


"""simple docstring"""







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 _A						(						lowercase       ,    lowercase       ,    lowercase=[]		):







    """simple docstring"""
    a 		=size[0] - overlap_pixels * 2
    a 		=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
    a 		=np.ones((size_y, size_x)       ,    dtype=np.uinta		) * 2_55
    a 		=np.pad(lowercase       ,    mode='''linear_ramp'''       ,    pad_width=lowercase       ,    end_values=0		)

    if "l" in remove_borders:
        a 		=mask[:, overlap_pixels : mask.shape[1]]
    if "r" in remove_borders:
        a 		=mask[:, 0 : mask.shape[1] - overlap_pixels]
    if "t" in remove_borders:
        a 		=mask[overlap_pixels : mask.shape[0], :]
    if "b" in remove_borders:
        a 		=mask[0 : mask.shape[0] - overlap_pixels, :]
    return mask
def _A						(						lowercase       ,    lowercase       ,    lowercase		):







    """simple docstring"""
    return max(lowercase       ,    min(lowercase       ,    lowercase		)		)
def _A						(						lowercase       ,    lowercase       ,    lowercase		):







    """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 _A						(						lowercase       ,    lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=list(lowercase		)
    rect[0] -= overlap
    rect[1] -= overlap
    rect[2] += overlap
    rect[3] += overlap
    a 		=clamp_rect(lowercase       ,    [0, 0]       ,    [image_size[0], image_size[1]]		)
    return rect
def _A						(						lowercase       ,    lowercase       ,    lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=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(lowercase       ,    (original_slice, 0)		)
    return result
def _A						(						lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=(original_image_slice * 4, 0, tile.size[0], tile.size[1])
    a 		=tile.crop(lowercase		)
    return tile
def _A						(						lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=n % d
    return n - divisor






class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 def __init__(		self ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A = 350 ,    )					->     int:
     super().__init__(
         vae=__A ,    text_encoder=__A ,    tokenizer=__A ,    unet=__A ,    low_res_scheduler=__A ,    scheduler=__A ,    max_noise_level=__A ,    )


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    **__A						)					->     Tuple:
     torch.manual_seed(0						)
     a 		=(
         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						),
     )
     a 		=add_overlap_rect(__A ,    __A ,    image.size						)
     a 		=image.crop(__A						)
     a 		=((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
     a 		=translated_slice_x - (original_image_slice / 2)
     a 		=max(0 ,    __A						)
     a 		=squeeze_tile(__A ,    __A ,    __A ,    __A						)
     a 		=to_input.size
     a 		=to_input.resize((tile_size, tile_size) ,    Image.BICUBIC						)
     a 		=super(__A ,    self						).__call__(image=__A ,    **__A						).images[0]
     a 		=upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) ,    Image.BICUBIC						)
     a 		=unsqueeze_tile(__A ,    __A						)
     a 		=upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) ,    Image.BICUBIC						)
     a 		=[]
     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'''						)
     a 		=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 ,    __A ,    __A ,    __A = 75 ,    __A = 9.0 ,    __A = 50 ,    __A = None ,    __A = 1 ,    __A = 0.0 ,    __A = None ,    __A = None ,    __A = None ,    __A = 1 ,    __A = 128 ,    __A = 32 ,    __A = 32 ,    )					->     Optional[int]:
     a 		=Image.new('''RGB''' ,    (image.size[0] * 4, image.size[1] * 4)						)
     a 		=math.ceil(image.size[0] / tile_size						)
     a 		=math.ceil(image.size[1] / tile_size						)
     a 		=tcx * tcy
     a 		=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 _A						(						):







    """simple docstring"""
    # Run a demo
    a 		='''stabilityai/stable-diffusion-x4-upscaler'''
    a 		=StableDiffusionTiledUpscalePipeline.from_pretrained(lowercase       ,    revision='''fp16'''       ,    torch_dtype=torch.floataa		)
    a 		=pipe.to('''cuda'''		)
    a 		=Image.open('''../../docs/source/imgs/diffusers_library.jpg'''		)

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

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


if __name__ == "__main__":
       main()





import os

import pytest

from transformers.dynamic_module_utils import get_imports


_lowerCamelCase	:		Any							       =   """
import os
"""

_lowerCamelCase	:		Optional[int]							       =   """
def foo():
    import os
    return False
"""

_lowerCamelCase	:		List[Any]							       =   """
def foo():
    def bar():
        if True:
            import os
        return False
    return bar()
"""

_lowerCamelCase	:		List[Any]							       =   """
import os

try:
    import bar
except ImportError:
    raise ValueError()
"""

_lowerCamelCase	:		Union[str, Any]							       =   """
import os

def foo():
    try:
        import bar
    except ImportError:
        raise ValueError()
"""

_lowerCamelCase	:		List[Any]							       =   """
import os

try:
    import bar
except (ImportError, AttributeError):
    raise ValueError()
"""

_lowerCamelCase	:		List[Any]							       =   """
import os

try:
    import bar
except ImportError as e:
    raise ValueError()
"""

_lowerCamelCase	:		str							       =   """
import os

try:
    import bar
except:
    raise ValueError()
"""

_lowerCamelCase	:		Optional[Any]							       =   """
import os

try:
    import bar
    import baz
except ImportError:
    raise ValueError()
"""

_lowerCamelCase	:		Any							       =   """
import os

try:
    import bar
    import baz
except ImportError:
    x = 1
    raise ValueError()
"""

_lowerCamelCase	:		Dict							       =   [
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]







@pytest.mark.parametrize('''case''' ,				lowercase_						)
def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       int:



       """simple docstring"""

       A__       =    os.path.join(lowercase_ ,				'''test_file.py'''						)
       with open(lowercase_ ,				'''w'''						) as _tmp_file:
              _tmp_file.write(lowercase_						)

       A__       =    get_imports(lowercase_						)
       assert parsed_imports == ["os"]


