






import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import (
       MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
       MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       MODEL_MAPPING,
       DeiTForImageClassification,
       DeiTForImageClassificationWithTeacher,
       DeiTForMaskedImageModeling,
       DeiTModel,
   )
   from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import DeiTImageProcessor


class 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


     def __init__(					self							:						int       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						Dict=13       ,			UpperCAmelCase_							:						Dict=30       ,			UpperCAmelCase_							:						Tuple=2       ,			UpperCAmelCase_							:						List[str]=3       ,			UpperCAmelCase_							:						int=True       ,			UpperCAmelCase_							:						Union[str, Any]=True       ,			UpperCAmelCase_							:						Dict=32       ,			UpperCAmelCase_							:						Any=5       ,			UpperCAmelCase_							:						str=4       ,			UpperCAmelCase_							:						Optional[Any]=37       ,			UpperCAmelCase_							:						Dict="gelu"       ,			UpperCAmelCase_							:						Union[str, Any]=0.1       ,			UpperCAmelCase_							:						Optional[int]=0.1       ,			UpperCAmelCase_							:						Dict=10       ,			UpperCAmelCase_							:						Union[str, Any]=0.02       ,			UpperCAmelCase_							:						str=3       ,			UpperCAmelCase_							:						List[Any]=None       ,			UpperCAmelCase_							:						List[str]=2       ,			):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     parent
       SCREAMING_SNAKE_CASE							:   List[str]						    =     batch_size
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     image_size
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     patch_size
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     num_channels
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     is_training
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     use_labels
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     hidden_size
       SCREAMING_SNAKE_CASE							:   Dict						    =     num_hidden_layers
       SCREAMING_SNAKE_CASE							:   Any						    =     num_attention_heads
       SCREAMING_SNAKE_CASE							:   int						    =     intermediate_size
       SCREAMING_SNAKE_CASE							:   int						    =     hidden_act
       SCREAMING_SNAKE_CASE							:   Dict						    =     hidden_dropout_prob
       SCREAMING_SNAKE_CASE							:   str						    =     attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE							:   int						    =     type_sequence_label_size
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     initializer_range
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     scope
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     encoder_stride

       # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     (image_size // patch_size) ** 2
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     num_patches + 2


     def 					_A							(					self							:						Union[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

       SCREAMING_SNAKE_CASE							:   List[str]						    =     None
       if self.use_labels:
         SCREAMING_SNAKE_CASE							:   Dict						    =     ids_tensor([self.batch_size]       ,			self.type_sequence_label_size   )

       SCREAMING_SNAKE_CASE							:   int						    =     self.get_config()

       return config, pixel_values, labels


     def 					_A							(					self							:						Optional[Any]   ):
       return DeiTConfig(
           image_size=self.image_size       ,			patch_size=self.patch_size       ,			num_channels=self.num_channels       ,			hidden_size=self.hidden_size       ,			num_hidden_layers=self.num_hidden_layers       ,			num_attention_heads=self.num_attention_heads       ,			intermediate_size=self.intermediate_size       ,			hidden_act=self.hidden_act       ,			hidden_dropout_prob=self.hidden_dropout_prob       ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,			is_decoder=UpperCAmelCase_       ,			initializer_range=self.initializer_range       ,			encoder_stride=self.encoder_stride       ,			)


     def 					_A							(					self							:						Any       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						Optional[Any]       ,			UpperCAmelCase_							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   int						    =     DeiTModel(config=UpperCAmelCase_   )
       model.to(UpperCAmelCase_   )
       model.eval()
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     model(UpperCAmelCase_   )
       self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, self.seq_length, self.hidden_size)   )


     def 					_A							(					self							:						List[Any]       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						Any   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     DeiTForMaskedImageModeling(config=UpperCAmelCase_   )
       model.to(UpperCAmelCase_   )
       model.eval()
       SCREAMING_SNAKE_CASE							:   int						    =     model(UpperCAmelCase_   )
       self.parent.assertEqual(
           result.reconstruction.shape       ,			(self.batch_size, self.num_channels, self.image_size, self.image_size)   )

