




import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
  from huggingface_hub import snapshot_download
  from pyctcdecode import BeamSearchDecoderCTC

  from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
  from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
  from transformers import WavaVecaForCTC







@require_pyctcdecode
class 						lowercase							(   unittest.TestCase		):

     def 					a__	(  self				)						->							str:
           _A							:     Any			=						"""| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
           _A							:     Tuple			=						dict(zip(_a		,     range(len(_a				)				)				)				)

           _A							:     List[str]			=						{
               """unk_token""": """<unk>""",
               """bos_token""": """<s>""",
               """eos_token""": """</s>""",
           }
           _A							:     Optional[Any]			=						{
               """feature_size""": 1,
               """padding_value""": 0.0,
               """sampling_rate""": 1_6000,
               """return_attention_mask""": False,
               """do_normalize""": True,
           }

           _A							:     Union[str, Any]			=						tempfile.mkdtemp()
           _A							:     Union[str, Any]			=						os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["""vocab_file"""]				)
           _A							:     Optional[Any]			=						os.path.join(self.tmpdirname		,     _a				)
           with open(self.vocab_file		,     """w"""		,     encoding="""utf-8"""				) as fp:
                 fp.write(json.dumps(_a				) + """\n"""				)

           with open(self.feature_extraction_file		,     """w"""		,     encoding="""utf-8"""				) as fp:
                 fp.write(json.dumps(_a				) + """\n"""				)

           # load decoder from hub
           _A							:     str			=						"""hf-internal-testing/ngram-beam-search-decoder"""

     def 					a__	(  self		,     **_a				)						->							Union[str, Any]:
           _A							:     str			=						self.add_kwargs_tokens_map.copy()
           kwargs.update(_a				)
           return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,     **_a				)

     def 					a__	(  self		,     **_a				)						->							Dict:
           return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname		,     **_a				)

     def 					a__	(  self		,     **_a				)						->							str:
           return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name		,     **_a				)

     def 					a__	(  self				)						->							Any:
           shutil.rmtree(self.tmpdirname				)

     def 					a__	(  self				)						->							str:
           _A							:     List[Any]			=						self.get_tokenizer()
           _A							:     Optional[Any]			=						self.get_feature_extractor()
           _A							:     List[str]			=						self.get_decoder()

           _A							:     List[str]			=						WavaVecaProcessorWithLM(tokenizer=_a		,     feature_extractor=_a		,     decoder=_a				)

           processor.save_pretrained(self.tmpdirname				)
           _A							:     List[str]			=						WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname				)

           # tokenizer
           self.assertEqual(processor.tokenizer.get_vocab()		,     tokenizer.get_vocab()				)
           self.assertIsInstance(processor.tokenizer		,     _a				)

           # feature extractor
           self.assertEqual(processor.feature_extractor.to_json_string()		,     feature_extractor.to_json_string()				)
           self.assertIsInstance(processor.feature_extractor		,     _a				)

           # decoder
           self.assertEqual(processor.decoder._alphabet.labels		,     decoder._alphabet.labels				)
           self.assertEqual(
               processor.decoder.model_container[decoder._model_key]._unigram_set		,     decoder.model_container[decoder._model_key]._unigram_set		,     )
           self.assertIsInstance(processor.decoder		,     _a				)

     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     List[Any]			=						WavaVecaProcessorWithLM(
               tokenizer=self.get_tokenizer()		,     feature_extractor=self.get_feature_extractor()		,     decoder=self.get_decoder()				)
           processor.save_pretrained(self.tmpdirname				)

           # make sure that error is thrown when decoder alphabet doesn't match
           _A							:     int			=						WavaVecaProcessorWithLM.from_pretrained(
               self.tmpdirname		,     alpha=5.0		,     beta=3.0		,     score_boundary=-7.0		,     unk_score_offset=3				)

           # decoder
           self.assertEqual(processor.language_model.alpha		,     5.0				)
           self.assertEqual(processor.language_model.beta		,     3.0				)
           self.assertEqual(processor.language_model.score_boundary		,     -7.0				)
           self.assertEqual(processor.language_model.unk_score_offset		,     3				)

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Dict			=						self.get_tokenizer()
           # add token to trigger raise
           tokenizer.add_tokens(["""xx"""]				)
           with self.assertRaisesRegex(_a		,     """include"""				):
                 WavaVecaProcessorWithLM(
                     tokenizer=_a		,     feature_extractor=self.get_feature_extractor()		,     decoder=self.get_decoder()				)

     def 					a__	(  self				)						->							List[str]:
           _A							:     str			=						self.get_feature_extractor()
           _A							:     Tuple			=						self.get_tokenizer()
           _A							:     List[Any]			=						self.get_decoder()

