import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
      from PIL import Image
else:





      class      lowerCAmelCase						:



           @staticmethod
           def 			a__  (					*lowerCAmelCase__				,      **lowerCAmelCase__       ):
                  pass





@is_pipeline_test
@require_torch
@require_vision
class      lowerCAmelCase						(					unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     Tuple		=MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	pipeline('visual-question-answering'				,      model='hf-internal-testing/tiny-vilt-random-vqa'       )
            _A=	[
                {
                    'image': Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       ),
                    'question': 'How many cats are there?',
                },
                {
                    'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
                    'question': 'How many cats are there?',
                },
            ]
            return vqa_pipeline, examples



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	vqa_pipeline(lowerCAmelCase__				,      top_k=1       )
            self.assertEqual(
                lowerCAmelCase__				,      [
                    [{'score': ANY(lowerCAmelCase__       ), 'answer': ANY(lowerCAmelCase__       )}],
                    [{'score': ANY(lowerCAmelCase__       ), 'answer': ANY(lowerCAmelCase__       )}],
                ]				,      )



     @require_torch
     def 			a__  (					self       ):
            _A=	pipeline('visual-question-answering'				,      model='hf-internal-testing/tiny-vilt-random-vqa'       )
            _A=	'./tests/fixtures/tests_samples/COCO/000000039769.png'
            _A=	'How many cats are there?'

            _A=	vqa_pipeline(image=lowerCAmelCase__				,      question='How many cats are there?'				,      top_k=2       )
            self.assertEqual(
                lowerCAmelCase__				,      [{'score': ANY(lowerCAmelCase__       ), 'answer': ANY(lowerCAmelCase__       )}, {'score': ANY(lowerCAmelCase__       ), 'answer': ANY(lowerCAmelCase__       )}]       )

            _A=	vqa_pipeline({'image': image, 'question': question}				,      top_k=2       )
            self.assertEqual(
                lowerCAmelCase__				,      [{'score': ANY(lowerCAmelCase__       ), 'answer': ANY(lowerCAmelCase__       )}, {'score': ANY(lowerCAmelCase__       ), 'answer': ANY(lowerCAmelCase__       )}]       )



     @slow
     @require_torch
     def 			a__  (					self       ):
            _A=	pipeline('visual-question-answering'				,      model='dandelin/vilt-b32-finetuned-vqa'       )
            _A=	'./tests/fixtures/tests_samples/COCO/000000039769.png'
            _A=	'How many cats are there?'

            _A=	vqa_pipeline(image=lowerCAmelCase__				,      question=lowerCAmelCase__				,      top_k=2       )
            self.assertEqual(
                nested_simplify(lowerCAmelCase__				,      decimals=4       )				,      [{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]       )

            _A=	vqa_pipeline({'image': image, 'question': question}				,      top_k=2       )
            self.assertEqual(
                nested_simplify(lowerCAmelCase__				,      decimals=4       )				,      [{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]       )

            _A=	vqa_pipeline(
                [{'image': image, 'question': question}, {'image': image, 'question': question}]				,      top_k=2       )
            self.assertEqual(
                nested_simplify(lowerCAmelCase__				,      decimals=4       )				,      [[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]] * 2				,      )





     @require_tf
     @unittest.skip('Visual question answering not implemented in TF'       )
     def 			a__  (					self       ):
            pass
from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch





@dataclass
class      lowerCAmelCase						:
     _SCREAMING_SNAKE_CASE     :     torch.Tensor  # [batch_size x 3]
     _SCREAMING_SNAKE_CASE     :     torch.Tensor  # [batch_size x 3]
     _SCREAMING_SNAKE_CASE     :     torch.Tensor  # [batch_size x 3]
     _SCREAMING_SNAKE_CASE     :     torch.Tensor  # [batch_size x 3]
     _SCREAMING_SNAKE_CASE     :     int
     _SCREAMING_SNAKE_CASE     :     int
     _SCREAMING_SNAKE_CASE     :     float
     _SCREAMING_SNAKE_CASE     :     float
     _SCREAMING_SNAKE_CASE     :     Tuple[int]



     def 			a__  (					self       ):
            assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
            assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
            assert len(self.x.shape       ) == len(self.y.shape       ) == len(self.z.shape       ) == len(self.origin.shape       ) == 2



     def 			a__  (					self       ):
            return torch.from_numpy(np.array([self.width, self.height]				,      dtype=np.floataa       )       )



     def 			a__  (					self       ):
            return torch.from_numpy(np.array([self.x_fov, self.y_fov]				,      dtype=np.floataa       )       )



     def 			a__  (					self       ):
            _A=	torch.arange(self.height * self.width       )
            _A=	torch.stack(
                [
                    pixel_indices % self.width,
                    torch.div(lowerCAmelCase__				,      self.width				,      rounding_mode='trunc'       ),
                ]				,      axis=1				,      )
            return coords



