



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   UpperCAmelCase_    (       unittest.TestCase):







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    "| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
														_lowerCAmelCase     :							Dict    =    dict(zip(__a,			range(len(__a))))

														_lowerCAmelCase     :							Tuple    =    {
														    "unk_token": "<unk>",
														    "bos_token": "<s>",
														    "eos_token": "</s>",
														}
														_lowerCAmelCase     :							int    =    {
														    "feature_size": 1,
														    "padding_value": 0.0,
														    "sampling_rate": 1_6000,
														    "return_attention_mask": False,
														    "do_normalize": True,
														}

														_lowerCAmelCase     :							int    =    tempfile.mkdtemp()
														_lowerCAmelCase     :							Any    =    os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES["vocab_file"])
														_lowerCAmelCase     :							List[str]    =    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
														_lowerCAmelCase     :							List[Any]    =    "hf-internal-testing/ngram-beam-search-decoder"







							def    snake_case__      (   self,			**__a):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    self.add_kwargs_tokens_map.copy()
														kwargs.update(__a)
														return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,			**__a)







							def    snake_case__      (   self,			**__a):

														'''simple docstring'''

														return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname,			**__a)







							def    snake_case__      (   self,			**__a):

														'''simple docstring'''

														return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name,			**__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														shutil.rmtree(self.tmpdirname)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    self.get_tokenizer()
														_lowerCAmelCase     :							Optional[int]    =    self.get_feature_extractor()
														_lowerCAmelCase     :							List[str]    =    self.get_decoder()

														_lowerCAmelCase     :							Optional[Any]    =    WavaVecaProcessorWithLM(tokenizer=__a,			feature_extractor=__a,			decoder=__a)

														processor.save_pretrained(self.tmpdirname)
														_lowerCAmelCase     :							Any    =    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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    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     :							str    =    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):

														'''simple docstring'''

														_lowerCAmelCase     :							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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    self.get_feature_extractor()
														_lowerCAmelCase     :							int    =    self.get_tokenizer()
														_lowerCAmelCase     :							Dict    =    self.get_decoder()

														_lowerCAmelCase     :							List[Any]    =    WavaVecaProcessorWithLM(tokenizer=__a,			feature_extractor=__a,			decoder=__a)

														_lowerCAmelCase     :							Dict    =    floats_list((3, 1000))

														_lowerCAmelCase     :							Optional[Any]    =    feature_extractor(__a,			return_tensors="np")
														_lowerCAmelCase     :							str    =    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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    self.get_feature_extractor()
														_lowerCAmelCase     :							Union[str, Any]    =    self.get_tokenizer()
														_lowerCAmelCase     :							List[str]    =    self.get_decoder()

														_lowerCAmelCase     :							Tuple    =    WavaVecaProcessorWithLM(tokenizer=__a,			feature_extractor=__a,			decoder=__a)

														_lowerCAmelCase     :							List[str]    =    "This is a test string"

														_lowerCAmelCase     :							List[Any]    =    processor(text=__a)

														_lowerCAmelCase     :							Union[str, Any]    =    tokenizer(__a)

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







							def    snake_case__      (   self,			__a=(2, 10, 16),			__a=77):

														'''simple docstring'''

														np.random.seed(__a)
														return np.random.rand(*__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    self.get_feature_extractor()
														_lowerCAmelCase     :							Union[str, Any]    =    self.get_tokenizer()
														_lowerCAmelCase     :							Dict    =    self.get_decoder()

														_lowerCAmelCase     :							List[Any]    =    WavaVecaProcessorWithLM(tokenizer=__a,			feature_extractor=__a,			decoder=__a)

														_lowerCAmelCase     :							Union[str, Any]    =    self._get_dummy_logits(shape=(10, 16),			seed=13)

														_lowerCAmelCase     :							int    =    processor.decode(__a)

														_lowerCAmelCase     :							List[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    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    self.get_feature_extractor()
														_lowerCAmelCase     :							Any    =    self.get_tokenizer()
														_lowerCAmelCase     :							List[str]    =    self.get_decoder()

														_lowerCAmelCase     :							int    =    WavaVecaProcessorWithLM(tokenizer=__a,			feature_extractor=__a,			decoder=__a)