           _A							:     Any			=						WavaVecaProcessorWithLM(tokenizer=_a		,     feature_extractor=_a		,     decoder=_a				)

           _A							:     Tuple			=						floats_list((3, 1000)				)

           _A							:     Union[str, Any]			=						feature_extractor(_a		,     return_tensors="""np"""				)
           _A							:     Tuple			=						processor(_a		,     return_tensors="""np"""				)

           for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()		,     input_processor[key].sum()		,     delta=1e-2				)

     def 					a__	(  self				)						->							str:
           _A							:     Any			=						self.get_feature_extractor()
           _A							:     List[str]			=						self.get_tokenizer()
           _A							:     List[Any]			=						self.get_decoder()

           _A							:     List[str]			=						WavaVecaProcessorWithLM(tokenizer=_a		,     feature_extractor=_a		,     decoder=_a				)

           _A							:     int			=						"""This is a test string"""

           _A							:     List[str]			=						processor(text=_a				)

           _A							:     Union[str, Any]			=						tokenizer(_a				)

           for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]		,     encoded_processor[key]				)

     def 					a__	(  self		,     _a=(2, 10, 16)		,     _a=77				)						->							Optional[int]:
           np.random.seed(_a				)
           return np.random.rand(*_a				)

     def 					a__	(  self				)						->							List[str]:
           _A							:     Dict			=						self.get_feature_extractor()
           _A							:     List[Any]			=						self.get_tokenizer()
           _A							:     Optional[int]			=						self.get_decoder()

           _A							:     Optional[Any]			=						WavaVecaProcessorWithLM(tokenizer=_a		,     feature_extractor=_a		,     decoder=_a				)

           _A							:     List[Any]			=						self._get_dummy_logits(shape=(10, 16)		,     seed=13				)

           _A							:     Dict			=						processor.decode(_a				)

           _A							:     Optional[Any]			=						decoder.decode_beams(_a				)[0]

           self.assertEqual(decoded_decoder[0]		,     decoded_processor.text				)
           self.assertEqual("""</s> <s> </s>"""		,     decoded_processor.text				)
           self.assertEqual(decoded_decoder[-2]		,     decoded_processor.logit_score				)
           self.assertEqual(decoded_decoder[-1]		,     decoded_processor.lm_score				)

     @parameterized.expand([[None], ["""fork"""], ["""spawn"""]]				)
     def 					a__	(  self		,     _a				)						->							int:
           _A							:     int			=						self.get_feature_extractor()
           _A							:     Any			=						self.get_tokenizer()
           _A							:     int			=						self.get_decoder()

           _A							:     Tuple			=						WavaVecaProcessorWithLM(tokenizer=_a		,     feature_extractor=_a		,     decoder=_a				)

           _A							:     Union[str, Any]			=						self._get_dummy_logits()

           # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
           #       otherwise, the LM won't be available to the pool's sub-processes.
           # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
           if pool_context is None:
                 _A							:     Any			=						processor.batch_decode(_a				)
           else:
                 with get_context(_a				).Pool() as pool:
                       _A							:     Optional[int]			=						processor.batch_decode(_a		,     _a				)

           _A							:     Optional[int]			=						list(_a				)

           with get_context("""fork"""				).Pool() as p:
                 _A							:     Tuple			=						decoder.decode_beams_batch(_a		,     _a				)

           _A			,   _A			,   _A							:     str			=						[], [], []
           for beams in decoded_beams:
                 texts_decoder.append(beams[0][0]				)
                 logit_scores_decoder.append(beams[0][-2]				)
                 lm_scores_decoder.append(beams[0][-1]				)

           self.assertListEqual(_a		,     decoded_processor.text				)
           self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""]		,     decoded_processor.text				)
           self.assertListEqual(_a		,     decoded_processor.logit_score				)
           self.assertListEqual(_a		,     decoded_processor.lm_score				)

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Any			=						self.get_feature_extractor()
           _A							:     str			=						self.get_tokenizer()
           _A							:     int			=						self.get_decoder()

           _A							:     Dict			=						WavaVecaProcessorWithLM(tokenizer=_a		,     feature_extractor=_a		,     decoder=_a				)

           _A							:     List[Any]			=						self._get_dummy_logits()

