





import math
from typing import Callable, List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[Any]      ,				snake_case__	:List[Any]      ,				snake_case__	:Any=[]  )   ->    Any:
  _lowercase						    =			size[0] - overlap_pixels * 2
  _lowercase						    =			size[1] - overlap_pixels * 2
  for letter in ["l", "r"]:
    if letter in remove_borders:
      size_x += overlap_pixels
  for letter in ["t", "b"]:
    if letter in remove_borders:
      size_y += overlap_pixels
  _lowercase						    =			np.ones((size_y, size_x)      ,				dtype=np.uinta  ) * 255
  _lowercase						    =			np.pad(snake_case__      ,				mode='linear_ramp'      ,				pad_width=snake_case__      ,				end_values=0  )

  if "l" in remove_borders:
    _lowercase						    =			mask[:, overlap_pixels : mask.shape[1]]
  if "r" in remove_borders:
    _lowercase						    =			mask[:, 0 : mask.shape[1] - overlap_pixels]
  if "t" in remove_borders:
    _lowercase						    =			mask[overlap_pixels : mask.shape[0], :]
  if "b" in remove_borders:
    _lowercase						    =			mask[0 : mask.shape[0] - overlap_pixels, :]
  return mask

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Optional[Any]      ,				snake_case__	:List[str]      ,				snake_case__	:List[str]  )   ->    Dict:
  return max(snake_case__      ,				min(snake_case__      ,				snake_case__  )  )

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:[int]      ,				snake_case__	:[int]      ,				snake_case__	:[int]  )   ->    Optional[Any]:
  return (
      clamp(rect[0]      ,				min[0]      ,				max[0]  ),
      clamp(rect[1]      ,				min[1]      ,				max[1]  ),
      clamp(rect[2]      ,				min[0]      ,				max[0]  ),
      clamp(rect[3]      ,				min[1]      ,				max[1]  ),
  )

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:[int]      ,				snake_case__	:int      ,				snake_case__	:[int]  )   ->    Dict:
  _lowercase						    =			list(snake_case__  )
  rect[0] -= overlap
  rect[1] -= overlap
  rect[2] += overlap
  rect[3] += overlap
  _lowercase						    =			clamp_rect(snake_case__      ,				[0, 0]      ,				[image_size[0], image_size[1]]  )
  return rect

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Tuple      ,				snake_case__	:int      ,				snake_case__	:Dict      ,				snake_case__	:str  )   ->    Tuple:
  _lowercase						    =			Image.new('RGB'      ,				(tile.size[0] + original_slice, tile.size[1])  )
  result.paste(
      original_image.resize((tile.size[0], tile.size[1])      ,				Image.BICUBIC  ).crop(
          (slice_x, 0, slice_x + original_slice, tile.size[1])  )      ,				(0, 0)      ,				)
  result.paste(snake_case__      ,				(original_slice, 0)  )
  return result

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Tuple      ,				snake_case__	:Tuple  )   ->    List[str]:
  _lowercase						    =			(original_image_slice * 4, 0, tile.size[0], tile.size[1])
  _lowercase						    =			tile.crop(snake_case__  )
  return tile

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[str]      ,				snake_case__	:Optional[int]  )   ->    Tuple:
  _lowercase						    =			n % d
  return n - divisor





class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       def __init__(     self						:						str    ,__A						:						AutoencoderKL    ,__A						:						CLIPTextModel    ,__A						:						CLIPTokenizer    ,__A						:						UNetaDConditionModel    ,__A						:						DDPMScheduler    ,__A						:						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]    ,__A						:						int = 350    ,)    ->       Union[str, Any]:
         super().__init__(
             vae=__A    ,text_encoder=__A    ,tokenizer=__A    ,unet=__A    ,low_res_scheduler=__A    ,scheduler=__A    ,max_noise_level=__A    ,)







