


import argparse
import logging
import os
import re

import tensorflow as tf

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


A	:		Tuple    =       logging.getLogger(__name__)

A	:		Optional[Any]    =       tf.data.AUTOTUNE






def 			UpperCamelCase (     )       ->  str:
		"""simple docstring"""




		lowercase__					      =		argparse.ArgumentParser(description="""Train a masked language model on TPU."""					)
		parser.add_argument(
		    """--pretrained_model_config"""   ,   type=__magic_name__   ,   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=__magic_name__   ,   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=__magic_name__   ,   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=__magic_name__   ,   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=__magic_name__   ,   help="""Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."""   ,   )

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

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

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

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

		parser.add_argument(
		    """--max_length"""   ,   type=__magic_name__   ,   default=512   ,   help="""Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"""   ,   )

		parser.add_argument(
		    """--mlm_probability"""   ,   type=__magic_name__   ,   default=0.1_5   ,   help="""Fraction of tokens to mask during training."""   ,   )

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

		lowercase__					      =		parser.parse_args()
		return args






def 			UpperCamelCase (     __magic_name__					:							str					)       ->  List[str]:
		"""simple docstring"""




		try:
				if args.tpu_name:
						lowercase__					      =		tf.distribute.cluster_resolver.TPUClusterResolver(
						    args.tpu_name   ,   zone=args.tpu_zone   ,   project=args.gcp_project					)
				else:
						lowercase__					      =		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(__magic_name__					)
		tf.tpu.experimental.initialize_tpu_system(__magic_name__					)

		return tpu






def 			UpperCamelCase (     __magic_name__					:							Tuple					)       ->  List[str]:
		"""simple docstring"""




		lowercase__					      =		0
		for file in file_list:
				lowercase__					      =		file.split("""/"""					)[-1]
				lowercase__					      =		re.search(R"""-\d+-(\d+)\.tfrecord"""   ,   __magic_name__					).group(1					)
				lowercase__					      =		int(__magic_name__					)
				num_samples += sample_count

		return num_samples






def 			UpperCamelCase (     __magic_name__					:							Optional[Any]   ,   __magic_name__					:							int   ,   __magic_name__					:							List[str]   ,   __magic_name__					:							List[Any]   ,   __magic_name__					:							List[Any]   ,   __magic_name__					:							Union[str, Any]=None					)       ->  str:
		"""simple docstring"""




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






def 			UpperCamelCase (     __magic_name__					:							Optional[Any]					)       ->  str:
		"""simple docstring"""




		if not args.no_tpu:
				lowercase__					      =		initialize_tpu(__magic_name__					)
				lowercase__					      =		tf.distribute.TPUStrategy(__magic_name__					)
		else:
				lowercase__					      =		tf.distribute.OneDeviceStrategy(device="""/gpu:0"""					)

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

		lowercase__					      =		AutoTokenizer.from_pretrained(args.tokenizer					)
		lowercase__					      =		AutoConfig.from_pretrained(args.pretrained_model_config					)
		lowercase__					      =		tokenizer.vocab_size

		lowercase__					      =		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}.'''					)
		lowercase__					      =		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}.'''					)

		lowercase__					      =		count_samples(__magic_name__					)

		lowercase__					      =		num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
		lowercase__					      =		steps_per_epoch * args.num_epochs

		with strategy.scope():
				lowercase__					      =		TFAutoModelForMaskedLM.from_config(__magic_name__					)
				model(model.dummy_inputs					)  # Pass some dummy inputs through the model to ensure all the weights are built
				lowercase__      ,     lowercase__					      =		create_optimizer(
				    num_train_steps=__magic_name__   ,   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=__magic_name__   ,   metrics=["""accuracy"""]					)

		def decode_fn(__magic_name__					:							int					):
				lowercase__					      =		{
				    """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(__magic_name__   ,   __magic_name__					)

		# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
		# use their methods in our data pipeline.
		lowercase__					      =		DataCollatorForLanguageModeling(
		    tokenizer=__magic_name__   ,   mlm_probability=args.mlm_probability   ,   mlm=__magic_name__   ,   return_tensors="""tf"""					)

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

		lowercase__					      =		args.per_replica_batch_size * strategy.num_replicas_in_sync

		lowercase__					      =		prepare_dataset(
		    __magic_name__   ,   decode_fn=__magic_name__   ,   mask_fn=__magic_name__   ,   batch_size=__magic_name__   ,   shuffle=__magic_name__   ,   shuffle_buffer_size=args.shuffle_buffer_size   ,   )

		lowercase__					      =		prepare_dataset(
		    __magic_name__   ,   decode_fn=__magic_name__   ,   mask_fn=__magic_name__   ,   batch_size=__magic_name__   ,   shuffle=__magic_name__   ,   )

		lowercase__					      =		[]
		if args.hub_model_id:
				callbacks.append(
				    PushToHubCallback(output_dir=args.output_dir   ,   hub_model_id=args.hub_model_id   ,   tokenizer=__magic_name__					)					)

