import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
     from cookiecutter.main import cookiecutter

     snake_case__				= True
except ImportError:
     snake_case__				= False

snake_case__				= logging.get_logger(__name__)  # pylint: disable=invalid-name
def       __magic_name__(  __UpperCAmelCase		)						->    Union[str, Any]:



      '''simple docstring'''

      return AddNewModelCommand(args.testing ,						args.testing_file ,						path=args.path		)







class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     @staticmethod
     def 		UpperCamelCase_						(					A_						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				parser.add_parser('''add-new-model'''						)
           add_new_model_parser.add_argument('''--testing'''				,				action='''store_true'''				,				help='''If in testing mode.'''						)
           add_new_model_parser.add_argument('''--testing_file'''				,				type=A_				,				help='''Configuration file on which to run.'''						)
           add_new_model_parser.add_argument(
               '''--path'''				,				type=A_				,				help='''Path to cookiecutter. Should only be used for testing purposes.'''						)
           add_new_model_parser.set_defaults(func=A_						)



     def __init__(					self				,				A_				,				A_				,				A_=None				,				*A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				testing
           _lowerCamelCase			    =				testing_file
           _lowerCamelCase			    =				path





     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           warnings.warn(
               '''The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '''
               '''It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '''
               '''checks, you should use `transformers-cli add-new-model-like` instead.'''						)
           if not _has_cookiecutter:
                 raise ImportError(
                     '''Model creation dependencies are required to use the `add_new_model` command. Install them by running '''
                     '''the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n'''						)
           # Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
           _lowerCamelCase			    =				[directory for directory in os.listdir() if '''cookiecutter-template-''' == directory[:22]]
           if len(A_						) > 0:
                 raise ValueError(
                     '''Several directories starting with `cookiecutter-template-` in current working directory. '''
                     '''Please clean your directory by removing all folders starting with `cookiecutter-template-` or '''
                     '''change your working directory.'''						)

           _lowerCamelCase			    =				(
               Path(A_						).parent.parent.parent.parent if self._path is None else Path(self._path						).parent.parent
           )
           _lowerCamelCase			    =				path_to_transformer_root / '''templates''' / '''adding_a_new_model'''

           # Execute cookiecutter
           if not self._testing:
                 cookiecutter(str(A_						)						)
           else:
                 with open(self._testing_file				,				'''r'''						) as configuration_file:
                       _lowerCamelCase			    =				json.load(A_						)

                 cookiecutter(
                     str(path_to_cookiecutter if self._path is None else self._path						)				,				no_input=A_				,				extra_context=A_				,				)

           _lowerCamelCase			    =				[directory for directory in os.listdir() if '''cookiecutter-template-''' in directory[:22]][0]

           # Retrieve configuration
           with open(directory + '''/configuration.json'''				,				'''r'''						) as configuration_file:
                 _lowerCamelCase			    =				json.load(A_						)

           _lowerCamelCase			    =				configuration['''lowercase_modelname''']
           _lowerCamelCase			    =				configuration['''generate_tensorflow_pytorch_and_flax''']
           os.remove(F'{directory}/configuration.json'						)

           _lowerCamelCase			    =				'''PyTorch''' in generate_tensorflow_pytorch_and_flax
           _lowerCamelCase			    =				'''TensorFlow''' in generate_tensorflow_pytorch_and_flax
           _lowerCamelCase			    =				'''Flax''' in generate_tensorflow_pytorch_and_flax

           _lowerCamelCase			    =				F'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
           os.makedirs(A_				,				exist_ok=A_						)
           os.makedirs(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}'				,				exist_ok=A_						)

           # Tests require submodules as they have parent imports
           with open(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py'				,				'''w'''						):
                 pass

           shutil.move(
               F'{directory}/__init__.py'				,				F'{model_dir}/__init__.py'				,				)
           shutil.move(
               F'{directory}/configuration_{lowercase_model_name}.py'				,				F'{model_dir}/configuration_{lowercase_model_name}.py'				,				)

           def remove_copy_lines(A_						):
                 with open(A_				,				'''r'''						) as f:
                       _lowerCamelCase			    =				f.readlines()
                 with open(A_				,				'''w'''						) as f:
                       for line in lines:
                             if "# Copied from transformers." not in line:
                                   f.write(A_						)

           if output_pytorch:
                 if not self._testing:
                       remove_copy_lines(F'{directory}/modeling_{lowercase_model_name}.py'						)

                 shutil.move(
                     F'{directory}/modeling_{lowercase_model_name}.py'				,				F'{model_dir}/modeling_{lowercase_model_name}.py'				,				)

