import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
     import tensorflow as tf
     from tensorflow.python.framework.errors_impl import ResourceExhaustedError

     from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
     import pyanvml.pyanvml as nvml

snake_case__				= logging.get_logger(__name__)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    List[str]:



      '''simple docstring'''

      def run_func(__UpperCAmelCase		):
            @wraps(__UpperCAmelCase		)
            def run_in_eager_mode(*__UpperCAmelCase ,						**__UpperCAmelCase		):
                  return func(*__UpperCAmelCase ,						**__UpperCAmelCase		)

            @wraps(__UpperCAmelCase		)
            @tf.function(experimental_compile=__UpperCAmelCase		)
            def run_in_graph_mode(*__UpperCAmelCase ,						**__UpperCAmelCase		):
                  return func(*__UpperCAmelCase ,						**__UpperCAmelCase		)

            if do_eager_mode is True:
                  if use_xla is not False:
                        raise ValueError(
                            '''Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'''		)
                  return run_in_eager_mode
            else:
                  return run_in_graph_mode

      return run_func
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    ["tf.Tensor"]:



      '''simple docstring'''

      _lowerCamelCase			    =				random.Random()
      _lowerCamelCase			    =				[rng.randint(0 ,						vocab_size - 1		) for i in range(batch_size * sequence_length		)]
      return tf.constant(__UpperCAmelCase ,						shape=(batch_size, sequence_length) ,						dtype=tf.intaa		)




class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  42
     A_										=  42
     A_										=  "TensorFlow"



     @property
     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           return tf.__version__



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				float:






           """simple docstring"""
           # initialize GPU on separate process
           _lowerCamelCase			    =				self.args.strategy
           if strategy is None:
                 raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''						)
           _lowerCamelCase			    =				self._prepare_inference_func(A_				,				A_				,				A_						)
           return self._measure_speed(_inference						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				float:






           """simple docstring"""
           _lowerCamelCase			    =				self.args.strategy
           if strategy is None:
                 raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''						)
           _lowerCamelCase			    =				self._prepare_train_func(A_				,				A_				,				A_						)
           return self._measure_speed(_train						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				[Memory, Optional[MemorySummary]]:






           """simple docstring"""
           # initialize GPU on separate process
           if self.args.is_gpu:
                 tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]				,				A_						)
           _lowerCamelCase			    =				self.args.strategy
           if strategy is None:
                 raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''						)
           _lowerCamelCase			    =				self._prepare_inference_func(A_				,				A_				,				A_						)
           return self._measure_memory(_inference						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				[Memory, Optional[MemorySummary]]:






           """simple docstring"""
           if self.args.is_gpu:
                 tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]				,				A_						)
           _lowerCamelCase			    =				self.args.strategy
           if strategy is None:
                 raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''						)

           _lowerCamelCase			    =				self._prepare_train_func(A_				,				A_				,				A_						)
           return self._measure_memory(_train						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				Callable[[], None]:






           """simple docstring"""
           _lowerCamelCase			    =				self.config_dict[model_name]

           if self.args.fpaa:
                 raise NotImplementedError('''Mixed precision is currently not supported.'''						)

           _lowerCamelCase			    =				(
               hasattr(A_				,				'''architectures'''						)
               and isinstance(config.architectures				,				A_						)
               and len(config.architectures						) > 0
           )
           if not self.args.only_pretrain_model and has_model_class_in_config:
                 try:
                       _lowerCamelCase			    =				'''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                       _lowerCamelCase			    =				__import__('''transformers'''				,				fromlist=[model_class]						)
                       _lowerCamelCase			    =				getattr(A_				,				A_						)
                       _lowerCamelCase			    =				model_cls(A_						)
                 except ImportError:
                       raise ImportError(
                           F'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
                           ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''						)
           else:
                 _lowerCamelCase			    =				TF_MODEL_MAPPING[config.__class__](A_						)

           # encoder-decoder has vocab size saved differently
           _lowerCamelCase			    =				config.vocab_size if hasattr(A_				,				'''vocab_size'''						) else config.encoder.vocab_size
           _lowerCamelCase			    =				random_input_ids(A_				,				A_				,				A_						)

           @run_with_tf_optimizations(self.args.eager_mode				,				self.args.use_xla						)
           def encoder_decoder_forward():
                 return model(A_				,				decoder_input_ids=A_				,				training=A_						)

           @run_with_tf_optimizations(self.args.eager_mode				,				self.args.use_xla						)
           def encoder_forward():
                 return model(A_				,				training=A_						)

           _lowerCamelCase			    =				encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

           return _inference



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				Callable[[], None]:






           """simple docstring"""
           _lowerCamelCase			    =				self.config_dict[model_name]

           if self.args.eager_mode is not False:
                 raise ValueError('''Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.'''						)

           if self.args.fpaa:
                 raise NotImplementedError('''Mixed precision is currently not supported.'''						)

