import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


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


if is_torch_available():
     from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

snake_case__				= 25_6047
snake_case__				= 25_6145



@require_sentencepiece
@require_tokenizers
class  UpperCamelCase				( lowercase_   ,  unittest.TestCase ):

     '''simple docstring'''

     A_										=  NllbTokenizer
     A_										=  NllbTokenizerFast
     A_										=  True
     A_										=  True
     A_										=  {}



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           super().setUp()

           # We have a SentencePiece fixture for testing
           _lowerCamelCase			    =				NllbTokenizer(lowerCamelCase_				,				keep_accents=lowerCamelCase_						)
           tokenizer.save_pretrained(self.tmpdirname						)



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				NllbTokenizer(lowerCamelCase_				,				keep_accents=lowerCamelCase_						)

           _lowerCamelCase			    =				tokenizer.tokenize('''This is a test'''						)
           self.assertListEqual(lowerCamelCase_				,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']						)

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

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

           _lowerCamelCase			    =				tokenizer.convert_ids_to_tokens(lowerCamelCase_						)
           self.assertListEqual(
               lowerCamelCase_				,				[
                   SPIECE_UNDERLINE + '''I''',
                   SPIECE_UNDERLINE + '''was''',
                   SPIECE_UNDERLINE + '''b''',
                   '''or''',
                   '''n''',
                   SPIECE_UNDERLINE + '''in''',
                   SPIECE_UNDERLINE + '''''',
                   '''<unk>''',
                   '''2''',
                   '''0''',
                   '''0''',
                   '''0''',
                   ''',''',
                   SPIECE_UNDERLINE + '''and''',
                   SPIECE_UNDERLINE + '''this''',
                   SPIECE_UNDERLINE + '''is''',
                   SPIECE_UNDERLINE + '''f''',
                   '''al''',
                   '''s''',
                   '''<unk>''',
                   '''.''',
               ]				,				)



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				(self.rust_tokenizer_class, """hf-internal-testing/tiny-random-nllb""", {})
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'						):
                       _lowerCamelCase			    =				self.rust_tokenizer_class.from_pretrained(lowerCamelCase_				,				**lowerCamelCase_						)
                       _lowerCamelCase			    =				self.tokenizer_class.from_pretrained(lowerCamelCase_				,				**lowerCamelCase_						)

                       _lowerCamelCase			    =				tempfile.mkdtemp()

                       _lowerCamelCase			    =				tokenizer_r.save_pretrained(lowerCamelCase_						)
                       _lowerCamelCase			    =				tokenizer_p.save_pretrained(lowerCamelCase_						)

                       # Checks it save with the same files + the tokenizer.json file for the fast one
                       self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files						)						)
                       _lowerCamelCase			    =				tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f						)
                       self.assertSequenceEqual(lowerCamelCase_				,				lowerCamelCase_						)

                       # Checks everything loads correctly in the same way
                       _lowerCamelCase			    =				tokenizer_r.from_pretrained(lowerCamelCase_						)
                       _lowerCamelCase			    =				tokenizer_p.from_pretrained(lowerCamelCase_						)

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

                       shutil.rmtree(lowerCamelCase_						)

                       # Save tokenizer rust, legacy_format=True
                       _lowerCamelCase			    =				tempfile.mkdtemp()

                       _lowerCamelCase			    =				tokenizer_r.save_pretrained(lowerCamelCase_				,				legacy_format=lowerCamelCase_						)
                       _lowerCamelCase			    =				tokenizer_p.save_pretrained(lowerCamelCase_						)

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

                       # Checks everything loads correctly in the same way
                       _lowerCamelCase			    =				tokenizer_r.from_pretrained(lowerCamelCase_						)
                       _lowerCamelCase			    =				tokenizer_p.from_pretrained(lowerCamelCase_						)

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

                       shutil.rmtree(lowerCamelCase_						)

                       # Save tokenizer rust, legacy_format=False
                       _lowerCamelCase			    =				tempfile.mkdtemp()

                       _lowerCamelCase			    =				tokenizer_r.save_pretrained(lowerCamelCase_				,				legacy_format=lowerCamelCase_						)
                       _lowerCamelCase			    =				tokenizer_p.save_pretrained(lowerCamelCase_						)

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

                       # Checks everything loads correctly in the same way
                       _lowerCamelCase			    =				tokenizer_r.from_pretrained(lowerCamelCase_						)
                       _lowerCamelCase			    =				tokenizer_p.from_pretrained(lowerCamelCase_						)

