
'''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 			lowerCAmelCase (				unittest.TestCase			):




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

								_SCREAMING_SNAKE_CASE				= {
								    "unk_token": "<unk>",
								    "bos_token": "<s>",
								    "eos_token": "</s>",
								}
								_SCREAMING_SNAKE_CASE				= {
								    "feature_size": 1,
								    "padding_value": 0.0,
								    "sampling_rate": 16_000,
								    "return_attention_mask": False,
								    "do_normalize": True,
								}

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

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

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




		def     lowercase       (				self					,						**UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= self.add_kwargs_tokens_map.copy()
								kwargs.update(UpperCamelCase      )
								return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname					,						**UpperCamelCase      )




		def     lowercase       (				self					,						**UpperCamelCase      ):
								return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname					,						**UpperCamelCase      )




		def     lowercase       (				self					,						**UpperCamelCase      ):
								return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name					,						**UpperCamelCase      )




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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_tokenizer()
								_SCREAMING_SNAKE_CASE				= self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE				= self.get_decoder()

								_SCREAMING_SNAKE_CASE				= WavaVecaProcessorWithLM(tokenizer=UpperCamelCase					,						feature_extractor=UpperCamelCase					,						decoder=UpperCamelCase      )

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

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

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

								# 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					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= 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
								_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_tokenizer()
								# add token to trigger raise
								tokenizer.add_tokens(["xx"]      )
								with self.assertRaisesRegex(UpperCamelCase					,						"include"      ):
														WavaVecaProcessorWithLM(
														    tokenizer=UpperCamelCase					,						feature_extractor=self.get_feature_extractor()					,						decoder=self.get_decoder()      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE				= self.get_tokenizer()
								_SCREAMING_SNAKE_CASE				= self.get_decoder()

								_SCREAMING_SNAKE_CASE				= WavaVecaProcessorWithLM(tokenizer=UpperCamelCase					,						feature_extractor=UpperCamelCase					,						decoder=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= floats_list((3, 1_000)      )

								_SCREAMING_SNAKE_CASE				= feature_extractor(UpperCamelCase					,						return_tensors="np"      )
								_SCREAMING_SNAKE_CASE				= processor(UpperCamelCase					,						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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE				= self.get_tokenizer()
								_SCREAMING_SNAKE_CASE				= self.get_decoder()

								_SCREAMING_SNAKE_CASE				= WavaVecaProcessorWithLM(tokenizer=UpperCamelCase					,						feature_extractor=UpperCamelCase					,						decoder=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= "This is a test string"

								_SCREAMING_SNAKE_CASE				= processor(text=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= tokenizer(UpperCamelCase      )

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




		def     lowercase       (				self					,						UpperCamelCase=(2, 10, 16)					,						UpperCamelCase=77      ):
								np.random.seed(UpperCamelCase      )
								return np.random.rand(*UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE				= self.get_tokenizer()
								_SCREAMING_SNAKE_CASE				= self.get_decoder()

								_SCREAMING_SNAKE_CASE				= WavaVecaProcessorWithLM(tokenizer=UpperCamelCase					,						feature_extractor=UpperCamelCase					,						decoder=UpperCamelCase      )

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

								_SCREAMING_SNAKE_CASE				= processor.decode(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= decoder.decode_beams(UpperCamelCase      )[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     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE				= self.get_tokenizer()
								_SCREAMING_SNAKE_CASE				= self.get_decoder()

								_SCREAMING_SNAKE_CASE				= WavaVecaProcessorWithLM(tokenizer=UpperCamelCase					,						feature_extractor=UpperCamelCase					,						decoder=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= 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:
														_SCREAMING_SNAKE_CASE				= processor.batch_decode(UpperCamelCase      )
								else:
														with get_context(UpperCamelCase      ).Pool() as pool:
																				_SCREAMING_SNAKE_CASE				= processor.batch_decode(UpperCamelCase					,						UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= list(UpperCamelCase      )

								with get_context("fork"      ).Pool() as p:
														_SCREAMING_SNAKE_CASE				= decoder.decode_beams_batch(UpperCamelCase					,						UpperCamelCase      )

