


import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_PRETRAINING_MAPPING,
							    MegatronBertForCausalLM,
							    MegatronBertForMaskedLM,
							    MegatronBertForMultipleChoice,
							    MegatronBertForNextSentencePrediction,
							    MegatronBertForPreTraining,
							    MegatronBertForQuestionAnswering,
							    MegatronBertForSequenceClassification,
							    MegatronBertForTokenClassification,
							    MegatronBertModel,
							)





class  lowerCAmelCase__:






				'''simple docstring'''





				def __init__(	self				:   int       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   List[str]=13       ,   __snake_case				:   List[Any]=7       ,   __snake_case				:   List[Any]=True       ,   __snake_case				:   List[str]=True       ,   __snake_case				:   str=True       ,   __snake_case				:   Optional[Any]=True       ,   __snake_case				:   Union[str, Any]=99       ,   __snake_case				:   Union[str, Any]=64       ,   __snake_case				:   Dict=32       ,   __snake_case				:   Tuple=5       ,   __snake_case				:   Optional[int]=4       ,   __snake_case				:   Optional[Any]=37       ,   __snake_case				:   Tuple="gelu"       ,   __snake_case				:   int=0.1       ,   __snake_case				:   List[Any]=0.1       ,   __snake_case				:   str=512       ,   __snake_case				:   int=16       ,   __snake_case				:   Any=2       ,   __snake_case				:   Optional[int]=0.02       ,   __snake_case				:   Tuple=3       ,   __snake_case				:   Any=4       ,   __snake_case				:   Dict=None       ,   ):





							'''simple docstring'''
							UpperCAmelCase_							:      str						=   parent
							UpperCAmelCase_							:      Optional[int]						=   batch_size
							UpperCAmelCase_							:      Tuple						=   seq_length
							UpperCAmelCase_							:      str						=   is_training
							UpperCAmelCase_							:      Tuple						=   use_input_mask
							UpperCAmelCase_							:      Tuple						=   use_token_type_ids
							UpperCAmelCase_							:      int						=   use_labels
							UpperCAmelCase_							:      Tuple						=   vocab_size
							UpperCAmelCase_							:      List[str]						=   hidden_size
							UpperCAmelCase_							:      Any						=   embedding_size
							UpperCAmelCase_							:      Union[str, Any]						=   num_hidden_layers
							UpperCAmelCase_							:      List[Any]						=   num_attention_heads
							UpperCAmelCase_							:      Optional[Any]						=   intermediate_size
							UpperCAmelCase_							:      Union[str, Any]						=   hidden_act
							UpperCAmelCase_							:      Optional[int]						=   hidden_dropout_prob
							UpperCAmelCase_							:      Optional[int]						=   attention_probs_dropout_prob
							UpperCAmelCase_							:      Optional[Any]						=   max_position_embeddings
							UpperCAmelCase_							:      str						=   type_vocab_size
							UpperCAmelCase_							:      int						=   type_sequence_label_size
							UpperCAmelCase_							:      List[str]						=   initializer_range
							UpperCAmelCase_							:      Union[str, Any]						=   num_labels
							UpperCAmelCase_							:      List[str]						=   num_choices
							UpperCAmelCase_							:      Dict						=   scope





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





							'''simple docstring'''
							UpperCAmelCase_							:      Any						=   ids_tensor([self.batch_size, self.seq_length]       ,   self.vocab_size	)

							UpperCAmelCase_							:      Union[str, Any]						=   None
							if self.use_input_mask:
										UpperCAmelCase_							:      Tuple						=   random_attention_mask([self.batch_size, self.seq_length]	)

							UpperCAmelCase_							:      List[str]						=   None
							if self.use_token_type_ids:
										UpperCAmelCase_							:      List[Any]						=   ids_tensor([self.batch_size, self.seq_length]       ,   self.type_vocab_size	)

							UpperCAmelCase_							:      Tuple						=   None
							UpperCAmelCase_							:      Any						=   None
							UpperCAmelCase_							:      List[Any]						=   None
							if self.use_labels:
										UpperCAmelCase_							:      Union[str, Any]						=   ids_tensor([self.batch_size]       ,   self.type_sequence_label_size	)
										UpperCAmelCase_							:      Tuple						=   ids_tensor([self.batch_size, self.seq_length]       ,   self.num_labels	)
										UpperCAmelCase_							:      Optional[int]						=   ids_tensor([self.batch_size]       ,   self.num_choices	)

