




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  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__=[]   )			->							List[Any]:





     '''simple docstring'''


     lowercase :       int       =			size[0] - overlap_pixels * 2
     lowercase :       List[Any]       =			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 :       Tuple       =			np.ones((size_y, size_x)			,		dtype=np.uinta   ) * 2_55
     lowercase :       int       =			np.pad(__magic_name__			,		mode='''linear_ramp'''			,		pad_width=__magic_name__			,		end_values=0   )

     if "l" in remove_borders:
          lowercase :       int       =			mask[:, overlap_pixels : mask.shape[1]]
     if "r" in remove_borders:
          lowercase :       int       =			mask[:, 0 : mask.shape[1] - overlap_pixels]
     if "t" in remove_borders:
          lowercase :       str       =			mask[overlap_pixels : mask.shape[0], :]
     if "b" in remove_borders:
          lowercase :       Optional[Any]       =			mask[0 : mask.shape[0] - overlap_pixels, :]
     return mask






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							Dict:





     '''simple docstring'''


     return max(__magic_name__			,		min(__magic_name__			,		__magic_name__   )   )






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							Any:





     '''simple docstring'''


     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  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							List[Any]:





     '''simple docstring'''


     lowercase :       int       =			list(__magic_name__   )
     rect[0] -= overlap
     rect[1] -= overlap
     rect[2] += overlap
     rect[3] += overlap
     lowercase :       List[str]       =			clamp_rect(__magic_name__			,		[0, 0]			,		[image_size[0], image_size[1]]   )
     return rect






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )			->							Optional[Any]:





     '''simple docstring'''


     lowercase :       Any       =			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(__magic_name__			,		(original_slice, 0)   )
     return result






def  snake_case(    __magic_name__			,		__magic_name__   )			->							Optional[Any]:





     '''simple docstring'''


     lowercase :       Optional[int]       =			(original_image_slice * 4, 0, tile.size[0], tile.size[1])
     lowercase :       Optional[int]       =			tile.crop(__magic_name__   )
     return tile






def  snake_case(    __magic_name__			,		__magic_name__   )			->							Dict:





     '''simple docstring'''


     lowercase :       Dict       =			n % d
     return n - divisor
class 						_A (  _lowerCamelCase				):






  def __init__(  self		:     Dict				,						_A		:     AutoencoderKL				,						_A		:     CLIPTextModel				,						_A		:     CLIPTokenizer				,						_A		:     UNetaDConditionModel				,						_A		:     DDPMScheduler				,						_A		:     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]				,						_A		:     int = 350				,						)		->   int:






       """simple docstring"""







       super().__init__(
           vae=_A				,						text_encoder=_A				,						tokenizer=_A				,						unet=_A				,						low_res_scheduler=_A				,						scheduler=_A				,						max_noise_level=_A				,						)






  def  __a      (  self		:     Any				,						_A		:     List[str]				,						_A		:     Optional[int]				,						_A		:     List[str]				,						_A		:     Dict				,						_A		:     Optional[Any]				,						_A		:     Dict				,						_A		:     Optional[Any]				,						**_A		:     str				)		->   Tuple:






       """simple docstring"""







       torch.manual_seed(0				)
       lowercase :       Any       =			(
           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 :       List[str]       =			add_overlap_rect(_A				,						_A				,						image.size				)
       lowercase :       Tuple       =			image.crop(_A				)
       lowercase :       Union[str, Any]       =			((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
       lowercase :       Any       =			translated_slice_x - (original_image_slice / 2)
       lowercase :       Dict       =			max(0				,						_A				)
       lowercase :       List[str]       =			squeeze_tile(_A				,						_A				,						_A				,						_A				)
       lowercase :       Optional[int]       =			to_input.size
       lowercase :       Any       =			to_input.resize((tile_size, tile_size)				,						Image.BICUBIC				)
       lowercase :       int       =			super(_A				,						self				).__call__(image=_A				,						**_A				).images[0]
       lowercase :       Tuple       =			upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)				,						Image.BICUBIC				)
       lowercase :       List[str]       =			unsqueeze_tile(_A				,						_A				)
       lowercase :       str       =			upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)				,						Image.BICUBIC				)
       lowercase :       str       =			[]
       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 :       Tuple       =			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		:     str				,						_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				,						)		->   Any:






