

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							:
 def __init__(   self						:   Any    ,  UpperCAmelCase						:   Tuple = "cpu"    ,  UpperCAmelCase						:   List[str] = "openai/clip-vit-large-patch14"			)    ->			None:
     lowerCamelCase__					:   str      				=	device
     lowerCamelCase__					:   Union[str, Any]      				=	CLIPTokenizerFast.from_pretrained(UpperCAmelCase			)
     lowerCamelCase__					:   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]
     lowerCamelCase__					:   Any      				=	[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]
     lowerCamelCase__					:   Optional[int]      				=	torchvision.transforms.Normalize(self.image_mean    ,  self.image_std			)
     lowerCamelCase__					:   str      				=	torchvision.transforms.Resize(224			)
     lowerCamelCase__					:   Optional[int]      				=	torchvision.transforms.CenterCrop(224			)
 def   A_			(   self						:   Dict    ,  UpperCAmelCase						:   Dict			)    ->			Any:
     lowerCamelCase__					:   str      				=	self.resize(UpperCAmelCase			)
     lowerCamelCase__					:   List[str]      				=	self.center_crop(UpperCAmelCase			)
     lowerCamelCase__					:   List[str]      				=	self.normalize(UpperCAmelCase			)
     return images







 def __call__(   self						:   Optional[int]    ,  UpperCAmelCase						:   List[Any]=None    ,  UpperCAmelCase						:   str=None    ,  **UpperCAmelCase						:   Tuple			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Optional[Any]      				=	self.tokenizer(text=UpperCAmelCase    ,  **UpperCAmelCase			)
     lowerCamelCase__					:   Tuple      				=	self.preprocess_img(UpperCAmelCase			)
     lowerCamelCase__					:   Optional[Any]      				=	{key: value.to(self.device			) for (key, value) in encoding.items()}
     return encoding


class      lowerCAmelCase							(      nn.Module    ):
 def __init__(   self						:   List[Any]    ,  UpperCAmelCase						:   Union[str, Any]=10    ,  UpperCAmelCase						:   Optional[Any]=0.0_1    ,  UpperCAmelCase						:   Union[str, Any]=None    ,  UpperCAmelCase						:   Optional[Any]=None    ,  UpperCAmelCase						:   Any=None    ,  UpperCAmelCase						:   str=None    ,  UpperCAmelCase						:   List[str]=None    ,  UpperCAmelCase						:   Dict=None    ,  UpperCAmelCase						:   Union[str, Any]=False    ,  UpperCAmelCase						:   Any=True    ,  UpperCAmelCase						:   Optional[int]="image"    ,  UpperCAmelCase						:   int=True    ,  UpperCAmelCase						:   List[str]=False    ,  UpperCAmelCase						:   str=False    ,  UpperCAmelCase						:   Dict=False    ,  )    ->			None:
     super().__init__()
     lowerCamelCase__					:   Optional[Any]      				=	None
     lowerCamelCase__					:   Any      				=	device if device else get_device()
     if vqgan:
         lowerCamelCase__					:   Tuple      				=	vqgan
     else:
         lowerCamelCase__					:   Optional[Any]      				=	load_vqgan(self.device    ,  conf_path=UpperCAmelCase    ,  ckpt_path=UpperCAmelCase			)
     self.vqgan.eval()
     if clip:
         lowerCamelCase__					:   List[str]      				=	clip
     else:
         lowerCamelCase__					:   Optional[Any]      				=	CLIPModel.from_pretrained('openai/clip-vit-base-patch32'			)
     self.clip.to(self.device			)
     lowerCamelCase__					:   Optional[int]      				=	ProcessorGradientFlow(device=self.device			)

