


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   a__	:

       def __init__(   self      :     int      ,							lowerCamelCase_      :     str = "cpu"      ,							lowerCamelCase_      :     str = "openai/clip-vit-large-patch14"    ):
            a_    :     Tuple										=    device
            a_    :     Optional[Any]										=    CLIPTokenizerFast.from_pretrained(lowerCamelCase_    )
            a_    :     Union[str, Any]										=    [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
            a_    :     int										=    [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
            a_    :     Optional[int]										=    torchvision.transforms.Normalize(self.image_mean      ,							self.image_std    )
            a_    :     Tuple										=    torchvision.transforms.Resize(2_2_4    )
            a_    :     Union[str, Any]										=    torchvision.transforms.CenterCrop(2_2_4    )

       def 						UpperCAmelCase(   self      :     Optional[int]      ,							lowerCamelCase_      :     Union[str, Any]    ):
            a_    :     str										=    self.resize(lowerCamelCase_    )
            a_    :     Any										=    self.center_crop(lowerCamelCase_    )
            a_    :     Tuple										=    self.normalize(lowerCamelCase_    )
            return images

       def __call__(   self      :     Tuple      ,							lowerCamelCase_      :     Union[str, Any]=None      ,							lowerCamelCase_      :     Dict=None      ,							**lowerCamelCase_      :     Dict    ):
            a_    :     str										=    self.tokenizer(text=lowerCamelCase_      ,							**lowerCamelCase_    )
            a_    :     Any										=    self.preprocess_img(lowerCamelCase_    )
            a_    :     Any										=    {key: value.to(self.device    ) for (key, value) in encoding.items()}
            return encoding


class   a__	(						nn.Module    ):

       def __init__(   self      :     str      ,							lowerCamelCase_      :     str=1_0      ,							lowerCamelCase_      :     Tuple=0.0_1      ,							lowerCamelCase_      :     str=None      ,							lowerCamelCase_      :     Union[str, Any]=None      ,							lowerCamelCase_      :     Dict=None      ,							lowerCamelCase_      :     Union[str, Any]=None      ,							lowerCamelCase_      :     str=None      ,							lowerCamelCase_      :     str=None      ,							lowerCamelCase_      :     Optional[Any]=False      ,							lowerCamelCase_      :     Tuple=True      ,							lowerCamelCase_      :     str="image"      ,							lowerCamelCase_      :     Any=True      ,							lowerCamelCase_      :     Optional[Any]=False      ,							lowerCamelCase_      :     Tuple=False      ,							lowerCamelCase_      :     str=False      ,							):
            super().__init__()
            a_    :     List[Any]										=    None
            a_    :     Dict										=    device if device else get_device()
            if vqgan:
                 a_    :     Any										=    vqgan
            else:
                 a_    :     List[Any]										=    load_vqgan(self.device      ,							conf_path=lowerCamelCase_      ,							ckpt_path=lowerCamelCase_    )
            self.vqgan.eval()
            if clip:
                 a_    :     List[Any]										=    clip
            else:
                 a_    :     int										=    CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""    )
            self.clip.to(self.device    )
            a_    :     str										=    ProcessorGradientFlow(device=self.device    )

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

       def 						UpperCAmelCase(   self      :     Optional[int]      ,							lowerCamelCase_      :     List[Any]=None      ,							lowerCamelCase_      :     List[Any]=None      ,							lowerCamelCase_      :     Optional[int]=5      ,							lowerCamelCase_      :     Union[str, Any]=True    ):
            a_    :     Optional[Any]										=    []
            if output_path is None:
                 a_    :     Tuple										=    """./animation.gif"""
            if input_path is None:
                 a_    :     str										=    self.save_path
            a_    :     Tuple										=    sorted(glob(input_path + """/*"""    )    )
            if not len(lowerCamelCase_    ):
                 raise ValueError(
                     """No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
                     """ function?)"""    )
            if len(lowerCamelCase_    ) == 1:
                 print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"""    )
            a_    :     Optional[int]										=    total_duration / len(lowerCamelCase_    )
            a_    :     Tuple										=    [frame_duration] * len(lowerCamelCase_    )
            if extend_frames:
                 a_    :     Tuple										=    1.5
                 a_    :     List[str]										=    3
            for file_name in paths:
                 if file_name.endswith(""".png"""    ):
                      images.append(imageio.imread(lowerCamelCase_    )    )
            imageio.mimsave(lowerCamelCase_      ,							lowerCamelCase_      ,							duration=lowerCamelCase_    )
            print(F'''gif saved to {output_path}'''    )

