






from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch




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






      def 			lowerCamelCase__    (    self	)	->Optional[Any]:







        '''simple docstring'''


        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 			lowerCamelCase__    (    self	)	->Union[str, Any]:







        '''simple docstring'''


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






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


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






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        _a							=	torch.arange(self.height * self.width	)
        _a							=	torch.stack(
            [
                pixel_indices % self.width,
                torch.div(a_     ,     self.width     ,     rounding_mode="trunc"	),
            ]     ,     axis=1     ,     )
        return coords






      @property
      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        _a			,	*_a							=	self.shape
        _a							=	int(np.prod(a_	)	)

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

        _a							=	rays.view(a_     ,     inner_batch_size * self.height * self.width     ,     2     ,     3	)

        return rays






      def 			lowerCamelCase__    (    self     ,     a_	)	->List[str]:







        '''simple docstring'''


        _a			,	*_a			,	_a							=	coords.shape
        assert n_coords == 2
        assert batch_size == self.origin.shape[0]

        _a							=	coords.view(a_     ,     -1     ,     2	)

        _a							=	self.resolution()
        _a							=	self.fov()

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

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






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_	)	->Union[str, Any]:







        '''simple docstring'''


        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=a_     ,     height=a_     ,     x_fov=self.x_fov     ,     y_fov=self.y_fov     ,     )







def        lowerCAmelCase (      UpperCamelCase_:							Optional[Any]					)       ->							Optional[Any]:







  '''simple docstring'''







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










"""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

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
     from PIL import Image




class    lowercase_    (_UpperCAmelCase     ):
      A__	:		str       			=				'''Salesforce/blip-image-captioning-base'''
      A__	:		Optional[int]       			=				(
          '''This is a tool that generates a description of an image. It takes an input named `image` which should be the '''
          '''image to caption, and returns a text that contains the description in English.'''
      )
      A__	:		str       			=				'''image_captioner'''
      A__	:		Tuple       			=				AutoModelForVisionaSeq

      A__	:		Tuple       			=				['''image''']
      A__	:		List[Any]       			=				['''text''']






      def __init__(    self     ,     *a_     ,     **a_	)	->Union[str, Any]:







        '''simple docstring'''


        requires_backends(self     ,     ["vision"]	)
        super().__init__(*a_     ,     **a_	)






      def 			lowerCamelCase__    (    self     ,     a_	)	->Union[str, Any]:







        '''simple docstring'''


        return self.pre_processor(images=a_     ,     return_tensors="pt"	)






      def 			lowerCamelCase__    (    self     ,     a_	)	->List[Any]:







        '''simple docstring'''


        return self.model.generate(**a_	)






      def 			lowerCamelCase__    (    self     ,     a_	)	->Tuple:







        '''simple docstring'''


        return self.pre_processor.batch_decode(a_     ,     skip_special_tokens=a_	)[0].strip()