														_lowerCAmelCase     :							Optional[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:
																					_lowerCAmelCase     :							List[Any]    =    processor.batch_decode(__a)
														else:
																					with get_context(__a).Pool() as pool:
																												_lowerCAmelCase     :							int    =    processor.batch_decode(__a,			__a)

														_lowerCAmelCase     :							List[Any]    =    list(__a)

														with get_context("fork").Pool() as p:
																					_lowerCAmelCase     :							Union[str, Any]    =    decoder.decode_beams_batch(__a,			__a)

														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							List[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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    self.get_feature_extractor()
														_lowerCAmelCase     :							Dict    =    self.get_tokenizer()
														_lowerCAmelCase     :							Union[str, Any]    =    self.get_decoder()

														_lowerCAmelCase     :							Any    =    WavaVecaProcessorWithLM(tokenizer=__a,			feature_extractor=__a,			decoder=__a)

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

														_lowerCAmelCase     :							Any    =    15
														_lowerCAmelCase     :							Optional[int]    =    -20.0
														_lowerCAmelCase     :							List[Any]    =    -4.0

														_lowerCAmelCase     :							Any    =    processor.batch_decode(
														    __a,			beam_width=__a,			beam_prune_logp=__a,			token_min_logp=__a,			)
														_lowerCAmelCase     :							Any    =    decoded_processor_out.text

														_lowerCAmelCase     :							str    =    list(__a)

														with get_context("fork").Pool() as pool:
																					_lowerCAmelCase     :							int    =    decoder.decode_beams_batch(
																					    __a,			__a,			beam_width=__a,			beam_prune_logp=__a,			token_min_logp=__a,			)

														_lowerCAmelCase     :							str    =    [d[0][0] for d in decoded_decoder_out]
														_lowerCAmelCase     :							int    =    [d[0][2] for d in decoded_decoder_out]
														_lowerCAmelCase     :							Dict    =    [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, -13.9_474],			__a,			atol=1E-3))







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    self.get_feature_extractor()
														_lowerCAmelCase     :							Dict    =    self.get_tokenizer()
														_lowerCAmelCase     :							List[str]    =    self.get_decoder()

														_lowerCAmelCase     :							Optional[Any]    =    WavaVecaProcessorWithLM(tokenizer=__a,			feature_extractor=__a,			decoder=__a)

														_lowerCAmelCase     :							Any    =    self._get_dummy_logits()

														_lowerCAmelCase     :							Union[str, Any]    =    2.0
														_lowerCAmelCase     :							str    =    5.0
														_lowerCAmelCase     :							List[Any]    =    -20.0
														_lowerCAmelCase     :							List[str]    =    True

														_lowerCAmelCase     :							Optional[int]    =    processor.batch_decode(
														    __a,			alpha=__a,			beta=__a,			unk_score_offset=__a,			lm_score_boundary=__a,			)
														_lowerCAmelCase     :							Optional[Any]    =    decoded_processor_out.text

														_lowerCAmelCase     :							List[str]    =    list(__a)
														decoder.reset_params(
														    alpha=__a,			beta=__a,			unk_score_offset=__a,			lm_score_boundary=__a,			)

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

														_lowerCAmelCase     :							Union[str, Any]    =    [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)
														_lowerCAmelCase     :							Tuple    =    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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm")

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

														_lowerCAmelCase     :							Any    =    os.listdir(__a)
														_lowerCAmelCase     :							str    =    ["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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    snapshot_download("hf-internal-testing/processor_with_lm")

														_lowerCAmelCase     :							Union[str, Any]    =    WavaVecaProcessorWithLM.from_pretrained(__a)

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

														_lowerCAmelCase     :							int    =    os.listdir(__a)
														_lowerCAmelCase     :							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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm")
														_lowerCAmelCase     :							Optional[Any]    =    AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm")

														_lowerCAmelCase     :							str    =    floats_list((3, 1000))

														_lowerCAmelCase     :							Optional[Any]    =    processor_wavaveca(__a,			return_tensors="np")
														_lowerCAmelCase     :							Tuple    =    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)

