



'''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      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__=[]   ):
			'''simple docstring'''





			A_     :						Optional[int]     =					size[0] - overlap_pixels * 2
			A_     :						Optional[int]     =					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_     :						Optional[Any]     =					np.ones((size_y, size_x)						,				dtype=np.uinta   ) * 2_55
			A_     :						int     =					np.pad(lowerCamelCase__						,				mode="""linear_ramp"""						,				pad_width=lowerCamelCase__						,				end_values=0   )

			if "l" in remove_borders:
						A_     :						List[Any]     =					mask[:, overlap_pixels : mask.shape[1]]
			if "r" in remove_borders:
						A_     :						Dict     =					mask[:, 0 : mask.shape[1] - overlap_pixels]
			if "t" in remove_borders:
						A_     :						Dict     =					mask[overlap_pixels : mask.shape[0], :]
			if "b" in remove_borders:
						A_     :						str     =					mask[0 : mask.shape[0] - overlap_pixels, :]
			return mask



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			return max(lowerCamelCase__						,				min(lowerCamelCase__						,				lowerCamelCase__   )   )



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''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      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Union[str, Any]     =					list(lowerCamelCase__   )
			rect[0] -= overlap
			rect[1] -= overlap
			rect[2] += overlap
			rect[3] += overlap
			A_     :						str     =					clamp_rect(lowerCamelCase__						,				[0, 0]						,				[image_size[0], image_size[1]]   )
			return rect



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Optional[int]     =					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(lowerCamelCase__						,				(original_slice, 0)   )
			return result



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Dict     =					(original_image_slice * 4, 0, tile.size[0], tile.size[1])
			A_     :						List[str]     =					tile.crop(lowerCamelCase__   )
			return tile



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Optional[Any]     =					n % d
			return n - divisor







class      _lowerCAmelCase							(      _a			):


	def __init__(self	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase = 350	,				):
				super().__init__(
				    vae=__lowerCamelCase	,				text_encoder=__lowerCamelCase	,				tokenizer=__lowerCamelCase	,				unet=__lowerCamelCase	,				low_res_scheduler=__lowerCamelCase	,				scheduler=__lowerCamelCase	,				max_noise_level=__lowerCamelCase	,				)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				**lowercase							):
				torch.manual_seed(0							)
				A_     :						int     =					(
				    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_     :						Optional[Any]     =					add_overlap_rect(__lowerCamelCase	,				__lowerCamelCase	,				image.size							)
				A_     :						Optional[Any]     =					image.crop(__lowerCamelCase							)
				A_     :						Tuple     =					((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
				A_     :						Union[str, Any]     =					translated_slice_x - (original_image_slice / 2)
				A_     :						Optional[int]     =					max(0	,				__lowerCamelCase							)
				A_     :						List[Any]     =					squeeze_tile(__lowerCamelCase	,				__lowerCamelCase	,				__lowerCamelCase	,				__lowerCamelCase							)
				A_     :						Dict     =					to_input.size
				A_     :						Dict     =					to_input.resize((tile_size, tile_size)	,				Image.BICUBIC							)
				A_     :						Any     =					super(__lowerCamelCase	,				self							).__call__(image=__lowerCamelCase	,				**__lowerCamelCase							).images[0]
				A_     :						Dict     =					upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)	,				Image.BICUBIC							)
				A_     :						Dict     =					unsqueeze_tile(__lowerCamelCase	,				__lowerCamelCase							)
				A_     :						Tuple     =					upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)	,				Image.BICUBIC							)
				A_     :						int     =					[]
				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_     :						List[str]     =					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	,				lowercase	,				lowercase	,				lowercase = 75	,				lowercase = 9.0	,				lowercase = 50	,				lowercase = None	,				lowercase = 1	,				lowercase = 0.0	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = 1	,				lowercase = 128	,				lowercase = 32	,				lowercase = 32	,				):
				A_     :						str     =					Image.new("""RGB"""	,				(image.size[0] * 4, image.size[1] * 4)							)
				A_     :						str     =					math.ceil(image.size[0] / tile_size							)
				A_     :						Any     =					math.ceil(image.size[1] / tile_size							)
				A_     :						Tuple     =					tcx * tcy
				A_     :						List[Any]     =					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      (			):
			'''simple docstring'''