           _lowerCamelCase			    =				(
               hasattr(A_				,				'''architectures'''						)
               and isinstance(config.architectures				,				A_						)
               and len(config.architectures						) > 0
           )
           if not self.args.only_pretrain_model and has_model_class_in_config:
                 try:
                       _lowerCamelCase			    =				'''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                       _lowerCamelCase			    =				__import__('''transformers'''				,				fromlist=[model_class]						)
                       _lowerCamelCase			    =				getattr(A_				,				A_						)
                       _lowerCamelCase			    =				model_cls(A_						)
                 except ImportError:
                       raise ImportError(
                           F'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
                           ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''						)
           else:
                 _lowerCamelCase			    =				TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](A_						)

           # encoder-decoder has vocab size saved differently
           _lowerCamelCase			    =				config.vocab_size if hasattr(A_				,				'''vocab_size'''						) else config.encoder.vocab_size
           _lowerCamelCase			    =				random_input_ids(A_				,				A_				,				A_						)

           @run_with_tf_optimizations(self.args.eager_mode				,				self.args.use_xla						)
           def encoder_decoder_train():
                 _lowerCamelCase			    =				model(A_				,				decoder_input_ids=A_				,				labels=A_				,				training=A_						)[0]
                 _lowerCamelCase			    =				tf.gradients(A_				,				model.trainable_variables						)
                 return gradients

           @run_with_tf_optimizations(self.args.eager_mode				,				self.args.use_xla						)
           def encoder_train():
                 _lowerCamelCase			    =				model(A_				,				labels=A_				,				training=A_						)[0]
                 _lowerCamelCase			    =				tf.gradients(A_				,				model.trainable_variables						)
                 return gradients

           _lowerCamelCase			    =				encoder_decoder_train if config.is_encoder_decoder else encoder_train

           return _train



     def 		UpperCamelCase_						(					self				,				A_						) ->				float:






           """simple docstring"""
           with self.args.strategy.scope():
                 try:
                       if self.args.is_tpu or self.args.use_xla:
                             # run additional 10 times to stabilize compilation for tpu
                             logger.info('''Do inference on TPU. Running model 5 times to stabilize compilation'''						)
                             timeit.repeat(A_				,				repeat=1				,				number=5						)

                       # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
                       _lowerCamelCase			    =				timeit.repeat(
                           A_				,				repeat=self.args.repeat				,				number=10				,				)

                       return min(A_						) / 10.0
                 except ResourceExhaustedError as e:
                       self.print_fn(F'Doesn\'t fit on GPU. {e}'						)





     def 		UpperCamelCase_						(					self				,				A_						) ->				[Memory, MemorySummary]:






           """simple docstring"""
           logger.info(
               '''Note that TensorFlow allocates more memory than '''
               '''it might need to speed up computation. '''
               '''The memory reported here corresponds to the memory '''
               '''reported by `nvidia-smi`, which can vary depending '''
               '''on total available memory on the GPU that is used.'''						)
           with self.args.strategy.scope():
                 try:
                       if self.args.trace_memory_line_by_line:
                             if not self.args.eager_mode:
                                   raise ValueError(
                                       '''`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'''
                                       ''' consumption line by line.'''						)
                             _lowerCamelCase			    =				start_memory_tracing('''transformers'''						)

                       if self.args.is_tpu:
                             # tpu
                             raise NotImplementedError(
                                 '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'''
                                 ''' with `args.memory=False`'''						)
                       elif self.args.is_gpu:
                             # gpu
                             if not is_pyanvml_available():
                                   logger.warning(
                                       '''py3nvml not installed, we won\'t log GPU memory usage. '''
                                       '''Install py3nvml (pip install py3nvml) to log information about GPU.'''						)
                                   _lowerCamelCase			    =				'''N/A'''
                             else:
                                   logger.info(
                                       '''Measuring total GPU usage on GPU device. Make sure to not have additional processes'''
                                       ''' running on the same GPU.'''						)
                                   # init nvml
                                   nvml.nvmlInit()
                                   func()
                                   _lowerCamelCase			    =				nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx						)
                                   _lowerCamelCase			    =				nvml.nvmlDeviceGetMemoryInfo(A_						)
                                   _lowerCamelCase			    =				meminfo.used
                                   _lowerCamelCase			    =				Memory(A_						)
                                   # shutdown nvml
                                   nvml.nvmlShutdown()
                       else:
                             # cpu
                             if self.args.trace_memory_line_by_line:
                                   logger.info(
                                       '''When enabling line by line tracing, the max peak memory for CPU is inaccurate in'''
                                       ''' TensorFlow.'''						)
                                   _lowerCamelCase			    =				None
                             else:
                                   _lowerCamelCase			    =				measure_peak_memory_cpu(A_						)
                                   _lowerCamelCase			    =				Memory(A_						) if isinstance(A_				,				A_						) else memory_bytes
                       if self.args.trace_memory_line_by_line:
                             _lowerCamelCase			    =				stop_memory_tracing(A_						)
                             if memory is None:
                                   _lowerCamelCase			    =				summary.total
                       else:
                             _lowerCamelCase			    =				None

                       return memory, summary
                 except ResourceExhaustedError as e:
                       self.print_fn(F'Doesn\'t fit on GPU. {e}'						)
                       return "N/A", None
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'						)						)