     lowerCamelCase__					:   Union[str, Any]      				=	iterations
     lowerCamelCase__					:   str      				=	lr
     lowerCamelCase__					:   List[Any]      				=	log
     lowerCamelCase__					:   List[str]      				=	make_grid
     lowerCamelCase__					:   List[Any]      				=	return_val
     lowerCamelCase__					:   Union[str, Any]      				=	quantize
     lowerCamelCase__					:   List[Any]      				=	self.vqgan.decoder.z_shape
 def   A_			(   self						:   List[str]    ,  UpperCAmelCase						:   Optional[Any]=None    ,  UpperCAmelCase						:   List[Any]=None    ,  UpperCAmelCase						:   Tuple=5    ,  UpperCAmelCase						:   Union[str, Any]=True			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	[]
     if output_path is None:
         lowerCamelCase__					:   Dict      				=	"./animation.gif"
     if input_path is None:
         lowerCamelCase__					:   Optional[Any]      				=	self.save_path
     lowerCamelCase__					:   Union[str, Any]      				=	sorted(glob(input_path + '/*'			)			)
     if not len(UpperCAmelCase			):
         raise ValueError(
             'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
             ' function?)'			)
     if len(UpperCAmelCase			) == 1:
         print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'			)
     lowerCamelCase__					:   List[str]      				=	total_duration / len(UpperCAmelCase			)
     lowerCamelCase__					:   str      				=	[frame_duration] * len(UpperCAmelCase			)
     if extend_frames:
         lowerCamelCase__					:   Dict      				=	1.5
         lowerCamelCase__					:   List[str]      				=	3
     for file_name in paths:
         if file_name.endswith('.png'			):
             images.append(imageio.imread(UpperCAmelCase			)			)
     imageio.mimsave(UpperCAmelCase    ,  UpperCAmelCase    ,  duration=UpperCAmelCase			)
     print(F"""gif saved to {output_path}"""			)
 def   A_			(   self						:   Optional[Any]    ,  UpperCAmelCase						:   int=None    ,  UpperCAmelCase						:   List[str]=None			)    ->			str:
     if not (path or img):
         raise ValueError('Input either path or tensor'			)
     if img is not None:
         raise NotImplementedError
     lowerCamelCase__					:   Dict      				=	preprocess(Image.open(UpperCAmelCase			)    ,  target_image_size=256			).to(self.device			)
     lowerCamelCase__					:   Tuple      				=	preprocess_vqgan(UpperCAmelCase			)
     lowerCamelCase__					:   int      				=	self.vqgan.encode(UpperCAmelCase			)
     return z
 def   A_			(   self						:   Optional[Any]    ,  UpperCAmelCase						:   Optional[int]			)    ->			List[Any]:
     lowerCamelCase__					:   Tuple      				=	self.latent.detach().requires_grad_()
     lowerCamelCase__					:   Dict      				=	base_latent + transform_vector
     if self.quantize:
         lowerCamelCase__					:   str      				=	self.vqgan.quantize(UpperCAmelCase			)
     else:
         lowerCamelCase__					:   int      				=	trans_latent
     return self.vqgan.decode(UpperCAmelCase			)
 def   A_			(   self						:   Dict    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   List[str]=None			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Tuple      				=	self.clip_preprocessor(text=UpperCAmelCase    ,  images=UpperCAmelCase    ,  return_tensors='pt'    ,  padding=UpperCAmelCase			)
     lowerCamelCase__					:   Tuple      				=	self.clip(**UpperCAmelCase			)
     lowerCamelCase__					:   Optional[Any]      				=	clip_outputs.logits_per_image
     if weights is not None:
         lowerCamelCase__					:   str      				=	similarity_logits * weights
     return similarity_logits.sum()
 def   A_			(   self						:   Any    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   Optional[int]			)    ->			int:
     lowerCamelCase__					:   List[str]      				=	self._get_clip_similarity(pos_prompts['prompts']    ,  UpperCAmelCase    ,  weights=(1 / pos_prompts['weights'])			)
     if neg_prompts:
         lowerCamelCase__					:   Tuple      				=	self._get_clip_similarity(neg_prompts['prompts']    ,  UpperCAmelCase    ,  weights=neg_prompts['weights']			)
     else:
         lowerCamelCase__					:   str      				=	torch.tensor([1]    ,  device=self.device			)
     lowerCamelCase__					:   Optional[int]      				=	-torch.log(UpperCAmelCase			) + torch.log(UpperCAmelCase			)
     return loss
 def   A_			(   self						:   Optional[Any]    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   Union[str, Any]			)    ->			Any:
     lowerCamelCase__					:   int      				=	torch.randn_like(self.latent    ,  requires_grad=UpperCAmelCase    ,  device=self.device			)
     lowerCamelCase__					:   Any      				=	torch.optim.Adam([vector]    ,  lr=self.lr			)