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

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

			A_     :						Optional[int]     =					pipe(image=lowerCamelCase__						,				prompt="""Black font, white background, vector"""						,				noise_level=40						,				callback=lowerCamelCase__   )
			final_image.save("""diffusers_library.jpg"""   )


if __name__ == "__main__":
							main()




'''simple docstring'''




import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


lowerCamelCase							:Optional[int]       = logging.getLogger(__name__)

lowerCamelCase							:Optional[int]       = tf.data.AUTOTUNE



def      a      (			):
			'''simple docstring'''





			A_     :						Any     =					argparse.ArgumentParser(description="""Train a masked language model on TPU."""   )
			parser.add_argument(
			    """--pretrained_model_config"""						,				type=lowerCamelCase__						,				default="""roberta-base"""						,				help="""The model config to use. Note that we don't copy the model's weights, only the config!"""						,				)
			parser.add_argument(
			    """--tokenizer"""						,				type=lowerCamelCase__						,				default="""unigram-tokenizer-wikitext"""						,				help="""The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model's vocab size."""						,				)

			parser.add_argument(
			    """--per_replica_batch_size"""						,				type=lowerCamelCase__						,				default=8						,				help="""Batch size per TPU core."""						,				)

			parser.add_argument(
			    """--no_tpu"""						,				action="""store_true"""						,				help="""If set, run on CPU and don't try to initialize a TPU. Useful for debugging on non-TPU instances."""						,				)

			parser.add_argument(
			    """--tpu_name"""						,				type=lowerCamelCase__						,				help="""Name of TPU resource to initialize. Should be blank on Colab, and 'local' on TPU VMs."""						,				default="""local"""						,				)

			parser.add_argument(
			    """--tpu_zone"""						,				type=lowerCamelCase__						,				help="""Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."""						,				)

			parser.add_argument(
			    """--gcp_project"""						,				type=lowerCamelCase__						,				help="""Google cloud project name. Only used for non-Colab TPU nodes."""   )

			parser.add_argument(
			    """--bfloat16"""						,				action="""store_true"""						,				help="""Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU."""						,				)

			parser.add_argument(
			    """--train_dataset"""						,				type=lowerCamelCase__						,				help="""Path to training dataset to load. If the path begins with `gs://`"""
			    """ then the dataset will be loaded from a Google Cloud Storage bucket."""						,				)

			parser.add_argument(
			    """--shuffle_buffer_size"""						,				type=lowerCamelCase__						,				default=2**18						,				help="""Size of the shuffle buffer (in samples)"""						,				)

			parser.add_argument(
			    """--eval_dataset"""						,				type=lowerCamelCase__						,				help="""Path to evaluation dataset to load. If the path begins with `gs://`"""
			    """ then the dataset will be loaded from a Google Cloud Storage bucket."""						,				)

			parser.add_argument(
			    """--num_epochs"""						,				type=lowerCamelCase__						,				default=1						,				help="""Number of epochs to train for."""						,				)

			parser.add_argument(
			    """--learning_rate"""						,				type=lowerCamelCase__						,				default=1E-4						,				help="""Learning rate to use for training."""						,				)

			parser.add_argument(
			    """--weight_decay_rate"""						,				type=lowerCamelCase__						,				default=1E-3						,				help="""Weight decay rate to use for training."""						,				)

			parser.add_argument(
			    """--max_length"""						,				type=lowerCamelCase__						,				default=5_12						,				help="""Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"""						,				)

			parser.add_argument(
			    """--mlm_probability"""						,				type=lowerCamelCase__						,				default=0.15						,				help="""Fraction of tokens to mask during training."""						,				)

			parser.add_argument("""--output_dir"""						,				type=lowerCamelCase__						,				required=lowerCamelCase__						,				help="""Path to save model checkpoints to."""   )
			parser.add_argument("""--hub_model_id"""						,				type=lowerCamelCase__						,				help="""Model ID to upload to on the Hugging Face Hub."""   )