       """simple docstring"""







       lowercase :       Tuple       =			Image.new('''RGB'''				,						(image.size[0] * 4, image.size[1] * 4)				)
       lowercase :       Tuple       =			math.ceil(image.size[0] / tile_size				)
       lowercase :       Dict       =			math.ceil(image.size[1] / tile_size				)
       lowercase :       List[Any]       =			tcx * tcy
       lowercase :       List[str]       =			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  snake_case(    )			->							Tuple:





     '''simple docstring'''


     lowercase :       int       =			'''stabilityai/stable-diffusion-x4-upscaler'''
     lowercase :       Union[str, Any]       =			StableDiffusionTiledUpscalePipeline.from_pretrained(__magic_name__			,		revision='''fp16'''			,		torch_dtype=torch.floataa   )
     lowercase :       Optional[Any]       =			pipe.to('''cuda'''   )
     lowercase :       Tuple       =			Image.open('''../../docs/source/imgs/diffusers_library.jpg'''   )

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

     lowercase :       Tuple       =			pipe(image=__magic_name__			,		prompt='''Black font, white background, vector'''			,		noise_level=40			,		callback=__magic_name__   )
     final_image.save('''diffusers_library.jpg'''   )


if __name__ == "__main__":
 main()





import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


lowerCAmelCase_						=			'bert-base-cased'
lowerCAmelCase_						=			'google/pegasus-xsum'
lowerCAmelCase_						=			[' Sam ate lunch today.', 'Sams lunch ingredients.']
lowerCAmelCase_						=			['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
lowerCAmelCase_						=			'patrickvonplaten/t5-tiny-random'
lowerCAmelCase_						=			'sshleifer/bart-tiny-random'
lowerCAmelCase_						=			'sshleifer/tiny-mbart'
lowerCAmelCase_						=			'sshleifer/tiny-marian-en-de'






def  snake_case(    __magic_name__			,		__magic_name__   )			->							Optional[Any]:





     '''simple docstring'''


     lowercase :       List[str]       =			'''\n'''.join(__magic_name__   )
     Path(__magic_name__   ).open('''w'''   ).writelines(__magic_name__   )






def  snake_case(    __magic_name__   )			->							Optional[int]:





     '''simple docstring'''


     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(__magic_name__			,		F"""{split}.source"""   )			,		__magic_name__   )
          _dump_articles(os.path.join(__magic_name__			,		F"""{split}.target"""   )			,		__magic_name__   )
     return tmp_dir



class 						_A (  _lowerCamelCase				):






  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]				,						)
  @slow
  def  __a      (  self		:     List[str]				,						_A		:     Optional[Any]				)		->   Dict:






       """simple docstring"""







       lowercase :       int       =			AutoTokenizer.from_pretrained(_A				)
       lowercase :       Optional[int]       =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
       lowercase :       List[str]       =			max(len(tokenizer.encode(_A				)				) for a in ARTICLES				)
       lowercase :       Optional[int]       =			max(len(tokenizer.encode(_A				)				) for a in SUMMARIES				)
       lowercase :       str       =			4
       lowercase :       List[Any]       =			8
       assert max_len_target > max_src_len  # Will be truncated
       assert max_len_source > max_src_len  # Will be truncated
       lowercase      ,	lowercase :       Optional[int]       =			'''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
       lowercase :       int       =			SeqaSeqDataset(
           _A				,						data_dir=_A				,						type_path='''train'''				,						max_source_length=_A				,						max_target_length=_A				,						src_lang=_A				,						tgt_lang=_A				,						)
       lowercase :       Optional[int]       =			DataLoader(_A				,						batch_size=2				,						collate_fn=train_dataset.collate_fn				)
       for batch in dataloader:
            assert isinstance(_A				,						_A				)
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_src_len
            # show that targets are the same len
            assert batch["labels"].shape[1] == max_tgt_len
            if tok_name != MBART_TINY:
                 continue
            # check language codes in correct place
            lowercase :       int       =			shift_tokens_right(batch['''labels''']				,						tokenizer.pad_token_id				)
            assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
            assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
            assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
            assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

