



"""simple docstring"""





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							,    _lowercase = "cpu"							,    _lowercase = "openai/clip-vit-large-patch14"   ) ->						None:







								'''simple docstring'''

								snake_case_   : List[str]						=			device
								snake_case_   : Tuple						=			CLIPTokenizerFast.from_pretrained(__a   )
								snake_case_   : Dict						=			[0.4814_5466, 0.457_8275, 0.4082_1073]
								snake_case_   : Dict						=			[0.2686_2954, 0.2613_0258, 0.2757_7711]
								snake_case_   : Tuple						=			torchvision.transforms.Normalize(self.image_mean							,    self.image_std   )
								snake_case_   : List[Any]						=			torchvision.transforms.Resize(2_2_4   )
								snake_case_   : Dict						=			torchvision.transforms.CenterCrop(2_2_4   )





		def    UpperCAmelCase__							(					self							,    _lowercase   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : List[str]						=			self.resize(__a   )
								snake_case_   : str						=			self.center_crop(__a   )
								snake_case_   : int						=			self.normalize(__a   )
								return images





		def __call__(					self							,    _lowercase=None							,    _lowercase=None							,    **_lowercase   ) ->						Tuple:







								'''simple docstring'''

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




class    _lowerCAmelCase    (				nn.Module		):





		"""simple docstring"""






		def __init__(					self							,    _lowercase=1_0							,    _lowercase=0.01							,    _lowercase=None							,    _lowercase=None							,    _lowercase=None							,    _lowercase=None							,    _lowercase=None							,    _lowercase=None							,    _lowercase=False							,    _lowercase=True							,    _lowercase="image"							,    _lowercase=True							,    _lowercase=False							,    _lowercase=False							,    _lowercase=False							,    ) ->						None:







								'''simple docstring'''

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

								snake_case_   : List[Any]						=			iterations
								snake_case_   : str						=			lr
								snake_case_   : Optional[int]						=			log
								snake_case_   : List[Any]						=			make_grid
								snake_case_   : Optional[int]						=			return_val
								snake_case_   : Optional[int]						=			quantize
								snake_case_   : List[Any]						=			self.vqgan.decoder.z_shape





		def    UpperCAmelCase__							(					self							,    _lowercase=None							,    _lowercase=None							,    _lowercase=5							,    _lowercase=True   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			[]
								if output_path is None:
														snake_case_   : Optional[int]						=			"""./animation.gif"""
								if input_path is None:
														snake_case_   : List[str]						=			self.save_path
								snake_case_   : 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?)"""   )
								snake_case_   : List[Any]						=			total_duration / len(__a   )
								snake_case_   : int						=			[frame_duration] * len(__a   )
								if extend_frames:
														snake_case_   : Tuple						=			1.5
														snake_case_   : 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    UpperCAmelCase__							(					self							,    _lowercase=None							,    _lowercase=None   ) ->						Optional[int]:







								'''simple docstring'''

								if not (path or img):
														raise ValueError("""Input either path or tensor"""   )
								if img is not None:
														raise NotImplementedError
								snake_case_   : Tuple						=			preprocess(Image.open(__a   )							,    target_image_size=2_5_6   ).to(self.device   )
								snake_case_   : Dict						=			preprocess_vqgan(__a   )
								snake_case_   : List[str]						=			self.vqgan.encode(__a   )
								return z





		def    UpperCAmelCase__							(					self							,    _lowercase   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : List[str]						=			self.latent.detach().requires_grad_()
								snake_case_   : int						=			base_latent + transform_vector
								if self.quantize:
														snake_case_   : Union[str, Any]						=			self.vqgan.quantize(__a   )
								else:
														snake_case_   : Optional[Any]						=			trans_latent
								return self.vqgan.decode(__a   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase=None   ) ->						Optional[Any]:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						str:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						int:







								'''simple docstring'''

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

								for i in range(self.iterations   ):
														optim.zero_grad()
														snake_case_   : Optional[Any]						=			self._add_vector(__a   )
														snake_case_   : Optional[Any]						=			loop_post_process(__a   )
														snake_case_   : Dict						=			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							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						List[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:
														snake_case_   : Dict						=			Image.open(__a   )
														snake_case_   : Optional[Any]						=			image.resize((2_5_6, 2_5_6)   )
														wandb.log("""Original Image"""							,    wandb.Image(__a   )   )





		def    UpperCAmelCase__							(					self							,    _lowercase   ) ->						Dict:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase=None							,    _lowercase=None							,    _lowercase=True							,    _lowercase=False							,    _lowercase=True							,    _lowercase=True							,    _lowercase=None							,    ) ->						Union[str, Any]:







								'''simple docstring'''

								if image_path:
														snake_case_   : str						=			self._get_latent(__a   )
								else:
														snake_case_   : List[str]						=			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."
								snake_case_   : int						=			self.process_prompts(__a   )
								snake_case_   : int						=			self.process_prompts(__a   )
								if save_final and save_path is None:
														snake_case_   : str						=			os.path.join("""./outputs/"""							,    """_""".join(pos_prompts["""prompts"""]   )   )
								if not os.path.exists(__a   ):
														os.makedirs(__a   )
								else:
														snake_case_   : Dict						=			save_path + """_""" + get_timestamp()
														os.makedirs(__a   )
								snake_case_   : Tuple						=			save_path

