






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 		_snake_case  :






			def __init__(       self		,					a = "cpu"		,					a = "openai/clip-vit-large-patch14")      ->       None:
						SCREAMING_SNAKE_CASE						= device
						SCREAMING_SNAKE_CASE						= CLIPTokenizerFast.from_pretrained(a)
						SCREAMING_SNAKE_CASE						= [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
						SCREAMING_SNAKE_CASE						= [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
						SCREAMING_SNAKE_CASE						= torchvision.transforms.Normalize(self.image_mean		,					self.image_std)
						SCREAMING_SNAKE_CASE						= torchvision.transforms.Resize(224)
						SCREAMING_SNAKE_CASE						= torchvision.transforms.CenterCrop(224)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= self.resize(a)
						SCREAMING_SNAKE_CASE						= self.center_crop(a)
						SCREAMING_SNAKE_CASE						= self.normalize(a)
						return images







			def __call__(       self		,					a=None		,					a=None		,					**a)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= self.tokenizer(text=a		,					**a)
						SCREAMING_SNAKE_CASE						= self.preprocess_img(a)
						SCREAMING_SNAKE_CASE						= {key: value.to(self.device) for (key, value) in encoding.items()}
						return encoding



class 		_snake_case  (  nn.Module					):






			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:
						super().__init__()
						SCREAMING_SNAKE_CASE						= None
						SCREAMING_SNAKE_CASE						= device if device else get_device()
						if vqgan:
									SCREAMING_SNAKE_CASE						= vqgan
						else:
									SCREAMING_SNAKE_CASE						= load_vqgan(self.device		,					conf_path=a		,					ckpt_path=a)
						self.vqgan.eval()
						if clip:
									SCREAMING_SNAKE_CASE						= clip
						else:
									SCREAMING_SNAKE_CASE						= CLIPModel.from_pretrained('openai/clip-vit-base-patch32')
						self.clip.to(self.device)
						SCREAMING_SNAKE_CASE						= ProcessorGradientFlow(device=self.device)

						SCREAMING_SNAKE_CASE						= iterations
						SCREAMING_SNAKE_CASE						= lr
						SCREAMING_SNAKE_CASE						= log
						SCREAMING_SNAKE_CASE						= make_grid
						SCREAMING_SNAKE_CASE						= return_val
						SCREAMING_SNAKE_CASE						= quantize
						SCREAMING_SNAKE_CASE						= self.vqgan.decoder.z_shape






			def    SCREAMING_SNAKE_CASE__    (       self		,					a=None		,					a=None		,					a=5		,					a=True)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= []
						if output_path is None:
									SCREAMING_SNAKE_CASE						= './animation.gif'
						if input_path is None:
									SCREAMING_SNAKE_CASE						= self.save_path
						SCREAMING_SNAKE_CASE						= 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?)')
						SCREAMING_SNAKE_CASE						= total_duration / len(a)
						SCREAMING_SNAKE_CASE						= [frame_duration] * len(a)
						if extend_frames:
									SCREAMING_SNAKE_CASE						= 1.5
									SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self		,					a=None		,					a=None)      ->       List[Any]:
						if not (path or img):
									raise ValueError('Input either path or tensor')
						if img is not None:
									raise NotImplementedError
						SCREAMING_SNAKE_CASE						= preprocess(Image.open(a)		,					target_image_size=256).to(self.device)
						SCREAMING_SNAKE_CASE						= preprocess_vqgan(a)
						SCREAMING_SNAKE_CASE				,						*SCREAMING_SNAKE_CASE						= self.vqgan.encode(a)
						return z






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= self.latent.detach().requires_grad_()
						SCREAMING_SNAKE_CASE						= base_latent + transform_vector
						if self.quantize:
									SCREAMING_SNAKE_CASE				,						*SCREAMING_SNAKE_CASE						= self.vqgan.quantize(a)
						else:
									SCREAMING_SNAKE_CASE						= trans_latent
						return self.vqgan.decode(a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a=None)      ->       Any:
						SCREAMING_SNAKE_CASE						= self.clip_preprocessor(text=a		,					images=a		,					return_tensors='pt'		,					padding=a)
						SCREAMING_SNAKE_CASE						= self.clip(**a)
						SCREAMING_SNAKE_CASE						= clip_outputs.logits_per_image
						if weights is not None:
									SCREAMING_SNAKE_CASE						= similarity_logits * weights
						return similarity_logits.sum()






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a)      ->       Any:
						SCREAMING_SNAKE_CASE						= self._get_clip_similarity(pos_prompts['prompts']		,					a		,					weights=(1 / pos_prompts['weights']))
						if neg_prompts:
									SCREAMING_SNAKE_CASE						= self._get_clip_similarity(neg_prompts['prompts']		,					a		,					weights=neg_prompts['weights'])
						else:
									SCREAMING_SNAKE_CASE						= torch.tensor([1]		,					device=self.device)
						SCREAMING_SNAKE_CASE						= -torch.log(a) + torch.log(a)
						return loss