       # test greyscale images
       SCREAMING_SNAKE_CASE							:   int						    =     1
       SCREAMING_SNAKE_CASE							:   str						    =     DeiTForMaskedImageModeling(UpperCAmelCase_   )
       model.to(UpperCAmelCase_   )
       model.eval()

       SCREAMING_SNAKE_CASE							:   Any						    =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
       SCREAMING_SNAKE_CASE							:   List[str]						    =     model(UpperCAmelCase_   )
       self.parent.assertEqual(result.reconstruction.shape       ,			(self.batch_size, 1, self.image_size, self.image_size)   )


     def 					_A							(					self							:						str       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   str						    =     self.type_sequence_label_size
       SCREAMING_SNAKE_CASE							:   Dict						    =     DeiTForImageClassification(UpperCAmelCase_   )
       model.to(UpperCAmelCase_   )
       model.eval()
       SCREAMING_SNAKE_CASE							:   int						    =     model(UpperCAmelCase_       ,			labels=UpperCAmelCase_   )
       self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.type_sequence_label_size)   )

       # test greyscale images
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     1
       SCREAMING_SNAKE_CASE							:   int						    =     DeiTForImageClassification(UpperCAmelCase_   )
       model.to(UpperCAmelCase_   )
       model.eval()

       SCREAMING_SNAKE_CASE							:   Any						    =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     model(UpperCAmelCase_       ,			labels=UpperCAmelCase_   )
       self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.type_sequence_label_size)   )






     def 					_A							(					self							:						Union[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE
       )    ,	(
           SCREAMING_SNAKE_CASE
       )    ,	(
           SCREAMING_SNAKE_CASE
       )    ,	
       )							:   str						    =     config_and_inputs
       SCREAMING_SNAKE_CASE							:   str						    =     {"pixel_values": pixel_values}
       return config, inputs_dict





@require_torch
class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase   ,		lowerCAmelCase   ,		unittest.TestCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Optional[Any]								=						(
         (
             DeiTModel,
             DeiTForImageClassification,
             DeiTForImageClassificationWithTeacher,
             DeiTForMaskedImageModeling,
         )
         if is_torch_available()
         else ()
     )
     UpperCamelCase_				:		Optional[int]								=						(
         {
             '''feature-extraction''': DeiTModel,
             '''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
         }
         if is_torch_available()
         else {}
     )

     UpperCamelCase_				:		str								=						False
     UpperCamelCase_				:		List[Any]								=						False
     UpperCamelCase_				:		Any								=						False


     def 					_A							(					self							:						Any   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     DeiTModelTester(self   )
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     ConfigTester(self       ,			config_class=UpperCAmelCase_       ,			has_text_modality=UpperCAmelCase_       ,			hidden_size=37   )


     def 					_A							(					self							:						Optional[Any]   ):
       self.config_tester.run_common_tests()


     @unittest.skip(reason="DeiT does not use inputs_embeds"   )
     def 					_A							(					self							:						Tuple   ):
       pass


     def 					_A							(					self							:						int   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Tuple						    =     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE							:   Tuple						    =     model_class(UpperCAmelCase_   )
         self.assertIsInstance(model.get_input_embeddings()       ,			(nn.Module)   )
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(UpperCAmelCase_       ,			nn.Linear   )   )


     def 					_A							(					self							:						Tuple   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Tuple						    =     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     model_class(UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   List[str]						    =     inspect.signature(model.forward   )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     [*signature.parameters.keys()]

         SCREAMING_SNAKE_CASE							:   str						    =     ["pixel_values"]
         self.assertListEqual(arg_names[:1]       ,			UpperCAmelCase_   )


     def 					_A							(					self							:						str   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCAmelCase_   )


     def 					_A							(					self							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   str						    =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase_   )