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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE				= self.get_tokenizer()
								_SCREAMING_SNAKE_CASE				= self.get_decoder()

								_SCREAMING_SNAKE_CASE				= WavaVecaProcessorWithLM(tokenizer=UpperCamelCase					,						feature_extractor=UpperCamelCase					,						decoder=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= self._get_dummy_logits()

								_SCREAMING_SNAKE_CASE				= 15
								_SCREAMING_SNAKE_CASE				= -20.0
								_SCREAMING_SNAKE_CASE				= -4.0

								_SCREAMING_SNAKE_CASE				= processor.batch_decode(
								    UpperCamelCase					,						beam_width=UpperCamelCase					,						beam_prune_logp=UpperCamelCase					,						token_min_logp=UpperCamelCase					,						)
								_SCREAMING_SNAKE_CASE				= decoded_processor_out.text

								_SCREAMING_SNAKE_CASE				= list(UpperCamelCase      )

								with get_context("fork"      ).Pool() as pool:
														_SCREAMING_SNAKE_CASE				= decoder.decode_beams_batch(
														    UpperCamelCase					,						UpperCamelCase					,						beam_width=UpperCamelCase					,						beam_prune_logp=UpperCamelCase					,						token_min_logp=UpperCamelCase					,						)

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

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

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

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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE				= self.get_tokenizer()
								_SCREAMING_SNAKE_CASE				= self.get_decoder()

								_SCREAMING_SNAKE_CASE				= WavaVecaProcessorWithLM(tokenizer=UpperCamelCase					,						feature_extractor=UpperCamelCase					,						decoder=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= self._get_dummy_logits()

								_SCREAMING_SNAKE_CASE				= 2.0
								_SCREAMING_SNAKE_CASE				= 5.0
								_SCREAMING_SNAKE_CASE				= -20.0
								_SCREAMING_SNAKE_CASE				= True

								_SCREAMING_SNAKE_CASE				= processor.batch_decode(
								    UpperCamelCase					,						alpha=UpperCamelCase					,						beta=UpperCamelCase					,						unk_score_offset=UpperCamelCase					,						lm_score_boundary=UpperCamelCase					,						)
								_SCREAMING_SNAKE_CASE				= decoded_processor_out.text

								_SCREAMING_SNAKE_CASE				= list(UpperCamelCase      )
								decoder.reset_params(
								    alpha=UpperCamelCase					,						beta=UpperCamelCase					,						unk_score_offset=UpperCamelCase					,						lm_score_boundary=UpperCamelCase					,						)

								with get_context("fork"      ).Pool() as pool:
														_SCREAMING_SNAKE_CASE				= decoder.decode_beams_batch(
														    UpperCamelCase					,						UpperCamelCase					,						)

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

								self.assertListEqual(UpperCamelCase					,						UpperCamelCase      )
								self.assertListEqual(["<s> </s> <s> </s> </s>", "</s> </s> <s> </s> </s>"]					,						UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= 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					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"      )

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

								_SCREAMING_SNAKE_CASE				= os.listdir(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= ["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(UpperCamelCase					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= snapshot_download("hf-internal-testing/processor_with_lm"      )

								_SCREAMING_SNAKE_CASE				= WavaVecaProcessorWithLM.from_pretrained(UpperCamelCase      )

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

								_SCREAMING_SNAKE_CASE				= os.listdir(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= os.listdir(UpperCamelCase      )

								local_decoder_files.sort()
								expected_decoder_files.sort()

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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"      )
								_SCREAMING_SNAKE_CASE				= AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm"      )

								_SCREAMING_SNAKE_CASE				= floats_list((3, 1_000)      )

								_SCREAMING_SNAKE_CASE				= processor_wavaveca(UpperCamelCase					,						return_tensors="np"      )
								_SCREAMING_SNAKE_CASE				= processor_auto(UpperCamelCase					,						return_tensors="np"      )

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

								_SCREAMING_SNAKE_CASE				= self._get_dummy_logits()

								_SCREAMING_SNAKE_CASE				= processor_wavaveca.batch_decode(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= processor_auto.batch_decode(UpperCamelCase      )