     for i in range(self.iterations			):
         optim.zero_grad()
         lowerCamelCase__					:   Tuple      				=	self._add_vector(UpperCAmelCase			)
         lowerCamelCase__					:   Union[str, Any]      				=	loop_post_process(UpperCAmelCase			)
         lowerCamelCase__					:   Optional[Any]      				=	self._get_CLIP_loss(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)
         print('CLIP loss'    ,  UpperCAmelCase			)
         if self.log:
             wandb.log({'CLIP Loss': clip_loss}			)
         clip_loss.backward(retain_graph=UpperCAmelCase			)
         optim.step()
         if self.return_val == "image":
             yield custom_to_pil(transformed_img[0]			)
         else:
             yield vector
 def   A_			(   self						:   Any    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   Dict			)    ->			List[str]:
     wandb.init(reinit=UpperCAmelCase    ,  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__					:   List[str]      				=	Image.open(UpperCAmelCase			)
         lowerCamelCase__					:   int      				=	image.resize((256, 256)			)
         wandb.log('Original Image'    ,  wandb.Image(UpperCAmelCase			)			)
 def   A_			(   self						:   Any    ,  UpperCAmelCase						:   Tuple			)    ->			int:
     if not prompts:
         return []
     lowerCamelCase__					:   int      				=	[]
     lowerCamelCase__					:   Any      				=	[]
     if isinstance(UpperCAmelCase    ,  UpperCAmelCase			):
         lowerCamelCase__					:   int      				=	[prompt.strip() for prompt in prompts.split('|'			)]
     for prompt in prompts:
         if isinstance(UpperCAmelCase    ,  (tuple, list)			):
             lowerCamelCase__					:   List[Any]      				=	prompt[0]
             lowerCamelCase__					:   List[Any]      				=	float(prompt[1]			)
         elif ":" in prompt:
             lowerCamelCase__					:   str      				=	prompt.split(':'			)
             lowerCamelCase__					:   int      				=	float(UpperCAmelCase			)
         else:
             lowerCamelCase__					:   int      				=	prompt
             lowerCamelCase__					:   Tuple      				=	1.0
         processed_prompts.append(UpperCAmelCase			)
         weights.append(UpperCAmelCase			)
     return {
         "prompts": processed_prompts,
         "weights": torch.tensor(UpperCAmelCase    ,  device=self.device			),
     }







 def   A_			(   self						:   int    ,  UpperCAmelCase						:   Optional[Any]    ,  UpperCAmelCase						:   str=None    ,  UpperCAmelCase						:   Optional[Any]=None    ,  UpperCAmelCase						:   Any=True    ,  UpperCAmelCase						:   Dict=False    ,  UpperCAmelCase						:   int=True    ,  UpperCAmelCase						:   Optional[Any]=True    ,  UpperCAmelCase						:   Any=None    ,  )    ->			Union[str, Any]:
     if image_path:
         lowerCamelCase__					:   Tuple      				=	self._get_latent(UpperCAmelCase			)
     else:
         lowerCamelCase__					:   str      				=	torch.randn(self.latent_dim    ,  device=self.device			)
     if self.log:
         self._init_logging(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)

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

     lowerCamelCase__					:   List[Any]      				=	self.vqgan.decode(self.latent			)[0]
     if show_intermediate:
         print('Original Image'			)
         show_pil(custom_to_pil(UpperCAmelCase			)			)

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












from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Any:
    lowerCamelCase__					:   Any      				=	[]
    for part_id in partition_order:
        lowerCamelCase__					:   List[str]      				=	df.where(F"""SPARK_PARTITION_ID() = {part_id}"""				).collect()
        for row_idx, row in enumerate(_UpperCAmelCase				):
            expected_row_ids_and_row_dicts.append((F"""{part_id}_{row_idx}""", row.asDict())				)
    return expected_row_ids_and_row_dicts


@require_not_windows
@require_dill_gt_0_3_2
def  SCREAMING_SNAKE_CASE   ( )    ->  List[Any]:
    lowerCamelCase__					:   List[str]      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   Any      				=	spark.range(100				).repartition(1				)
    lowerCamelCase__					:   str      				=	Spark(_UpperCAmelCase				)
    # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
    # that each partition can hold 2 rows.
    spark_builder._repartition_df_if_needed(max_shard_size=16				)
    # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
    assert spark_builder.df.rdd.getNumPartitions() == 50