			A_     :						Dict     =					parser.parse_args()
			return args



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			try:
						if args.tpu_name:
									A_     :						Optional[Any]     =					tf.distribute.cluster_resolver.TPUClusterResolver(
									    args.tpu_name						,				zone=args.tpu_zone						,				project=args.gcp_project   )
						else:
									A_     :						Union[str, Any]     =					tf.distribute.cluster_resolver.TPUClusterResolver()
			except ValueError:
						raise RuntimeError(
						    """Couldn't connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or """
						    """--gcp_project. When running on a TPU VM, use --tpu_name local."""   )

			tf.config.experimental_connect_to_cluster(lowerCamelCase__   )
			tf.tpu.experimental.initialize_tpu_system(lowerCamelCase__   )

			return tpu



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						List[str]     =					0
			for file in file_list:
						A_     :						Optional[Any]     =					file.split("""/"""   )[-1]
						A_     :						Optional[int]     =					re.search(r"""-\d+-(\d+)\.tfrecord"""						,				lowerCamelCase__   ).group(1   )
						A_     :						Any     =					int(lowerCamelCase__   )
						num_samples += sample_count

			return num_samples



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__=None   ):
			'''simple docstring'''





			A_     :						List[Any]     =					count_samples(lowerCamelCase__   )
			A_     :						List[str]     =					tf.data.Dataset.from_tensor_slices(lowerCamelCase__   )
			if shuffle:
						A_     :						Optional[int]     =					dataset.shuffle(len(lowerCamelCase__   )   )
			A_     :						Tuple     =					tf.data.TFRecordDataset(lowerCamelCase__						,				num_parallel_reads=lowerCamelCase__   )
			# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
			A_     :						Union[str, Any]     =					dataset.apply(tf.data.experimental.assert_cardinality(lowerCamelCase__   )   )
			A_     :						Any     =					dataset.map(lowerCamelCase__						,				num_parallel_calls=lowerCamelCase__   )
			if shuffle:
						assert shuffle_buffer_size is not None
						A_     :						List[str]     =					dataset.shuffle(args.shuffle_buffer_size   )
			A_     :						str     =					dataset.batch(lowerCamelCase__						,				drop_remainder=lowerCamelCase__   )
			A_     :						str     =					dataset.map(lowerCamelCase__						,				num_parallel_calls=lowerCamelCase__   )
			A_     :						Optional[int]     =					dataset.prefetch(lowerCamelCase__   )
			return dataset



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			if not args.no_tpu:
						A_     :						Dict     =					initialize_tpu(lowerCamelCase__   )
						A_     :						str     =					tf.distribute.TPUStrategy(lowerCamelCase__   )
			else:
						A_     :						Union[str, Any]     =					tf.distribute.OneDeviceStrategy(device="""/gpu:0"""   )

			if args.bfloataa:
						tf.keras.mixed_precision.set_global_policy("""mixed_bfloat16"""   )

			A_     :						Optional[Any]     =					AutoTokenizer.from_pretrained(args.tokenizer   )
			A_     :						str     =					AutoConfig.from_pretrained(args.pretrained_model_config   )
			A_     :						Optional[int]     =					tokenizer.vocab_size

			A_     :						Dict     =					tf.io.gfile.glob(os.path.join(args.train_dataset						,				"""*.tfrecord"""   )   )
			if not training_records:
						raise ValueError(f'No .tfrecord files found in {args.train_dataset}.'   )
			A_     :						Optional[int]     =					tf.io.gfile.glob(os.path.join(args.eval_dataset						,				"""*.tfrecord"""   )   )
			if not eval_records:
						raise ValueError(f'No .tfrecord files found in {args.eval_dataset}.'   )