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

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





				def 					_lowerCamelCase (	self				:   Any	):





							'''simple docstring'''
							return MegatronBertConfig(
							    vocab_size=self.vocab_size       ,   hidden_size=self.hidden_size       ,   num_hidden_layers=self.num_hidden_layers       ,   num_attention_heads=self.num_attention_heads       ,   intermediate_size=self.intermediate_size       ,   embedding_size=self.embedding_size       ,   hidden_act=self.hidden_act       ,   hidden_dropout_prob=self.hidden_dropout_prob       ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,   max_position_embeddings=self.max_position_embeddings       ,   type_vocab_size=self.type_vocab_size       ,   is_decoder=_lowercase       ,   initializer_range=self.initializer_range       ,   )





				def 					_lowerCamelCase (	self				:   Tuple       ,   __snake_case				:   Dict       ,   __snake_case				:   Optional[Any]       ,   __snake_case				:   str       ,   __snake_case				:   List[str]       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Optional[int]	):





							'''simple docstring'''
							UpperCAmelCase_							:      Union[str, Any]						=   MegatronBertModel(config=_lowercase	)
							model.to(_lowercase	)
							model.eval()
							UpperCAmelCase_							:      Tuple						=   model(_lowercase       ,   attention_mask=_lowercase       ,   token_type_ids=_lowercase	)
							UpperCAmelCase_							:      List[str]						=   model(_lowercase       ,   token_type_ids=_lowercase	)
							UpperCAmelCase_							:      List[Any]						=   model(_lowercase	)

							self.parent.assertEqual(result.last_hidden_state.shape       ,   (self.batch_size, self.seq_length, self.hidden_size)	)
							self.parent.assertEqual(result.pooler_output.shape       ,   (self.batch_size, self.hidden_size)	)





				def 					_lowerCamelCase (	self				:   str       ,   __snake_case				:   List[Any]       ,   __snake_case				:   Optional[Any]       ,   __snake_case				:   Tuple       ,   __snake_case				:   Optional[Any]       ,   __snake_case				:   str       ,   __snake_case				:   List[str]       ,   __snake_case				:   Optional[Any]	):





							'''simple docstring'''
							UpperCAmelCase_							:      Optional[Any]						=   MegatronBertForMaskedLM(config=_lowercase	)
							model.to(_lowercase	)
							model.eval()
							UpperCAmelCase_							:      Union[str, Any]						=   model(_lowercase       ,   attention_mask=_lowercase       ,   token_type_ids=_lowercase       ,   labels=_lowercase	)
							self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.seq_length, self.vocab_size)	)





				def 					_lowerCamelCase (	self				:   List[Any]       ,   __snake_case				:   List[str]       ,   __snake_case				:   int       ,   __snake_case				:   Dict       ,   __snake_case				:   List[Any]       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Any       ,   __snake_case				:   str	):





							'''simple docstring'''
							UpperCAmelCase_							:      Dict						=   MegatronBertForCausalLM(config=_lowercase	)
							model.to(_lowercase	)
							model.eval()
							UpperCAmelCase_							:      Union[str, Any]						=   model(_lowercase       ,   attention_mask=_lowercase       ,   token_type_ids=_lowercase       ,   labels=_lowercase	)
							self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.seq_length, self.vocab_size)	)





				def 					_lowerCamelCase (	self				:   List[Any]       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   List[Any]       ,   __snake_case				:   List[str]       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   List[str]       ,   __snake_case				:   List[str]       ,   __snake_case				:   Union[str, Any]	):





							'''simple docstring'''
							UpperCAmelCase_							:      Optional[Any]						=   MegatronBertForNextSentencePrediction(config=_lowercase	)
							model.to(_lowercase	)
							model.eval()
							UpperCAmelCase_							:      Dict						=   model(
							    _lowercase       ,   attention_mask=_lowercase       ,   token_type_ids=_lowercase       ,   labels=_lowercase       ,   )
							self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, 2)	)





				def 					_lowerCamelCase (	self				:   int       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Tuple       ,   __snake_case				:   str       ,   __snake_case				:   Any       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Dict       ,   __snake_case				:   Union[str, Any]	):





