



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__	:
     """simple docstring"""



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






           '''simple docstring'''



           lowerCamelCase             =		device
           lowerCamelCase             =		CLIPTokenizerFast.from_pretrained(__a     )
           lowerCamelCase             =		[0.4814_5466, 0.457_8275, 0.4082_1073]
           lowerCamelCase             =		[0.2686_2954, 0.2613_0258, 0.2757_7711]
           lowerCamelCase             =		torchvision.transforms.Normalize(self.image_mean					,      self.image_std     )
           lowerCamelCase             =		torchvision.transforms.Resize(2_24     )
           lowerCamelCase             =		torchvision.transforms.CenterCrop(2_24     )







     def     _a   (self					,      __a     ):






           '''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     ):






           '''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  lowerCamelCase__	(    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					,      ):






           '''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     _a   (self					,      __a=None					,      __a=None					,      __a=5					,      __a=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(__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     _a   (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             =		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     _a   (self					,      __a     ):






           '''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     _a   (self					,      __a					,      __a					,      __a=None     ):






           '''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     _a   (self					,      __a					,      __a					,      __a     ):






           '''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     _a   (self					,      __a					,      __a					,      __a     ):






           '''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     _a   (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             =		Image.open(__a     )
                 lowerCamelCase             =		image.resize((2_56, 2_56)     )
                 wandb.log("Original Image"					,      wandb.Image(__a     )     )







     def     _a   (self					,      __a     ):






           '''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     _a   (self					,      __a					,      __a=None					,      __a=None					,      __a=True					,      __a=False					,      __a=True					,      __a=True					,      __a=None					,      ):






           '''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"""     )     )




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
         OpenAIGPTConfig,
         OpenAIGPTDoubleHeadsModel,
         OpenAIGPTForSequenceClassification,
         OpenAIGPTLMHeadModel,
         OpenAIGPTModel,
     )





class  lowerCamelCase__	:
     """simple docstring"""



     def __init__(self					,      __a					,      __a=13					,      __a=7					,      __a=True					,      __a=True					,      __a=True					,      __a=99					,      __a=32					,      __a=5					,      __a=4					,      __a=37					,      __a="gelu"					,      __a=0.1					,      __a=0.1					,      __a=5_12					,      __a=16					,      __a=2					,      __a=0.02					,      __a=3					,      __a=4					,      __a=None					,      ):






           '''simple docstring'''



           lowerCamelCase             =		parent
           lowerCamelCase             =		batch_size
           lowerCamelCase             =		seq_length
           lowerCamelCase             =		is_training
           lowerCamelCase             =		use_token_type_ids
           lowerCamelCase             =		use_labels
           lowerCamelCase             =		vocab_size
           lowerCamelCase             =		hidden_size
           lowerCamelCase             =		num_hidden_layers
           lowerCamelCase             =		num_attention_heads
           lowerCamelCase             =		intermediate_size
           lowerCamelCase             =		hidden_act
           lowerCamelCase             =		hidden_dropout_prob
           lowerCamelCase             =		attention_probs_dropout_prob
           lowerCamelCase             =		max_position_embeddings
           lowerCamelCase             =		type_vocab_size
           lowerCamelCase             =		type_sequence_label_size
           lowerCamelCase             =		initializer_range
           lowerCamelCase             =		num_labels
           lowerCamelCase             =		num_choices
           lowerCamelCase             =		scope
           lowerCamelCase             =		self.vocab_size - 1







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size     )

           lowerCamelCase             =		None
           if self.use_token_type_ids:
                 lowerCamelCase             =		ids_tensor([self.batch_size, self.seq_length]					,      self.type_vocab_size     )

           lowerCamelCase             =		None
           lowerCamelCase             =		None
           lowerCamelCase             =		None
           if self.use_labels:
                 lowerCamelCase             =		ids_tensor([self.batch_size]					,      self.type_sequence_label_size     )
                 lowerCamelCase             =		ids_tensor([self.batch_size, self.seq_length]					,      self.num_labels     )
                 lowerCamelCase             =		ids_tensor([self.batch_size]					,      self.num_choices     )

           lowerCamelCase             =		OpenAIGPTConfig(
               vocab_size=self.vocab_size					,      n_embd=self.hidden_size					,      n_layer=self.num_hidden_layers					,      n_head=self.num_attention_heads					,      n_positions=self.max_position_embeddings					,      pad_token_id=self.pad_token_id					,      )

           lowerCamelCase             =		ids_tensor([self.num_hidden_layers, self.num_attention_heads]					,      2     )

           return (
               config,
               input_ids,
               head_mask,
               token_type_ids,
               sequence_labels,
               token_labels,
               choice_labels,
           )







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      *__a     ):