           _A							:     Union[str, Any]			=						15
           _A							:     str			=						-20.0
           _A							:     Optional[int]			=						-4.0

           _A							:     Union[str, Any]			=						processor.batch_decode(
               _a		,     beam_width=_a		,     beam_prune_logp=_a		,     token_min_logp=_a		,     )
           _A							:     str			=						decoded_processor_out.text

           _A							:     Dict			=						list(_a				)

           with get_context("""fork"""				).Pool() as pool:
                 _A							:     Union[str, Any]			=						decoder.decode_beams_batch(
                     _a		,     _a		,     beam_width=_a		,     beam_prune_logp=_a		,     token_min_logp=_a		,     )

           _A							:     Optional[int]			=						[d[0][0] for d in decoded_decoder_out]
           _A							:     int			=						[d[0][2] for d in decoded_decoder_out]
           _A							:     Optional[int]			=						[d[0][3] for d in decoded_decoder_out]

           self.assertListEqual(_a		,     _a				)
           self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""]		,     _a				)

           self.assertTrue(np.array_equal(_a		,     decoded_processor_out.logit_score				)				)
           self.assertTrue(np.allclose([-20.054, -18.447]		,     _a		,     atol=1e-3				)				)

           self.assertTrue(np.array_equal(_a		,     decoded_processor_out.lm_score				)				)
           self.assertTrue(np.allclose([-15.554, -13.9474]		,     _a		,     atol=1e-3				)				)

     def 					a__	(  self				)						->							str:
           _A							:     Any			=						self.get_feature_extractor()
           _A							:     Dict			=						self.get_tokenizer()
           _A							:     Any			=						self.get_decoder()

           _A							:     Any			=						WavaVecaProcessorWithLM(tokenizer=_a		,     feature_extractor=_a		,     decoder=_a				)

           _A							:     Dict			=						self._get_dummy_logits()

           _A							:     Any			=						2.0
           _A							:     int			=						5.0
           _A							:     Union[str, Any]			=						-20.0
           _A							:     str			=						True

           _A							:     Optional[Any]			=						processor.batch_decode(
               _a		,     alpha=_a		,     beta=_a		,     unk_score_offset=_a		,     lm_score_boundary=_a		,     )
           _A							:     Union[str, Any]			=						decoded_processor_out.text

           _A							:     Tuple			=						list(_a				)
           decoder.reset_params(
               alpha=_a		,     beta=_a		,     unk_score_offset=_a		,     lm_score_boundary=_a		,     )

           with get_context("""fork"""				).Pool() as pool:
                 _A							:     Optional[Any]			=						decoder.decode_beams_batch(
                     _a		,     _a		,     )

           _A							:     Dict			=						[d[0][0] for d in decoded_decoder_out]

           self.assertListEqual(_a		,     _a				)
           self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""]		,     _a				)
           _A							:     List[Any]			=						processor.decoder.model_container[processor.decoder._model_key]
           self.assertEqual(lm_model.alpha		,     2.0				)
           self.assertEqual(lm_model.beta		,     5.0				)
           self.assertEqual(lm_model.unk_score_offset		,     -20.0				)
           self.assertEqual(lm_model.score_boundary		,     _a				)

     def 					a__	(  self				)						->							Any:
           _A							:     Tuple			=						WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""				)

           _A							:     Optional[int]			=						processor.decoder.model_container[processor.decoder._model_key]
           _A							:     Optional[int]			=						Path(language_model._kenlm_model.path.decode("""utf-8"""				)				).parent.parent.absolute()

           _A							:     Any			=						os.listdir(_a				)
           _A							:     int			=						["""alphabet.json""", """language_model"""]

           downloaded_decoder_files.sort()
           expected_decoder_files.sort()

           # test that only decoder relevant files from
           # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
           # are downloaded and none of the rest (e.g. README.md, ...)
           self.assertListEqual(_a		,     _a				)

     def 					a__	(  self				)						->							Dict:
           _A							:     int			=						snapshot_download("""hf-internal-testing/processor_with_lm"""				)

           _A							:     Optional[Any]			=						WavaVecaProcessorWithLM.from_pretrained(_a				)

           _A							:     Optional[int]			=						processor.decoder.model_container[processor.decoder._model_key]
           _A							:     str			=						Path(language_model._kenlm_model.path.decode("""utf-8"""				)				).parent.parent.absolute()

           _A							:     Optional[int]			=						os.listdir(_a				)
           _A							:     List[str]			=						os.listdir(_a				)

           local_decoder_files.sort()
           expected_decoder_files.sort()

