
from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class 		UpperCamelCase__  :
   _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 				lowerCAmelCase      (self			: Optional[Any]       ):
    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			: List[Any]       ):
    return torch.from_numpy(np.array([self.width, self.height] ,    dtype=np.floataa       )       )
   def 				lowerCAmelCase      (self			: List[Any]       ):
    return torch.from_numpy(np.array([self.x_fov, self.y_fov] ,    dtype=np.floataa       )       )
   def 				lowerCAmelCase      (self			: Any       ):
    __a			:  str		    =  torch.arange(self.height * self.width       )
    __a			:  int		    =  torch.stack(
        [
            pixel_indices % self.width,
            torch.div(snake_case_ ,    self.width ,    rounding_mode='''trunc'''       ),
        ] ,    axis=1 ,    )
    return coords
   @property
   def 				lowerCAmelCase      (self			: List[str]       ):
    __a    ,	*__a			:  Any		    =  self.shape
    __a			:  Tuple		    =  int(np.prod(snake_case_       )       )

    __a			:  int		    =  self.get_image_coords()
    __a			:  Optional[int]		    =  torch.broadcast_to(coords.unsqueeze(0       ) ,    [batch_size * inner_batch_size, *coords.shape]       )
    __a			:  Union[str, Any]		    =  self.get_camera_rays(snake_case_       )

    __a			:  Tuple		    =  rays.view(snake_case_ ,    inner_batch_size * self.height * self.width ,    2 ,    3       )

    return rays
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: torch.Tensor       ):
    __a    ,	*__a    ,	__a			:  Optional[int]		    =  coords.shape
    assert n_coords == 2
    assert batch_size == self.origin.shape[0]

    __a			:  Optional[int]		    =  coords.view(snake_case_ ,    -1 ,    2       )

    __a			:  Dict		    =  self.resolution()
    __a			:  List[Any]		    =  self.fov()

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

    __a			:  Tuple		    =  fracs.view(snake_case_ ,    -1 ,    2       )
    __a			:  int		    =  (
        self.z.view(snake_case_ ,    1 ,    3       )
        + self.x.view(snake_case_ ,    1 ,    3       ) * fracs[:, :, :1]
        + self.y.view(snake_case_ ,    1 ,    3       ) * fracs[:, :, 1:]
    )
    __a			:  Optional[Any]		    =  directions / directions.norm(dim=-1 ,    keepdim=snake_case_       )
    __a			:  List[Any]		    =  torch.stack(
        [
            torch.broadcast_to(self.origin.view(snake_case_ ,    1 ,    3       ) ,    [batch_size, directions.shape[1], 3]       ),
            directions,
        ] ,    dim=2 ,    )
    return rays.view(snake_case_ ,    *snake_case_ ,    2 ,    3       )







   def 				lowerCAmelCase      (self			: List[str] ,    snake_case_			: int ,    snake_case_			: int       ):
    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=snake_case_ ,    height=snake_case_ ,    x_fov=self.x_fov ,    y_fov=self.y_fov ,    )





def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ):
 __a			:  List[str]		    =  []
 __a			:  Optional[Any]		    =  []
 __a			:  Any		    =  []
 __a			:  List[str]		    =  []
 for theta in np.linspace(0  ,       2 * np.pi  ,       num=2_0  ):
  __a			:  Optional[Any]		    =  np.array([np.sin(lowerCAmelCase__  ), np.cos(lowerCAmelCase__  ), -0.5]  )
  z /= np.sqrt(np.sum(z**2  )  )
  __a			:  str		    =  -z * 4
  __a			:  Tuple		    =  np.array([np.cos(lowerCAmelCase__  ), -np.sin(lowerCAmelCase__  ), 0.0]  )
  __a			:  Tuple		    =  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__  ))  ,       )


# Copyright 2023 The HuggingFace 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__          ={
    'configuration_efficientnet': [
        'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientNetConfig',
        'EfficientNetOnnxConfig',
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['EfficientNetImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
          'EfficientNetForImageClassification',
          'EfficientNetModel',
          'EfficientNetPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_efficientnet import (
          EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
          EfficientNetConfig,
          EfficientNetOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_efficientnet import EfficientNetImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_efficientnet import (
                EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                EfficientNetForImageClassification,
                EfficientNetModel,
                EfficientNetPreTrainedModel,
            )

else:
      import sys

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure)
