




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(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			Union[str, Any]=[]				)    -> int:
							'''simple docstring'''


							__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(UpperCamelCase__				,       mode='''linear_ramp'''				,       pad_width=UpperCamelCase__				,       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 		_A(       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Any				)    -> str:
							'''simple docstring'''


							return max(UpperCamelCase__				,       min(UpperCamelCase__				,       UpperCamelCase__				)				)







def 		_A(       UpperCamelCase__							:			[int]				,       UpperCamelCase__							:			[int]				,       UpperCamelCase__							:			[int]				)    -> Optional[int]:
							'''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(       UpperCamelCase__							:			[int]				,       UpperCamelCase__							:			int				,       UpperCamelCase__							:			[int]				)    -> int:
							'''simple docstring'''


							__lowercase      =    list(UpperCamelCase__				)
							rect[0] -= overlap
							rect[1] -= overlap
							rect[2] += overlap
							rect[3] += overlap
							__lowercase      =    clamp_rect(UpperCamelCase__				,       [0, 0]				,       [image_size[0], image_size[1]]				)
							return rect







def 		_A(       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			List[str]				,       UpperCamelCase__							:			str				,       UpperCamelCase__							:			Any				)    -> Union[str, Any]:
							'''simple docstring'''


							__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(UpperCamelCase__				,       (original_slice, 0)				)
							return result







def 		_A(       UpperCamelCase__							:			Optional[Any]				,       UpperCamelCase__							:			Optional[int]				)    -> Any:
							'''simple docstring'''


							__lowercase      =    (original_image_slice * 4, 0, tile.size[0], tile.size[1])
							__lowercase      =    tile.crop(UpperCamelCase__				)
							return tile







def 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Union[str, Any]				)    -> Optional[int]:
							'''simple docstring'''


							__lowercase      =    n % d
							return n - divisor







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def __init__( self :     Optional[int]				,							lowerCamelCase__ :     AutoencoderKL				,							lowerCamelCase__ :     CLIPTextModel				,							lowerCamelCase__ :     CLIPTokenizer				,							lowerCamelCase__ :     UNetaDConditionModel				,							lowerCamelCase__ :     DDPMScheduler				,							lowerCamelCase__ :     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]				,							lowerCamelCase__ :     int = 350				,							)    ->						Tuple:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Tuple				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Optional[int]				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     Optional[int]				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     Dict				,							**lowerCamelCase__ :     Optional[int]				)    ->						Optional[Any]:
													"""simple docstring"""


													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(lowerCamelCase__				,							lowerCamelCase__				,							image.size				)
													__lowercase      =    image.crop(lowerCamelCase__				)
													__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				,							lowerCamelCase__				)
													__lowercase      =    squeeze_tile(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)
													__lowercase      =    to_input.size
													__lowercase      =    to_input.resize((tile_size, tile_size)				,							Image.BICUBIC				)
													__lowercase      =    super(lowerCamelCase__				,							self				).__call__(image=lowerCamelCase__				,							**lowerCamelCase__				).images[0]
													__lowercase      =    upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)				,							Image.BICUBIC				)
													__lowercase      =    unsqueeze_tile(lowerCamelCase__				,							lowerCamelCase__				)
													__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=lowerCamelCase__				)				,							mode='''L'''				,							)
													final_image.paste(
													    lowerCamelCase__				,							(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)				,							lowerCamelCase__				)






						@torch.no_grad()
						def __call__( self :     str				,							lowerCamelCase__ :     Union[str, List[str]]				,							lowerCamelCase__ :     Union[PIL.Image.Image, List[PIL.Image.Image]]				,							lowerCamelCase__ :     int = 75				,							lowerCamelCase__ :     float = 9.0				,							lowerCamelCase__ :     int = 50				,							lowerCamelCase__ :     Optional[Union[str, List[str]]] = None				,							lowerCamelCase__ :     Optional[int] = 1				,							lowerCamelCase__ :     float = 0.0				,							lowerCamelCase__ :     Optional[torch.Generator] = None				,							lowerCamelCase__ :     Optional[torch.FloatTensor] = None				,							lowerCamelCase__ :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,							lowerCamelCase__ :     int = 1				,							lowerCamelCase__ :     int = 128				,							lowerCamelCase__ :     int = 32				,							lowerCamelCase__ :     int = 32				,							)    ->						Optional[Any]:
													"""simple docstring"""


													__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(lowerCamelCase__				):
																				for x in range(lowerCamelCase__				):
																											self._process_tile(
																											    lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							prompt=lowerCamelCase__				,							num_inference_steps=lowerCamelCase__				,							guidance_scale=lowerCamelCase__				,							noise_level=lowerCamelCase__				,							negative_prompt=lowerCamelCase__				,							num_images_per_prompt=lowerCamelCase__				,							eta=lowerCamelCase__				,							generator=lowerCamelCase__				,							latents=lowerCamelCase__				,							)
																											current_count += 1
																											if callback is not None:
																																		callback({'''progress''': current_count / total_tile_count, '''image''': final_image}				)
													return final_image







def 		_A(       )    -> str:
							'''simple docstring'''