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     Optional[int]=None      ,							lowerCamelCase_      :     Union[str, Any]=None    ):
            if not (path or img):
                 raise ValueError("""Input either path or tensor"""    )
            if img is not None:
                 raise NotImplementedError
            a_    :     List[Any]										=    preprocess(Image.open(lowerCamelCase_    )      ,							target_image_size=2_5_6    ).to(self.device    )
            a_    :     Tuple										=    preprocess_vqgan(lowerCamelCase_    )
            a_			,			*a_    :     Union[str, Any]										=    self.vqgan.encode(lowerCamelCase_    )
            return z

       def 						UpperCAmelCase(   self      :     Optional[int]      ,							lowerCamelCase_      :     Union[str, Any]    ):
            a_    :     Union[str, Any]										=    self.latent.detach().requires_grad_()
            a_    :     Dict										=    base_latent + transform_vector
            if self.quantize:
                 a_			,			*a_    :     Optional[Any]										=    self.vqgan.quantize(lowerCamelCase_    )
            else:
                 a_    :     Optional[Any]										=    trans_latent
            return self.vqgan.decode(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     Tuple=None    ):
            a_    :     Optional[Any]										=    self.clip_preprocessor(text=lowerCamelCase_      ,							images=lowerCamelCase_      ,							return_tensors="""pt"""      ,							padding=lowerCamelCase_    )
            a_    :     List[Any]										=    self.clip(**lowerCamelCase_    )
            a_    :     Any										=    clip_outputs.logits_per_image
            if weights is not None:
                 a_    :     Any										=    similarity_logits * weights
            return similarity_logits.sum()

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

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     Dict      ,							lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     Dict    ):
            a_    :     str										=    torch.randn_like(self.latent      ,							requires_grad=lowerCamelCase_      ,							device=self.device    )
            a_    :     Tuple										=    torch.optim.Adam([vector]      ,							lr=self.lr    )

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

       def 						UpperCAmelCase(   self      :     Optional[Any]      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     Union[str, Any]      ,							lowerCamelCase_      :     Optional[int]    ):
            wandb.init(reinit=lowerCamelCase_      ,							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:
                 a_    :     Tuple										=    Image.open(lowerCamelCase_    )
                 a_    :     Tuple										=    image.resize((2_5_6, 2_5_6)    )
                 wandb.log("""Original Image"""      ,							wandb.Image(lowerCamelCase_    )    )

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

       def 						UpperCAmelCase(   self      :     Any      ,							lowerCamelCase_      :     Dict      ,							lowerCamelCase_      :     str=None      ,							lowerCamelCase_      :     Dict=None      ,							lowerCamelCase_      :     int=True      ,							lowerCamelCase_      :     Optional[Any]=False      ,							lowerCamelCase_      :     Optional[int]=True      ,							lowerCamelCase_      :     List[str]=True      ,							lowerCamelCase_      :     Dict=None      ,							):
            if image_path:
                 a_    :     List[Any]										=    self._get_latent(lowerCamelCase_    )
            else:
                 a_    :     Union[str, Any]										=    torch.randn(self.latent_dim      ,							device=self.device    )
            if self.log:
                 self._init_logging(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_    )

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

            a_    :     Optional[int]										=    self.vqgan.decode(self.latent    )[0]
            if show_intermediate:
                 print("""Original Image"""    )
                 show_pil(custom_to_pil(lowerCamelCase_    )    )

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







import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser



def  _a			(						__UpperCamelCase=None  ,  __UpperCamelCase=None ):
     return field(default_factory=lambda: default  ,  metadata=__UpperCamelCase )


