


'''simple docstring'''

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 					a__ (       unittest.TestCase					):







				"""simple docstring"""



				def     _snake_case     (self						):
										__lowerCAmelCase											= "| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
										__lowerCAmelCase											= dict(zip(lowerCAmelCase_				,	range(len(lowerCAmelCase_						)						)						)						)

										__lowerCAmelCase											= {
										    "unk_token": "<unk>",
										    "bos_token": "<s>",
										    "eos_token": "</s>",
										}
										__lowerCAmelCase											= {
										    "feature_size": 1,
										    "padding_value": 0.0,
										    "sampling_rate": 1_60_00,
										    "return_attention_mask": False,
										    "do_normalize": True,
										}

										__lowerCAmelCase											= tempfile.mkdtemp()
										__lowerCAmelCase											= os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['''vocab_file''']						)
										__lowerCAmelCase											= os.path.join(self.tmpdirname				,	lowerCAmelCase_						)
										with open(self.vocab_file				,	'''w'''				,	encoding='''utf-8'''						) as fp:
																fp.write(json.dumps(lowerCAmelCase_						) + '''\n'''						)

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

										# load decoder from hub
										__lowerCAmelCase											= "hf-internal-testing/ngram-beam-search-decoder"




				def     _snake_case     (self				,	**__lowercase						):
										__lowerCAmelCase											= self.add_kwargs_tokens_map.copy()
										kwargs.update(lowerCAmelCase_						)
										return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname				,	**lowerCAmelCase_						)




				def     _snake_case     (self				,	**__lowercase						):
										return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname				,	**lowerCAmelCase_						)




				def     _snake_case     (self				,	**__lowercase						):
										return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name				,	**lowerCAmelCase_						)




				def     _snake_case     (self						):
										shutil.rmtree(self.tmpdirname						)




				def     _snake_case     (self						):
										__lowerCAmelCase											= self.get_tokenizer()
										__lowerCAmelCase											= self.get_feature_extractor()
										__lowerCAmelCase											= self.get_decoder()

										__lowerCAmelCase											= WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_				,	feature_extractor=lowerCAmelCase_				,	decoder=lowerCAmelCase_						)

										processor.save_pretrained(self.tmpdirname						)
										__lowerCAmelCase											= WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname						)

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

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

										# 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				,	lowerCAmelCase_						)




				def     _snake_case     (self						):
										__lowerCAmelCase											= 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
										__lowerCAmelCase											= 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     _snake_case     (self						):
										__lowerCAmelCase											= self.get_tokenizer()
										# add token to trigger raise
										tokenizer.add_tokens(['''xx''']						)
										with self.assertRaisesRegex(lowerCAmelCase_				,	'''include'''						):
																WavaVecaProcessorWithLM(
																    tokenizer=lowerCAmelCase_				,	feature_extractor=self.get_feature_extractor()				,	decoder=self.get_decoder()						)




				def     _snake_case     (self						):
										__lowerCAmelCase											= self.get_feature_extractor()
										__lowerCAmelCase											= self.get_tokenizer()
										__lowerCAmelCase											= self.get_decoder()

										__lowerCAmelCase											= WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_				,	feature_extractor=lowerCAmelCase_				,	decoder=lowerCAmelCase_						)

										__lowerCAmelCase											= floats_list((3, 10_00)						)

										__lowerCAmelCase											= feature_extractor(lowerCAmelCase_				,	return_tensors='''np'''						)
										__lowerCAmelCase											= processor(lowerCAmelCase_				,	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     _snake_case     (self						):
										__lowerCAmelCase											= self.get_feature_extractor()
										__lowerCAmelCase											= self.get_tokenizer()
										__lowerCAmelCase											= self.get_decoder()

										__lowerCAmelCase											= WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_				,	feature_extractor=lowerCAmelCase_				,	decoder=lowerCAmelCase_						)

										__lowerCAmelCase											= "This is a test string"

										__lowerCAmelCase											= processor(text=lowerCAmelCase_						)

										__lowerCAmelCase											= tokenizer(lowerCAmelCase_						)

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




				def     _snake_case     (self				,	__lowercase=(2, 10, 16)				,	__lowercase=77						):
										np.random.seed(lowerCAmelCase_						)
										return np.random.rand(*lowerCAmelCase_						)




				def     _snake_case     (self						):
										__lowerCAmelCase											= self.get_feature_extractor()
										__lowerCAmelCase											= self.get_tokenizer()
										__lowerCAmelCase											= self.get_decoder()

