





'''simple docstring'''







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

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


snake_case_     :       Optional[Any]									=  logging.get_logger(__name__)  # pylint: disable=invalid-name


class 					__a      (lowerCamelCase     ):

							def __init__(							self							:     Union[str, Any]       ,     __magic_name__							:     int       ,     __magic_name__							:     List[str]=7_68   )					->       Tuple:







													"""simple docstring"""
													super().__init__(__magic_name__   )
													UpperCAmelCase_			:      str     						=  proj_size

													UpperCAmelCase_			:      Dict     						=  CLIPVisionModel(__magic_name__   )
													UpperCAmelCase_			:      Optional[Any]     						=  PaintByExampleMapper(__magic_name__   )
													UpperCAmelCase_			:      Tuple     						=  nn.LayerNorm(config.hidden_size   )
													UpperCAmelCase_			:      Dict     						=  nn.Linear(config.hidden_size       ,     self.proj_size   )

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



							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     Tuple=False   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  self.model(pixel_values=__magic_name__   )
													UpperCAmelCase_			:      Union[str, Any]     						=  clip_output.pooler_output
													UpperCAmelCase_			:      Any     						=  self.mapper(latent_states[:, None]   )
													UpperCAmelCase_			:      Dict     						=  self.final_layer_norm(__magic_name__   )
													UpperCAmelCase_			:      str     						=  self.proj_out(__magic_name__   )
													if return_uncond_vector:
																			return latent_states, self.uncond_vector

													return latent_states





class 					__a      (nn.Module     ):

							def __init__(							self							:     Union[str, Any]       ,     __magic_name__							:     Any   )					->       Tuple:







													"""simple docstring"""
													super().__init__()
													UpperCAmelCase_			:      int     						=  (config.num_hidden_layers + 1) // 5
													UpperCAmelCase_			:      Dict     						=  config.hidden_size
													UpperCAmelCase_			:      Optional[int]     						=  1
													UpperCAmelCase_			:      Union[str, Any]     						=  nn.ModuleList(
													    [
													        BasicTransformerBlock(__magic_name__       ,     __magic_name__       ,     __magic_name__       ,     activation_fn='''gelu'''       ,     attention_bias=__magic_name__   )
													        for _ in range(__magic_name__   )
													    ]   )



							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     List[str]   )					->       List[str]:







													"""simple docstring"""
													for block in self.blocks:
																			UpperCAmelCase_			:      Any     						=  block(__magic_name__   )

													return hidden_states












'''simple docstring'''







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
					from PIL import Image


class 					__a      (lowerCamelCase     ):
							__a					:   int									=       "dandelin/vilt-b32-finetuned-vqa"
							__a					:   Any									=       (
							    "This is a tool that answers a question about an image. It takes an input named `image` which should be the "
							    "image containing the information, as well as a `question` which should be the question in English. It "
							    "returns a text that is the answer to the question."
							)
							__a					:   Any									=       "image_qa"
							__a					:   str									=       AutoProcessor
							__a					:   Any									=       AutoModelForVisualQuestionAnswering

							__a					:   List[Any]									=       ["image", "text"]
							__a					:   int									=       ["text"]

							def __init__(							self							:     Tuple       ,     *__magic_name__							:     Any       ,     **__magic_name__							:     Any   )					->       Tuple:







													"""simple docstring"""
													requires_backends(self       ,     ['''vision''']   )
													super().__init__(*__magic_name__       ,     **__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Union[str, Any]       ,     __magic_name__							:     "Image"       ,     __magic_name__							:     str   )					->       Tuple:







													"""simple docstring"""
													return self.pre_processor(__magic_name__       ,     __magic_name__       ,     return_tensors='''pt'''   )

							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     List[str]   )					->       Optional[Any]:







													"""simple docstring"""
													with torch.no_grad():
																			return self.model(**__magic_name__   ).logits



							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     int   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  outputs.argmax(-1   ).item()
													return self.model.config.idalabel[idx]





