


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
       import os

       # The slow tests are often failing with OOM error on GPU
       # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
       # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
       __UpperCamelCase				:  int      					=							'platform'

       import jax
       import jax.numpy as jnp

       from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
           FlaxBlenderbotSmallForConditionalGeneration,
           FlaxBlenderbotSmallModel,
           shift_tokens_right,
       )
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase=None			,       __lowercase=None			,       __lowercase=None			,       __lowercase=None			,       __lowercase=None			,       __lowercase=None			,       ):
   if attention_mask is None:
      UpperCAmelCase_							:      int						=   np.where(input_ids != config.pad_token_id			,       1			,       0       )
   if decoder_attention_mask is None:
      UpperCAmelCase_							:      List[str]						=   np.where(decoder_input_ids != config.pad_token_id			,       1			,       0       )
   if head_mask is None:
      UpperCAmelCase_							:      Dict						=   np.ones((config.encoder_layers, config.encoder_attention_heads)       )
   if decoder_head_mask is None:
      UpperCAmelCase_							:      List[str]						=   np.ones((config.decoder_layers, config.decoder_attention_heads)       )
   if cross_attn_head_mask is None:
      UpperCAmelCase_							:      List[str]						=   np.ones((config.decoder_layers, config.decoder_attention_heads)       )
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": attention_mask,
   }





class  lowerCAmelCase__:






    '''simple docstring'''





    def __init__(	self				:   Any       ,   __snake_case				:   Dict       ,   __snake_case				:   str=13       ,   __snake_case				:   List[str]=7       ,   __snake_case				:   str=True       ,   __snake_case				:   List[str]=False       ,   __snake_case				:   str=99       ,   __snake_case				:   Optional[Any]=16       ,   __snake_case				:   Tuple=2       ,   __snake_case				:   Any=4       ,   __snake_case				:   Optional[int]=4       ,   __snake_case				:   Optional[int]="gelu"       ,   __snake_case				:   Union[str, Any]=0.1       ,   __snake_case				:   List[Any]=0.1       ,   __snake_case				:   List[str]=32       ,   __snake_case				:   Tuple=2       ,   __snake_case				:   int=1       ,   __snake_case				:   Dict=0       ,   __snake_case				:   Dict=0.02       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   parent
       UpperCAmelCase_							:      Optional[int]						=   batch_size
       UpperCAmelCase_							:      Optional[Any]						=   seq_length
       UpperCAmelCase_							:      Optional[int]						=   is_training
       UpperCAmelCase_							:      List[str]						=   use_labels
       UpperCAmelCase_							:      List[str]						=   vocab_size
       UpperCAmelCase_							:      Tuple						=   hidden_size
       UpperCAmelCase_							:      str						=   num_hidden_layers
       UpperCAmelCase_							:      str						=   num_attention_heads
       UpperCAmelCase_							:      Union[str, Any]						=   intermediate_size
       UpperCAmelCase_							:      Tuple						=   hidden_act
       UpperCAmelCase_							:      int						=   hidden_dropout_prob
       UpperCAmelCase_							:      List[Any]						=   attention_probs_dropout_prob
       UpperCAmelCase_							:      str						=   max_position_embeddings
       UpperCAmelCase_							:      int						=   eos_token_id
       UpperCAmelCase_							:      Optional[Any]						=   pad_token_id
       UpperCAmelCase_							:      Union[str, Any]						=   bos_token_id
       UpperCAmelCase_							:      Optional[int]						=   initializer_range





    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       UpperCAmelCase_							:      Any						=   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       ,   self.vocab_size	)       ,   3       ,   self.vocab_size	)
       UpperCAmelCase_							:      Optional[Any]						=   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       ,   dtype=np.intaa	))       ,   -1	)

       UpperCAmelCase_							:      int						=   shift_tokens_right(__snake_case       ,   1       ,   2	)