										__lowerCAmelCase											= WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_				,	feature_extractor=lowerCAmelCase_				,	decoder=lowerCAmelCase_						)

										__lowerCAmelCase											= self._get_dummy_logits(shape=(10, 16)				,	seed=13						)

										__lowerCAmelCase											= processor.decode(lowerCAmelCase_						)

										__lowerCAmelCase											= decoder.decode_beams(lowerCAmelCase_						)[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     _snake_case     (self				,	__lowercase						):
										__lowerCAmelCase											= self.get_feature_extractor()
										__lowerCAmelCase											= self.get_tokenizer()
										__lowerCAmelCase											= self.get_decoder()

										__lowerCAmelCase											= WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_				,	feature_extractor=lowerCAmelCase_				,	decoder=lowerCAmelCase_						)

										__lowerCAmelCase											= 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:
																__lowerCAmelCase											= processor.batch_decode(lowerCAmelCase_						)
										else:
																with get_context(lowerCAmelCase_						).Pool() as pool:
																						__lowerCAmelCase											= processor.batch_decode(lowerCAmelCase_				,	lowerCAmelCase_						)

										__lowerCAmelCase											= list(lowerCAmelCase_						)

										with get_context('''fork'''						).Pool() as p:
																__lowerCAmelCase											= decoder.decode_beams_batch(lowerCAmelCase_				,	lowerCAmelCase_						)

										__lowerCAmelCase											= [], [], []
										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(lowerCAmelCase_				,	decoded_processor.text						)
										self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>''']				,	decoded_processor.text						)
										self.assertListEqual(lowerCAmelCase_				,	decoded_processor.logit_score						)
										self.assertListEqual(lowerCAmelCase_				,	decoded_processor.lm_score						)




				def     _snake_case     (self						):
										__lowerCAmelCase											= self.get_feature_extractor()
										__lowerCAmelCase											= self.get_tokenizer()
										__lowerCAmelCase											= self.get_decoder()

										__lowerCAmelCase											= WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_				,	feature_extractor=lowerCAmelCase_				,	decoder=lowerCAmelCase_						)

										__lowerCAmelCase											= self._get_dummy_logits()

										__lowerCAmelCase											= 15
										__lowerCAmelCase											= -20.0
										__lowerCAmelCase											= -4.0

										__lowerCAmelCase											= processor.batch_decode(
										    lowerCAmelCase_				,	beam_width=lowerCAmelCase_				,	beam_prune_logp=lowerCAmelCase_				,	token_min_logp=lowerCAmelCase_				,	)
										__lowerCAmelCase											= decoded_processor_out.text

										__lowerCAmelCase											= list(lowerCAmelCase_						)

										with get_context('''fork'''						).Pool() as pool:
																__lowerCAmelCase											= decoder.decode_beams_batch(
																    lowerCAmelCase_				,	lowerCAmelCase_				,	beam_width=lowerCAmelCase_				,	beam_prune_logp=lowerCAmelCase_				,	token_min_logp=lowerCAmelCase_				,	)

										__lowerCAmelCase											= [d[0][0] for d in decoded_decoder_out]
										__lowerCAmelCase											= [d[0][2] for d in decoded_decoder_out]
										__lowerCAmelCase											= [d[0][3] for d in decoded_decoder_out]

										self.assertListEqual(lowerCAmelCase_				,	lowerCAmelCase_						)
										self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>''']				,	lowerCAmelCase_						)

										self.assertTrue(np.array_equal(lowerCAmelCase_				,	decoded_processor_out.logit_score						)						)
										self.assertTrue(np.allclose([-20.0_54, -18.4_47]				,	lowerCAmelCase_				,	atol=1e-3						)						)

										self.assertTrue(np.array_equal(lowerCAmelCase_				,	decoded_processor_out.lm_score						)						)
										self.assertTrue(np.allclose([-15.5_54, -13.94_74]				,	lowerCAmelCase_				,	atol=1e-3						)						)




				def     _snake_case     (self						):
										__lowerCAmelCase											= self.get_feature_extractor()
										__lowerCAmelCase											= self.get_tokenizer()
										__lowerCAmelCase											= self.get_decoder()

										__lowerCAmelCase											= WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_				,	feature_extractor=lowerCAmelCase_				,	decoder=lowerCAmelCase_						)

										__lowerCAmelCase											= self._get_dummy_logits()

										__lowerCAmelCase											= 2.0
										__lowerCAmelCase											= 5.0
										__lowerCAmelCase											= -20.0
										__lowerCAmelCase											= True

