



"""simple docstring"""

import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


a_       =       get_tests_dir('''fixtures/test_sentencepiece.model''')

if is_torch_available():
			from transformers.models.mbart.modeling_mbart import shift_tokens_right

a_       =       250004
a_       =       250020







@require_sentencepiece
@require_tokenizers
class  __lowercase  (       SCREAMING_SNAKE_CASE__       ,     unittest.TestCase):







						"""simple docstring"""







						_A		:				Tuple											=		MBartaaTokenizer
						_A		:				List[Any]											=		MBartaaTokenizerFast
						_A		:				Tuple											=		True
						_A		:				Any											=		True







						def     __UpperCamelCase				(self							):
											super().setUp()

											# We have a SentencePiece fixture for testing
											snake_case_       :		List[Any]			       =       MBartaaTokenizer(snake_case__ ,		src_lang="""en_XX""" ,		tgt_lang="""ro_RO""" ,		keep_accents=snake_case__							)
											tokenizer.save_pretrained(self.tmpdirname							)







						def     __UpperCamelCase				(self							):
											snake_case_       :		Any			       =       """<s>"""
											snake_case_       :		Any			       =       0

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__							) ,		snake_case__							)
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__							) ,		snake_case__							)







						def     __UpperCamelCase				(self							):
											snake_case_       :		Dict			       =       list(self.get_tokenizer().get_vocab().keys()							)

											self.assertEqual(vocab_keys[0] ,		"""<s>"""							)
											self.assertEqual(vocab_keys[1] ,		"""<pad>"""							)
											self.assertEqual(vocab_keys[-1] ,		"""<mask>"""							)
											self.assertEqual(len(snake_case__							) ,		10_54							)







						def     __UpperCamelCase				(self							):
											self.assertEqual(self.get_tokenizer().vocab_size ,		10_54							)







						def     __UpperCamelCase				(self							):
											snake_case_       :		Any			       =       MBartaaTokenizer(snake_case__ ,		src_lang="""en_XX""" ,		tgt_lang="""ro_RO""" ,		keep_accents=snake_case__							)

											snake_case_       :		Optional[int]			       =       tokenizer.tokenize("""This is a test"""							)
											self.assertListEqual(snake_case__ ,		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

											self.assertListEqual(
											    tokenizer.convert_tokens_to_ids(snake_case__							) ,		[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] ,		)

											snake_case_       :		Any			       =       tokenizer.tokenize("""I was born in 92000, and this is falsé."""							)
											self.assertListEqual(
											    snake_case__ ,		[SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """9""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """é""", """."""] ,		)
											snake_case_       :		List[Any]			       =       tokenizer.convert_tokens_to_ids(snake_case__							)
											self.assertListEqual(
											    snake_case__ ,		[
											        value + tokenizer.fairseq_offset
											        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
											    ] ,		)

											snake_case_       :		Optional[int]			       =       tokenizer.convert_ids_to_tokens(snake_case__							)
											self.assertListEqual(
											    snake_case__ ,		[SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """<unk>""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """<unk>""", """."""] ,		)







						@slow
						def     __UpperCamelCase				(self							):
											snake_case_       :		Tuple			       =       {"""input_ids""": [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=snake_case__ ,		model_name="""facebook/mbart-large-50""" ,		revision="""d3913889c59cd5c9e456b269c376325eabad57e2""" ,		)







						def     __UpperCamelCase				(self							):

											if not self.test_slow_tokenizer:
																# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																return

											snake_case_       :		List[Any]			       =       (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart50""", {})
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'							):
																					snake_case_       :		Optional[Any]			       =       self.rust_tokenizer_class.from_pretrained(snake_case__ ,		**snake_case__							)
																					snake_case_       :		Optional[Any]			       =       self.tokenizer_class.from_pretrained(snake_case__ ,		**snake_case__							)

																					snake_case_       :		Tuple			       =       tempfile.mkdtemp()

																					snake_case_       :		List[Any]			       =       tokenizer_r.save_pretrained(snake_case__							)
																					snake_case_       :		str			       =       tokenizer_p.save_pretrained(snake_case__							)

																					# Checks it save with the same files + the tokenizer.json file for the fast one
																					self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files							)							)
																					snake_case_       :		List[str]			       =       tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f							)
																					self.assertSequenceEqual(snake_case__ ,		snake_case__							)

																					# Checks everything loads correctly in the same way
																					snake_case_       :		Tuple			       =       tokenizer_r.from_pretrained(snake_case__							)
																					snake_case_       :		Any			       =       tokenizer_p.from_pretrained(snake_case__							)

																					# Check special tokens are set accordingly on Rust and Python
																					for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(snake_case__ ,		snake_case__							)							)
																										# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																										# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																					shutil.rmtree(snake_case__							)

																					# Save tokenizer rust, legacy_format=True
																					snake_case_       :		Tuple			       =       tempfile.mkdtemp()

																					snake_case_       :		List[Any]			       =       tokenizer_r.save_pretrained(snake_case__ ,		legacy_format=snake_case__							)
																					snake_case_       :		List[str]			       =       tokenizer_p.save_pretrained(snake_case__							)

