


"""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 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """dandelin/vilt-b32-finetuned-vqa"""
						lowerCAmelCase       =     (
						    """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."""
						)
						lowerCAmelCase       =     """image_qa"""
						lowerCAmelCase       =     AutoProcessor
						lowerCAmelCase       =     AutoModelForVisualQuestionAnswering

						lowerCAmelCase       =     ["""image""", """text"""]
						lowerCAmelCase       =     ["""text"""]







						def __init__(							self   :    List[Any]			,			*_lowercase   :    List[str]			,			**_lowercase   :    Dict		):
								requires_backends(self			,			['vision']		)
								super().__init__(*_lowercase			,			**_lowercase		)







						def __a						(							self   :    Union[str, Any]			,			_lowercase   :    "Image"			,			_lowercase   :    str		):
								return self.pre_processor(_lowercase			,			_lowercase			,			return_tensors='pt'		)







						def __a						(							self   :    Optional[Any]			,			_lowercase   :    Union[str, Any]		):
								with torch.no_grad():
										return self.model(**_lowercase		).logits







						def __a						(							self   :    List[str]			,			_lowercase   :    Dict		):
								A					       = outputs.argmax(-1		).item()
								return self.model.config.idalabel[idx]









"""simple docstring"""


import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible



class 			lowerCamelCase__						(   unittest.TestCase			):







						def __a						(							self   :    Dict		):
								A					       = [
								    'safety_checker/pytorch_model.bin',
								    'safety_checker/model.safetensors',
								    'vae/diffusion_pytorch_model.bin',
								    'vae/diffusion_pytorch_model.safetensors',
								    'text_encoder/pytorch_model.bin',
								    'text_encoder/model.safetensors',
								    'unet/diffusion_pytorch_model.bin',
								    'unet/diffusion_pytorch_model.safetensors',
								]
								self.assertTrue(is_safetensors_compatible(_lowercase		)		)







						def __a						(							self   :    Union[str, Any]		):
								A					       = [
								    'unet/diffusion_pytorch_model.bin',
								    'unet/diffusion_pytorch_model.safetensors',
								]
								self.assertTrue(is_safetensors_compatible(_lowercase		)		)







						def __a						(							self   :    int		):
								A					       = [
								    'safety_checker/pytorch_model.bin',
								    'safety_checker/model.safetensors',
								    'vae/diffusion_pytorch_model.bin',
								    'vae/diffusion_pytorch_model.safetensors',
								    'text_encoder/pytorch_model.bin',
								    'text_encoder/model.safetensors',
								    'unet/diffusion_pytorch_model.bin',
								    # Removed: 'unet/diffusion_pytorch_model.safetensors',
								]
								self.assertFalse(is_safetensors_compatible(_lowercase		)		)







						def __a						(							self   :    Dict		):
								A					       = [
								    'text_encoder/pytorch_model.bin',
								    'text_encoder/model.safetensors',
								]
								self.assertTrue(is_safetensors_compatible(_lowercase		)		)







						def __a						(							self   :    Union[str, Any]		):
								A					       = [
								    'safety_checker/pytorch_model.bin',
								    'safety_checker/model.safetensors',
								    'vae/diffusion_pytorch_model.bin',
								    'vae/diffusion_pytorch_model.safetensors',
								    'text_encoder/pytorch_model.bin',
								    # Removed: 'text_encoder/model.safetensors',
								    'unet/diffusion_pytorch_model.bin',
								    'unet/diffusion_pytorch_model.safetensors',
								]
								self.assertFalse(is_safetensors_compatible(_lowercase		)		)







						def __a						(							self   :    Any		):
								A					       = [
								    'safety_checker/pytorch_model.fp16.bin',
								    'safety_checker/model.fp16.safetensors',
								    'vae/diffusion_pytorch_model.fp16.bin',
								    'vae/diffusion_pytorch_model.fp16.safetensors',
								    'text_encoder/pytorch_model.fp16.bin',
								    'text_encoder/model.fp16.safetensors',
								    'unet/diffusion_pytorch_model.fp16.bin',
								    'unet/diffusion_pytorch_model.fp16.safetensors',
								]
								A					       = 'fp16'
								self.assertTrue(is_safetensors_compatible(_lowercase			,			variant=_lowercase		)		)







						def __a						(							self   :    Union[str, Any]		):
								A					       = [
								    'unet/diffusion_pytorch_model.fp16.bin',
								    'unet/diffusion_pytorch_model.fp16.safetensors',
								]
								A					       = 'fp16'
								self.assertTrue(is_safetensors_compatible(_lowercase			,			variant=_lowercase		)		)







						def __a						(							self   :    Optional[Any]		):
								# pass variant but use the non-variant filenames
								A					       = [
								    'unet/diffusion_pytorch_model.bin',
								    'unet/diffusion_pytorch_model.safetensors',
								]
								A					       = 'fp16'
								self.assertTrue(is_safetensors_compatible(_lowercase			,			variant=_lowercase		)		)







						def __a						(							self   :    Dict		):
								A					       = [
								    'safety_checker/pytorch_model.fp16.bin',
								    'safety_checker/model.fp16.safetensors',
								    'vae/diffusion_pytorch_model.fp16.bin',
								    'vae/diffusion_pytorch_model.fp16.safetensors',
								    'text_encoder/pytorch_model.fp16.bin',
								    'text_encoder/model.fp16.safetensors',
								    'unet/diffusion_pytorch_model.fp16.bin',
								    # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
								]
								A					       = 'fp16'
								self.assertFalse(is_safetensors_compatible(_lowercase			,			variant=_lowercase		)		)







						def __a						(							self   :    Dict		):
								A					       = [
								    'text_encoder/pytorch_model.fp16.bin',
								    'text_encoder/model.fp16.safetensors',
								]
								A					       = 'fp16'
								self.assertTrue(is_safetensors_compatible(_lowercase			,			variant=_lowercase		)		)







						def __a						(							self   :    Any		):
								# pass variant but use the non-variant filenames
								A					       = [
								    'text_encoder/pytorch_model.bin',
								    'text_encoder/model.safetensors',
								]
								A					       = 'fp16'
								self.assertTrue(is_safetensors_compatible(_lowercase			,			variant=_lowercase		)		)







						def __a						(							self   :    List[str]		):
								A					       = [
								    'safety_checker/pytorch_model.fp16.bin',
								    'safety_checker/model.fp16.safetensors',
								    'vae/diffusion_pytorch_model.fp16.bin',
								    'vae/diffusion_pytorch_model.fp16.safetensors',
								    'text_encoder/pytorch_model.fp16.bin',
								    # 'text_encoder/model.fp16.safetensors',
								    'unet/diffusion_pytorch_model.fp16.bin',
								    'unet/diffusion_pytorch_model.fp16.safetensors',
								]
								A					       = 'fp16'
								self.assertFalse(is_safetensors_compatible(_lowercase			,			variant=_lowercase		)		)





