


'''simple docstring'''



import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import YolosForObjectDetection, YolosModel
      from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor



class      a    :


     """simple docstring"""






     def __init__(    self					:		Any							,			snake_case					:		List[str]							,			snake_case					:		Optional[int]=13							,			snake_case					:		List[str]=[30, 30]							,			snake_case					:		str=2							,			snake_case					:		Dict=3							,			snake_case					:		Optional[Any]=True							,			snake_case					:		Optional[Any]=True							,			snake_case					:		List[Any]=32							,			snake_case					:		Optional[int]=5							,			snake_case					:		List[str]=4							,			snake_case					:		List[str]=37							,			snake_case					:		List[str]="gelu"							,			snake_case					:		Dict=0.1							,			snake_case					:		Dict=0.1							,			snake_case					:		Optional[Any]=10							,			snake_case					:		Optional[Any]=0.02							,			snake_case					:		Tuple=3							,			snake_case					:		List[Any]=None							,			snake_case					:		Optional[int]=8							,			snake_case					:		Any=10							,			)						->							str:
          __UpperCAmelCase     :			List[Any]  							=      parent
          __UpperCAmelCase     :			Any  							=      batch_size
          __UpperCAmelCase     :			List[Any]  							=      image_size
          __UpperCAmelCase     :			Optional[int]  							=      patch_size
          __UpperCAmelCase     :			List[str]  							=      num_channels
          __UpperCAmelCase     :			int  							=      is_training
          __UpperCAmelCase     :			Optional[int]  							=      use_labels
          __UpperCAmelCase     :			Optional[int]  							=      hidden_size
          __UpperCAmelCase     :			Union[str, Any]  							=      num_hidden_layers
          __UpperCAmelCase     :			Optional[Any]  							=      num_attention_heads
          __UpperCAmelCase     :			str  							=      intermediate_size
          __UpperCAmelCase     :			str  							=      hidden_act
          __UpperCAmelCase     :			List[str]  							=      hidden_dropout_prob
          __UpperCAmelCase     :			Any  							=      attention_probs_dropout_prob
          __UpperCAmelCase     :			str  							=      type_sequence_label_size
          __UpperCAmelCase     :			Dict  							=      initializer_range
          __UpperCAmelCase     :			str  							=      num_labels
          __UpperCAmelCase     :			int  							=      scope
          __UpperCAmelCase     :			Any  							=      n_targets
          __UpperCAmelCase     :			List[Any]  							=      num_detection_tokens
          # we set the expected sequence length (which is used in several tests)
          # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
          __UpperCAmelCase     :			str  							=      (image_size[1] // patch_size) * (image_size[0] // patch_size)
          __UpperCAmelCase     :			Optional[int]  							=      num_patches + 1 + self.num_detection_tokens

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Any  							=      floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]      )

          __UpperCAmelCase     :			Dict  							=      None
          if self.use_labels:
               # labels is a list of Dict (each Dict being the labels for a given example in the batch)
               __UpperCAmelCase     :			Union[str, Any]  							=      []
               for i in range(self.batch_size      ):
                    __UpperCAmelCase     :			Optional[int]  							=      {}
                    __UpperCAmelCase     :			Any  							=      torch.randint(
                        high=self.num_labels							,			size=(self.n_targets,)							,			device=snake_case      )
                    __UpperCAmelCase     :			List[Any]  							=      torch.rand(self.n_targets							,			4							,			device=snake_case      )
                    labels.append(snake_case      )

          __UpperCAmelCase     :			Union[str, Any]  							=      self.get_config()

          return config, pixel_values, labels

     def 			lowerCamelCase__			(    self					:		Any      )						->							Optional[int]:
          return YolosConfig(
              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=snake_case							,			initializer_range=self.initializer_range							,			num_detection_tokens=self.num_detection_tokens							,			num_labels=self.num_labels							,			)

     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		Optional[int]							,			snake_case					:		Dict							,			snake_case					:		Optional[Any]      )						->							Optional[Any]:
          __UpperCAmelCase     :			List[str]  							=      YolosModel(config=snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			int  							=      model(snake_case      )
          self.parent.assertEqual(
              result.last_hidden_state.shape							,			(self.batch_size, self.expected_seq_len, self.hidden_size)      )

     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		Optional[int]							,			snake_case					:		str							,			snake_case					:		Tuple      )						->							str:
          __UpperCAmelCase     :			Optional[int]  							=      YolosForObjectDetection(snake_case      )
          model.to(snake_case      )
          model.eval()