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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE				= self.get_tokenizer()
								_SCREAMING_SNAKE_CASE				= self.get_decoder()

								_SCREAMING_SNAKE_CASE				= WavaVecaProcessorWithLM(tokenizer=UpperCamelCase					,						feature_extractor=UpperCamelCase					,						decoder=UpperCamelCase      )

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




		@staticmethod
		def     lowercase       (				UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= [d[key] for d in offsets]
								return retrieved_list




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"      )
								_SCREAMING_SNAKE_CASE				= self._get_dummy_logits()[0]

								_SCREAMING_SNAKE_CASE				= processor.decode(UpperCamelCase					,						output_word_offsets=UpperCamelCase      )
								# 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(UpperCamelCase					,						UpperCamelCase      )      )

								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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"      )
								_SCREAMING_SNAKE_CASE				= self._get_dummy_logits()

								_SCREAMING_SNAKE_CASE				= processor.batch_decode(UpperCamelCase					,						output_word_offsets=UpperCamelCase      )

								# 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(UpperCamelCase					,						UpperCamelCase      )      )

								self.assertListEqual(
								    [" ".join(self.get_from_offsets(UpperCamelCase					,						"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     lowercase       (				self      ):
								import torch

								_SCREAMING_SNAKE_CASE				= load_dataset("common_voice"					,						"en"					,						split="train"					,						streaming=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= ds.cast_column("audio"					,						datasets.Audio(sampling_rate=16_000      )      )
								_SCREAMING_SNAKE_CASE				= iter(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= next(UpperCamelCase      )

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

								with torch.no_grad():
														_SCREAMING_SNAKE_CASE				= model(UpperCamelCase      ).logits.cpu().numpy()

								_SCREAMING_SNAKE_CASE				= processor.decode(logits[0]					,						output_word_offsets=UpperCamelCase      )

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

								_SCREAMING_SNAKE_CASE				= "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(UpperCamelCase					,						"word"      )      )					,						UpperCamelCase      )
								self.assertEqual(" ".join(self.get_from_offsets(UpperCamelCase					,						"word"      )      )					,						output.text      )

								# output times
								_SCREAMING_SNAKE_CASE				= torch.tensor(self.get_from_offsets(UpperCamelCase					,						"start_time"      )      )
								_SCREAMING_SNAKE_CASE				= torch.tensor(self.get_from_offsets(UpperCamelCase					,						"end_time"      )      )

								# fmt: off
								_SCREAMING_SNAKE_CASE				= torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99]      )
								_SCREAMING_SNAKE_CASE				= torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94]      )
								# fmt: on

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

'''simple docstring'''







import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


_snake_case				:   Optional[int]     =				(
    """https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"""
)


_snake_case				:   Dict     =				logging.get_logger(__name__)  # pylint: disable=invalid-name



def 			_a		(	):
						_SCREAMING_SNAKE_CASE				= "https://pypi.org/pypi/diffusers/json"
						_SCREAMING_SNAKE_CASE				= json.loads(request.urlopen(_SCREAMING_SNAKE_CASE  ).read()  )["releases"].keys()
						return sorted(_SCREAMING_SNAKE_CASE	,			key=lambda _SCREAMING_SNAKE_CASE  : version.Version(_SCREAMING_SNAKE_CASE  )  )



def 			_a		(	):
						# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
						if HF_MODULES_CACHE in sys.path:
												return

						sys.path.append(_SCREAMING_SNAKE_CASE  )
						os.makedirs(_SCREAMING_SNAKE_CASE	,			exist_ok=_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= Path(_SCREAMING_SNAKE_CASE  ) / "__init__.py"
						if not init_path.exists():
												init_path.touch()



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, os.PathLike]  ):
						init_hf_modules()
						_SCREAMING_SNAKE_CASE				= Path(_SCREAMING_SNAKE_CASE  ) / name
						# If the parent module does not exist yet, recursively create it.
						if not dynamic_module_path.parent.exists():
												create_dynamic_module(dynamic_module_path.parent  )
						os.makedirs(_SCREAMING_SNAKE_CASE	,			exist_ok=_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= dynamic_module_path / "__init__.py"
						if not init_path.exists():
												init_path.touch()