       UpperCAmelCase_							:      List[Any]						=   BlenderbotSmallConfig(
           vocab_size=self.vocab_size       ,   d_model=self.hidden_size       ,   encoder_layers=self.num_hidden_layers       ,   decoder_layers=self.num_hidden_layers       ,   encoder_attention_heads=self.num_attention_heads       ,   decoder_attention_heads=self.num_attention_heads       ,   encoder_ffn_dim=self.intermediate_size       ,   decoder_ffn_dim=self.intermediate_size       ,   dropout=self.hidden_dropout_prob       ,   attention_dropout=self.attention_probs_dropout_prob       ,   max_position_embeddings=self.max_position_embeddings       ,   eos_token_id=self.eos_token_id       ,   bos_token_id=self.bos_token_id       ,   pad_token_id=self.pad_token_id       ,   initializer_range=self.initializer_range       ,   use_cache=__snake_case       ,   )
       UpperCAmelCase_							:      Union[str, Any]						=   prepare_blenderbot_inputs_dict(__snake_case       ,   __snake_case       ,   __snake_case	)
       return config, inputs_dict





    def 					_lowerCamelCase (	self				:   List[str]	):





       '''simple docstring'''
       UpperCAmelCase_		,      UpperCAmelCase_							:      Tuple						=   self.prepare_config_and_inputs()
       return config, inputs_dict





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





       '''simple docstring'''
       UpperCAmelCase_							:      Dict						=   20
       UpperCAmelCase_							:      Optional[int]						=   model_class_name(__snake_case	)

       UpperCAmelCase_							:      Tuple						=   model.encode(inputs_dict['''input_ids''']	)

       UpperCAmelCase_		,      UpperCAmelCase_							:      Union[str, Any]						=   (
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       UpperCAmelCase_							:      str						=   model.init_cache(decoder_input_ids.shape[0]       ,   __snake_case       ,   __snake_case	)
       UpperCAmelCase_							:      List[Any]						=   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,   dtype='''i4'''	)

       UpperCAmelCase_							:      str						=   jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]       ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,   )
       UpperCAmelCase_							:      List[str]						=   model.decode(
           decoder_input_ids[:, :-1]       ,   __snake_case       ,   decoder_attention_mask=__snake_case       ,   past_key_values=__snake_case       ,   decoder_position_ids=__snake_case       ,   )

       UpperCAmelCase_							:      str						=   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,   dtype='''i4'''	)
       UpperCAmelCase_							:      Optional[int]						=   model.decode(
           decoder_input_ids[:, -1:]       ,   __snake_case       ,   decoder_attention_mask=__snake_case       ,   past_key_values=outputs_cache.past_key_values       ,   decoder_position_ids=__snake_case       ,   )

       UpperCAmelCase_							:      List[str]						=   model.decode(__snake_case       ,   __snake_case	)

       UpperCAmelCase_							:      Union[str, Any]						=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
       self.parent.assertTrue(diff < 1E-3       ,   msg=f'''Max diff is {diff}'''	)





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





       '''simple docstring'''
       UpperCAmelCase_							:      Dict						=   20
       UpperCAmelCase_							:      List[Any]						=   model_class_name(__snake_case	)

       UpperCAmelCase_							:      Any						=   model.encode(inputs_dict['''input_ids''']	)

       UpperCAmelCase_		,      UpperCAmelCase_							:      Union[str, Any]						=   (
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       UpperCAmelCase_							:      Dict						=   jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])	),
           ]       ,   axis=-1       ,   )

       UpperCAmelCase_							:      Union[str, Any]						=   model.init_cache(decoder_input_ids.shape[0]       ,   __snake_case       ,   __snake_case	)
       UpperCAmelCase_							:      int						=   jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]       ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,   )

       UpperCAmelCase_							:      List[str]						=   model.decode(
           decoder_input_ids[:, :-1]       ,   __snake_case       ,   decoder_attention_mask=__snake_case       ,   past_key_values=__snake_case       ,   decoder_position_ids=__snake_case       ,   )
       UpperCAmelCase_							:      str						=   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,   dtype='''i4'''	)
       UpperCAmelCase_							:      List[Any]						=   model.decode(
           decoder_input_ids[:, -1:]       ,   __snake_case       ,   past_key_values=outputs_cache.past_key_values       ,   decoder_attention_mask=__snake_case       ,   decoder_position_ids=__snake_case       ,   )