     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   str						    =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_   )


     def 					_A							(					self							:						List[Any]       ,			UpperCAmelCase_							:						Any       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						Dict=False   ):
       SCREAMING_SNAKE_CASE							:   str						    =     super()._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_       ,			return_labels=UpperCAmelCase_   )

       if return_labels:
         if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
           del inputs_dict["labels"]

       return inputs_dict


     def 					_A							(					self							:						Optional[Any]   ):
       if not self.model_tester.is_training:
         return

       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   int						    =     self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     True

       for model_class in self.all_model_classes:
         # DeiTForImageClassificationWithTeacher supports inference-only
         if (
             model_class in get_values(UpperCAmelCase_   )
             or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
         ):
           continue
         SCREAMING_SNAKE_CASE							:   int						    =     model_class(UpperCAmelCase_   )
         model.to(UpperCAmelCase_   )
         model.train()
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_       ,			return_labels=UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   Any						    =     model(**UpperCAmelCase_   ).loss
         loss.backward()


     def 					_A							(					self							:						str   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
         return

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     False
       SCREAMING_SNAKE_CASE							:   int						    =     True

       for model_class in self.all_model_classes:
         if model_class in get_values(UpperCAmelCase_   ) or not model_class.supports_gradient_checkpointing:
           continue
         # DeiTForImageClassificationWithTeacher supports inference-only
         if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
           continue
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     model_class(UpperCAmelCase_   )
         model.gradient_checkpointing_enable()
         model.to(UpperCAmelCase_   )
         model.train()
         SCREAMING_SNAKE_CASE							:   Tuple						    =     self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_       ,			return_labels=UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   Dict						    =     model(**UpperCAmelCase_   ).loss
         loss.backward()


     def 					_A							(					self							:						Dict   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   int						    =     self.model_tester.prepare_config_and_inputs_for_common()

       SCREAMING_SNAKE_CASE							:   int						    =     [
           {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
           {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
           {"title": "regression", "num_labels": 1, "dtype": torch.float},
       ]

       for model_class in self.all_model_classes:
         if (
             model_class
             not in [
                 *get_values(UpperCAmelCase_   ),
                 *get_values(UpperCAmelCase_   ),
             ]
             or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
         ):
           continue

         for problem_type in problem_types:
           with self.subTest(msg=f'''Testing {model_class} with {problem_type['title']}'''   ):
             SCREAMING_SNAKE_CASE							:   str						    =     problem_type["title"]
             SCREAMING_SNAKE_CASE							:   Optional[int]						    =     problem_type["num_labels"]

             SCREAMING_SNAKE_CASE							:   str						    =     model_class(UpperCAmelCase_   )
             model.to(UpperCAmelCase_   )
             model.train()

             SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_       ,			return_labels=UpperCAmelCase_   )

             if problem_type["num_labels"] > 1:
               SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     inputs["labels"].unsqueeze(1   ).repeat(1       ,			problem_type["num_labels"]   )

             SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     inputs["labels"].to(problem_type["dtype"]   )

             # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
             # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
             # they have the same size." which is a symptom something in wrong for the regression problem.
             # See https://github.com/huggingface/transformers/issues/11780
             with warnings.catch_warnings(record=UpperCAmelCase_   ) as warning_list:
               SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     model(**UpperCAmelCase_   ).loss
             for w in warning_list:
               if "Using a target size that is different to the input size" in str(w.message   ):
                 raise ValueError(
                     f'''Something is going wrong in the regression problem: intercepted {w.message}'''   )

             loss.backward()






     @slow
     def 					_A							(					self							:						int   ):
       for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         SCREAMING_SNAKE_CASE							:   List[str]						    =     DeiTModel.from_pretrained(UpperCAmelCase_   )
         self.assertIsNotNone(UpperCAmelCase_   )





def 	lowerCamelCase__      (  ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Tuple						    =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
  return image


@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE      (      unittest.TestCase       ):
     '''simple docstring'''