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]  ):
						with open(_SCREAMING_SNAKE_CASE	,			"r"	,			encoding="utf-8"  ) as f:
												_SCREAMING_SNAKE_CASE				= f.read()

						# Imports of the form `import .xxx`
						_SCREAMING_SNAKE_CASE				= re.findall("^\s*import\s+\.(\S+)\s*$"	,			_SCREAMING_SNAKE_CASE	,			flags=re.MULTILINE  )
						# Imports of the form `from .xxx import yyy`
						relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import"	,			_SCREAMING_SNAKE_CASE	,			flags=re.MULTILINE  )
						# Unique-ify
						return list(set(_SCREAMING_SNAKE_CASE  )  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]  ):
						_SCREAMING_SNAKE_CASE				= False
						_SCREAMING_SNAKE_CASE				= [module_file]
						_SCREAMING_SNAKE_CASE				= []

						# Let's recurse through all relative imports
						while not no_change:
												_SCREAMING_SNAKE_CASE				= []
												for f in files_to_check:
																		new_imports.extend(get_relative_imports(_SCREAMING_SNAKE_CASE  )  )

												_SCREAMING_SNAKE_CASE				= Path(_SCREAMING_SNAKE_CASE  ).parent
												_SCREAMING_SNAKE_CASE				= [str(module_path / m  ) for m in new_imports]
												_SCREAMING_SNAKE_CASE				= [f for f in new_import_files if f not in all_relative_imports]
												_SCREAMING_SNAKE_CASE				= [F'{f}.py' for f in new_import_files]

												_SCREAMING_SNAKE_CASE				= len(_SCREAMING_SNAKE_CASE  ) == 0
												all_relative_imports.extend(_SCREAMING_SNAKE_CASE  )

						return all_relative_imports



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str  ):
						with open(_SCREAMING_SNAKE_CASE	,			"r"	,			encoding="utf-8"  ) as f:
												_SCREAMING_SNAKE_CASE				= f.read()

						# Imports of the form `import xxx`
						_SCREAMING_SNAKE_CASE				= re.findall("^\s*import\s+(\S+)\s*$"	,			_SCREAMING_SNAKE_CASE	,			flags=re.MULTILINE  )
						# Imports of the form `from xxx import yyy`
						imports += re.findall("^\s*from\s+(\S+)\s+import"	,			_SCREAMING_SNAKE_CASE	,			flags=re.MULTILINE  )
						# Only keep the top-level module
						_SCREAMING_SNAKE_CASE				= [imp.split("."  )[0] for imp in imports if not imp.startswith("."  )]

						# Unique-ify and test we got them all
						_SCREAMING_SNAKE_CASE				= list(set(_SCREAMING_SNAKE_CASE  )  )
						_SCREAMING_SNAKE_CASE				= []
						for imp in imports:
												try:
																		importlib.import_module(_SCREAMING_SNAKE_CASE  )
												except ImportError:
																		missing_packages.append(_SCREAMING_SNAKE_CASE  )

						if len(_SCREAMING_SNAKE_CASE  ) > 0:
												raise ImportError(
												    "This modeling file requires the following packages that were not found in your environment: "
												    F'{", ".join(_SCREAMING_SNAKE_CASE  )}. Run `pip install {" ".join(_SCREAMING_SNAKE_CASE  )}`'  )

						return get_relative_imports(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   int  ):
						_SCREAMING_SNAKE_CASE				= module_path.replace(os.path.sep	,			"."  )
						_SCREAMING_SNAKE_CASE				= importlib.import_module(_SCREAMING_SNAKE_CASE  )

						if class_name is None:
												return find_pipeline_class(_SCREAMING_SNAKE_CASE  )
						return getattr(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						from ..pipelines import DiffusionPipeline

						_SCREAMING_SNAKE_CASE				= dict(inspect.getmembers(_SCREAMING_SNAKE_CASE	,			inspect.isclass  )  )