          __UpperCAmelCase     :			int  							=      model(pixel_values=snake_case      )
          __UpperCAmelCase     :			str  							=      model(snake_case      )

          self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_detection_tokens, self.num_labels + 1)      )
          self.parent.assertEqual(result.pred_boxes.shape							,			(self.batch_size, self.num_detection_tokens, 4)      )

          __UpperCAmelCase     :			Tuple  							=      model(pixel_values=snake_case							,			labels=snake_case      )

          self.parent.assertEqual(result.loss.shape							,			()      )
          self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_detection_tokens, self.num_labels + 1)      )
          self.parent.assertEqual(result.pred_boxes.shape							,			(self.batch_size, self.num_detection_tokens, 4)      )






     def 			lowerCamelCase__			(    self					:		int      )						->							Optional[Any]:
          __UpperCAmelCase     :			List[str]  							=      self.prepare_config_and_inputs()
          __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			str  							=      config_and_inputs
          __UpperCAmelCase     :			Optional[Any]  							=      {'''pixel_values''': pixel_values}
          return config, inputs_dict



@require_torch
class      a    (		_a     ,  _a     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Dict							       =       (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE	:			Dict							       =       (
         {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
     )

     SCREAMING_SNAKE_CASE	:			List[Any]							       =       False
     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       False
     SCREAMING_SNAKE_CASE	:			Any							       =       False
     SCREAMING_SNAKE_CASE	:			Any							       =       False

     def 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		List[Any]							,			snake_case					:		Any							,			snake_case					:		List[Any]=False      )						->							Optional[int]:
          __UpperCAmelCase     :			Optional[int]  							=      super()._prepare_for_class(snake_case							,			snake_case							,			return_labels=snake_case      )

          if return_labels:
               if model_class.__name__ == "YolosForObjectDetection":
                    __UpperCAmelCase     :			List[Any]  							=      []
                    for i in range(self.model_tester.batch_size      ):
                         __UpperCAmelCase     :			Optional[int]  							=      {}
                         __UpperCAmelCase     :			str  							=      torch.ones(
                             size=(self.model_tester.n_targets,)							,			device=snake_case							,			dtype=torch.long      )
                         __UpperCAmelCase     :			List[Any]  							=      torch.ones(
                             self.model_tester.n_targets							,			4							,			device=snake_case							,			dtype=torch.float      )
                         labels.append(snake_case      )
                    __UpperCAmelCase     :			Optional[int]  							=      labels

          return inputs_dict

     def 			lowerCamelCase__			(    self					:		Any      )						->							Optional[int]:
          __UpperCAmelCase     :			Dict  							=      YolosModelTester(self      )
          __UpperCAmelCase     :			List[str]  							=      ConfigTester(self							,			config_class=snake_case							,			has_text_modality=snake_case							,			hidden_size=37      )

     def 			lowerCamelCase__			(    self					:		str      )						->							List[Any]:
          self.config_tester.run_common_tests()

     def 			lowerCamelCase__			(    self					:		Dict      )						->							Union[str, Any]:
          # YOLOS does not use inputs_embeds
          pass

     def 			lowerCamelCase__			(    self					:		Any      )						->							str:
          __UpperCAmelCase							,       __UpperCAmelCase     :			int  							=      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __UpperCAmelCase     :			Any  							=      model_class(snake_case      )
               self.assertIsInstance(model.get_input_embeddings()							,			(nn.Module)      )
               __UpperCAmelCase     :			Dict  							=      model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(snake_case							,			nn.Linear      )      )

     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							Tuple:
          __UpperCAmelCase							,       __UpperCAmelCase     :			Dict  							=      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __UpperCAmelCase     :			List[Any]  							=      model_class(snake_case      )
               __UpperCAmelCase     :			Dict  							=      inspect.signature(model.forward      )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __UpperCAmelCase     :			Tuple  							=      [*signature.parameters.keys()]

               __UpperCAmelCase     :			List[str]  							=      ['''pixel_values''']
               self.assertListEqual(arg_names[:1]							,			snake_case      )

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Optional[Any]  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							List[Any]:
          __UpperCAmelCase							,       __UpperCAmelCase     :			List[Any]  							=      self.model_tester.prepare_config_and_inputs_for_common()
          __UpperCAmelCase     :			str  							=      True