           # test that both decoder form hub and local files in cache are the same
           self.assertListEqual(_a		,     _a				)

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     List[str]			=						WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""				)
           _A							:     Union[str, Any]			=						AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm"""				)

           _A							:     List[str]			=						floats_list((3, 1000)				)

           _A							:     Union[str, Any]			=						processor_wavaveca(_a		,     return_tensors="""np"""				)
           _A							:     Any			=						processor_auto(_a		,     return_tensors="""np"""				)

           for key in input_wavaveca.keys():
                 self.assertAlmostEqual(input_wavaveca[key].sum()		,     input_auto[key].sum()		,     delta=1e-2				)

           _A							:     Tuple			=						self._get_dummy_logits()

           _A							:     List[str]			=						processor_wavaveca.batch_decode(_a				)
           _A							:     Any			=						processor_auto.batch_decode(_a				)

           self.assertListEqual(decoded_wavaveca.text		,     decoded_auto.text				)

     def 					a__	(  self				)						->							Dict:
           _A							:     Dict			=						self.get_feature_extractor()
           _A							:     Optional[int]			=						self.get_tokenizer()
           _A							:     int			=						self.get_decoder()

           _A							:     List[str]			=						WavaVecaProcessorWithLM(tokenizer=_a		,     feature_extractor=_a		,     decoder=_a				)

           self.assertListEqual(
               processor.model_input_names		,     feature_extractor.model_input_names		,     msg="""`processor` and `feature_extractor` model input names do not match"""		,     )

     @staticmethod
     def 					a__	(  _a		,     _a				)						->							int:
           _A							:     List[Any]			=						[d[key] for d in offsets]
           return retrieved_list

     def 					a__	(  self				)						->							int:
           _A							:     Any			=						WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""				)
           _A							:     Optional[int]			=						self._get_dummy_logits()[0]

           _A							:     str			=						processor.decode(_a		,     output_word_offsets=_a				)
           # check Wav2Vec2CTCTokenizerOutput keys for word
           self.assertEqual(len(outputs.keys()				)		,     4				)
           self.assertTrue("""text""" in outputs				)
           self.assertTrue("""word_offsets""" in outputs				)
           self.assertTrue(isinstance(_a		,     _a				)				)

           self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""]		,     """word"""				)				)		,     outputs.text				)
           self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]		,     """word"""				)		,     ["""<s>""", """<s>""", """</s>"""]				)
           self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]		,     """start_offset"""				)		,     [0, 2, 4]				)
           self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]		,     """end_offset"""				)		,     [1, 3, 5]				)

     def 					a__	(  self				)						->							int:
           _A							:     Dict			=						WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""				)
           _A							:     Tuple			=						self._get_dummy_logits()

           _A							:     int			=						processor.batch_decode(_a		,     output_word_offsets=_a				)

           # check Wav2Vec2CTCTokenizerOutput keys for word
           self.assertEqual(len(outputs.keys()				)		,     4				)
           self.assertTrue("""text""" in outputs				)
           self.assertTrue("""word_offsets""" in outputs				)
           self.assertTrue(isinstance(_a		,     _a				)				)

           self.assertListEqual(
               [""" """.join(self.get_from_offsets(_a		,     """word"""				)				) for o in outputs["""word_offsets"""]]		,     outputs.text				)
           self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]		,     """word"""				)		,     ["""<s>""", """<s>""", """</s>"""]				)
           self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]		,     """start_offset"""				)		,     [0, 2, 4]				)
           self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]		,     """end_offset"""				)		,     [1, 3, 5]				)



     @slow
     @require_torch
     @require_torchaudio
     def 					a__	(  self				)						->							Tuple:
           import torch

           _A							:     Union[str, Any]			=						load_dataset("""common_voice"""		,     """en"""		,     split="""train"""		,     streaming=_a				)
           _A							:     List[Any]			=						ds.cast_column("""audio"""		,     datasets.Audio(sampling_rate=1_6000				)				)
           _A							:     Optional[Any]			=						iter(_a				)
           _A							:     Any			=						next(_a				)

           _A							:     str			=						AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""				)
           _A							:     Optional[int]			=						WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""				)

           # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
           _A							:     Dict			=						processor(sample["""audio"""]["""array"""]		,     return_tensors="""pt"""				).input_values

           with torch.no_grad():
                 _A							:     Dict			=						model(_a				).logits.cpu().numpy()