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

                       shutil.rmtree(lowerCamelCase_						)



     @require_torch
     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""



           if not self.test_seqaseq:
                 return

           _lowerCamelCase			    =				self.get_tokenizers()
           for tokenizer in tokenizers:
                 with self.subTest(F'{tokenizer.__class__.__name__}'						):
                       # Longer text that will definitely require truncation.
                       _lowerCamelCase			    =				[
                           """ UN Chief Says There Is No Military Solution in Syria""",
                           """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"""
                           """ Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"""
                           """ will only worsen the violence and misery for millions of people.""",
                       ]
                       _lowerCamelCase			    =				[
                           """Şeful ONU declară că nu există o soluţie militară în Siria""",
                           """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"""
                           """ Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"""
                           """ că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
                       ]
                       try:
                             _lowerCamelCase			    =				tokenizer.prepare_seqaseq_batch(
                                 src_texts=lowerCamelCase_				,				tgt_texts=lowerCamelCase_				,				max_length=3				,				max_target_length=10				,				return_tensors='''pt'''				,				src_lang='''eng_Latn'''				,				tgt_lang='''ron_Latn'''				,				)
                       except NotImplementedError:
                             return
                       self.assertEqual(batch.input_ids.shape[1]				,				3						)
                       self.assertEqual(batch.labels.shape[1]				,				10						)
                       # max_target_length will default to max_length if not specified
                       _lowerCamelCase			    =				tokenizer.prepare_seqaseq_batch(
                           lowerCamelCase_				,				tgt_texts=lowerCamelCase_				,				max_length=3				,				return_tensors='''pt'''						)
                       self.assertEqual(batch.input_ids.shape[1]				,				3						)
                       self.assertEqual(batch.labels.shape[1]				,				3						)

                       _lowerCamelCase			    =				tokenizer.prepare_seqaseq_batch(
                           src_texts=lowerCamelCase_				,				max_length=3				,				max_target_length=10				,				return_tensors='''pt'''						)
                       self.assertEqual(batch_encoder_only.input_ids.shape[1]				,				3						)
                       self.assertEqual(batch_encoder_only.attention_mask.shape[1]				,				3						)
                       self.assertNotIn('''decoder_input_ids'''				,				lowerCamelCase_						)



     @unittest.skip('''Unfortunately way too slow to build a BPE with SentencePiece.'''						)
     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           pass





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






           """simple docstring"""



           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'						):
                       _lowerCamelCase			    =				[AddedToken('''<special>'''				,				lstrip=lowerCamelCase_						)]

                       _lowerCamelCase			    =				self.rust_tokenizer_class.from_pretrained(
                           lowerCamelCase_				,				additional_special_tokens=lowerCamelCase_				,				**lowerCamelCase_						)
                       _lowerCamelCase			    =				tokenizer_r.encode('''Hey this is a <special> token'''						)

                       _lowerCamelCase			    =				tokenizer_r.encode('''<special>'''				,				add_special_tokens=lowerCamelCase_						)[0]

                       self.assertTrue(special_token_id in r_output						)

                       if self.test_slow_tokenizer:
                             _lowerCamelCase			    =				self.rust_tokenizer_class.from_pretrained(
                                 lowerCamelCase_				,				additional_special_tokens=lowerCamelCase_				,				**lowerCamelCase_				,				)
                             _lowerCamelCase			    =				self.tokenizer_class.from_pretrained(
                                 lowerCamelCase_				,				additional_special_tokens=lowerCamelCase_				,				**lowerCamelCase_						)

                             _lowerCamelCase			    =				tokenizer_p.encode('''Hey this is a <special> token'''						)

                             _lowerCamelCase			    =				tokenizer_cr.encode('''Hey this is a <special> token'''						)

                             self.assertEqual(lowerCamelCase_				,				lowerCamelCase_						)
                             self.assertEqual(lowerCamelCase_				,				lowerCamelCase_						)
                             self.assertTrue(special_token_id in p_output						)
                             self.assertTrue(special_token_id in cr_output						)





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

     '''simple docstring'''