																					# Checks it save with the same files
																					self.assertSequenceEqual(snake_case__ ,		snake_case__							)

																					# Checks everything loads correctly in the same way
																					snake_case_       :		List[str]			       =       tokenizer_r.from_pretrained(snake_case__							)
																					snake_case_       :		Union[str, Any]			       =       tokenizer_p.from_pretrained(snake_case__							)

																					# Check special tokens are set accordingly on Rust and Python
																					for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(snake_case__ ,		snake_case__							)							)

																					shutil.rmtree(snake_case__							)

																					# Save tokenizer rust, legacy_format=False
																					snake_case_       :		Tuple			       =       tempfile.mkdtemp()

																					snake_case_       :		Dict			       =       tokenizer_r.save_pretrained(snake_case__ ,		legacy_format=snake_case__							)
																					snake_case_       :		Any			       =       tokenizer_p.save_pretrained(snake_case__							)

																					# Checks it saved the tokenizer.json file
																					self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files							)							)

																					# Checks everything loads correctly in the same way
																					snake_case_       :		str			       =       tokenizer_r.from_pretrained(snake_case__							)
																					snake_case_       :		Dict			       =       tokenizer_p.from_pretrained(snake_case__							)

																					# Check special tokens are set accordingly on Rust and Python
																					for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(snake_case__ ,		snake_case__							)							)

																					shutil.rmtree(snake_case__							)







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







						"""simple docstring"""







						_A		:				str											=		"""facebook/mbart-large-50-one-to-many-mmt"""
						_A		:				List[Any]											=		[
						    """ UN Chief Says There Is No Military Solution in Syria""",
						    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
						]
						_A		:				str											=		[
						    """Şeful ONU declară că nu există o soluţie militară în Siria""",
						    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
						    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
						    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
						]
						_A		:				int											=		[EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2]







						@classmethod
						def     __UpperCamelCase				(cls							):
											snake_case_       :		Optional[int]			       =       MBartaaTokenizer.from_pretrained(
											    cls.checkpoint_name ,		src_lang="""en_XX""" ,		tgt_lang="""ro_RO"""							)
											snake_case_       :		Tuple			       =       1
											return cls







						def     __UpperCamelCase				(self							):
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""] ,		25_00_01							)
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""] ,		25_00_04							)
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""] ,		25_00_20							)
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""mr_IN"""] ,		25_00_38							)







						def     __UpperCamelCase				(self							):
											snake_case_       :		Optional[int]			       =       self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
											self.assertListEqual(self.expected_src_tokens ,		snake_case__							)







						def     __UpperCamelCase				(self							):
											self.assertIn(snake_case__ ,		self.tokenizer.all_special_ids							)
											snake_case_       :		Tuple			       =       [RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
											snake_case_       :		Tuple			       =       self.tokenizer.decode(snake_case__ ,		skip_special_tokens=snake_case__							)
											snake_case_       :		List[str]			       =       self.tokenizer.decode(generated_ids[1:] ,		skip_special_tokens=snake_case__							)
											self.assertEqual(snake_case__ ,		snake_case__							)
											self.assertNotIn(self.tokenizer.eos_token ,		snake_case__							)







						def     __UpperCamelCase				(self							):
											snake_case_       :		Any			       =       ["""this is gunna be a long sentence """ * 20]
											assert isinstance(src_text[0] ,		snake_case__							)
											snake_case_       :		Any			       =       10
											snake_case_       :		Optional[Any]			       =       self.tokenizer(snake_case__ ,		max_length=snake_case__ ,		truncation=snake_case__							).input_ids[0]
											self.assertEqual(ids[0] ,		snake_case__							)
											self.assertEqual(ids[-1] ,		2							)
											self.assertEqual(len(snake_case__							) ,		snake_case__							)







						def     __UpperCamelCase				(self							):
											self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]							) ,		[25_00_53, 25_00_01]							)







						def     __UpperCamelCase				(self							):
											snake_case_       :		List[str]			       =       tempfile.mkdtemp()
											snake_case_       :		str			       =       self.tokenizer.fairseq_tokens_to_ids
											self.tokenizer.save_pretrained(snake_case__							)
											snake_case_       :		List[str]			       =       MBartaaTokenizer.from_pretrained(snake_case__							)
											self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,		snake_case__							)







						@require_torch
						def     __UpperCamelCase				(self							):
											snake_case_       :		Union[str, Any]			       =       self.tokenizer(self.src_text ,		text_target=self.tgt_text ,		padding=snake_case__ ,		return_tensors="""pt"""							)
											snake_case_       :		str			       =       shift_tokens_right(batch["""labels"""] ,		self.tokenizer.pad_token_id							)

											# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
											assert batch.input_ids[1][0] == EN_CODE
											assert batch.input_ids[1][-1] == 2
											assert batch.labels[1][0] == RO_CODE
											assert batch.labels[1][-1] == 2
											assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]