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

														_lowerCAmelCase     :							Dict    =    processor_wavaveca.batch_decode(__a)
														_lowerCAmelCase     :							Optional[Any]    =    processor_auto.batch_decode(__a)

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    self.get_feature_extractor()
														_lowerCAmelCase     :							Tuple    =    self.get_tokenizer()
														_lowerCAmelCase     :							Tuple    =    self.get_decoder()

														_lowerCAmelCase     :							Tuple    =    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    snake_case__      (   __a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    [d[key] for d in offsets]
														return retrieved_list







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm")
														_lowerCAmelCase     :							List[str]    =    self._get_dummy_logits()[0]

														_lowerCAmelCase     :							Optional[int]    =    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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm")
														_lowerCAmelCase     :							List[str]    =    self._get_dummy_logits()

														_lowerCAmelCase     :							Optional[Any]    =    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    snake_case__      (   self):

														'''simple docstring'''

														import torch

														_lowerCAmelCase     :							Any    =    load_dataset("common_voice",			"en",			split="train",			streaming=__a)
														_lowerCAmelCase     :							Any    =    ds.cast_column("audio",			datasets.Audio(sampling_rate=1_6000))
														_lowerCAmelCase     :							Dict    =    iter(__a)
														_lowerCAmelCase     :							Optional[int]    =    next(__a)

														_lowerCAmelCase     :							Optional[Any]    =    AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm")
														_lowerCAmelCase     :							Union[str, Any]    =    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     :							List[Any]    =    processor(sample["audio"]["array"],			return_tensors="pt").input_values

														with torch.no_grad():
																					_lowerCAmelCase     :							List[str]    =    model(__a).logits.cpu().numpy()

														_lowerCAmelCase     :							Dict    =    processor.decode(logits[0],			output_word_offsets=__a)

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

														_lowerCAmelCase     :							str    =    "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
														_lowerCAmelCase     :							List[str]    =    torch.tensor(self.get_from_offsets(__a,			"start_time"))
														_lowerCAmelCase     :							str    =    torch.tensor(self.get_from_offsets(__a,			"end_time"))

														# fmt: off
														_lowerCAmelCase     :							int    =    torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599])
														_lowerCAmelCase     :							List[Any]    =    torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94])
														# fmt: on

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





import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil



class   UpperCAmelCase_    :







							def __init__(   self,			__a = "cpu",			__a = "openai/clip-vit-large-patch14"):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    device
														_lowerCAmelCase     :							Optional[int]    =    CLIPTokenizerFast.from_pretrained(__a)
														_lowerCAmelCase     :							Any    =    [0.48_145_466, 0.4_578_275, 0.40_821_073]
														_lowerCAmelCase     :							Union[str, Any]    =    [0.26_862_954, 0.26_130_258, 0.27_577_711]
														_lowerCAmelCase     :							Tuple    =    torchvision.transforms.Normalize(self.image_mean,			self.image_std)
														_lowerCAmelCase     :							Optional[int]    =    torchvision.transforms.Resize(224)
														_lowerCAmelCase     :							Dict    =    torchvision.transforms.CenterCrop(224)







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    self.resize(__a)
														_lowerCAmelCase     :							List[str]    =    self.center_crop(__a)
														_lowerCAmelCase     :							Optional[Any]    =    self.normalize(__a)
														return images







							def __call__(   self,			__a=None,			__a=None,			**__a):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    self.tokenizer(text=__a,			**__a)
														_lowerCAmelCase     :							List[str]    =    self.preprocess_img(__a)
														_lowerCAmelCase     :							Tuple    =    {key: value.to(self.device) for (key, value) in encoding.items()}
														return encoding



class   UpperCAmelCase_    (       nn.Module):







							def __init__(   self,			__a=10,			__a=0.01,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=False,			__a=True,			__a="image",			__a=True,			__a=False,			__a=False,			__a=False,			):