       UpperCAmelCase_							:      Optional[int]						=   model.decode(__snake_case       ,   __snake_case       ,   decoder_attention_mask=__snake_case	)

       UpperCAmelCase_							:      int						=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
       self.parent.assertTrue(diff < 1E-3       ,   msg=f'''Max diff is {diff}'''	)





@require_flax
class  lowerCAmelCase__(    unittest.TestCase						):






    '''simple docstring'''





    A_  :     List[str]			= 9_9





    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Dict						=   np.array(
           [
               [71, 82, 18, 33, 46, 91, 2],
               [68, 34, 26, 58, 30, 82, 2],
               [5, 97, 17, 39, 94, 40, 2],
               [76, 83, 94, 25, 70, 78, 2],
               [87, 59, 41, 35, 48, 66, 2],
               [55, 13, 16, 58, 5, 2, 1],  # note padding
               [64, 27, 31, 51, 12, 75, 2],
               [52, 64, 86, 17, 83, 39, 2],
               [48, 61, 9, 24, 71, 82, 2],
               [26, 1, 60, 48, 22, 13, 2],
               [21, 5, 62, 28, 14, 76, 2],
               [45, 98, 37, 86, 59, 48, 2],
               [70, 70, 50, 9, 28, 0, 2],
           ]       ,   dtype=np.intaa       ,   )

       UpperCAmelCase_							:      List[Any]						=   input_ids.shape[0]
       UpperCAmelCase_							:      Union[str, Any]						=   BlenderbotSmallConfig(
           vocab_size=self.vocab_size       ,   d_model=24       ,   encoder_layers=2       ,   decoder_layers=2       ,   encoder_attention_heads=2       ,   decoder_attention_heads=2       ,   encoder_ffn_dim=32       ,   decoder_ffn_dim=32       ,   max_position_embeddings=48       ,   eos_token_id=2       ,   pad_token_id=1       ,   bos_token_id=0       ,   )
       return config, input_ids, batch_size





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      str						=   self._get_config_and_data()
       UpperCAmelCase_							:      Dict						=   FlaxBlenderbotSmallForConditionalGeneration(__snake_case	)
       UpperCAmelCase_							:      int						=   lm_model(input_ids=__snake_case	)
       UpperCAmelCase_							:      List[str]						=   (batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape       ,   __snake_case	)





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      Dict						=   BlenderbotSmallConfig(
           vocab_size=self.vocab_size       ,   d_model=14       ,   encoder_layers=2       ,   decoder_layers=2       ,   encoder_attention_heads=2       ,   decoder_attention_heads=2       ,   encoder_ffn_dim=8       ,   decoder_ffn_dim=8       ,   max_position_embeddings=48       ,   )
       UpperCAmelCase_							:      int						=   FlaxBlenderbotSmallForConditionalGeneration(__snake_case	)
       UpperCAmelCase_							:      Union[str, Any]						=   np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]       ,   dtype=np.intaa	)
       UpperCAmelCase_							:      Union[str, Any]						=   np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]       ,   dtype=np.intaa	)
       UpperCAmelCase_							:      int						=   lm_model(input_ids=__snake_case       ,   decoder_input_ids=__snake_case	)
       UpperCAmelCase_							:      str						=   (*summary.shape, config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape       ,   __snake_case	)





    def 					_lowerCamelCase (	self				:   Optional[Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]       ,   dtype=np.intaa	)
       UpperCAmelCase_							:      Union[str, Any]						=   shift_tokens_right(__snake_case       ,   1       ,   2	)
       UpperCAmelCase_							:      Optional[Any]						=   np.equal(__snake_case       ,   1	).astype(np.floataa	).sum()
       UpperCAmelCase_							:      Any						=   np.equal(__snake_case       ,   1	).astype(np.floataa	).sum()
       self.assertEqual(shifted.shape       ,   input_ids.shape	)
       self.assertEqual(__snake_case       ,   n_pad_before - 1	)
       self.assertTrue(np.equal(shifted[:, 0]       ,   2	).all()	)