     A_										=  'facebook/nllb-200-distilled-600M'
     A_										=  [
         ' UN Chief Says There Is No Military Solution in Syria',
         ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
     ]
     A_										=  [
         'Şeful ONU declară că nu există o soluţie militară în Siria',
         'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
         ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
         ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
     ]
     A_										=  [
         256_047,
         16_297,
         134_408,
         8_165,
         248_066,
         14_734,
         950,
         1_135,
         105_721,
         3_573,
         83,
         27_352,
         108,
         49_486,
         2,
     ]



     @classmethod
     def 		UpperCamelCase_						(					cls						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				NllbTokenizer.from_pretrained(
               cls.checkpoint_name				,				src_lang='''eng_Latn'''				,				tgt_lang='''ron_Latn'''						)
           _lowerCamelCase			    =				1
           return cls



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Arab''']				,				25_60_01						)
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Latn''']				,				25_60_02						)
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''fra_Latn''']				,				25_60_57						)



     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
           self.assertListEqual(self.expected_src_tokens				,				lowerCamelCase_						)



     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           self.assertIn(lowerCamelCase_				,				self.tokenizer.all_special_ids						)
           # fmt: off
           _lowerCamelCase			    =				[RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
           # fmt: on

           _lowerCamelCase			    =				self.tokenizer.decode(lowerCamelCase_				,				skip_special_tokens=lowerCamelCase_						)
           _lowerCamelCase			    =				self.tokenizer.decode(generated_ids[1:]				,				skip_special_tokens=lowerCamelCase_						)
           self.assertEqual(lowerCamelCase_				,				lowerCamelCase_						)
           self.assertNotIn(self.tokenizer.eos_token				,				lowerCamelCase_						)



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				["""this is gunna be a long sentence """ * 20]
           assert isinstance(src_text[0]				,				lowerCamelCase_						)
           _lowerCamelCase			    =				10
           _lowerCamelCase			    =				self.tokenizer(lowerCamelCase_				,				max_length=lowerCamelCase_				,				truncation=lowerCamelCase_						).input_ids[0]
           self.assertEqual(ids[-1]				,				2						)
           self.assertEqual(ids[0]				,				lowerCamelCase_						)
           self.assertEqual(len(lowerCamelCase_						)				,				lowerCamelCase_						)



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






           """simple docstring"""
           self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']						)				,				[25_62_03, 3]						)



     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				tempfile.mkdtemp()
           _lowerCamelCase			    =				self.tokenizer.fairseq_tokens_to_ids
           self.tokenizer.save_pretrained(lowerCamelCase_						)
           _lowerCamelCase			    =				NllbTokenizer.from_pretrained(lowerCamelCase_						)
           self.assertDictEqual(new_tok.fairseq_tokens_to_ids				,				lowerCamelCase_						)



     @require_torch
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.tokenizer(
               self.src_text				,				text_target=self.tgt_text				,				padding=lowerCamelCase_				,				truncation=lowerCamelCase_				,				max_length=len(self.expected_src_tokens						)				,				return_tensors='''pt'''				,				)
           _lowerCamelCase			    =				shift_tokens_right(
               batch['''labels''']				,				self.tokenizer.pad_token_id				,				self.tokenizer.lang_code_to_id['''ron_Latn''']						)

           self.assertIsInstance(lowerCamelCase_				,				lowerCamelCase_						)

           self.assertEqual((2, 15)				,				batch.input_ids.shape						)
           self.assertEqual((2, 15)				,				batch.attention_mask.shape						)
           _lowerCamelCase			    =				batch.input_ids.tolist()[0]
           self.assertListEqual(self.expected_src_tokens				,				lowerCamelCase_						)
           self.assertEqual(lowerCamelCase_				,				batch.decoder_input_ids[0, 0]						)  # EOS
           # Test that special tokens are reset
           self.assertEqual(self.tokenizer.prefix_tokens				,				[EN_CODE]						)
           self.assertEqual(self.tokenizer.suffix_tokens				,				[self.tokenizer.eos_token_id]						)



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.tokenizer(self.src_text				,				padding=lowerCamelCase_				,				truncation=lowerCamelCase_				,				max_length=3				,				return_tensors='''pt'''						)
           _lowerCamelCase			    =				self.tokenizer(
               text_target=self.tgt_text				,				padding=lowerCamelCase_				,				truncation=lowerCamelCase_				,				max_length=10				,				return_tensors='''pt'''						)
           _lowerCamelCase			    =				targets["""input_ids"""]
           _lowerCamelCase			    =				shift_tokens_right(
               lowerCamelCase_				,				self.tokenizer.pad_token_id				,				decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]				,				)