			A_     :						Dict     =					count_samples(lowerCamelCase__   )

			A_     :						Optional[Any]     =					num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
			A_     :						Dict     =					steps_per_epoch * args.num_epochs

			with strategy.scope():
						A_     :						Optional[int]     =					TFAutoModelForMaskedLM.from_config(lowerCamelCase__   )
						model(model.dummy_inputs   )  # Pass some dummy inputs through the model to ensure all the weights are built
						A_,   A_     :						Any     =					create_optimizer(
						    num_train_steps=lowerCamelCase__						,				num_warmup_steps=total_train_steps // 20						,				init_lr=args.learning_rate						,				weight_decay_rate=args.weight_decay_rate						,				)

						# Transformers models compute the right loss for their task by default when labels are passed, and will
						# use this for training unless you specify your own loss function in compile().
						model.compile(optimizer=lowerCamelCase__						,				metrics=["""accuracy"""]   )

			def decode_fn(lowerCamelCase__   ):
						A_     :						Optional[int]     =					{
						    """input_ids""": tf.io.FixedLenFeature(dtype=tf.intaa						,				shape=(args.max_length,)   ),
						    """attention_mask""": tf.io.FixedLenFeature(dtype=tf.intaa						,				shape=(args.max_length,)   ),
						}
						return tf.io.parse_single_example(lowerCamelCase__						,				lowerCamelCase__   )

			# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
			# use their methods in our data pipeline.
			A_     :						Optional[int]     =					DataCollatorForLanguageModeling(
			    tokenizer=lowerCamelCase__						,				mlm_probability=args.mlm_probability						,				mlm=lowerCamelCase__						,				return_tensors="""tf"""   )

			def mask_with_collator(lowerCamelCase__   ):
						# TF really needs an isin() function
						A_     :						str     =					(
						    ~tf.cast(batch["""attention_mask"""]						,				tf.bool   )
						    | (batch["""input_ids"""] == tokenizer.cls_token_id)
						    | (batch["""input_ids"""] == tokenizer.sep_token_id)
						)
						A_,   A_     :						Union[str, Any]     =					data_collator.tf_mask_tokens(
						    batch["""input_ids"""]						,				vocab_size=len(lowerCamelCase__   )						,				mask_token_id=tokenizer.mask_token_id						,				special_tokens_mask=lowerCamelCase__						,				)
						return batch

			A_     :						str     =					args.per_replica_batch_size * strategy.num_replicas_in_sync

			A_     :						Tuple     =					prepare_dataset(
			    lowerCamelCase__						,				decode_fn=lowerCamelCase__						,				mask_fn=lowerCamelCase__						,				batch_size=lowerCamelCase__						,				shuffle=lowerCamelCase__						,				shuffle_buffer_size=args.shuffle_buffer_size						,				)

			A_     :						Optional[Any]     =					prepare_dataset(
			    lowerCamelCase__						,				decode_fn=lowerCamelCase__						,				mask_fn=lowerCamelCase__						,				batch_size=lowerCamelCase__						,				shuffle=lowerCamelCase__						,				)

			A_     :						Union[str, Any]     =					[]
			if args.hub_model_id:
						callbacks.append(
						    PushToHubCallback(output_dir=args.output_dir						,				hub_model_id=args.hub_model_id						,				tokenizer=lowerCamelCase__   )   )

			model.fit(
			    lowerCamelCase__						,				validation_data=lowerCamelCase__						,				epochs=args.num_epochs						,				callbacks=lowerCamelCase__						,				)

			model.save_pretrained(args.output_dir   )


if __name__ == "__main__":
						lowerCamelCase							:Tuple       = parse_args()
						main(args)