



import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs



class        lowerCAmelCase__					(  _lowerCamelCase       ):


				def __init__(							self      :  Union[str, Any]						, __UpperCamelCase      :  str						, __UpperCamelCase      :  int=None						, __UpperCamelCase      :  int=True						, __UpperCamelCase      :  List[Any]=None						, **__UpperCamelCase      :  Union[str, Any]       )		->     Union[str, Any]:
											A      						=    parent
											A      						=    config_class
											A      						=    has_text_modality
											A      						=    kwargs
											A      						=    common_properties


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Tuple:
											A      						=    self.config_class(**self.inputs_dict       )
											A      						=    (
											    ['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(__UpperCamelCase						, __UpperCamelCase       )						, msg=f'''`{prop}` does not exist'''       )

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


				def __UpperCamelCase						(							self      :  List[Any]       )		->     str:
											A      						=    self.config_class(**self.inputs_dict       )
											A      						=    json.loads(config.to_json_string()       )
											for key, value in self.inputs_dict.items():
																		self.parent.assertEqual(obj[key]						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Optional[int]:
											A      						=    self.config_class(**self.inputs_dict       )

											with tempfile.TemporaryDirectory() as tmpdirname:
																		A      						=    os.path.join(__UpperCamelCase						, 'config.json'       )
																		config_first.to_json_file(__UpperCamelCase       )
																		A      						=    self.config_class.from_json_file(__UpperCamelCase       )

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


				def __UpperCamelCase						(							self      :  List[Any]       )		->     str:
											A      						=    self.config_class(**self.inputs_dict       )

											with tempfile.TemporaryDirectory() as tmpdirname:
																		config_first.save_pretrained(__UpperCamelCase       )
																		A      						=    self.config_class.from_pretrained(__UpperCamelCase       )

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


				def __UpperCamelCase						(							self      :  Dict       )		->     Optional[Any]:
											A      						=    self.config_class(**self.inputs_dict       )

											A      						=    'test'
											with tempfile.TemporaryDirectory() as tmpdirname:
																		A      						=    os.path.join(__UpperCamelCase						, __UpperCamelCase       )
																		config_first.save_pretrained(__UpperCamelCase       )
																		A      						=    self.config_class.from_pretrained(__UpperCamelCase						, subfolder=__UpperCamelCase       )

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


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Tuple:
											A      						=    self.config_class(**self.inputs_dict						, num_labels=5       )
											self.parent.assertEqual(len(config.idalabel       )						, 5       )
											self.parent.assertEqual(len(config.labelaid       )						, 5       )

											A      						=    3
											self.parent.assertEqual(len(config.idalabel       )						, 3       )
											self.parent.assertEqual(len(config.labelaid       )						, 3       )


				def __UpperCamelCase						(							self      :  Dict       )		->     str:
											if self.config_class.is_composition:
																		return
											A      						=    self.config_class()
											self.parent.assertIsNotNone(__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[str]       )		->     Dict:
											A      						=    copy.deepcopy(__UpperCamelCase       )
											A      						=    self.config_class(**__UpperCamelCase       )
											A      						=    []
											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(__UpperCamelCase						, __UpperCamelCase       ) != value:
																									wrong_values.append((key, getattr(__UpperCamelCase						, __UpperCamelCase       ), value)       )

											if len(__UpperCamelCase       ) > 0:
																		A      						=    '\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 __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Optional[int]:
											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()

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__       ))      ,					)