							__lowercase      =    '''stabilityai/stable-diffusion-x4-upscaler'''
							__lowercase      =    StableDiffusionTiledUpscalePipeline.from_pretrained(UpperCamelCase__				,       revision='''fp16'''				,       torch_dtype=torch.floataa				)
							__lowercase      =    pipe.to('''cuda'''				)
							__lowercase      =    Image.open('''../../docs/source/imgs/diffusers_library.jpg'''				)

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

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


if __name__ == "__main__":
			main()












import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)







def 		_A(       UpperCamelCase__							:			Tuple				)    -> Tuple:
							'''simple docstring'''


							__lowercase      =    checkpoints.load_tax_checkpoint(UpperCamelCase__				)
							__lowercase      =    flatten_dict(UpperCamelCase__				)
							return flax_params







def 		_A(       UpperCamelCase__							:			str				)    -> Dict:
							'''simple docstring'''


							__lowercase      =    {}

							__lowercase      =    {
							    '''token_embedder''': '''embeddings''',
							    '''encoder_norm''': '''layernorm''',
							    '''kernel''': '''weight''',
							    '''.out''': '''.output''',
							    '''scale''': '''weight''',
							    '''embedders_0.pos_embedding''': '''row_embedder.weight''',
							    '''embedders_1.pos_embedding''': '''column_embedder.weight''',
							}

							__lowercase      =    {
							    '''query''': '''attention.query''',
							    '''key''': '''attention.key''',
							    '''value''': '''attention.value''',
							    '''output.dense''': '''output''',
							    '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
							    '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
							    '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
							    '''mlp.''': '''mlp.DenseReluDense.''',
							    '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
							    '''self_attention.o''': '''self_attention.attention.o''',
							    '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
							    '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
							    '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
							    '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
							}

							for key in flax_dict.keys():
														if "target" in key:
																					# remove the first prefix from the key
																					__lowercase      =    '''.'''.join(key[1:]				)

																					# rename the key
																					for old, new in CONVERSION_MAPPING.items():
																												__lowercase      =    new_key.replace(UpperCamelCase__				,       UpperCamelCase__				)

																					if "decoder" in new_key:
																												for old, new in DECODER_CONVERSION_MAPPING.items():
																																			__lowercase      =    new_key.replace(UpperCamelCase__				,       UpperCamelCase__				)

																					if "layers" in new_key and "decoder" not in new_key:
																												# use regex to replace the layer number
																												__lowercase      =    re.sub(r'''layers_(\d+)'''				,       r'''layer.\1'''				,       UpperCamelCase__				)
																												__lowercase      =    new_key.replace('''encoder'''				,       '''encoder.encoder'''				)

																					elif "layers" in new_key and "decoder" in new_key:
																												# use regex to replace the layer number
																												__lowercase      =    re.sub(r'''layers_(\d+)'''				,       r'''layer.\1'''				,       UpperCamelCase__				)

																					__lowercase      =    flax_dict[key]

							__lowercase      =    {}
							# convert converted_dict into torch format
							for key in converted_dict.keys():
														if ("embed_tokens" not in key) and ("embedder" not in key):
																					__lowercase      =    torch.from_numpy(converted_dict[key].T				)
														else:
																					__lowercase      =    torch.from_numpy(converted_dict[key]				)

							return converted_torch_dict







def 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			int				,       UpperCamelCase__							:			List[str]=False				,       UpperCamelCase__							:			List[str]=False				)    -> Any:
							'''simple docstring'''


							__lowercase      =    get_flax_param(UpperCamelCase__				)

							if not use_large:
														__lowercase      =    PixaStructVisionConfig()
														__lowercase      =    PixaStructTextConfig()
							else:
														__lowercase      =    PixaStructVisionConfig(
														    hidden_size=1536				,       d_ff=3968				,       num_attention_heads=24				,       num_hidden_layers=18				)
														__lowercase      =    PixaStructTextConfig(hidden_size=1536				,       d_ff=3968				,       num_heads=24				,       num_layers=18				)
							__lowercase      =    PixaStructConfig(
							    vision_config=encoder_config.to_dict()				,       text_config=decoder_config.to_dict()				,       is_vqa=UpperCamelCase__				)

							__lowercase      =    PixaStructForConditionalGeneration(UpperCamelCase__				)

							__lowercase      =    rename_and_convert_flax_params(UpperCamelCase__				)
							model.load_state_dict(UpperCamelCase__				)

							__lowercase      =    AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''				)
							__lowercase      =    PixaStructImageProcessor()
							__lowercase      =    PixaStructProcessor(image_processor=UpperCamelCase__				,       tokenizer=UpperCamelCase__				)

							if use_large:
														__lowercase      =    4096

							__lowercase      =    True

							# mkdir if needed
							os.makedirs(UpperCamelCase__				,       exist_ok=UpperCamelCase__				)

							model.save_pretrained(UpperCamelCase__				)
							processor.save_pretrained(UpperCamelCase__				)

							print('''Model saved in {}'''.format(UpperCamelCase__				)				)


if __name__ == "__main__":
			UpperCAmelCase__				     = argparse.ArgumentParser()
			parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
			parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			parser.add_argument("--use_large", action="store_true", help="Use large model.")
			parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
			UpperCAmelCase__				     = parser.parse_args()

			convert_pixastruct_original_pytorch_checkpoint_to_hf(
			    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
			)