							'''simple docstring'''
							UpperCAmelCase_							:      Tuple						=   MegatronBertForPreTraining(config=_lowercase	)
							model.to(_lowercase	)
							model.eval()
							UpperCAmelCase_							:      Union[str, Any]						=   model(
							    _lowercase       ,   attention_mask=_lowercase       ,   token_type_ids=_lowercase       ,   labels=_lowercase       ,   next_sentence_label=_lowercase       ,   )
							self.parent.assertEqual(result.prediction_logits.shape       ,   (self.batch_size, self.seq_length, self.vocab_size)	)
							self.parent.assertEqual(result.seq_relationship_logits.shape       ,   (self.batch_size, 2)	)





				def 					_lowerCamelCase (	self				:   Tuple       ,   __snake_case				:   List[str]       ,   __snake_case				:   int       ,   __snake_case				:   Tuple       ,   __snake_case				:   str       ,   __snake_case				:   Dict       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Optional[Any]	):





							'''simple docstring'''
							UpperCAmelCase_							:      Optional[Any]						=   MegatronBertForQuestionAnswering(config=_lowercase	)
							model.to(_lowercase	)
							model.eval()
							UpperCAmelCase_							:      List[str]						=   model(
							    _lowercase       ,   attention_mask=_lowercase       ,   token_type_ids=_lowercase       ,   start_positions=_lowercase       ,   end_positions=_lowercase       ,   )
							self.parent.assertEqual(result.start_logits.shape       ,   (self.batch_size, self.seq_length)	)
							self.parent.assertEqual(result.end_logits.shape       ,   (self.batch_size, self.seq_length)	)





				def 					_lowerCamelCase (	self				:   Union[str, Any]       ,   __snake_case				:   List[Any]       ,   __snake_case				:   List[str]       ,   __snake_case				:   Any       ,   __snake_case				:   Dict       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   List[str]       ,   __snake_case				:   List[Any]	):





							'''simple docstring'''
							UpperCAmelCase_							:      Dict						=   self.num_labels
							UpperCAmelCase_							:      Optional[int]						=   MegatronBertForSequenceClassification(_lowercase	)
							model.to(_lowercase	)
							model.eval()
							UpperCAmelCase_							:      str						=   model(_lowercase       ,   attention_mask=_lowercase       ,   token_type_ids=_lowercase       ,   labels=_lowercase	)
							self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.num_labels)	)





				def 					_lowerCamelCase (	self				:   Dict       ,   __snake_case				:   str       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   List[Any]       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Dict       ,   __snake_case				:   List[Any]	):





							'''simple docstring'''
							UpperCAmelCase_							:      str						=   self.num_labels
							UpperCAmelCase_							:      List[str]						=   MegatronBertForTokenClassification(config=_lowercase	)
							model.to(_lowercase	)
							model.eval()
							UpperCAmelCase_							:      Optional[int]						=   model(_lowercase       ,   attention_mask=_lowercase       ,   token_type_ids=_lowercase       ,   labels=_lowercase	)
							self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.seq_length, self.num_labels)	)





				def 					_lowerCamelCase (	self				:   str       ,   __snake_case				:   List[Any]       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Any       ,   __snake_case				:   List[str]       ,   __snake_case				:   Tuple       ,   __snake_case				:   int       ,   __snake_case				:   Optional[Any]	):





							'''simple docstring'''
							UpperCAmelCase_							:      Dict						=   self.num_choices
							UpperCAmelCase_							:      Tuple						=   MegatronBertForMultipleChoice(config=_lowercase	)
							model.to(_lowercase	)
							model.eval()
							UpperCAmelCase_							:      str						=   input_ids.unsqueeze(1	).expand(-1       ,   self.num_choices       ,   -1	).contiguous()
							UpperCAmelCase_							:      Optional[Any]						=   token_type_ids.unsqueeze(1	).expand(-1       ,   self.num_choices       ,   -1	).contiguous()
							UpperCAmelCase_							:      List[str]						=   input_mask.unsqueeze(1	).expand(-1       ,   self.num_choices       ,   -1	).contiguous()
							UpperCAmelCase_							:      Tuple						=   model(
							    _lowercase       ,   attention_mask=_lowercase       ,   token_type_ids=_lowercase       ,   labels=_lowercase       ,   )
							self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.num_choices)	)





				def 					_lowerCamelCase (	self				:   Any	):





							'''simple docstring'''
							UpperCAmelCase_							:      Optional[int]						=   self.prepare_config_and_inputs()
							(
							    UpperCAmelCase_      
							)							:      Tuple						=   config_and_inputs
							UpperCAmelCase_							:      List[Any]						=   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
							return config, inputs_dict





@require_torch
class  lowerCAmelCase__(    UpperCAmelCase_    ,    UpperCAmelCase_    ,    unittest.TestCase						):