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



     @require_torch
     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				self.tokenizer._build_translation_inputs(
               '''A test'''				,				return_tensors='''pt'''				,				src_lang='''eng_Latn'''				,				tgt_lang='''fra_Latn'''						)

           self.assertEqual(
               nested_simplify(lowerCamelCase_						)				,				{
                   # A, test, EOS, en_XX
                   '''input_ids''': [[25_60_47, 70, 73_56, 2]],
                   '''attention_mask''': [[1, 1, 1, 1]],
                   # ar_AR
                   '''forced_bos_token_id''': 25_60_57,
               }				,				)





     @require_torch
     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				True
           _lowerCamelCase			    =				self.tokenizer(
               '''UN Chief says there is no military solution in Syria'''				,				src_lang='''eng_Latn'''				,				tgt_lang='''fra_Latn'''						)
           self.assertEqual(
               inputs.input_ids				,				[1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47]						)

           _lowerCamelCase			    =				False
           _lowerCamelCase			    =				self.tokenizer(
               '''UN Chief says there is no military solution in Syria'''				,				src_lang='''eng_Latn'''				,				tgt_lang='''fra_Latn'''						)
           self.assertEqual(
               inputs.input_ids				,				[25_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2]						)
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				:

     '''simple docstring'''

     def __init__(					self				,				A_ = "cpu"				,				A_ = "openai/clip-vit-large-patch14"						) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				device
           _lowerCamelCase			    =				CLIPTokenizerFast.from_pretrained(A_						)
           _lowerCamelCase			    =				[0.48145466, 0.4578275, 0.40821073]
           _lowerCamelCase			    =				[0.26862954, 0.26130258, 0.27577711]
           _lowerCamelCase			    =				torchvision.transforms.Normalize(self.image_mean				,				self.image_std						)
           _lowerCamelCase			    =				torchvision.transforms.Resize(2_24						)
           _lowerCamelCase			    =				torchvision.transforms.CenterCrop(2_24						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.resize(A_						)
           _lowerCamelCase			    =				self.center_crop(A_						)
           _lowerCamelCase			    =				self.normalize(A_						)
           return images





     def __call__(					self				,				A_=None				,				A_=None				,				**A_						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.tokenizer(text=A_				,				**A_						)
           _lowerCamelCase			    =				self.preprocess_img(A_						)
           _lowerCamelCase			    =				{key: value.to(self.device						) for (key, value) in encoding.items()}
           return encoding





class  UpperCamelCase				( nn.Module ):

     '''simple docstring'''

     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				,				) ->				None:






           """simple docstring"""
           super().__init__()
           _lowerCamelCase			    =				None
           _lowerCamelCase			    =				device if device else get_device()
           if vqgan:
                 _lowerCamelCase			    =				vqgan
           else:
                 _lowerCamelCase			    =				load_vqgan(self.device				,				conf_path=A_				,				ckpt_path=A_						)
           self.vqgan.eval()
           if clip:
                 _lowerCamelCase			    =				clip
           else:
                 _lowerCamelCase			    =				CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''						)
           self.clip.to(self.device						)
           _lowerCamelCase			    =				ProcessorGradientFlow(device=self.device						)

           _lowerCamelCase			    =				iterations
           _lowerCamelCase			    =				lr
           _lowerCamelCase			    =				log
           _lowerCamelCase			    =				make_grid
           _lowerCamelCase			    =				return_val
           _lowerCamelCase			    =				quantize
           _lowerCamelCase			    =				self.vqgan.decoder.z_shape



     def 		UpperCamelCase_						(					self				,				A_=None				,				A_=None				,				A_=5				,				A_=True						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				[]
           if output_path is None:
                 _lowerCamelCase			    =				'''./animation.gif'''
           if input_path is None:
                 _lowerCamelCase			    =				self.save_path
           _lowerCamelCase			    =				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			    =				total_duration / len(A_						)
           _lowerCamelCase			    =				[frame_duration] * len(A_						)
           if extend_frames:
                 _lowerCamelCase			    =				1.5
                 _lowerCamelCase			    =				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 		UpperCamelCase_						(					self				,				A_=None				,				A_=None						) ->				Union[str, Any]:






           """simple docstring"""
           if not (path or img):
                 raise ValueError('''Input either path or tensor'''						)
           if img is not None:
                 raise NotImplementedError
           _lowerCamelCase			    =				preprocess(Image.open(A_						)				,				target_image_size=2_56						).to(self.device						)
           _lowerCamelCase			    =				preprocess_vqgan(A_						)
           _lowerCamelCase    ,					*_lowerCamelCase			    =				self.vqgan.encode(A_						)
           return z



     def 		UpperCamelCase_						(					self				,				A_						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				self.latent.detach().requires_grad_()
           _lowerCamelCase			    =				base_latent + transform_vector
           if self.quantize:
                 _lowerCamelCase    ,					*_lowerCamelCase			    =				self.vqgan.quantize(A_						)
           else:
                 _lowerCamelCase			    =				trans_latent
           return self.vqgan.decode(A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_=None						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				self.clip_preprocessor(text=A_				,				images=A_				,				return_tensors='''pt'''				,				padding=A_						)
           _lowerCamelCase			    =				self.clip(**A_						)
           _lowerCamelCase			    =				clip_outputs.logits_per_image
           if weights is not None:
                 _lowerCamelCase			    =				similarity_logits * weights
           return similarity_logits.sum()



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				self._get_clip_similarity(pos_prompts['''prompts''']				,				A_				,				weights=(1 / pos_prompts['''weights'''])						)
           if neg_prompts:
                 _lowerCamelCase			    =				self._get_clip_similarity(neg_prompts['''prompts''']				,				A_				,				weights=neg_prompts['''weights''']						)
           else:
                 _lowerCamelCase			    =				torch.tensor([1]				,				device=self.device						)
           _lowerCamelCase			    =				-torch.log(A_						) + torch.log(A_						)
           return loss



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				torch.randn_like(self.latent				,				requires_grad=A_				,				device=self.device						)
           _lowerCamelCase			    =				torch.optim.Adam([vector]				,				lr=self.lr						)

           for i in range(self.iterations						):
                 optim.zero_grad()
                 _lowerCamelCase			    =				self._add_vector(A_						)
                 _lowerCamelCase			    =				loop_post_process(A_						)
                 _lowerCamelCase			    =				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 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				Any:






           """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			    =				Image.open(A_						)
                 _lowerCamelCase			    =				image.resize((2_56, 2_56)						)
                 wandb.log('''Original Image'''				,				wandb.Image(A_						)						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				int:






           """simple docstring"""
           if not prompts:
                 return []
           _lowerCamelCase			    =				[]
           _lowerCamelCase			    =				[]
           if isinstance(A_				,				A_						):
                 _lowerCamelCase			    =				[prompt.strip() for prompt in prompts.split('''|'''						)]
           for prompt in prompts:
                 if isinstance(A_				,				(tuple, list)						):
                       _lowerCamelCase			    =				prompt[0]
                       _lowerCamelCase			    =				float(prompt[1]						)
                 elif ":" in prompt:
                       _lowerCamelCase    ,					_lowerCamelCase			    =				prompt.split(''':'''						)
                       _lowerCamelCase			    =				float(A_						)
                 else:
                       _lowerCamelCase			    =				prompt
                       _lowerCamelCase			    =				1.0
                 processed_prompts.append(A_						)
                 weights.append(A_						)
           return {
               "prompts": processed_prompts,
               "weights": torch.tensor(A_				,				device=self.device						),
           }





     def 		UpperCamelCase_						(					self				,				A_				,				A_=None				,				A_=None				,				A_=True				,				A_=False				,				A_=True				,				A_=True				,				A_=None				,				) ->				str:






           """simple docstring"""
           if image_path:
                 _lowerCamelCase			    =				self._get_latent(A_						)
           else:
                 _lowerCamelCase			    =				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			    =				self.process_prompts(A_						)
           _lowerCamelCase			    =				self.process_prompts(A_						)
           if save_final and save_path is None:
                 _lowerCamelCase			    =				os.path.join('''./outputs/'''				,				'''_'''.join(pos_prompts['''prompts''']						)						)
           if not os.path.exists(A_						):
                 os.makedirs(A_						)
           else:
                 _lowerCamelCase			    =				save_path + '''_''' + get_timestamp()
                 os.makedirs(A_						)
           _lowerCamelCase			    =				save_path

           _lowerCamelCase			    =				self.vqgan.decode(self.latent						)[0]
           if show_intermediate:
                 print('''Original Image'''						)
                 show_pil(custom_to_pil(A_						)						)

           _lowerCamelCase			    =				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'						)						)