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

								snake_case_   : Optional[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'   )   )






"""simple docstring"""





import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.roformer.modeling_flax_roformer import (
					    FlaxRoFormerForMaskedLM,
					    FlaxRoFormerForMultipleChoice,
					    FlaxRoFormerForQuestionAnswering,
					    FlaxRoFormerForSequenceClassification,
					    FlaxRoFormerForTokenClassification,
					    FlaxRoFormerModel,
					)



class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		def __init__(					self							,    _lowercase							,    _lowercase=1_3							,    _lowercase=7							,    _lowercase=True							,    _lowercase=True							,    _lowercase=True							,    _lowercase=True							,    _lowercase=9_9							,    _lowercase=3_2							,    _lowercase=5							,    _lowercase=4							,    _lowercase=3_7							,    _lowercase="gelu"							,    _lowercase=0.1							,    _lowercase=0.1							,    _lowercase=5_1_2							,    _lowercase=1_6							,    _lowercase=2							,    _lowercase=0.02							,    _lowercase=4							,    ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : Tuple						=			parent
								snake_case_   : List[str]						=			batch_size
								snake_case_   : int						=			seq_length
								snake_case_   : List[Any]						=			is_training
								snake_case_   : Optional[int]						=			use_attention_mask
								snake_case_   : Optional[Any]						=			use_token_type_ids
								snake_case_   : Union[str, Any]						=			use_labels
								snake_case_   : str						=			vocab_size
								snake_case_   : List[str]						=			hidden_size
								snake_case_   : List[str]						=			num_hidden_layers
								snake_case_   : int						=			num_attention_heads
								snake_case_   : Dict						=			intermediate_size
								snake_case_   : Union[str, Any]						=			hidden_act
								snake_case_   : List[str]						=			hidden_dropout_prob
								snake_case_   : Any						=			attention_probs_dropout_prob
								snake_case_   : List[str]						=			max_position_embeddings
								snake_case_   : Union[str, Any]						=			type_vocab_size
								snake_case_   : str						=			type_sequence_label_size
								snake_case_   : Dict						=			initializer_range
								snake_case_   : str						=			num_choices





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







								'''simple docstring'''

								snake_case_   : List[Any]						=			ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size   )

								snake_case_   : Tuple						=			None
								if self.use_attention_mask:
														snake_case_   : Union[str, Any]						=			random_attention_mask([self.batch_size, self.seq_length]   )

								snake_case_   : Optional[int]						=			None
								if self.use_token_type_ids:
														snake_case_   : str						=			ids_tensor([self.batch_size, self.seq_length]							,    self.type_vocab_size   )

								snake_case_   : Union[str, Any]						=			RoFormerConfig(
								    vocab_size=self.vocab_size							,    hidden_size=self.hidden_size							,    num_hidden_layers=self.num_hidden_layers							,    num_attention_heads=self.num_attention_heads							,    intermediate_size=self.intermediate_size							,    hidden_act=self.hidden_act							,    hidden_dropout_prob=self.hidden_dropout_prob							,    attention_probs_dropout_prob=self.attention_probs_dropout_prob							,    max_position_embeddings=self.max_position_embeddings							,    type_vocab_size=self.type_vocab_size							,    is_decoder=_lowercase							,    initializer_range=self.initializer_range							,    )

								return config, input_ids, token_type_ids, attention_mask





		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : str						=			self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_   : List[Any]						=			config_and_inputs
								snake_case_   : Tuple						=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
								return config, inputs_dict




@require_flax
class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       True

		_lowerCamelCase			  =       (
		    (
		        FlaxRoFormerModel,
		        FlaxRoFormerForMaskedLM,
		        FlaxRoFormerForSequenceClassification,
		        FlaxRoFormerForTokenClassification,
		        FlaxRoFormerForMultipleChoice,
		        FlaxRoFormerForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)





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







								'''simple docstring'''

								snake_case_   : Dict						=			FlaxRoFormerModelTester(self   )





		@slow
		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								for model_class_name in self.all_model_classes:
														snake_case_   : Tuple						=			model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""							,    from_pt=_lowercase   )
														snake_case_   : List[str]						=			model(np.ones((1, 1)   )   )
														self.assertIsNotNone(_lowercase   )




@require_flax
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		@slow
		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : Dict						=			FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""   )
								snake_case_   : Tuple						=			jnp.array([[0, 1, 2, 3, 4, 5]]   )
								snake_case_   : Dict						=			model(_lowercase   )[0]

								snake_case_   : Optional[int]						=			5_0_0_0_0

								snake_case_   : Union[str, Any]						=			(1, 6, vocab_size)
								self.assertEqual(output.shape							,    _lowercase   )

								snake_case_   : Dict						=			jnp.array(
								    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]   )

								self.assertTrue(jnp.allclose(output[:, :3, :3]							,    _lowercase							,    atol=1E-4   )   )