				'''simple docstring'''





				A_  :     Dict			= (
				    (
				        MegatronBertModel,
				        MegatronBertForMaskedLM,
				        MegatronBertForCausalLM,
				        MegatronBertForMultipleChoice,
				        MegatronBertForNextSentencePrediction,
				        MegatronBertForPreTraining,
				        MegatronBertForQuestionAnswering,
				        MegatronBertForSequenceClassification,
				        MegatronBertForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				A_  :     List[str]			= (
				    {
				        'feature-extraction': MegatronBertModel,
				        'fill-mask': MegatronBertForMaskedLM,
				        'question-answering': MegatronBertForQuestionAnswering,
				        'text-classification': MegatronBertForSequenceClassification,
				        'text-generation': MegatronBertForCausalLM,
				        'token-classification': MegatronBertForTokenClassification,
				        'zero-shot': MegatronBertForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				A_  :     Any			= True
				# test_resize_embeddings = False
				A_  :     List[str]			= False





				def 					_lowerCamelCase (	self				:   Union[str, Any]       ,   __snake_case				:   List[str]       ,   __snake_case				:   List[Any]       ,   __snake_case				:   Dict=False	):





							'''simple docstring'''
							UpperCAmelCase_							:      Any						=   super()._prepare_for_class(_lowercase       ,   _lowercase       ,   return_labels=_lowercase	)

							if return_labels:
										if model_class in get_values(_lowercase	):
													UpperCAmelCase_							:      int						=   torch.zeros(
													    (self.model_tester.batch_size, self.model_tester.seq_length)       ,   dtype=torch.long       ,   device=_lowercase	)
													UpperCAmelCase_							:      List[Any]						=   torch.zeros(
													    self.model_tester.batch_size       ,   dtype=torch.long       ,   device=_lowercase	)
							return inputs_dict





				def 					_lowerCamelCase (	self				:   Tuple	):





							'''simple docstring'''
							UpperCAmelCase_							:      Optional[Any]						=   MegatronBertModelTester(self	)
							UpperCAmelCase_							:      Optional[int]						=   ConfigTester(self       ,   config_class=_lowercase       ,   hidden_size=37	)





				def 					_lowerCamelCase (	self				:   List[str]	):





							'''simple docstring'''
							self.config_tester.run_common_tests()





				def 					_lowerCamelCase (	self				:   Optional[Any]	):





							'''simple docstring'''
							UpperCAmelCase_							:      Optional[int]						=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_megatron_bert_model(*_lowercase	)





				def 					_lowerCamelCase (	self				:   Optional[Any]	):





							'''simple docstring'''
							UpperCAmelCase_							:      Union[str, Any]						=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_megatron_bert_for_masked_lm(*_lowercase	)





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





							'''simple docstring'''
							UpperCAmelCase_							:      Union[str, Any]						=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*_lowercase	)





				def 					_lowerCamelCase (	self				:   Optional[int]	):





							'''simple docstring'''
							UpperCAmelCase_							:      Tuple						=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*_lowercase	)





				def 					_lowerCamelCase (	self				:   Tuple	):





							'''simple docstring'''
							UpperCAmelCase_							:      Tuple						=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_megatron_bert_for_pretraining(*_lowercase	)





				def 					_lowerCamelCase (	self				:   Tuple	):





							'''simple docstring'''
							UpperCAmelCase_							:      Dict						=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_megatron_bert_for_question_answering(*_lowercase	)





				def 					_lowerCamelCase (	self				:   Dict	):





							'''simple docstring'''
							UpperCAmelCase_							:      Optional[Any]						=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*_lowercase	)





				def 					_lowerCamelCase (	self				:   Optional[int]	):





							'''simple docstring'''
							UpperCAmelCase_							:      Tuple						=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_megatron_bert_for_token_classification(*_lowercase	)





def      snake_case_						( __lowercase       ):
			return torch.tensor(
			    __lowercase			,       dtype=torch.long			,       device=__lowercase			,       )


__UpperCamelCase				:  Any      					=							1E-4





@require_torch
@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase__(    unittest.TestCase						):






				'''simple docstring'''





				@slow
				@unittest.skip('''Model is not available.'''	)
				def 					_lowerCamelCase (	self				:   Union[str, Any]	):