       def  __UpperCAmelCase			(     self						:						Dict    ,__A						:						Dict    ,__A						:						Tuple    ,__A						:						List[Any]    ,__A						:						str    ,__A						:						Optional[Any]    ,__A						:						Union[str, Any]    ,__A						:						Optional[int]    ,**__A						:						List[Any]	)    ->       int:
         torch.manual_seed(0	)
         _lowercase						    =			(
             min(image.size[0] - (tile_size + original_image_slice)    ,x * tile_size	),
             min(image.size[1] - (tile_size + original_image_slice)    ,y * tile_size	),
             min(image.size[0]    ,(x + 1) * tile_size	),
             min(image.size[1]    ,(y + 1) * tile_size	),
         )
         _lowercase						    =			add_overlap_rect(__A    ,__A    ,image.size	)
         _lowercase						    =			image.crop(__A	)
         _lowercase						    =			((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
         _lowercase						    =			translated_slice_x - (original_image_slice / 2)
         _lowercase						    =			max(0    ,__A	)
         _lowercase						    =			squeeze_tile(__A    ,__A    ,__A    ,__A	)
         _lowercase						    =			to_input.size
         _lowercase						    =			to_input.resize((tile_size, tile_size)    ,Image.BICUBIC	)
         _lowercase						    =			super(__A    ,self	).__call__(image=__A    ,**__A	).images[0]
         _lowercase						    =			upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)    ,Image.BICUBIC	)
         _lowercase						    =			unsqueeze_tile(__A    ,__A	)
         _lowercase						    =			upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)    ,Image.BICUBIC	)
         _lowercase						    =			[]
         if x == 0:
           remove_borders.append('l'	)
         elif crop_rect[2] == image.size[0]:
           remove_borders.append('r'	)
         if y == 0:
           remove_borders.append('t'	)
         elif crop_rect[3] == image.size[1]:
           remove_borders.append('b'	)
         _lowercase						    =			Image.fromarray(
             make_transparency_mask(
                 (upscaled_tile.size[0], upscaled_tile.size[1])    ,tile_border * 4    ,remove_borders=__A	)    ,mode='L'    ,)
         final_image.paste(
             __A    ,(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)    ,__A	)







       @torch.no_grad()
       def __call__(     self						:						List[Any]    ,__A						:						Union[str, List[str]]    ,__A						:						Union[PIL.Image.Image, List[PIL.Image.Image]]    ,__A						:						int = 75    ,__A						:						float = 9.0    ,__A						:						int = 50    ,__A						:						Optional[Union[str, List[str]]] = None    ,__A						:						Optional[int] = 1    ,__A						:						float = 0.0    ,__A						:						Optional[torch.Generator] = None    ,__A						:						Optional[torch.FloatTensor] = None    ,__A						:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None    ,__A						:						int = 1    ,__A						:						int = 128    ,__A						:						int = 32    ,__A						:						int = 32    ,)    ->       int:
         _lowercase						    =			Image.new('RGB'    ,(image.size[0] * 4, image.size[1] * 4)	)
         _lowercase						    =			math.ceil(image.size[0] / tile_size	)
         _lowercase						    =			math.ceil(image.size[1] / tile_size	)
         _lowercase						    =			tcx * tcy
         _lowercase						    =			0
         for y in range(__A	):
           for x in range(__A	):
             self._process_tile(
                 __A    ,__A    ,__A    ,__A    ,__A    ,__A    ,__A    ,prompt=__A    ,num_inference_steps=__A    ,guidance_scale=__A    ,noise_level=__A    ,negative_prompt=__A    ,num_images_per_prompt=__A    ,eta=__A    ,generator=__A    ,latents=__A    ,)
             current_count += 1
             if callback is not None:
               callback({'progress': current_count / total_tile_count, 'image': final_image}	)
         return final_image

def 				SCREAMING_SNAKE_CASE__							(	)   ->    List[Any]:
  # Run a demo
  _lowercase						    =			'stabilityai/stable-diffusion-x4-upscaler'
  _lowercase						    =			StableDiffusionTiledUpscalePipeline.from_pretrained(snake_case__      ,				revision='fp16'      ,				torch_dtype=torch.floataa  )
  _lowercase						    =			pipe.to('cuda'  )
  _lowercase						    =			Image.open('../../docs/source/imgs/diffusers_library.jpg'  )

  def callback(snake_case__	:int  ):
    print(F"""progress: {obj["progress"]:.4f}"""  )
    obj["image"].save('diffusers_library_progress.jpg'  )

  _lowercase						    =			pipe(image=snake_case__      ,				prompt='Black font, white background, vector'      ,				noise_level=40      ,				callback=snake_case__  )
  final_image.save('diffusers_library.jpg'  )


if __name__ == "__main__":
      main()





import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
				raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")

# TF training parameters
SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       False
SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       False

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[Any]:


			'''simple docstring'''







			return TrainCommand(SCREAMING_SNAKE_CASE )



class       a_       (     SCREAMING_SNAKE_CASE__     ):





