

'''simple docstring'''


from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch




@dataclass
class     a			:






						"""simple docstring"""




						__UpperCAmelCase			     =    42  # [batch_size x 3]
						__UpperCAmelCase			     =    42  # [batch_size x 3]
						__UpperCAmelCase			     =    42  # [batch_size x 3]
						__UpperCAmelCase			     =    42  # [batch_size x 3]
						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    42







						def       __magic_name__							(							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       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  torch.arange(self.height * self.width  )
								snake_case__						:       int       						=  torch.stack(
								    [
								        pixel_indices % self.width,
								        torch.div(snake_case_			,			self.width			,			rounding_mode='''trunc'''  ),
								    ]			,			axis=1			,			)
								return coords







						@property
						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						,       *snake_case__						:       str       						=  self.shape
								snake_case__						:       List[str]       						=  int(np.prod(snake_case_  )  )

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

								snake_case__						:       List[Any]       						=  rays.view(snake_case_			,			inner_batch_size * self.height * self.width			,			2			,			3  )

								return rays







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      torch.Tensor  ):



								'''simple docstring'''



								snake_case__						,       *snake_case__						,       snake_case__						:       Any       						=  coords.shape
								assert n_coords == 2
								assert batch_size == self.origin.shape[0]

								snake_case__						:       Tuple       						=  coords.view(snake_case_			,			-1			,			2  )

								snake_case__						:       List[Any]       						=  self.resolution()
								snake_case__						:       int       						=  self.fov()

								snake_case__						:       Optional[int]       						=  (flat.float() / (res - 1)) * 2 - 1
								snake_case__						:       Dict       						=  fracs * torch.tan(fov / 2  )

								snake_case__						:       List[Any]       						=  fracs.view(snake_case_			,			-1			,			2  )
								snake_case__						:       Dict       						=  (
								    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:]
								)
								snake_case__						:       int       						=  directions / directions.norm(dim=-1			,			keepdim=snake_case_  )
								snake_case__						:       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       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      int			,			snake_case_					:      int  ):



								'''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=snake_case_			,			height=snake_case_			,			x_fov=self.x_fov			,			y_fov=self.y_fov			,			)





def 	_a       (				__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		snake_case__						:       Optional[Any]       						=  []
		snake_case__						:       Optional[int]       						=  []
		snake_case__						:       Optional[int]       						=  []
		snake_case__						:       List[Any]       						=  []
		for theta in np.linspace(0   ,							2 * np.pi   ,							num=20     ):
				snake_case__						:       Tuple       						=  np.array([np.sin(__lowerCAmelCase     ), np.cos(__lowerCAmelCase     ), -0.5]     )
				z /= np.sqrt(np.sum(z**2     )     )
				snake_case__						:       int       						=  -z * 4
				snake_case__						:       List[str]       						=  np.array([np.cos(__lowerCAmelCase     ), -np.sin(__lowerCAmelCase     ), 0.0]     )
				snake_case__						:       Optional[Any]       						=  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     ))   ,							)






'''simple docstring'''


import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						def __init__(							self					:      Dict			,			snake_case_					:      str			,			snake_case_					:      Optional[int]=None			,			snake_case_					:      List[Any]=True			,			snake_case_					:      Any=None			,			**snake_case_					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  parent
								snake_case__						:       List[Any]       						=  config_class
								snake_case__						:       Optional[int]       						=  has_text_modality
								snake_case__						:       str       						=  kwargs
								snake_case__						:       Dict       						=  common_properties







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  self.config_class(**self.inputs_dict  )
								snake_case__						:       List[Any]       						=  (
								    ['''hidden_size''', '''num_attention_heads''', '''num_hidden_layers''']
								    if self.common_properties is None
								    else self.common_properties
								)

								# Add common fields for text models
								if self.has_text_modality:
										common_properties.extend(['''vocab_size''']  )

								# Test that config has the common properties as getters
								for prop in common_properties:
										self.parent.assertTrue(hasattr(snake_case_			,			snake_case_  )			,			msg=F"""`{prop}` does not exist"""  )