                 shutil.move(
                     F'{directory}/test_modeling_{lowercase_model_name}.py'				,				F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py'				,				)
           else:
                 os.remove(F'{directory}/modeling_{lowercase_model_name}.py'						)
                 os.remove(F'{directory}/test_modeling_{lowercase_model_name}.py'						)

           if output_tensorflow:
                 if not self._testing:
                       remove_copy_lines(F'{directory}/modeling_tf_{lowercase_model_name}.py'						)

                 shutil.move(
                     F'{directory}/modeling_tf_{lowercase_model_name}.py'				,				F'{model_dir}/modeling_tf_{lowercase_model_name}.py'				,				)

                 shutil.move(
                     F'{directory}/test_modeling_tf_{lowercase_model_name}.py'				,				F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py'				,				)
           else:
                 os.remove(F'{directory}/modeling_tf_{lowercase_model_name}.py'						)
                 os.remove(F'{directory}/test_modeling_tf_{lowercase_model_name}.py'						)

           if output_flax:
                 if not self._testing:
                       remove_copy_lines(F'{directory}/modeling_flax_{lowercase_model_name}.py'						)

                 shutil.move(
                     F'{directory}/modeling_flax_{lowercase_model_name}.py'				,				F'{model_dir}/modeling_flax_{lowercase_model_name}.py'				,				)

                 shutil.move(
                     F'{directory}/test_modeling_flax_{lowercase_model_name}.py'				,				F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py'				,				)
           else:
                 os.remove(F'{directory}/modeling_flax_{lowercase_model_name}.py'						)
                 os.remove(F'{directory}/test_modeling_flax_{lowercase_model_name}.py'						)

           shutil.move(
               F'{directory}/{lowercase_model_name}.md'				,				F'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md'				,				)

           shutil.move(
               F'{directory}/tokenization_{lowercase_model_name}.py'				,				F'{model_dir}/tokenization_{lowercase_model_name}.py'				,				)

           shutil.move(
               F'{directory}/tokenization_fast_{lowercase_model_name}.py'				,				F'{model_dir}/tokenization_{lowercase_model_name}_fast.py'				,				)

           from os import fdopen, remove
           from shutil import copymode, move
           from tempfile import mkstemp

           def replace(A_				,				A_				,				A_						):
                 # Create temp file
                 _lowerCamelCase    ,					_lowerCamelCase			    =				mkstemp()
                 _lowerCamelCase			    =				False
                 with fdopen(A_				,				'''w'''						) as new_file:
                       with open(A_						) as old_file:
                             for line in old_file:
                                   new_file.write(A_						)
                                   if line_to_copy_below in line:
                                         _lowerCamelCase			    =				True
                                         for line_to_copy in lines_to_copy:
                                               new_file.write(A_						)

                 if not line_found:
                       raise ValueError(F'Line {line_to_copy_below} was not found in file.'						)

                 # Copy the file permissions from the old file to the new file
                 copymode(A_				,				A_						)
                 # Remove original file
                 remove(A_						)
                 # Move new file
                 move(A_				,				A_						)

           def skip_units(A_						):
                 return (
                     ("generating PyTorch" in line and not output_pytorch)
                     or ("generating TensorFlow" in line and not output_tensorflow)
                     or ("generating Flax" in line and not output_flax)
                 )

           def replace_in_files(A_						):
                 with open(A_						) as datafile:
                       _lowerCamelCase			    =				[]
                       _lowerCamelCase			    =				False
                       _lowerCamelCase			    =				False
                       for line in datafile:
                             if "# To replace in: " in line and "##" not in line:
                                   _lowerCamelCase			    =				line.split('''"'''						)[1]
                                   _lowerCamelCase			    =				skip_units(A_						)
                             elif "# Below: " in line and "##" not in line:
                                   _lowerCamelCase			    =				line.split('''"'''						)[1]
                                   _lowerCamelCase			    =				skip_units(A_						)
                             elif "# End." in line and "##" not in line:
                                   if not skip_file and not skip_snippet:
                                         replace(A_				,				A_				,				A_						)

                                   _lowerCamelCase			    =				[]
                             elif "# Replace with" in line and "##" not in line:
                                   _lowerCamelCase			    =				[]
                             elif "##" not in line:
                                   lines_to_copy.append(A_						)

                 remove(A_						)

           replace_in_files(F'{directory}/to_replace_{lowercase_model_name}.py'						)
           os.rmdir(A_						)
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'						)						)