														'''simple docstring'''

														super().__init__()
														_lowerCAmelCase     :							List[str]    =    None
														_lowerCAmelCase     :							List[str]    =    device if device else get_device()
														if vqgan:
																					_lowerCAmelCase     :							Union[str, Any]    =    vqgan
														else:
																					_lowerCAmelCase     :							Optional[Any]    =    load_vqgan(self.device,			conf_path=__a,			ckpt_path=__a)
														self.vqgan.eval()
														if clip:
																					_lowerCAmelCase     :							str    =    clip
														else:
																					_lowerCAmelCase     :							int    =    CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
														self.clip.to(self.device)
														_lowerCAmelCase     :							Optional[int]    =    ProcessorGradientFlow(device=self.device)

														_lowerCAmelCase     :							Any    =    iterations
														_lowerCAmelCase     :							List[Any]    =    lr
														_lowerCAmelCase     :							Tuple    =    log
														_lowerCAmelCase     :							List[str]    =    make_grid
														_lowerCAmelCase     :							int    =    return_val
														_lowerCAmelCase     :							Dict    =    quantize
														_lowerCAmelCase     :							Any    =    self.vqgan.decoder.z_shape







							def    snake_case__      (   self,			__a=None,			__a=None,			__a=5,			__a=True):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    []
														if output_path is None:
																					_lowerCAmelCase     :							List[Any]    =    "./animation.gif"
														if input_path is None:
																					_lowerCAmelCase     :							str    =    self.save_path
														_lowerCAmelCase     :							str    =    sorted(glob(input_path + "/*"))
														if not len(__a):
																					raise ValueError(
																					    "No images found in save path, aborting (did you pass save_intermediate=True to the generate"
																					    " function?)")
														if len(__a) == 1:
																					print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)")
														_lowerCAmelCase     :							Optional[int]    =    total_duration / len(__a)
														_lowerCAmelCase     :							Union[str, Any]    =    [frame_duration] * len(__a)
														if extend_frames:
																					_lowerCAmelCase     :							Any    =    1.5
																					_lowerCAmelCase     :							List[str]    =    3
														for file_name in paths:
																					if file_name.endswith(".png"):
																												images.append(imageio.imread(__a))
														imageio.mimsave(__a,			__a,			duration=__a)
														print(f"gif saved to {output_path}")







							def    snake_case__      (   self,			__a=None,			__a=None):

														'''simple docstring'''

														if not (path or img):
																					raise ValueError("Input either path or tensor")
														if img is not None:
																					raise NotImplementedError
														_lowerCAmelCase     :							Dict    =    preprocess(Image.open(__a),			target_image_size=256).to(self.device)
														_lowerCAmelCase     :							Dict    =    preprocess_vqgan(__a)
														_lowerCAmelCase   ,    *_lowerCAmelCase     :							str    =    self.vqgan.encode(__a)
														return z







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    self.latent.detach().requires_grad_()
														_lowerCAmelCase     :							Dict    =    base_latent + transform_vector
														if self.quantize:
																					_lowerCAmelCase   ,    *_lowerCAmelCase     :							List[Any]    =    self.vqgan.quantize(__a)
														else:
																					_lowerCAmelCase     :							Any    =    trans_latent
														return self.vqgan.decode(__a)







							def    snake_case__      (   self,			__a,			__a,			__a=None):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    self.clip_preprocessor(text=__a,			images=__a,			return_tensors="pt",			padding=__a)
														_lowerCAmelCase     :							Optional[int]    =    self.clip(**__a)
														_lowerCAmelCase     :							Any    =    clip_outputs.logits_per_image
														if weights is not None:
																					_lowerCAmelCase     :							Tuple    =    similarity_logits * weights
														return similarity_logits.sum()







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    self._get_clip_similarity(pos_prompts["prompts"],			__a,			weights=(1 / pos_prompts["weights"]))
														if neg_prompts:
																					_lowerCAmelCase     :							List[Any]    =    self._get_clip_similarity(neg_prompts["prompts"],			__a,			weights=neg_prompts["weights"])
														else:
																					_lowerCAmelCase     :							Union[str, Any]    =    torch.tensor([1],			device=self.device)
														_lowerCAmelCase     :							List[str]    =    -torch.log(__a) + torch.log(__a)
														return loss