@require_flax
class  lowerCAmelCase__(    snake_case__    ,    unittest.TestCase    ,    snake_case__						):






    '''simple docstring'''





    A_  :     Tuple			= True
    A_  :     Dict			= (
        (
            FlaxBlenderbotSmallModel,
            FlaxBlenderbotSmallForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    A_  :     List[Any]			= (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()





    def 					_lowerCamelCase (	self				:   List[Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   FlaxBlenderbotSmallModelTester(self	)





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_		,      UpperCAmelCase_							:      List[str]						=   self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(__snake_case       ,   __snake_case       ,   __snake_case	)





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[Any]						=   self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward_with_attn_mask(__snake_case       ,   __snake_case       ,   __snake_case	)





    def 					_lowerCamelCase (	self				:   Optional[Any]	):





       '''simple docstring'''
       UpperCAmelCase_		,      UpperCAmelCase_							:      Tuple						=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__	):
             UpperCAmelCase_							:      List[str]						=   self._prepare_for_class(__snake_case       ,   __snake_case	)
             UpperCAmelCase_							:      int						=   model_class(__snake_case	)

             @jax.jit
             def encode_jitted(__snake_case				:   Union[str, Any]       ,   __snake_case				:   Dict=None       ,   **__snake_case				:   List[Any]	):
                return model.encode(input_ids=__snake_case       ,   attention_mask=__snake_case	)

             with self.subTest('''JIT Enabled'''	):
                UpperCAmelCase_							:      Dict						=   encode_jitted(**__snake_case	).to_tuple()

             with self.subTest('''JIT Disabled'''	):
                with jax.disable_jit():
                   UpperCAmelCase_							:      Optional[Any]						=   encode_jitted(**__snake_case	).to_tuple()

             self.assertEqual(len(__snake_case	)       ,   len(__snake_case	)	)
             for jitted_output, output in zip(__snake_case       ,   __snake_case	):
                self.assertEqual(jitted_output.shape       ,   output.shape	)





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_		,      UpperCAmelCase_							:      Dict						=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__	):
             UpperCAmelCase_							:      Optional[int]						=   model_class(__snake_case	)
             UpperCAmelCase_							:      Any						=   model.encode(inputs_dict['''input_ids''']       ,   inputs_dict['''attention_mask''']	)

             UpperCAmelCase_							:      Any						=   {
                 '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                 '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                 '''encoder_outputs''': encoder_outputs,
             }

             @jax.jit
             def decode_jitted(__snake_case				:   Optional[int]       ,   __snake_case				:   Tuple       ,   __snake_case				:   Union[str, Any]	):
                return model.decode(
                    decoder_input_ids=__snake_case       ,   decoder_attention_mask=__snake_case       ,   encoder_outputs=__snake_case       ,   )

             with self.subTest('''JIT Enabled'''	):
                UpperCAmelCase_							:      int						=   decode_jitted(**__snake_case	).to_tuple()

             with self.subTest('''JIT Disabled'''	):
                with jax.disable_jit():
                   UpperCAmelCase_							:      List[Any]						=   decode_jitted(**__snake_case	).to_tuple()

             self.assertEqual(len(__snake_case	)       ,   len(__snake_case	)	)
             for jitted_output, output in zip(__snake_case       ,   __snake_case	):
                self.assertEqual(jitted_output.shape       ,   output.shape	)





    @slow
    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       for model_class_name in self.all_model_classes:
          UpperCAmelCase_							:      Optional[int]						=   model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''	)
          # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
          UpperCAmelCase_							:      Any						=   np.ones((1, 1)	) * model.config.eos_token_id
          UpperCAmelCase_							:      Union[str, Any]						=   model(__snake_case	)
          self.assertIsNotNone(__snake_case	)



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       ))			,       )