



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  __lowerCAmelCase     :




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



    '''simple docstring'''
    __lowerCamelCase													=    device
    __lowerCamelCase													=    CLIPTokenizerFast.from_pretrained(__UpperCAmelCase				)
    __lowerCamelCase													=    [0.48_145_466, 0.4_578_275, 0.40_821_073]
    __lowerCamelCase													=    [0.26_862_954, 0.26_130_258, 0.27_577_711]
    __lowerCamelCase													=    torchvision.transforms.Normalize(self.image_mean	,				self.image_std				)
    __lowerCamelCase													=    torchvision.transforms.Resize(224				)
    __lowerCamelCase													=    torchvision.transforms.CenterCrop(224				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    self.resize(__UpperCAmelCase				)
    __lowerCamelCase													=    self.center_crop(__UpperCAmelCase				)
    __lowerCamelCase													=    self.normalize(__UpperCAmelCase				)
    return images




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



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



class  __lowerCAmelCase     (	nn.Module   ):




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



    '''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=__UpperCAmelCase	,				ckpt_path=__UpperCAmelCase				)
    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        lowerCamelCase      (						self	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None	,				__UpperCAmelCase=5	,				__UpperCAmelCase=True				):



    '''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(__UpperCAmelCase				):
       raise ValueError(
           '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
           ''' function?)'''				)
    if len(__UpperCAmelCase				) == 1:
       print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''				)
    __lowerCamelCase													=    total_duration / len(__UpperCAmelCase				)
    __lowerCamelCase													=    [frame_duration] * len(__UpperCAmelCase				)
    if extend_frames:
       __lowerCamelCase													=    1.5
       __lowerCamelCase													=    3
    for file_name in paths:
       if file_name.endswith('''.png'''				):
          images.append(imageio.imread(__UpperCAmelCase				)				)
    imageio.mimsave(__UpperCAmelCase	,				__UpperCAmelCase	,				duration=__UpperCAmelCase				)
    print(F"""gif saved to {output_path}"""				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None				):



    '''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(__UpperCAmelCase				)	,				target_image_size=256				).to(self.device				)
    __lowerCamelCase													=    preprocess_vqgan(__UpperCAmelCase				)
    __lowerCamelCase							,*__lowerCamelCase													=    self.vqgan.encode(__UpperCAmelCase				)
    return z




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    self.latent.detach().requires_grad_()
    __lowerCamelCase													=    base_latent + transform_vector
    if self.quantize:
       __lowerCamelCase							,*__lowerCamelCase													=    self.vqgan.quantize(__UpperCAmelCase				)
    else:
       __lowerCamelCase													=    trans_latent
    return self.vqgan.decode(__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase=None				):



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




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



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




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    torch.randn_like(self.latent	,				requires_grad=__UpperCAmelCase	,				device=self.device				)
    __lowerCamelCase													=    torch.optim.Adam([vector]	,				lr=self.lr				)

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




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    wandb.init(reinit=__UpperCAmelCase	,				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(__UpperCAmelCase				)
       __lowerCamelCase													=    image.resize((256, 256)				)
       wandb.log('''Original Image'''	,				wandb.Image(__UpperCAmelCase				)				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



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




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None	,				__UpperCAmelCase=True	,				__UpperCAmelCase=False	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=None	,				):



    '''simple docstring'''
    if image_path:
       __lowerCamelCase													=    self._get_latent(__UpperCAmelCase				)
    else:
       __lowerCamelCase													=    torch.randn(self.latent_dim	,				device=self.device				)
    if self.log:
       self._init_logging(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)

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

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

    __lowerCamelCase													=    loop_post_process(__UpperCAmelCase				)
    for iter, transformed_img in enumerate(self._optimize_CLIP(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)				):
       if show_intermediate:
          show_pil(__UpperCAmelCase				)
       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(__UpperCAmelCase				)}				)
    if show_final:
       show_pil(__UpperCAmelCase				)
    if save_final:
       transformed_img.save(os.path.join(self.save_path	,				F"""iter_{iter:03d}_final.png"""				)				)











import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a_			=					get_tests_dir("""fixtures/test_sentencepiece.model""")



@require_sentencepiece
class  __lowerCAmelCase     (	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  XLMProphetNetTokenizer
 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  True




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    super().setUp()

    # We have a SentencePiece fixture for testing
    __lowerCamelCase													=    XLMProphetNetTokenizer(__UpperCAmelCase	,				keep_accents=__UpperCAmelCase				)
    tokenizer.save_pretrained(self.tmpdirname				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''[PAD]'''
    __lowerCamelCase													=    0

    self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase				)	,				__UpperCAmelCase				)
    self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase				)	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    list(self.get_tokenizer().get_vocab().keys()				)

    self.assertEqual(vocab_keys[0]	,				'''[PAD]'''				)
    self.assertEqual(vocab_keys[1]	,				'''[CLS]'''				)
    self.assertEqual(vocab_keys[-1]	,				'''j'''				)
    self.assertEqual(len(__UpperCAmelCase				)	,				1012				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    self.assertEqual(self.get_tokenizer().vocab_size	,				1012				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    XLMProphetNetTokenizer(__UpperCAmelCase	,				keep_accents=__UpperCAmelCase				)

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

    self.assertListEqual(
        tokenizer.convert_tokens_to_ids(__UpperCAmelCase				)	,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]	,				)

    __lowerCamelCase													=    tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
    self.assertListEqual(
        __UpperCAmelCase	,				[
            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(__UpperCAmelCase				)
    self.assertListEqual(
        __UpperCAmelCase	,				[
            value + tokenizer.fairseq_offset
            for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
        ]	,				)

    __lowerCamelCase													=    tokenizer.convert_ids_to_tokens(__UpperCAmelCase				)
    self.assertListEqual(
        __UpperCAmelCase	,				[
            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]''',
            '''.''',
        ]	,				)




 @cached_property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''				)




 @slow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''Hello World!'''
    __lowerCamelCase													=    [35389, 6672, 49, 2]
    self.assertListEqual(__UpperCAmelCase	,				self.big_tokenizer.encode(__UpperCAmelCase				)				)




 @slow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    # fmt: off
    __lowerCamelCase													=    {'''input_ids''': [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
    # fmt: on

    self.tokenizer_integration_test_util(
        expected_encoding=__UpperCAmelCase	,				model_name='''microsoft/xprophetnet-large-wiki100-cased'''	,				revision='''1acad1643ddd54a44df6a1b797ada8373685d90e'''	,				)