							'''simple docstring'''
							UpperCAmelCase_							:      Optional[int]						=   'nvidia/megatron-bert-uncased-345m'
							if "MYDIR" in os.environ:
										UpperCAmelCase_							:      Optional[Any]						=   os.path.join(os.environ['''MYDIR''']       ,   _lowercase	)
							UpperCAmelCase_							:      List[str]						=   MegatronBertModel.from_pretrained(_lowercase	)
							model.to(_lowercase	)
							model.half()
							UpperCAmelCase_							:      Tuple						=   _long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]	)
							with torch.no_grad():
										UpperCAmelCase_							:      List[Any]						=   model(_lowercase	)[0]
							UpperCAmelCase_							:      int						=   torch.Size((1, 9, 1_024)	)
							self.assertEqual(output.shape       ,   _lowercase	)

							UpperCAmelCase_							:      Union[str, Any]						=   [-0.6_040, -0.2_517, -0.1_025, 0.3_420, -0.6_758, -0.0_017, -0.1_089, -0.1_990, 0.5_728]
							for ii in range(3	):
										for jj in range(3	):
													UpperCAmelCase_							:      int						=   output[0, ii, jj]
													UpperCAmelCase_							:      Any						=   expected[3 * ii + jj]
													UpperCAmelCase_							:      int						=   'ii={} jj={} a={} b={}'.format(_lowercase       ,   _lowercase       ,   _lowercase       ,   _lowercase	)
													self.assertTrue(math.isclose(_lowercase       ,   _lowercase       ,   rel_tol=_lowercase       ,   abs_tol=_lowercase	)       ,   msg=_lowercase	)



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch





@dataclass
class  lowerCAmelCase__:






    '''simple docstring'''





    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     int
    A_  :     int
    A_  :     float
    A_  :     float
    A_  :     Tuple[int]





    def 					_lowerCamelCase (	self				:   List[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 					_lowerCamelCase (	self				:   Tuple	):





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





    def 					_lowerCamelCase (	self				:   Tuple	):





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





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   torch.arange(self.height * self.width	)
       UpperCAmelCase_							:      Any						=   torch.stack(
           [
               pixel_indices % self.width,
               torch.div(__snake_case       ,   self.width       ,   rounding_mode='''trunc'''	),
           ]       ,   axis=1       ,   )
       return coords





    @property
    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_		,      *UpperCAmelCase_							:      List[Any]						=   self.shape
       UpperCAmelCase_							:      Optional[Any]						=   int(np.prod(__snake_case	)	)

       UpperCAmelCase_							:      str						=   self.get_image_coords()
       UpperCAmelCase_							:      List[str]						=   torch.broadcast_to(coords.unsqueeze(0	)       ,   [batch_size * inner_batch_size, *coords.shape]	)
       UpperCAmelCase_							:      Tuple						=   self.get_camera_rays(__snake_case	)

       UpperCAmelCase_							:      Union[str, Any]						=   rays.view(__snake_case       ,   inner_batch_size * self.height * self.width       ,   2       ,   3	)

       return rays





    def 					_lowerCamelCase (	self				:   Dict       ,   __snake_case				:   torch.Tensor	):





       '''simple docstring'''
       UpperCAmelCase_		,      *UpperCAmelCase_		,      UpperCAmelCase_							:      int						=   coords.shape
       assert n_coords == 2
       assert batch_size == self.origin.shape[0]

       UpperCAmelCase_							:      str						=   coords.view(__snake_case       ,   -1       ,   2	)

       UpperCAmelCase_							:      List[Any]						=   self.resolution()
       UpperCAmelCase_							:      Optional[Any]						=   self.fov()

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

       UpperCAmelCase_							:      Optional[Any]						=   fracs.view(__snake_case       ,   -1       ,   2	)
       UpperCAmelCase_							:      List[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_							:      Optional[int]						=   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	):





       '''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      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      List[Any]						=   []
   UpperCAmelCase_							:      int						=   []
   UpperCAmelCase_							:      Any						=   []
   UpperCAmelCase_							:      Tuple						=   []
   for theta in np.linspace(0			,       2 * np.pi			,       num=2_0       ):
      UpperCAmelCase_							:      Tuple						=   np.array([np.sin(__lowercase       ), np.cos(__lowercase       ), -0.5]       )
      z /= np.sqrt(np.sum(z**2       )       )
      UpperCAmelCase_							:      str						=   -z * 4
      UpperCAmelCase_							:      List[Any]						=   np.array([np.cos(__lowercase       ), -np.sin(__lowercase       ), 0.0]       )
      UpperCAmelCase_							:      Tuple						=   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       ))			,       )