						_SCREAMING_SNAKE_CASE				= None
						for cls_name, cls in cls_members.items():
												if (
												    cls_name != DiffusionPipeline.__name__
												    and issubclass(cls	,			_SCREAMING_SNAKE_CASE  )
												    and cls.__module__.split("."  )[0] != "diffusers"
												):
																		if pipeline_class is not None:
																								raise ValueError(
																								    F'Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'
																								    F' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'
																								    F' {loaded_module}.'  )
																		_SCREAMING_SNAKE_CASE				= cls

						return pipeline_class



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, os.PathLike]	,			_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   Optional[Union[str, os.PathLike]] = None	,			_SCREAMING_SNAKE_CASE		:   bool = False	,			_SCREAMING_SNAKE_CASE		:   bool = False	,			_SCREAMING_SNAKE_CASE		:   Optional[Dict[str, str]] = None	,			_SCREAMING_SNAKE_CASE		:   Optional[Union[bool, str]] = None	,			_SCREAMING_SNAKE_CASE		:   Optional[str] = None	,			_SCREAMING_SNAKE_CASE		:   bool = False	,			):
						_SCREAMING_SNAKE_CASE				= str(_SCREAMING_SNAKE_CASE  )

						_SCREAMING_SNAKE_CASE				= os.path.join(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

						if os.path.isfile(_SCREAMING_SNAKE_CASE  ):
												_SCREAMING_SNAKE_CASE				= module_file_or_url
												_SCREAMING_SNAKE_CASE				= "local"
						elif pretrained_model_name_or_path.count("/"  ) == 0:
												_SCREAMING_SNAKE_CASE				= get_diffusers_versions()
												# cut ".dev0"
												_SCREAMING_SNAKE_CASE				= "v" + ".".join(__version__.split("."  )[:3]  )

												# retrieve github version that matches
												if revision is None:
																		_SCREAMING_SNAKE_CASE				= latest_version if latest_version[1:] in available_versions else "main"
																		logger.info(F'Defaulting to latest_version: {revision}.'  )
												elif revision in available_versions:
																		_SCREAMING_SNAKE_CASE				= F'v{revision}'
												elif revision == "main":
																		_SCREAMING_SNAKE_CASE				= revision
												else:
																		raise ValueError(
																		    F'`custom_revision`: {revision} does not exist. Please make sure to choose one of'
																		    F' {", ".join(available_versions + ["main"]  )}.'  )

												# community pipeline on GitHub
												_SCREAMING_SNAKE_CASE				= COMMUNITY_PIPELINES_URL.format(revision=_SCREAMING_SNAKE_CASE	,			pipeline=_SCREAMING_SNAKE_CASE  )
												try:
																		_SCREAMING_SNAKE_CASE				= cached_download(
																		    _SCREAMING_SNAKE_CASE	,			cache_dir=_SCREAMING_SNAKE_CASE	,			force_download=_SCREAMING_SNAKE_CASE	,			proxies=_SCREAMING_SNAKE_CASE	,			resume_download=_SCREAMING_SNAKE_CASE	,			local_files_only=_SCREAMING_SNAKE_CASE	,			use_auth_token=_SCREAMING_SNAKE_CASE	,			)
																		_SCREAMING_SNAKE_CASE				= "git"
																		_SCREAMING_SNAKE_CASE				= pretrained_model_name_or_path + ".py"
												except EnvironmentError:
																		logger.error(F'Could not locate the {module_file} inside {pretrained_model_name_or_path}.'  )
																		raise
						else:
												try:
																		# Load from URL or cache if already cached
																		_SCREAMING_SNAKE_CASE				= hf_hub_download(
																		    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			cache_dir=_SCREAMING_SNAKE_CASE	,			force_download=_SCREAMING_SNAKE_CASE	,			proxies=_SCREAMING_SNAKE_CASE	,			resume_download=_SCREAMING_SNAKE_CASE	,			local_files_only=_SCREAMING_SNAKE_CASE	,			use_auth_token=_SCREAMING_SNAKE_CASE	,			)
																		_SCREAMING_SNAKE_CASE				= os.path.join("local"	,			"--".join(pretrained_model_name_or_path.split("/"  )  )  )
												except EnvironmentError:
																		logger.error(F'Could not locate the {module_file} inside {pretrained_model_name_or_path}.'  )
																		raise