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    torch.randn_like(self.latent,			requires_grad=__a,			device=self.device)
														_lowerCAmelCase     :							Optional[int]    =    torch.optim.Adam([vector],			lr=self.lr)

														for i in range(self.iterations):
																					optim.zero_grad()
																					_lowerCAmelCase     :							Any    =    self._add_vector(__a)
																					_lowerCAmelCase     :							Optional[Any]    =    loop_post_process(__a)
																					_lowerCAmelCase     :							Optional[Any]    =    self._get_CLIP_loss(__a,			__a,			__a)
																					print("CLIP loss",			__a)
																					if self.log:
																												wandb.log({"CLIP Loss": clip_loss})
																					clip_loss.backward(retain_graph=__a)
																					optim.step()
																					if self.return_val == "image":
																												yield custom_to_pil(transformed_img[0])
																					else:
																												yield vector







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														wandb.init(reinit=__a,			project="face-editor")
														wandb.config.update({"Positive Prompts": positive_prompts})
														wandb.config.update({"Negative Prompts": negative_prompts})
														wandb.config.update({"lr": self.lr, "iterations": self.iterations})
														if image_path:
																					_lowerCAmelCase     :							str    =    Image.open(__a)
																					_lowerCAmelCase     :							int    =    image.resize((256, 256))
																					wandb.log("Original Image",			wandb.Image(__a))







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														if not prompts:
																					return []
														_lowerCAmelCase     :							int    =    []
														_lowerCAmelCase     :							List[str]    =    []
														if isinstance(__a,			__a):
																					_lowerCAmelCase     :							Union[str, Any]    =    [prompt.strip() for prompt in prompts.split("|")]
														for prompt in prompts:
																					if isinstance(__a,			(tuple, list)):
																												_lowerCAmelCase     :							Optional[Any]    =    prompt[0]
																												_lowerCAmelCase     :							Union[str, Any]    =    float(prompt[1])
																					elif ":" in prompt:
																												_lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    prompt.split(":")
																												_lowerCAmelCase     :							Optional[Any]    =    float(__a)
																					else:
																												_lowerCAmelCase     :							Optional[int]    =    prompt
																												_lowerCAmelCase     :							List[Any]    =    1.0
																					processed_prompts.append(__a)
																					weights.append(__a)
														return {
														    "prompts": processed_prompts,
														    "weights": torch.tensor(__a,			device=self.device),
														}







							def    snake_case__      (   self,			__a,			__a=None,			__a=None,			__a=True,			__a=False,			__a=True,			__a=True,			__a=None,			):

														'''simple docstring'''

														if image_path:
																					_lowerCAmelCase     :							List[Any]    =    self._get_latent(__a)
														else:
																					_lowerCAmelCase     :							Any    =    torch.randn(self.latent_dim,			device=self.device)
														if self.log:
																					self._init_logging(__a,			__a,			__a)

														assert pos_prompts, "You must provide at least one positive prompt."
														_lowerCAmelCase     :							int    =    self.process_prompts(__a)
														_lowerCAmelCase     :							List[str]    =    self.process_prompts(__a)
														if save_final and save_path is None:
																					_lowerCAmelCase     :							int    =    os.path.join("./outputs/",			"_".join(pos_prompts["prompts"]))
														if not os.path.exists(__a):
																					os.makedirs(__a)
														else:
																					_lowerCAmelCase     :							Tuple    =    save_path + "_" + get_timestamp()
																					os.makedirs(__a)
														_lowerCAmelCase     :							Tuple    =    save_path

														_lowerCAmelCase     :							List[Any]    =    self.vqgan.decode(self.latent)[0]
														if show_intermediate:
																					print("Original Image")
																					show_pil(custom_to_pil(__a))

														_lowerCAmelCase     :							int    =    loop_post_process(__a)
														for iter, transformed_img in enumerate(self._optimize_CLIP(__a,			__a,			__a)):
																					if show_intermediate:
																												show_pil(__a)
																					if save_intermediate:
																												transformed_img.save(os.path.join(self.save_path,			f"iter_{iter:03d}.png"))
																					if self.log:
																												wandb.log({"Image": wandb.Image(__a)})
														if show_final:
																					show_pil(__a)
														if save_final:
																					transformed_img.save(os.path.join(self.save_path,			f"iter_{iter:03d}_final.png"))