								# Test that config has the common properties as setter
								for idx, name in enumerate(snake_case_  ):
										try:
												setattr(snake_case_			,			snake_case_			,			snake_case_  )
												self.parent.assertEqual(
												    getattr(snake_case_			,			snake_case_  )			,			snake_case_			,			msg=F"""`{name} value {idx} expected, but was {getattr(snake_case_			,			snake_case_  )}"""  )
										except NotImplementedError:
												# Some models might not be able to implement setters for common_properties
												# In that case, a NotImplementedError is raised
												pass

        # Test if config class can be called with Config(prop_name=..)
								for idx, name in enumerate(snake_case_  ):
										try:
												snake_case__						:       Dict       						=  self.config_class(**{name: idx}  )
												self.parent.assertEqual(
												    getattr(snake_case_			,			snake_case_  )			,			snake_case_			,			msg=F"""`{name} value {idx} expected, but was {getattr(snake_case_			,			snake_case_  )}"""  )
										except NotImplementedError:
												# Some models might not be able to implement setters for common_properties
												# In that case, a NotImplementedError is raised
												pass







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  self.config_class(**self.inputs_dict  )
								snake_case__						:       str       						=  json.loads(config.to_json_string()  )
								for key, value in self.inputs_dict.items():
										self.parent.assertEqual(obj[key]			,			snake_case_  )







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  self.config_class(**self.inputs_dict  )

								with tempfile.TemporaryDirectory() as tmpdirname:
										snake_case__						:       List[Any]       						=  os.path.join(snake_case_			,			'''config.json'''  )
										config_first.to_json_file(snake_case_  )
										snake_case__						:       Dict       						=  self.config_class.from_json_file(snake_case_  )

								self.parent.assertEqual(config_second.to_dict()			,			config_first.to_dict()  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       int       						=  self.config_class(**self.inputs_dict  )

								with tempfile.TemporaryDirectory() as tmpdirname:
										config_first.save_pretrained(snake_case_  )
										snake_case__						:       Optional[Any]       						=  self.config_class.from_pretrained(snake_case_  )

								self.parent.assertEqual(config_second.to_dict()			,			config_first.to_dict()  )







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       int       						=  self.config_class(**self.inputs_dict  )

								snake_case__						:       str       						=  '''test'''
								with tempfile.TemporaryDirectory() as tmpdirname:
										snake_case__						:       List[str]       						=  os.path.join(snake_case_			,			snake_case_  )
										config_first.save_pretrained(snake_case_  )
										snake_case__						:       List[str]       						=  self.config_class.from_pretrained(snake_case_			,			subfolder=snake_case_  )

								self.parent.assertEqual(config_second.to_dict()			,			config_first.to_dict()  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  self.config_class(**self.inputs_dict			,			num_labels=5  )
								self.parent.assertEqual(len(config.idalabel  )			,			5  )
								self.parent.assertEqual(len(config.labelaid  )			,			5  )

								snake_case__						:       List[Any]       						=  3
								self.parent.assertEqual(len(config.idalabel  )			,			3  )
								self.parent.assertEqual(len(config.labelaid  )			,			3  )







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								if self.config_class.is_composition:
										return
								snake_case__						:       Any       						=  self.config_class()
								self.parent.assertIsNotNone(snake_case_  )







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  copy.deepcopy(snake_case_  )
								snake_case__						:       Union[str, Any]       						=  self.config_class(**snake_case_  )
								snake_case__						:       List[str]       						=  []
								for key, value in config_common_kwargs.items():
										if key == "torch_dtype":
												if not is_torch_available():
														continue
												else:
														import torch

														if config.torch_dtype != torch.floataa:
																wrong_values.append(('''torch_dtype''', config.torch_dtype, torch.floataa)  )
										elif getattr(snake_case_			,			snake_case_  ) != value:
												wrong_values.append((key, getattr(snake_case_			,			snake_case_  ), value)  )

								if len(snake_case_  ) > 0:
										snake_case__						:       Dict       						=  '''\n'''.join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values]  )
										raise ValueError(F"""The following keys were not properly set in the config:\n{errors}"""  )







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								self.create_and_test_config_common_properties()
								self.create_and_test_config_to_json_string()
								self.create_and_test_config_to_json_file()
								self.create_and_test_config_from_and_save_pretrained()
								self.create_and_test_config_from_and_save_pretrained_subfolder()
								self.create_and_test_config_with_num_labels()
								self.check_config_can_be_init_without_params()
								self.check_config_arguments_init()