    # Check we have all the requirements in our environment
						_SCREAMING_SNAKE_CASE				= check_imports(_SCREAMING_SNAKE_CASE  )

						# Now we move the module inside our cached dynamic modules.
						_SCREAMING_SNAKE_CASE				= DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
						create_dynamic_module(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= Path(_SCREAMING_SNAKE_CASE  ) / full_submodule
						if submodule == "local" or submodule == "git":
												# We always copy local files (we could hash the file to see if there was a change, and give them the name of
												# that hash, to only copy when there is a modification but it seems overkill for now).
												# The only reason we do the copy is to avoid putting too many folders in sys.path.
												shutil.copy(_SCREAMING_SNAKE_CASE	,			submodule_path / module_file  )
												for module_needed in modules_needed:
																		_SCREAMING_SNAKE_CASE				= F'{module_needed}.py'
																		shutil.copy(os.path.join(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )	,			submodule_path / module_needed  )
						else:
												# Get the commit hash
												# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
												if isinstance(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ):
																		_SCREAMING_SNAKE_CASE				= use_auth_token
												elif use_auth_token is True:
																		_SCREAMING_SNAKE_CASE				= HfFolder.get_token()
												else:
																		_SCREAMING_SNAKE_CASE				= None

												_SCREAMING_SNAKE_CASE				= model_info(_SCREAMING_SNAKE_CASE	,			revision=_SCREAMING_SNAKE_CASE	,			token=_SCREAMING_SNAKE_CASE  ).sha

												# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
												# benefit of versioning.
												_SCREAMING_SNAKE_CASE				= submodule_path / commit_hash
												_SCREAMING_SNAKE_CASE				= full_submodule + os.path.sep + commit_hash
												create_dynamic_module(_SCREAMING_SNAKE_CASE  )

												if not (submodule_path / module_file).exists():
																		shutil.copy(_SCREAMING_SNAKE_CASE	,			submodule_path / module_file  )
												# Make sure we also have every file with relative
												for module_needed in modules_needed:
																		if not (submodule_path / module_needed).exists():
																								get_cached_module_file(
																								    _SCREAMING_SNAKE_CASE	,			F'{module_needed}.py'	,			cache_dir=_SCREAMING_SNAKE_CASE	,			force_download=_SCREAMING_SNAKE_CASE	,			resume_download=_SCREAMING_SNAKE_CASE	,			proxies=_SCREAMING_SNAKE_CASE	,			use_auth_token=_SCREAMING_SNAKE_CASE	,			revision=_SCREAMING_SNAKE_CASE	,			local_files_only=_SCREAMING_SNAKE_CASE	,			)
						return os.path.join(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )




def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, os.PathLike]	,			_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   Optional[str] = None	,			_SCREAMING_SNAKE_CASE		:   Optional[Union[str, os.PathLike]] = None	,			_SCREAMING_SNAKE_CASE		:   bool = False	,			_SCREAMING_SNAKE_CASE		:   bool = False	,			_SCREAMING_SNAKE_CASE		:   Optional[Dict[str, str]] = None	,			_SCREAMING_SNAKE_CASE		:   Optional[Union[bool, str]] = None	,			_SCREAMING_SNAKE_CASE		:   Optional[str] = None	,			_SCREAMING_SNAKE_CASE		:   bool = False	,			**_SCREAMING_SNAKE_CASE		:   Tuple	,			):
						_SCREAMING_SNAKE_CASE				= get_cached_module_file(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			cache_dir=_SCREAMING_SNAKE_CASE	,			force_download=_SCREAMING_SNAKE_CASE	,			resume_download=_SCREAMING_SNAKE_CASE	,			proxies=_SCREAMING_SNAKE_CASE	,			use_auth_token=_SCREAMING_SNAKE_CASE	,			revision=_SCREAMING_SNAKE_CASE	,			local_files_only=_SCREAMING_SNAKE_CASE	,			)
						return get_class_in_module(_SCREAMING_SNAKE_CASE	,			final_module.replace(".py"	,			""  )  )