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a)      ->       str:
						SCREAMING_SNAKE_CASE						= torch.randn_like(self.latent		,					requires_grad=a		,					device=self.device)
						SCREAMING_SNAKE_CASE						= torch.optim.Adam([vector]		,					lr=self.lr)

						for i in range(self.iterations):
									optim.zero_grad()
									SCREAMING_SNAKE_CASE						= self._add_vector(a)
									SCREAMING_SNAKE_CASE						= loop_post_process(a)
									SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a)      ->       int:
						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:
									SCREAMING_SNAKE_CASE						= Image.open(a)
									SCREAMING_SNAKE_CASE						= image.resize((256, 256))
									wandb.log('Original Image'		,					wandb.Image(a))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
						if not prompts:
									return []
						SCREAMING_SNAKE_CASE						= []
						SCREAMING_SNAKE_CASE						= []
						if isinstance(a		,					a):
									SCREAMING_SNAKE_CASE						= [prompt.strip() for prompt in prompts.split('|')]
						for prompt in prompts:
									if isinstance(a		,					(tuple, list)):
												SCREAMING_SNAKE_CASE						= prompt[0]
												SCREAMING_SNAKE_CASE						= float(prompt[1])
									elif ":" in prompt:
												SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= prompt.split(':')
												SCREAMING_SNAKE_CASE						= float(a)
									else:
												SCREAMING_SNAKE_CASE						= prompt
												SCREAMING_SNAKE_CASE						= 1.0
									processed_prompts.append(a)
									weights.append(a)
						return {
						    "prompts": processed_prompts,
						    "weights": torch.tensor(a		,					device=self.device),
						}







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a=None		,					a=None		,					a=True		,					a=False		,					a=True		,					a=True		,					a=None		,					)      ->       str:
						if image_path:
									SCREAMING_SNAKE_CASE						= self._get_latent(a)
						else:
									SCREAMING_SNAKE_CASE						= 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."
						SCREAMING_SNAKE_CASE						= self.process_prompts(a)
						SCREAMING_SNAKE_CASE						= self.process_prompts(a)
						if save_final and save_path is None:
									SCREAMING_SNAKE_CASE						= os.path.join('./outputs/'		,					'_'.join(pos_prompts['prompts']))
						if not os.path.exists(a):
									os.makedirs(a)
						else:
									SCREAMING_SNAKE_CASE						= save_path + '_' + get_timestamp()
									os.makedirs(a)
						SCREAMING_SNAKE_CASE						= save_path

						SCREAMING_SNAKE_CASE						= self.vqgan.decode(self.latent)[0]
						if show_intermediate:
									print('Original Image')
									show_pil(custom_to_pil(a))

						SCREAMING_SNAKE_CASE						= 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'''))












# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


a_		:						Dict             =    Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

a_		:						List[str]             =    {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

a_		:						Union[str, Any]             =    'zero2'
a_		:						List[Any]             =    'zero3'
a_		:						List[str]             =    [ZEROa, ZEROa]






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			# customize the test name generator function as we want both params to appear in the sub-test
			# name, as by default it shows only the first param
			SCREAMING_SNAKE_CASE						= parameterized.to_safe_name('_'.join(str(_UpperCAmelCase) for x in param.args))
			return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
a_		:						List[Any]             =    list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 		_snake_case  (  A__					):






			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[str]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@require_torch_multi_gpu
			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       int:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@require_torch_multi_gpu
			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Dict:
						# XXX: run_asr is premature and doesn't save any results
						# so all we check for now is that the process didn't fail
						pass






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = 10		,					a = True		,					a = True		,					a = True		,					)      ->       Dict:
						SCREAMING_SNAKE_CASE						= models[model]

						SCREAMING_SNAKE_CASE						= self.run_trainer(
						    stage=a		,					model_name=a		,					eval_steps=a		,					num_train_epochs=1		,					distributed=a		,					fpaa=a		,					)

						self.do_checks(a)

						return output_dir






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = 10		,					a = 1		,					a = True		,					a = True		,					)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.get_auto_remove_tmp_dir('./xxx'		,					after=a)
						SCREAMING_SNAKE_CASE						= f'''
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(a)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        '''.split()

						if fpaa:
									args.extend(['--fp16'])

						# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
						# hence the separate config files
						SCREAMING_SNAKE_CASE						= f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
						SCREAMING_SNAKE_CASE						= [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
						SCREAMING_SNAKE_CASE						= self.get_launcher(a)

						SCREAMING_SNAKE_CASE						= launcher + script + args + ds_args
						# keep for quick debug
						# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
						execute_subprocess_async(a		,					env=self.get_env())

						return output_dir







			def    SCREAMING_SNAKE_CASE__    (       self		,					a=False)      ->       Optional[int]:
						# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
						# - it won't be able to handle that
						# 2. for now testing with just 2 gpus max (since some quality tests may give different
						# results with mode gpus because we use very little data)
						SCREAMING_SNAKE_CASE						= min(2		,					get_gpu_count()) if distributed else 1
						return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()