            break  # No need to test every batch






  @parameterized.expand([BART_TINY, BERT_BASE_CASED]				)
  def  __a      (  self		:     int				,						_A		:     Tuple				)		->   List[str]:






       """simple docstring"""







       lowercase :       int       =			AutoTokenizer.from_pretrained(_A				)
       lowercase :       int       =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
       lowercase :       Optional[int]       =			max(len(tokenizer.encode(_A				)				) for a in ARTICLES				)
       lowercase :       List[Any]       =			max(len(tokenizer.encode(_A				)				) for a in SUMMARIES				)
       lowercase :       List[Any]       =			4
       lowercase :       Any       =			LegacySeqaSeqDataset(
           _A				,						data_dir=_A				,						type_path='''train'''				,						max_source_length=20				,						max_target_length=_A				,						)
       lowercase :       Optional[Any]       =			DataLoader(_A				,						batch_size=2				,						collate_fn=train_dataset.collate_fn				)
       for batch in dataloader:
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_len_source
            assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
            # show that targets were truncated
            assert batch["labels"].shape[1] == trunc_target  # Truncated
            assert max_len_target > trunc_target  # Truncated
            break  # No need to test every batch






  def  __a      (  self		:     List[str]				)		->   int:






       """simple docstring"""







       lowercase :       Tuple       =			AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''				)

       lowercase :       List[str]       =			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
       lowercase :       Optional[int]       =			tmp_dir.joinpath('''train.source'''				).open().readlines()
       lowercase :       List[Any]       =			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
       pack_data_dir(_A				,						_A				,						128				,						_A				)
       lowercase :       Dict       =			{x.name for x in tmp_dir.iterdir()}
       lowercase :       Optional[Any]       =			{x.name for x in save_dir.iterdir()}
       lowercase :       int       =			save_dir.joinpath('''train.source'''				).open().readlines()
       # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
       # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
       assert len(_A				) < len(_A				)
       assert len(_A				) == 1
       assert len(packed_examples[0]				) == sum(len(_A				) for x in orig_examples				)
       assert orig_paths == new_paths






  @pytest.mark.skipif(not FAIRSEQ_AVAILABLE				,						reason='''This test requires fairseq'''				)
  def  __a      (  self		:     Any				)		->   Dict:






       """simple docstring"""







       if not FAIRSEQ_AVAILABLE:
            return
       lowercase      ,	lowercase      ,	lowercase :       Any       =			self._get_dataset(max_len=64				)
       lowercase :       List[Any]       =			64
       lowercase :       Any       =			ds.make_dynamic_sampler(_A				,						required_batch_size_multiple=_A				)
       lowercase :       Tuple       =			[len(_A				) for x in batch_sampler]
       assert len(set(_A				)				) > 1  # it's not dynamic batch size if every batch is the same length
       assert sum(_A				) == len(_A				)  # no dropped or added examples
       lowercase :       str       =			DataLoader(_A				,						batch_sampler=_A				,						collate_fn=ds.collate_fn				,						num_workers=2				)
       lowercase :       Optional[int]       =			[]
       lowercase :       str       =			[]
       for batch in data_loader:
            lowercase :       Tuple       =			batch['''input_ids'''].shape
            lowercase :       List[Any]       =			src_shape[0]
            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
            lowercase :       Dict       =			np.product(batch['''input_ids'''].shape				)
            num_src_per_batch.append(_A				)
            if num_src_tokens > (max_tokens * 1.1):
                 failures.append(_A				)
       assert num_src_per_batch[0] == max(_A				)
       if failures:
            raise AssertionError(f"""too many tokens in {len(_A				)} batches"""				)






  def  __a      (  self		:     int				)		->   Any:






       """simple docstring"""







       lowercase      ,	lowercase      ,	lowercase :       Tuple       =			self._get_dataset(max_len=512				)
       lowercase :       Tuple       =			2
       lowercase :       Union[str, Any]       =			ds.make_sortish_sampler(_A				,						shuffle=_A				)

       lowercase :       List[Any]       =			DataLoader(_A				,						batch_size=_A				,						collate_fn=ds.collate_fn				,						num_workers=2				)
       lowercase :       List[Any]       =			DataLoader(_A				,						batch_size=_A				,						collate_fn=ds.collate_fn				,						num_workers=2				,						sampler=_A				)

       lowercase :       int       =			tokenizer.pad_token_id

       def count_pad_tokens(_A		:     List[Any]				,						_A		:     Union[str, Any]="input_ids"				):
            return [batch[k].eq(_A				).sum().item() for batch in data_loader]

       assert sum(count_pad_tokens(_A				,						k='''labels'''				)				) < sum(count_pad_tokens(_A				,						k='''labels'''				)				)
       assert sum(count_pad_tokens(_A				)				) < sum(count_pad_tokens(_A				)				)
       assert len(_A				) == len(_A				)






  def  __a      (  self		:     Any				,						_A		:     Union[str, Any]=1_000				,						_A		:     str=128				)		->   List[Any]:






       """simple docstring"""







       if os.getenv('''USE_REAL_DATA'''				,						_A				):
            lowercase :       Optional[Any]       =			'''examples/seq2seq/wmt_en_ro'''
            lowercase :       Optional[int]       =			max_len * 2 * 64
            if not Path(_A				).joinpath('''train.len'''				).exists():
                 save_len_file(_A				,						_A				)
       else:
            lowercase :       Tuple       =			'''examples/seq2seq/test_data/wmt_en_ro'''
            lowercase :       Optional[Any]       =			max_len * 4
            save_len_file(_A				,						_A				)

       lowercase :       Optional[Any]       =			AutoTokenizer.from_pretrained(_A				)
       lowercase :       Union[str, Any]       =			SeqaSeqDataset(
           _A				,						data_dir=_A				,						type_path='''train'''				,						max_source_length=_A				,						max_target_length=_A				,						n_obs=_A				,						)
       return ds, max_tokens, tokenizer






  def  __a      (  self		:     List[str]				)		->   List[str]:






       """simple docstring"""







       lowercase      ,	lowercase      ,	lowercase :       Union[str, Any]       =			self._get_dataset()
       lowercase :       int       =			set(DistributedSortishSampler(_A				,						256				,						num_replicas=2				,						rank=0				,						add_extra_examples=_A				)				)
       lowercase :       Dict       =			set(DistributedSortishSampler(_A				,						256				,						num_replicas=2				,						rank=1				,						add_extra_examples=_A				)				)
       assert idsa.intersection(_A				) == set()






  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]				,						)
  def  __a      (  self		:     Union[str, Any]				,						_A		:     Tuple				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			AutoTokenizer.from_pretrained(_A				,						use_fast=_A				)
       if tok_name == MBART_TINY:
            lowercase :       Tuple       =			SeqaSeqDataset(
                _A				,						data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				,						type_path='''train'''				,						max_source_length=4				,						max_target_length=8				,						src_lang='''EN'''				,						tgt_lang='''FR'''				,						)
            lowercase :       Union[str, Any]       =			train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
            lowercase :       List[Any]       =			SeqaSeqDataset(
                _A				,						data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				,						type_path='''train'''				,						max_source_length=4				,						max_target_length=8				,						)
            lowercase :       Dict       =			train_dataset.dataset_kwargs
            assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
            assert len(_A				) == 1 if tok_name == BART_TINY else len(_A				) == 0