          # in YOLOS, the seq_len is different
          __UpperCAmelCase     :			int  							=      self.model_tester.expected_seq_len
          for model_class in self.all_model_classes:
               __UpperCAmelCase     :			Any  							=      True
               __UpperCAmelCase     :			int  							=      False
               __UpperCAmelCase     :			List[Any]  							=      True
               __UpperCAmelCase     :			str  							=      model_class(snake_case      )
               model.to(snake_case      )
               model.eval()
               with torch.no_grad():
                    __UpperCAmelCase     :			Tuple  							=      model(**self._prepare_for_class(snake_case							,			snake_case      )      )
               __UpperCAmelCase     :			Union[str, Any]  							=      outputs.attentions
               self.assertEqual(len(snake_case      )							,			self.model_tester.num_hidden_layers      )

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               __UpperCAmelCase     :			Any  							=      True
               __UpperCAmelCase     :			str  							=      model_class(snake_case      )
               model.to(snake_case      )
               model.eval()
               with torch.no_grad():
                    __UpperCAmelCase     :			int  							=      model(**self._prepare_for_class(snake_case							,			snake_case      )      )
               __UpperCAmelCase     :			str  							=      outputs.attentions
               self.assertEqual(len(snake_case      )							,			self.model_tester.num_hidden_layers      )

               self.assertListEqual(
                   list(attentions[0].shape[-3:]      )							,			[self.model_tester.num_attention_heads, seq_len, seq_len]							,			)
               __UpperCAmelCase     :			Optional[int]  							=      len(snake_case      )

               # Check attention is always last and order is fine
               __UpperCAmelCase     :			Optional[int]  							=      True
               __UpperCAmelCase     :			List[Any]  							=      True
               __UpperCAmelCase     :			Union[str, Any]  							=      model_class(snake_case      )
               model.to(snake_case      )
               model.eval()
               with torch.no_grad():
                    __UpperCAmelCase     :			Tuple  							=      model(**self._prepare_for_class(snake_case							,			snake_case      )      )

               __UpperCAmelCase     :			str  							=      1
               self.assertEqual(out_len + added_hidden_states							,			len(snake_case      )      )

               __UpperCAmelCase     :			int  							=      outputs.attentions

               self.assertEqual(len(snake_case      )							,			self.model_tester.num_hidden_layers      )
               self.assertListEqual(
                   list(self_attentions[0].shape[-3:]      )							,			[self.model_tester.num_attention_heads, seq_len, seq_len]							,			)

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							List[Any]:
          def check_hidden_states_output(snake_case					:		Any							,			snake_case					:		int							,			snake_case					:		Any      ):
               __UpperCAmelCase     :			Dict  							=      model_class(snake_case      )
               model.to(snake_case      )
               model.eval()

               with torch.no_grad():
                    __UpperCAmelCase     :			List[Any]  							=      model(**self._prepare_for_class(snake_case							,			snake_case      )      )

               __UpperCAmelCase     :			List[Any]  							=      outputs.hidden_states

               __UpperCAmelCase     :			List[Any]  							=      getattr(
                   self.model_tester							,			'''expected_num_hidden_layers'''							,			self.model_tester.num_hidden_layers + 1      )
               self.assertEqual(len(snake_case      )							,			snake_case      )

               # YOLOS has a different seq_length
               __UpperCAmelCase     :			List[str]  							=      self.model_tester.expected_seq_len

               self.assertListEqual(
                   list(hidden_states[0].shape[-2:]      )							,			[seq_length, self.model_tester.hidden_size]							,			)

          __UpperCAmelCase							,       __UpperCAmelCase     :			Tuple  							=      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __UpperCAmelCase     :			Union[str, Any]  							=      True
               check_hidden_states_output(snake_case							,			snake_case							,			snake_case      )

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __UpperCAmelCase     :			str  							=      True

               check_hidden_states_output(snake_case							,			snake_case							,			snake_case      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							List[Any]:
          __UpperCAmelCase     :			List[str]  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_object_detection(*snake_case      )






     @slow
     def 			lowerCamelCase__			(    self					:		str      )						->							Optional[int]:
          for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCAmelCase     :			Any  							=      YolosModel.from_pretrained(snake_case      )
               self.assertIsNotNone(snake_case      )




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			List[Any]  							=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
     return image



@require_torch
@require_vision
class      a    (		unittest.TestCase	):


     """simple docstring"""






     @cached_property
     def 			lowerCamelCase__			(    self					:		Dict      )						->							Optional[int]:
          return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''      ) if is_vision_available() else None






     @slow
     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							int:
          __UpperCAmelCase     :			Tuple  							=      YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''      ).to(snake_case      )