     @cached_property
     def 					_A							(					self							:						Tuple   ):
       return (
           DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"   )
           if is_vision_available()
           else None
       )


     @slow
     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   str						    =     DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"   ).to(
           UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.default_image_processor
       SCREAMING_SNAKE_CASE							:   Dict						    =     prepare_img()
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     image_processor(images=UpperCAmelCase_       ,			return_tensors="pt"   ).to(UpperCAmelCase_   )

       # forward pass
       with torch.no_grad():
         SCREAMING_SNAKE_CASE							:   str						    =     model(**UpperCAmelCase_   )

       # verify the logits
       SCREAMING_SNAKE_CASE							:   int						    =     torch.Size((1, 1000)   )
       self.assertEqual(outputs.logits.shape       ,			UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   int						    =     torch.tensor([-1.0_266, 0.1_912, -1.2_861]   ).to(UpperCAmelCase_   )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,			UpperCAmelCase_       ,			atol=1E-4   )   )






     @slow
     @require_accelerate
     @require_torch_gpu
     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     DeiTModel.from_pretrained(
           "facebook/deit-base-distilled-patch16-224"       ,			torch_dtype=torch.floataa       ,			device_map="auto"   )
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.default_image_processor

       SCREAMING_SNAKE_CASE							:   str						    =     prepare_img()
       SCREAMING_SNAKE_CASE							:   int						    =     image_processor(images=UpperCAmelCase_       ,			return_tensors="pt"   )
       SCREAMING_SNAKE_CASE							:   Any						    =     inputs.pixel_values.to(UpperCAmelCase_   )

       # forward pass to make sure inference works in fp16
       with torch.no_grad():
         SCREAMING_SNAKE_CASE							:   Any						    =     model(UpperCAmelCase_   )








from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch




@dataclass
class        _SCREAMING_SNAKE_CASE	:
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					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        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					return torch.from_numpy(np.array([self.width, self.height]		,    dtype=np.floataa					)					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					return torch.from_numpy(np.array([self.x_fov, self.y_fov]		,    dtype=np.floataa					)					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			torch.Tensor:
					lowerCamelCase_         =     torch.arange(self.height * self.width					)
					lowerCamelCase_         =     torch.stack(
					    [
					        pixel_indices % self.width,
					        torch.div(lowercase		,    self.width		,    rounding_mode="trunc"					),
					    ]		,    axis=1		,    )
					return coords



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					lowerCamelCase_      ,       *lowerCamelCase_         =     self.shape
					lowerCamelCase_         =     int(np.prod(lowercase					)					)

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

					lowerCamelCase_         =     rays.view(lowercase		,    inner_batch_size * self.height * self.width		,    2		,    3					)

					return rays



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			torch.Tensor:
					lowerCamelCase_      ,       *lowerCamelCase_      ,       lowerCamelCase_         =     coords.shape
					assert n_coords == 2
					assert batch_size == self.origin.shape[0]

					lowerCamelCase_         =     coords.view(lowercase		,    -1		,    2					)

					lowerCamelCase_         =     self.resolution()
					lowerCamelCase_         =     self.fov()

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

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



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase					)							->			"DifferentiableProjectiveCamera":
					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=lowercase		,    height=lowercase		,    x_fov=self.x_fov		,    y_fov=self.y_fov		,    )





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     []
				lowerCamelCase_         =     []
				lowerCamelCase_         =     []
				lowerCamelCase_         =     []
				for theta in np.linspace(0      ,					2 * np.pi      ,					num=2_0       ):
								lowerCamelCase_         =     np.array([np.sin(lowerCamelCase__       ), np.cos(lowerCamelCase__       ), -0.5]       )
								z /= np.sqrt(np.sum(z**2       )       )
								lowerCamelCase_         =     -z * 4
								lowerCamelCase_         =     np.array([np.cos(lowerCamelCase__       ), -np.sin(lowerCamelCase__       ), 0.0]       )
								lowerCamelCase_         =     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__       ))      ,					)