						@require_torch
						def     __UpperCamelCase				(self							):
											snake_case_       :		int			       =       self.tokenizer(
											    self.src_text ,		text_target=self.tgt_text ,		padding=snake_case__ ,		truncation=snake_case__ ,		max_length=len(self.expected_src_tokens							) ,		return_tensors="""pt""" ,		)
											snake_case_       :		Tuple			       =       shift_tokens_right(batch["""labels"""] ,		self.tokenizer.pad_token_id							)

											self.assertIsInstance(snake_case__ ,		snake_case__							)

											self.assertEqual((2, 14) ,		batch.input_ids.shape							)
											self.assertEqual((2, 14) ,		batch.attention_mask.shape							)
											snake_case_       :		str			       =       batch.input_ids.tolist()[0]
											self.assertListEqual(self.expected_src_tokens ,		snake_case__							)
											self.assertEqual(2 ,		batch.decoder_input_ids[0, 0]							)  # decoder_start_token_id
											# Test that special tokens are reset
											self.assertEqual(self.tokenizer.prefix_tokens ,		[EN_CODE]							)
											self.assertEqual(self.tokenizer.suffix_tokens ,		[self.tokenizer.eos_token_id]							)







						def     __UpperCamelCase				(self							):
											snake_case_       :		List[str]			       =       self.tokenizer(self.src_text ,		padding=snake_case__ ,		truncation=snake_case__ ,		max_length=3 ,		return_tensors="""pt"""							)
											snake_case_       :		Union[str, Any]			       =       self.tokenizer(
											    text_target=self.tgt_text ,		padding=snake_case__ ,		truncation=snake_case__ ,		max_length=10 ,		return_tensors="""pt"""							)
											snake_case_       :		Optional[Any]			       =       targets["""input_ids"""]
											snake_case_       :		Dict			       =       shift_tokens_right(snake_case__ ,		self.tokenizer.pad_token_id							)

											self.assertEqual(batch.input_ids.shape[1] ,		3							)
											self.assertEqual(batch.decoder_input_ids.shape[1] ,		10							)







						@require_torch
						def     __UpperCamelCase				(self							):
											snake_case_       :		int			       =       self.tokenizer._build_translation_inputs(
											    """A test""" ,		return_tensors="""pt""" ,		src_lang="""en_XX""" ,		tgt_lang="""ar_AR"""							)

											self.assertEqual(
											    nested_simplify(snake_case__							) ,		{
											        # en_XX, A, test, EOS
											        """input_ids""": [[25_00_04, 62, 30_34, 2]],
											        """attention_mask""": [[1, 1, 1, 1]],
											        # ar_AR
											        """forced_bos_token_id""": 25_00_01,
											    } ,		)










from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class    lowercase_		:
						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 	__a					(			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 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

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



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

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				*SCREAMING_SNAKE_CASE_					      =							self.shape
								SCREAMING_SNAKE_CASE_					      =							int(np.prod(snake_case__  )  )

								SCREAMING_SNAKE_CASE_					      =							self.get_image_coords()
								SCREAMING_SNAKE_CASE_					      =							torch.broadcast_to(coords.unsqueeze(0  )					,							[batch_size * inner_batch_size, *coords.shape]  )
								SCREAMING_SNAKE_CASE_					      =							self.get_camera_rays(snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							rays.view(snake_case__					,							inner_batch_size * self.height * self.width					,							2					,							3  )

								return rays



						def 	__a					(			self	:   Optional[Any]					,							snake_case__	:   torch.Tensor  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				*SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							coords.shape
								assert n_coords == 2
								assert batch_size == self.origin.shape[0]

								SCREAMING_SNAKE_CASE_					      =							coords.view(snake_case__					,							-1					,							2  )

								SCREAMING_SNAKE_CASE_					      =							self.resolution()
								SCREAMING_SNAKE_CASE_					      =							self.fov()

								SCREAMING_SNAKE_CASE_					      =							(flat.float() / (res - 1)) * 2 - 1
								SCREAMING_SNAKE_CASE_					      =							fracs * torch.tan(fov / 2  )

								SCREAMING_SNAKE_CASE_					      =							fracs.view(snake_case__					,							-1					,							2  )
								SCREAMING_SNAKE_CASE_					      =							(
								    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:]
								)
								SCREAMING_SNAKE_CASE_					      =							directions / directions.norm(dim=-1					,							keepdim=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							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 	__a					(			self	:   Optional[int]					,							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 )->	DifferentiableProjectiveCamera:
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		for theta in np.linspace(0    ,						2 * np.pi    ,						num=20 ):
				SCREAMING_SNAKE_CASE_					      =							np.array([np.sin(lowerCAmelCase ), np.cos(lowerCAmelCase ), -0.5] )
				z /= np.sqrt(np.sum(z**2 ) )
				SCREAMING_SNAKE_CASE_					      =							-z * 4
				SCREAMING_SNAKE_CASE_					      =							np.array([np.cos(lowerCAmelCase ), -np.sin(lowerCAmelCase ), 0.0] )
				SCREAMING_SNAKE_CASE_					      =							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 ))    ,						)