          __UpperCAmelCase     :			Tuple  							=      self.default_image_processor
          __UpperCAmelCase     :			str  							=      prepare_img()
          __UpperCAmelCase     :			List[str]  							=      image_processor(images=snake_case							,			return_tensors='''pt'''      ).to(snake_case      )

          # forward pass
          with torch.no_grad():
               __UpperCAmelCase     :			int  							=      model(inputs.pixel_values      )

          # verify outputs
          __UpperCAmelCase     :			List[Any]  							=      torch.Size((1, 100, 92)      )
          self.assertEqual(outputs.logits.shape							,			snake_case      )

          __UpperCAmelCase     :			int  							=      torch.tensor(
              [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]							,			device=snake_case							,			)
          __UpperCAmelCase     :			int  							=      torch.tensor(
              [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]							,			device=snake_case      )
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]							,			snake_case							,			atol=1E-4      )      )
          self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]							,			snake_case							,			atol=1E-4      )      )

          # verify postprocessing
          __UpperCAmelCase     :			Dict  							=      image_processor.post_process_object_detection(
              snake_case							,			threshold=0.3							,			target_sizes=[image.size[::-1]]      )[0]
          __UpperCAmelCase     :			int  							=      torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]      ).to(snake_case      )
          __UpperCAmelCase     :			List[Any]  							=      [75, 75, 17, 63, 17]
          __UpperCAmelCase     :			Any  							=      torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495]      ).to(snake_case      )

          self.assertEqual(len(results['''scores''']      )							,			5      )
          self.assertTrue(torch.allclose(results['''scores''']							,			snake_case							,			atol=1E-4      )      )
          self.assertSequenceEqual(results['''labels'''].tolist()							,			snake_case      )
          self.assertTrue(torch.allclose(results['''boxes'''][0, :]							,			snake_case      )      )



'''simple docstring'''



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class      a    :


     """simple docstring"""






     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					:		Any      )						->							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 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							str:
          return torch.from_numpy(np.array([self.width, self.height]							,			dtype=np.floataa      )      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							Optional[Any]:
          return torch.from_numpy(np.array([self.x_fov, self.y_fov]							,			dtype=np.floataa      )      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							torch.Tensor:
          __UpperCAmelCase     :			Dict  							=      torch.arange(self.height * self.width      )
          __UpperCAmelCase     :			Dict  							=      torch.stack(
              [
                  pixel_indices % self.width,
                  torch.div(snake_case							,			self.width							,			rounding_mode='''trunc'''      ),
              ]							,			axis=1							,			)
          return coords

     @property
     def 			lowerCamelCase__			(    self					:		Any      )						->							int:
          __UpperCAmelCase							,       *__UpperCAmelCase     :			str  							=      self.shape
          __UpperCAmelCase     :			Dict  							=      int(np.prod(snake_case      )      )

          __UpperCAmelCase     :			Tuple  							=      self.get_image_coords()
          __UpperCAmelCase     :			List[Any]  							=      torch.broadcast_to(coords.unsqueeze(0      )							,			[batch_size * inner_batch_size, *coords.shape]      )
          __UpperCAmelCase     :			Any  							=      self.get_camera_rays(snake_case      )

          __UpperCAmelCase     :			List[str]  							=      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      )						->							torch.Tensor:
          __UpperCAmelCase							,       *__UpperCAmelCase							,       __UpperCAmelCase     :			List[str]  							=      coords.shape
          assert n_coords == 2
          assert batch_size == self.origin.shape[0]

          __UpperCAmelCase     :			List[str]  							=      coords.view(snake_case							,			-1							,			2      )

          __UpperCAmelCase     :			Optional[Any]  							=      self.resolution()
          __UpperCAmelCase     :			Tuple  							=      self.fov()

          __UpperCAmelCase     :			Optional[int]  							=      (flat.float() / (res - 1)) * 2 - 1
          __UpperCAmelCase     :			Union[str, Any]  							=      fracs * torch.tan(fov / 2      )

          __UpperCAmelCase     :			str  							=      fracs.view(snake_case							,			-1							,			2      )
          __UpperCAmelCase     :			Any  							=      (
              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:]
          )
          __UpperCAmelCase     :			Union[str, Any]  							=      directions / directions.norm(dim=-1							,			keepdim=snake_case      )
          __UpperCAmelCase     :			Union[str, 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					:		Any							,			snake_case					:		int							,			snake_case					:		int      )						->							"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=snake_case							,			height=snake_case							,			x_fov=self.x_fov							,			y_fov=self.y_fov							,			)




def      _a     (						_lowercase :     int  ):





     '''simple docstring'''






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