@dataclass
class   a__	:
       lowerCamelCase__:     str							    =     field(
           metadata={"""help""": """The csv file to plot."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Disable logarithmic scale when plotting"""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={
               """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
           }						,       )
       lowerCamelCase__:     Optional[str]							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}						,       )
       lowerCamelCase__:     Optional[List[str]]							    =     list_field(
           default=lowerCAmelCase_						,       metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}    )



def  _a			(						__UpperCamelCase ):
     try:
          int(__UpperCamelCase )
          return True
     except ValueError:
          return False



def  _a			(						__UpperCamelCase ):
     try:
          float(__UpperCamelCase )
          return True
     except ValueError:
          return False


class   a__	:

       def __init__(   self      :     int      ,							lowerCamelCase_      :     int    ):
            a_    :     Union[str, Any]										=    args
            a_    :     Optional[int]										=    defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

            with open(self.args.csv_file      ,							newline=""""""    ) as csv_file:
                 a_    :     List[Any]										=    csv.DictReader(lowerCamelCase_    )
                 for row in reader:
                      a_    :     List[str]										=    row["""model"""]
                      self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]    )    )
                      self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]    )    )
                      if can_convert_to_int(row["""result"""]    ):
                           # value is not None
                           a_    :     Optional[Any]										=    int(row["""result"""]    )
                      elif can_convert_to_float(row["""result"""]    ):
                           # value is not None
                           a_    :     Dict										=    float(row["""result"""]    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            a_			,			a_    :     Tuple										=    plt.subplots()
            a_    :     List[Any]										=    """Time usage""" if self.args.is_time else """Memory usage"""
            a_    :     Optional[int]										=    title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

            if not self.args.no_log_scale:
                 # set logarithm scales
                 ax.set_xscale("""log"""    )
                 ax.set_yscale("""log"""    )

            for axis in [ax.xaxis, ax.yaxis]:
                 axis.set_major_formatter(ScalarFormatter()    )

            for model_name_idx, model_name in enumerate(self.result_dict.keys()    ):
                 a_    :     List[Any]										=    sorted(set(self.result_dict[model_name]["""bsz"""]    )    )
                 a_    :     Union[str, Any]										=    sorted(set(self.result_dict[model_name]["""seq_len"""]    )    )
                 a_    :     List[str]										=    self.result_dict[model_name]["""result"""]

                 ((a_)			,			(a_))    :     List[Any]										=    (
                     (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                 )

                 a_    :     str										=    (
                     model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
                 )

                 for inner_loop_value in inner_loop_array:
                      if self.args.plot_along_batch:
                           a_    :     Optional[int]										=    np.asarray(
                               [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,							dtype=lowerCamelCase_      ,							)
                      else:
                           a_    :     int										=    np.asarray(
                               [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]      ,							dtype=np.floataa      ,							)

                      ((a_)			,			(a_))    :     List[str]										=    (
                          ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
                      )

                      a_    :     List[Any]										=    np.asarray(lowerCamelCase_      ,							lowerCamelCase_    )[: len(lowerCamelCase_    )]
                      plt.scatter(
                          lowerCamelCase_      ,							lowerCamelCase_      ,							label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''    )
                      plt.plot(lowerCamelCase_      ,							lowerCamelCase_      ,							"""--"""    )

                 title_str += F''' {label_model_name} vs.'''

            a_    :     int										=    title_str[:-4]
            a_    :     Tuple										=    """Time in s""" if self.args.is_time else """Memory in MB"""

            # plot
            plt.title(lowerCamelCase_    )
            plt.xlabel(lowerCamelCase_    )
            plt.ylabel(lowerCamelCase_    )
            plt.legend()

            if self.args.figure_png_file is not None:
                 plt.savefig(self.args.figure_png_file    )
            else:
                 plt.show()



def  _a			(						):
     a_    :     Tuple										=    HfArgumentParser(__UpperCamelCase )
     a_    :     Dict										=    parser.parse_args_into_dataclasses()[0]
     a_    :     Dict										=    Plot(args=__UpperCamelCase )
     plot.plot()


if __name__ == "__main__":
       main()