           _A							:     Optional[int]			=						processor.decode(logits[0]		,     output_word_offsets=_a				)

           _A							:     List[str]			=						model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
           _A							:     List[Any]			=						[
               {
                   """start_time""": d["""start_offset"""] * time_offset,
                   """end_time""": d["""end_offset"""] * time_offset,
                   """word""": d["""word"""],
               }
               for d in output["""word_offsets"""]
           ]

           _A							:     Optional[Any]			=						"""WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""

           # output words
           self.assertEqual(""" """.join(self.get_from_offsets(_a		,     """word"""				)				)		,     _a				)
           self.assertEqual(""" """.join(self.get_from_offsets(_a		,     """word"""				)				)		,     output.text				)

           # output times
           _A							:     Optional[Any]			=						torch.tensor(self.get_from_offsets(_a		,     """start_time"""				)				)
           _A							:     int			=						torch.tensor(self.get_from_offsets(_a		,     """end_time"""				)				)

           # fmt: off
           _A							:     Dict			=						torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599]				)
           _A							:     str			=						torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94]				)
           # fmt: on

           self.assertTrue(torch.allclose(_a		,     _a		,     atol=0.01				)				)
           self.assertTrue(torch.allclose(_a		,     _a		,     atol=0.01				)				)







from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch







@dataclass
class     __lowerCAmelCase :

			"""simple docstring"""







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


			def       lowercase_    (      self							)    ->      Union[str, 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       lowercase_    (      self							)    ->      Optional[int]:



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


			def       lowercase_    (      self							)    ->      Optional[Any]:



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


			def       lowercase_    (      self							)    ->      torch.Tensor:



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


			@property
			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase       ,						*__lowerCamelCase		       =    self.shape
							__lowerCamelCase		       =    int(np.prod(lowerCamelCase__							)							)

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

							__lowerCamelCase		       =    rays.view(lowerCamelCase__			,						inner_batch_size * self.height * self.width			,						2			,						3							)

							return rays


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      torch.Tensor:



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

							__lowerCamelCase		       =    coords.view(lowerCamelCase__			,						-1			,						2							)

							__lowerCamelCase		       =    self.resolution()
							__lowerCamelCase		       =    self.fov()

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

							__lowerCamelCase		       =    fracs.view(lowerCamelCase__			,						-1			,						2							)
							__lowerCamelCase		       =    (
							    self.z.view(lowerCamelCase__			,						1			,						3							)
							    + self.x.view(lowerCamelCase__			,						1			,						3							) * fracs[:, :, :1]
							    + self.y.view(lowerCamelCase__			,						1			,						3							) * fracs[:, :, 1:]
							)
							__lowerCamelCase		       =    directions / directions.norm(dim=-1			,						keepdim=lowerCamelCase__							)
							__lowerCamelCase		       =    torch.stack(
							    [
							        torch.broadcast_to(self.origin.view(lowerCamelCase__			,						1			,						3							)			,						[batch_size, directions.shape[1], 3]							),
							        directions,
							    ]			,						dim=2			,						)
							return rays.view(lowerCamelCase__			,						*lowerCamelCase__			,						2			,						3							)






			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      "DifferentiableProjectiveCamera":



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







def 			lowerCamelCase_		(       UpperCamelCase__				:      int      )     ->       DifferentiableProjectiveCamera:





				"""simple docstring"""


				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    []
				for theta in np.linspace(0 ,     2 * np.pi ,     num=20      ):
								__lowerCamelCase		       =    np.array([np.sin(UpperCamelCase__      ), np.cos(UpperCamelCase__      ), -0.5]      )
								z /= np.sqrt(np.sum(z**2      )      )
								__lowerCamelCase		       =    -z * 4
								__lowerCamelCase		       =    np.array([np.cos(UpperCamelCase__      ), -np.sin(UpperCamelCase__      ), 0.0]      )
								__lowerCamelCase		       =    np.cross(UpperCamelCase__ ,     UpperCamelCase__      )
								origins.append(UpperCamelCase__      )
								xs.append(UpperCamelCase__      )
								ys.append(UpperCamelCase__      )
								zs.append(UpperCamelCase__      )
				return DifferentiableProjectiveCamera(
				    origin=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     x=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     y=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     z=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     width=UpperCamelCase__ ,     height=UpperCamelCase__ ,     x_fov=0.7 ,     y_fov=0.7 ,     shape=(1, len(UpperCamelCase__      )) ,     )