           '''simple docstring'''



           lowerCamelCase             =		OpenAIGPTModel(config=__a     )
           model.to(__a     )
           model.eval()

           lowerCamelCase             =		model(__a					,      token_type_ids=__a					,      head_mask=__a     )
           lowerCamelCase             =		model(__a					,      token_type_ids=__a     )
           lowerCamelCase             =		model(__a     )

           self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)     )







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      *__a     ):






           '''simple docstring'''



           lowerCamelCase             =		OpenAIGPTLMHeadModel(__a     )
           model.to(__a     )
           model.eval()

           lowerCamelCase             =		model(__a					,      token_type_ids=__a					,      labels=__a     )
           self.parent.assertEqual(result.loss.shape					,      ()     )
           self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)     )







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      *__a     ):






           '''simple docstring'''



           lowerCamelCase             =		OpenAIGPTDoubleHeadsModel(__a     )
           model.to(__a     )
           model.eval()

           lowerCamelCase             =		model(__a					,      token_type_ids=__a					,      labels=__a     )
           self.parent.assertEqual(result.loss.shape					,      ()     )
           self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)     )







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      *__a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.num_labels
           lowerCamelCase             =		OpenAIGPTForSequenceClassification(__a     )
           model.to(__a     )
           model.eval()

           lowerCamelCase             =		ids_tensor([self.batch_size]					,      self.type_sequence_label_size     )
           lowerCamelCase             =		model(__a					,      token_type_ids=__a					,      labels=__a     )
           self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.prepare_config_and_inputs()
           (
               (
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				
           )             =		config_and_inputs
           lowerCamelCase             =		{
               "input_ids": input_ids,
               "token_type_ids": token_type_ids,
               "head_mask": head_mask,
           }

           return config, inputs_dict





@require_torch
class  lowerCamelCase__	(    UpperCAmelCase_							,     UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase):
     """simple docstring"""



     _A      =      (
         (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
         if is_torch_available()
         else ()
     )
     _A      =      (
         (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
     )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
     _A      =      (
         {
             'feature-extraction': OpenAIGPTModel,
             'text-classification': OpenAIGPTForSequenceClassification,
             'text-generation': OpenAIGPTLMHeadModel,
             'zero-shot': OpenAIGPTForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      __a     ):






           '''simple docstring'''



           if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
                 # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
                 # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
                 # tiny config could not be created.
                 return True

           return False







     def     _a   (self					,      __a					,      __a					,      __a=False     ):






           '''simple docstring'''



           lowerCamelCase             =		super()._prepare_for_class(__a					,      __a					,      return_labels=__a     )

           if return_labels:
                 if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                       lowerCamelCase             =		torch.zeros(
                           (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)					,      dtype=torch.long					,      device=__a					,      )
                       lowerCamelCase             =		inputs_dict["labels"]
                       lowerCamelCase             =		inputs_dict["labels"]
                       lowerCamelCase             =		torch.zeros(
                           (self.model_tester.batch_size, self.model_tester.num_choices)					,      dtype=torch.long					,      device=__a					,      )
                       lowerCamelCase             =		torch.zeros(
                           self.model_tester.batch_size					,      dtype=torch.long					,      device=__a     )
           return inputs_dict







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		OpenAIGPTModelTester(self     )
           lowerCamelCase             =		ConfigTester(self					,      config_class=__a					,      n_embd=37     )







     def     _a   (self     ):






           '''simple docstring'''



           self.config_tester.run_common_tests()







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_model(*__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_lm_head_model(*__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_double_lm_head_model(*__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__a     )







     @slow
     def     _a   (self     ):






           '''simple docstring'''



           for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase             =		OpenAIGPTModel.from_pretrained(__a     )
                 self.assertIsNotNone(__a     )





@require_torch
class  lowerCamelCase__	(    unittest.TestCase):
     """simple docstring"""



     @slow
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		OpenAIGPTLMHeadModel.from_pretrained("openai-gpt"     )
           model.to(__a     )
           lowerCamelCase             =		torch.tensor([[4_81, 47_35, 5_44]]					,      dtype=torch.long					,      device=__a     )  # the president is
           lowerCamelCase             =		[
               4_81,
               47_35,
               5_44,
               2_46,
               9_63,
               8_70,
               7_62,
               2_39,
               2_44,
               4_04_77,
               2_44,
               2_49,
               7_19,
               8_81,
               4_87,
               5_44,
               2_40,
               2_44,
               6_03,
               4_81,
           ]  # the president is a very good man. " \n " i\'m sure he is, " said the

           lowerCamelCase             =		model.generate(__a					,      do_sample=__a     )
           self.assertListEqual(output_ids[0].tolist()					,      __a     )