     @property
     def 			a__  (					self       ):
            _A,			*_A=	self.shape
            _A=	int(np.prod(lowerCAmelCase__       )       )

            _A=	self.get_image_coords()
            _A=	torch.broadcast_to(coords.unsqueeze(0       )				,      [batch_size * inner_batch_size, *coords.shape]       )
            _A=	self.get_camera_rays(lowerCAmelCase__       )

            _A=	rays.view(lowerCAmelCase__				,      inner_batch_size * self.height * self.width				,      2				,      3       )

            return rays



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A,			*_A,			_A=	coords.shape
            assert n_coords == 2
            assert batch_size == self.origin.shape[0]

            _A=	coords.view(lowerCAmelCase__				,      -1				,      2       )

            _A=	self.resolution()
            _A=	self.fov()

            _A=	(flat.float() / (res - 1)) * 2 - 1
            _A=	fracs * torch.tan(fov / 2       )

            _A=	fracs.view(lowerCAmelCase__				,      -1				,      2       )
            _A=	(
                self.z.view(lowerCAmelCase__				,      1				,      3       )
                + self.x.view(lowerCAmelCase__				,      1				,      3       ) * fracs[:, :, :1]
                + self.y.view(lowerCAmelCase__				,      1				,      3       ) * fracs[:, :, 1:]
            )
            _A=	directions / directions.norm(dim=-1				,      keepdim=lowerCAmelCase__       )
            _A=	torch.stack(
                [
                    torch.broadcast_to(self.origin.view(lowerCAmelCase__				,      1				,      3       )				,      [batch_size, directions.shape[1], 3]       ),
                    directions,
                ]				,      dim=2				,      )
            return rays.view(lowerCAmelCase__				,      *lowerCAmelCase__				,      2				,      3       )





     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            assert width * self.height == height * self.width, "The aspect ratio should not change."
            return DifferentiableProjectiveCamera(
                origin=self.origin				,      x=self.x				,      y=self.y				,      z=self.z				,      width=lowerCAmelCase__				,      height=lowerCAmelCase__				,      x_fov=self.x_fov				,      y_fov=self.y_fov				,      )


def 							UpperCamelCase				(    lowerCAmelCase_   )		->						DifferentiableProjectiveCamera:




       '''simple docstring'''






       _A=	[]
       _A=	[]
       _A=	[]
       _A=	[]
       for theta in np.linspace(0       ,					2 * np.pi       ,					num=20   ):
              _A=	np.array([np.sin(lowerCAmelCase_   ), np.cos(lowerCAmelCase_   ), -0.5]   )
              z /= np.sqrt(np.sum(z**2   )   )
              _A=	-z * 4
              _A=	np.array([np.cos(lowerCAmelCase_   ), -np.sin(lowerCAmelCase_   ), 0.0]   )
              _A=	np.cross(lowerCAmelCase_       ,					lowerCAmelCase_   )
              origins.append(lowerCAmelCase_   )
              xs.append(lowerCAmelCase_   )
              ys.append(lowerCAmelCase_   )
              zs.append(lowerCAmelCase_   )
       return DifferentiableProjectiveCamera(
           origin=torch.from_numpy(np.stack(lowerCAmelCase_       ,					axis=0   )   ).float()       ,					x=torch.from_numpy(np.stack(lowerCAmelCase_       ,					axis=0   )   ).float()       ,					y=torch.from_numpy(np.stack(lowerCAmelCase_       ,					axis=0   )   ).float()       ,					z=torch.from_numpy(np.stack(lowerCAmelCase_       ,					axis=0   )   ).float()       ,					width=lowerCAmelCase_       ,					height=lowerCAmelCase_       ,					x_fov=0.7       ,					y_fov=0.7       ,					shape=(1, len(lowerCAmelCase_   ))       ,					)