										__lowerCAmelCase											= processor.batch_decode(
										    lowerCAmelCase_				,	alpha=lowerCAmelCase_				,	beta=lowerCAmelCase_				,	unk_score_offset=lowerCAmelCase_				,	lm_score_boundary=lowerCAmelCase_				,	)
										__lowerCAmelCase											= decoded_processor_out.text

										__lowerCAmelCase											= list(lowerCAmelCase_						)
										decoder.reset_params(
										    alpha=lowerCAmelCase_				,	beta=lowerCAmelCase_				,	unk_score_offset=lowerCAmelCase_				,	lm_score_boundary=lowerCAmelCase_				,	)

										with get_context('''fork'''						).Pool() as pool:
																__lowerCAmelCase											= decoder.decode_beams_batch(
																    lowerCAmelCase_				,	lowerCAmelCase_				,	)

										__lowerCAmelCase											= [d[0][0] for d in decoded_decoder_out]

										self.assertListEqual(lowerCAmelCase_				,	lowerCAmelCase_						)
										self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>''']				,	lowerCAmelCase_						)
										__lowerCAmelCase											= 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				,	lowerCAmelCase_						)




				def     _snake_case     (self						):
										__lowerCAmelCase											= WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)

										__lowerCAmelCase											= processor.decoder.model_container[processor.decoder._model_key]
										__lowerCAmelCase											= Path(language_model._kenlm_model.path.decode('''utf-8'''						)						).parent.parent.absolute()

										__lowerCAmelCase											= os.listdir(lowerCAmelCase_						)
										__lowerCAmelCase											= ["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(lowerCAmelCase_				,	lowerCAmelCase_						)




				def     _snake_case     (self						):
										__lowerCAmelCase											= snapshot_download('''hf-internal-testing/processor_with_lm'''						)

										__lowerCAmelCase											= WavaVecaProcessorWithLM.from_pretrained(lowerCAmelCase_						)

										__lowerCAmelCase											= processor.decoder.model_container[processor.decoder._model_key]
										__lowerCAmelCase											= Path(language_model._kenlm_model.path.decode('''utf-8'''						)						).parent.parent.absolute()

										__lowerCAmelCase											= os.listdir(lowerCAmelCase_						)
										__lowerCAmelCase											= os.listdir(lowerCAmelCase_						)

										local_decoder_files.sort()
										expected_decoder_files.sort()

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




				def     _snake_case     (self						):
										__lowerCAmelCase											= WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)
										__lowerCAmelCase											= AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm'''						)

										__lowerCAmelCase											= floats_list((3, 10_00)						)

										__lowerCAmelCase											= processor_wavaveca(lowerCAmelCase_				,	return_tensors='''np'''						)
										__lowerCAmelCase											= processor_auto(lowerCAmelCase_				,	return_tensors='''np'''						)

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

										__lowerCAmelCase											= self._get_dummy_logits()

										__lowerCAmelCase											= processor_wavaveca.batch_decode(lowerCAmelCase_						)
										__lowerCAmelCase											= processor_auto.batch_decode(lowerCAmelCase_						)

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




				def     _snake_case     (self						):
										__lowerCAmelCase											= self.get_feature_extractor()
										__lowerCAmelCase											= self.get_tokenizer()
										__lowerCAmelCase											= self.get_decoder()

										__lowerCAmelCase											= WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_				,	feature_extractor=lowerCAmelCase_				,	decoder=lowerCAmelCase_						)

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




				@staticmethod
				def     _snake_case     (__lowercase				,	__lowercase						):
										__lowerCAmelCase											= [d[key] for d in offsets]
										return retrieved_list




				def     _snake_case     (self						):
										__lowerCAmelCase											= WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)
										__lowerCAmelCase											= self._get_dummy_logits()[0]

										__lowerCAmelCase											= processor.decode(lowerCAmelCase_				,	output_word_offsets=lowerCAmelCase_						)
										# 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(lowerCAmelCase_				,	lowerCAmelCase_						)						)

										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     _snake_case     (self						):
										__lowerCAmelCase											= WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)
										__lowerCAmelCase											= self._get_dummy_logits()

										__lowerCAmelCase											= processor.batch_decode(lowerCAmelCase_				,	output_word_offsets=lowerCAmelCase_						)

										# 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(lowerCAmelCase_				,	lowerCAmelCase_						)						)

										self.assertListEqual(
										    [''' '''.join(self.get_from_offsets(lowerCAmelCase_				,	'''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     _snake_case     (self						):
										import torch