@require_not_windows
@require_dill_gt_0_3_2
def  SCREAMING_SNAKE_CASE   ( )    ->  Tuple:
    lowerCamelCase__					:   Any      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   Optional[int]      				=	spark.range(10				).repartition(2				)
    lowerCamelCase__					:   Dict      				=	[1, 0]
    lowerCamelCase__					:   str      				=	_generate_iterable_examples(_UpperCAmelCase		,			_UpperCAmelCase				)  # Reverse the partitions.
    lowerCamelCase__					:   Any      				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase		,			_UpperCAmelCase				)

    for i, (row_id, row_dict) in enumerate(generate_fn()				):
        lowerCamelCase__				,							lowerCamelCase__					:   Tuple      				=	expected_row_ids_and_row_dicts[i]
        assert row_id == expected_row_id
        assert row_dict == expected_row_dict


@require_not_windows
@require_dill_gt_0_3_2
def  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    lowerCamelCase__					:   Dict      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   Dict      				=	spark.range(10				).repartition(1				)
    lowerCamelCase__					:   List[str]      				=	SparkExamplesIterable(_UpperCAmelCase				)
    assert it.n_shards == 1
    for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
        assert row_id == F"""0_{i}"""
        assert row_dict == {"id": i}


@require_not_windows
@require_dill_gt_0_3_2
def  SCREAMING_SNAKE_CASE   ( )    ->  Any:
    lowerCamelCase__					:   Dict      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   str      				=	spark.range(30				).repartition(3				)
    # Mock the generator so that shuffle reverses the partition indices.
    with patch('numpy.random.Generator'				) as generator_mock:
        lowerCamelCase__					:   int      				=	lambda _UpperCAmelCase				: x.reverse()
        lowerCamelCase__					:   Any      				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase		,			[2, 1, 0]				)

        lowerCamelCase__					:   Tuple      				=	SparkExamplesIterable(_UpperCAmelCase				).shuffle_data_sources(_UpperCAmelCase				)
        assert shuffled_it.n_shards == 3
        for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
            lowerCamelCase__				,							lowerCamelCase__					:   Union[str, Any]      				=	expected_row_ids_and_row_dicts[i]
            assert row_id == expected_row_id
            assert row_dict == expected_row_dict


@require_not_windows
@require_dill_gt_0_3_2
def  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    lowerCamelCase__					:   Optional[Any]      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   Tuple      				=	spark.range(20				).repartition(4				)

    # Partitions 0 and 2
    lowerCamelCase__					:   Optional[Any]      				=	SparkExamplesIterable(_UpperCAmelCase				).shard_data_sources(worker_id=0		,			num_workers=2				)
    assert shard_it_a.n_shards == 2
    lowerCamelCase__					:   List[str]      				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase		,			[0, 2]				)
    for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
        lowerCamelCase__				,							lowerCamelCase__					:   int      				=	expected_row_ids_and_row_dicts_a[i]
        assert row_id == expected_row_id
        assert row_dict == expected_row_dict

    # Partitions 1 and 3
    lowerCamelCase__					:   Dict      				=	SparkExamplesIterable(_UpperCAmelCase				).shard_data_sources(worker_id=1		,			num_workers=2				)
    assert shard_it_a.n_shards == 2
    lowerCamelCase__					:   Optional[int]      				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase		,			[1, 3]				)
    for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
        lowerCamelCase__				,							lowerCamelCase__					:   Any      				=	expected_row_ids_and_row_dicts_a[i]
        assert row_id == expected_row_id
        assert row_dict == expected_row_dict


@require_not_windows
@require_dill_gt_0_3_2
def  SCREAMING_SNAKE_CASE   ( )    ->  Dict:
    lowerCamelCase__					:   int      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   str      				=	spark.range(100				).repartition(1				)
    lowerCamelCase__					:   Dict      				=	Spark(_UpperCAmelCase				)
    # Choose a small max_shard_size for maximum partitioning.
    spark_builder._repartition_df_if_needed(max_shard_size=1				)
    # The new number of partitions should not be greater than the number of rows.
    assert spark_builder.df.rdd.getNumPartitions() == 100





