'				,						attention_bias=lowerCAmelCase_  )
										        for _ in range(lowerCAmelCase_  )
										    ]  )






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							Optional[Any]:
										for block in self.blocks:
															_snake_case									=				block(lowerCAmelCase_  )

										return hidden_states










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_      							=     {
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_      							=     [
					    """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LukeForEntityClassification""",
					    """LukeForEntityPairClassification""",
					    """LukeForEntitySpanClassification""",
					    """LukeForMultipleChoice""",
					    """LukeForQuestionAnswering""",
					    """LukeForSequenceClassification""",
					    """LukeForTokenClassification""",
					    """LukeForMaskedLM""",
					    """LukeModel""",
					    """LukePreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
					from .tokenization_luke import LukeTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_luke import (
										    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LukeForEntityClassification,
										    LukeForEntityPairClassification,
										    LukeForEntitySpanClassification,
										    LukeForMaskedLM,
										    LukeForMultipleChoice,
										    LukeForQuestionAnswering,
										    LukeForSequenceClassification,
										    LukeForTokenClassification,
										    LukeModel,
										    LukePreTrainedModel,
										)

else:
					import sys

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