										__lowerCAmelCase											= load_dataset('''common_voice'''				,	'''en'''				,	split='''train'''				,	streaming=lowerCAmelCase_						)
										__lowerCAmelCase											= ds.cast_column('''audio'''				,	datasets.Audio(sampling_rate=1_60_00						)						)
										__lowerCAmelCase											= iter(lowerCAmelCase_						)
										__lowerCAmelCase											= next(lowerCAmelCase_						)

										__lowerCAmelCase											= AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''						)
										__lowerCAmelCase											= 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
										__lowerCAmelCase											= processor(sample['''audio''']['''array''']				,	return_tensors='''pt'''						).input_values

										with torch.no_grad():
																__lowerCAmelCase											= model(lowerCAmelCase_						).logits.cpu().numpy()

										__lowerCAmelCase											= processor.decode(logits[0]				,	output_word_offsets=lowerCAmelCase_						)

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

										__lowerCAmelCase											= "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(lowerCAmelCase_				,	'''word'''						)						)				,	lowerCAmelCase_						)
										self.assertEqual(''' '''.join(self.get_from_offsets(lowerCAmelCase_				,	'''word'''						)						)				,	output.text						)

										# output times
										__lowerCAmelCase											= torch.tensor(self.get_from_offsets(lowerCAmelCase_				,	'''start_time'''						)						)
										__lowerCAmelCase											= torch.tensor(self.get_from_offsets(lowerCAmelCase_				,	'''end_time'''						)						)

										# fmt: off
										__lowerCAmelCase											= torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9]						)
										__lowerCAmelCase											= torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4]						)
										# fmt: on

										self.assertTrue(torch.allclose(lowerCAmelCase_				,	lowerCAmelCase_				,	atol=0.0_1						)						)
										self.assertTrue(torch.allclose(lowerCAmelCase_				,	lowerCAmelCase_				,	atol=0.0_1						)						)









"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin







@flax.struct.dataclass
class 			UpperCamelCase_       :
	# setable values
	__magic_name__	      =				None
	__magic_name__	      =				None
	__magic_name__	      =				None  # sigma(t_i)





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: List[str]						)						->			Optional[Any]:
				return cls()







@dataclass
class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				42
	__magic_name__	      =				42
	__magic_name__	      =				42







class 			UpperCamelCase_       (__A				,						__A     ):





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			str:
				return True





	@register_to_config
	def __init__(      self					: List[str]	,	lowerCAmelCase_					: float = 0.0_2	,	lowerCAmelCase_					: float = 100	,	lowerCAmelCase_					: float = 1.0_0_7	,	lowerCAmelCase_					: float = 80	,	lowerCAmelCase_					: float = 0.0_5	,	lowerCAmelCase_					: float = 50	,	)						->			Union[str, Any]:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			Tuple:
				return KarrasVeSchedulerState.create()





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Tuple = ()						)						->			KarrasVeSchedulerState:
				UpperCAmelCase_     : Dict			     =					jnp.arange(0	,	lowerCAmelCase_						)[::-1].copy()
				UpperCAmelCase_     : Dict			     =					[
				    (
				        self.config.sigma_max**2
				        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
				    )
				    for i in timesteps
				]

				return state.replace(
				    num_inference_steps=lowerCAmelCase_	,	schedule=jnp.array(lowerCAmelCase_	,	dtype=jnp.floataa						)	,	timesteps=lowerCAmelCase_	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: random.KeyArray	,	)						->			Tuple[jnp.ndarray, float]:
				if self.config.s_min <= sigma <= self.config.s_max:
							UpperCAmelCase_     : Any			     =					min(self.config.s_churn / state.num_inference_steps	,	2**0.5 - 1						)
				else:
							UpperCAmelCase_     : Optional[int]			     =					0

				# sample eps ~ N(0, S_noise^2 * I)
				UpperCAmelCase_     : List[Any]			     =					random.split(lowerCAmelCase_	,	num=1						)
				UpperCAmelCase_     : List[str]			     =					self.config.s_noise * random.normal(key=lowerCAmelCase_	,	shape=sample.shape						)
				UpperCAmelCase_     : Optional[Any]			     =					sigma + gamma * sigma
				UpperCAmelCase_     : Any			     =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + sigma_hat * model_output
				UpperCAmelCase_     : List[Any]			     =					(sample_hat - pred_original_sample) / sigma_hat
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + (sigma_prev - sigma_hat) * derivative

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : str			     =					sample_prev + sigma_prev * model_output
				UpperCAmelCase_     : Any			     =					(sample_prev - pred_original_sample) / sigma_prev
				UpperCAmelCase_     : int			     =					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Optional[Any]						)						->			Dict:
				raise NotImplementedError()