	@staticmethod
	def 						A_(							SCREAMING_SNAKE_CASE      )  ->					Optional[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						parser.add_parser('train'      ,     help='CLI tool to train a model on a task.'      )

				train_parser.add_argument(
				    '--train_data'      ,     type=SCREAMING_SNAKE_CASE      ,     required=SCREAMING_SNAKE_CASE      ,     help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'      ,     )
				train_parser.add_argument(
				    '--column_label'      ,     type=SCREAMING_SNAKE_CASE      ,     default=0      ,     help='Column of the dataset csv file with example labels.'      )
				train_parser.add_argument(
				    '--column_text'      ,     type=SCREAMING_SNAKE_CASE      ,     default=1      ,     help='Column of the dataset csv file with example texts.'      )
				train_parser.add_argument(
				    '--column_id'      ,     type=SCREAMING_SNAKE_CASE      ,     default=2      ,     help='Column of the dataset csv file with example ids.'      )
				train_parser.add_argument(
				    '--skip_first_row'      ,     action='store_true'      ,     help='Skip the first row of the csv file (headers).'      )

				train_parser.add_argument('--validation_data'      ,     type=SCREAMING_SNAKE_CASE      ,     default=''      ,     help='path to validation dataset.'      )
				train_parser.add_argument(
				    '--validation_split'      ,     type=SCREAMING_SNAKE_CASE      ,     default=0.1      ,     help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.'      ,     )

				train_parser.add_argument('--output'      ,     type=SCREAMING_SNAKE_CASE      ,     default='./'      ,     help='path to saved the trained model.'      )

				train_parser.add_argument(
				    '--task'      ,     type=SCREAMING_SNAKE_CASE      ,     default='text_classification'      ,     help='Task to train the model on.'      )
				train_parser.add_argument(
				    '--model'      ,     type=SCREAMING_SNAKE_CASE      ,     default='bert-base-uncased'      ,     help='Model\'s name or path to stored model.'      )
				train_parser.add_argument('--train_batch_size'      ,     type=SCREAMING_SNAKE_CASE      ,     default=32      ,     help='Batch size for training.'      )
				train_parser.add_argument('--valid_batch_size'      ,     type=SCREAMING_SNAKE_CASE      ,     default=64      ,     help='Batch size for validation.'      )
				train_parser.add_argument('--learning_rate'      ,     type=SCREAMING_SNAKE_CASE      ,     default=3e-5      ,     help='Learning rate.'      )
				train_parser.add_argument('--adam_epsilon'      ,     type=SCREAMING_SNAKE_CASE      ,     default=1e-08      ,     help='Epsilon for Adam optimizer.'      )
				train_parser.set_defaults(func=SCREAMING_SNAKE_CASE      )





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						logging.get_logger('transformers-cli/training'      )

				SCREAMING_SNAKE_CASE_								=						'tf' if is_tf_available() else 'torch'

				os.makedirs(args.output      ,     exist_ok=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						args.output

				SCREAMING_SNAKE_CASE_								=						args.column_label
				SCREAMING_SNAKE_CASE_								=						args.column_text
				SCREAMING_SNAKE_CASE_								=						args.column_id

				self.logger.info(f'Loading {args.task} pipeline for {args.model}'      )
				if args.task == "text_classification":
							SCREAMING_SNAKE_CASE_								=						TextClassificationPipeline.from_pretrained(args.model      )
				elif args.task == "token_classification":
							raise NotImplementedError
				elif args.task == "question_answering":
							raise NotImplementedError

				self.logger.info(f'Loading dataset from {args.train_data}'      )
				SCREAMING_SNAKE_CASE_								=						Processor.create_from_csv(
				    args.train_data      ,     column_label=args.column_label      ,     column_text=args.column_text      ,     column_id=args.column_id      ,     skip_first_row=args.skip_first_row      ,     )
				SCREAMING_SNAKE_CASE_								=						None
				if args.validation_data:
							self.logger.info(f'Loading validation dataset from {args.validation_data}'      )
							SCREAMING_SNAKE_CASE_								=						Processor.create_from_csv(
							    args.validation_data      ,     column_label=args.column_label      ,     column_text=args.column_text      ,     column_id=args.column_id      ,     skip_first_row=args.skip_first_row      ,     )

				SCREAMING_SNAKE_CASE_								=						args.validation_split
				SCREAMING_SNAKE_CASE_								=						args.train_batch_size
				SCREAMING_SNAKE_CASE_								=						args.valid_batch_size
				SCREAMING_SNAKE_CASE_								=						args.learning_rate
				SCREAMING_SNAKE_CASE_								=						args.adam_epsilon





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				if self.framework == "tf":
							return self.run_tf()
				return self.run_torch()





	def 						A_(							self      )  ->					Optional[int]:




				"""simple docstring"""



				raise NotImplementedError





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				self.pipeline.fit(
				    self.train_dataset      ,     validation_data=self.valid_dataset      ,     validation_split=self.validation_split      ,     learning_rate=self.learning_rate      ,     adam_epsilon=self.adam_epsilon      ,     train_batch_size=self.train_batch_size      ,     valid_batch_size=self.valid_batch_size      ,     )

				# Save trained pipeline
				self.pipeline.save_pretrained(self.output      )