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

		model.save_pretrained(args.output_dir					)


if __name__ == "__main__":
	A	:		Union[str, Any]    =       parse_args()
	main(args)





import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 							A   (	UpperCAmelCase__			,  UpperCAmelCase__			,  UpperCAmelCase__			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			StableDiffusionInpaintPipeline
					A__      =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					A__      =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					A__      =			frozenset(
					    []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					A__      =			frozenset([]							)

					def   lowerCamelCase__					(self   :						Tuple		)						->       List[Any]:




							"""simple docstring"""


							torch.manual_seed(0		)
							lowercase__					      =		UNetaDConditionModel(
							    block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=9				, out_channels=4				, down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				, up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				, cross_attention_dim=32				, attention_head_dim=(2, 4)				, use_linear_projection=_UpperCAmelCase				, )
							lowercase__					      =		PNDMScheduler(skip_prk_steps=_UpperCAmelCase		)
							torch.manual_seed(0		)
							lowercase__					      =		AutoencoderKL(
							    block_out_channels=[32, 64]				, in_channels=3				, out_channels=3				, down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				, up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				, latent_channels=4				, sample_size=128				, )
							torch.manual_seed(0		)
							lowercase__					      =		CLIPTextConfig(
							    bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1E-0_5				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=1000				, hidden_act="""gelu"""				, projection_dim=512				, )
							lowercase__					      =		CLIPTextModel(_UpperCAmelCase		)
							lowercase__					      =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

							lowercase__					      =		{
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						Optional[Any]=0		)						->       List[str]:




							"""simple docstring"""


							lowercase__					      =		floats_tensor((1, 3, 32, 32)				, rng=random.Random(_UpperCAmelCase		)		).to(_UpperCAmelCase		)
							lowercase__					      =		image.cpu().permute(0				, 2				, 3				, 1		)[0]
							lowercase__					      =		Image.fromarray(np.uinta(_UpperCAmelCase		)		).convert("""RGB"""		).resize((64, 64)		)
							lowercase__					      =		Image.fromarray(np.uinta(image + 4		)		).convert("""RGB"""		).resize((64, 64)		)
							if str(_UpperCAmelCase		).startswith("""mps"""		):
									lowercase__					      =		torch.manual_seed(_UpperCAmelCase		)
							else:
									lowercase__					      =		torch.Generator(device=_UpperCAmelCase		).manual_seed(_UpperCAmelCase		)
							lowercase__					      =		{
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """image""": init_image,
							    """mask_image""": mask_image,
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 6.0,
							    """output_type""": """numpy""",
							}
							return inputs

					def   lowerCamelCase__					(self   :						Optional[int]		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowercase__					      =		self.get_dummy_components()
							lowercase__					      =		StableDiffusionInpaintPipeline(**_UpperCAmelCase		)
							lowercase__					      =		sd_pipe.to(_UpperCAmelCase		)
							sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

							lowercase__					      =		self.get_dummy_inputs(_UpperCAmelCase		)
							lowercase__					      =		sd_pipe(**_UpperCAmelCase		).images
							lowercase__					      =		image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							lowercase__					      =		np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]		)

							assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2


					def   lowerCamelCase__					(self   :						str		)						->       int:




							"""simple docstring"""


							super().test_inference_batch_single_identical(expected_max_diff=3E-3		)






@slow
@require_torch_gpu
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					def   lowerCamelCase__					(self   :						str		)						->       Optional[Any]:




							"""simple docstring"""


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()

					def   lowerCamelCase__					(self   :						Union[str, Any]		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/sd2-inpaint/init_image.png"""		)
							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""		)
							lowercase__					      =		load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
							    """/yellow_cat_sitting_on_a_park_bench.npy"""		)

							lowercase__					      =		"""stabilityai/stable-diffusion-2-inpainting"""
							lowercase__					      =		StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase				, safety_checker=_UpperCAmelCase		)
							pipe.to(_UpperCAmelCase		)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase		)
							pipe.enable_attention_slicing()

							lowercase__					      =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

							lowercase__					      =		torch.manual_seed(0		)
							lowercase__					      =		pipe(
							    prompt=_UpperCAmelCase				, image=_UpperCAmelCase				, mask_image=_UpperCAmelCase				, generator=_UpperCAmelCase				, output_type="""np"""				, )
							lowercase__					      =		output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image		).max() < 9E-3

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




							"""simple docstring"""


							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/sd2-inpaint/init_image.png"""		)
							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""		)
							lowercase__					      =		load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
							    """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""		)

							lowercase__					      =		"""stabilityai/stable-diffusion-2-inpainting"""
							lowercase__					      =		StableDiffusionInpaintPipeline.from_pretrained(
							    _UpperCAmelCase				, torch_dtype=torch.floataa				, safety_checker=_UpperCAmelCase				, )
							pipe.to(_UpperCAmelCase		)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase		)
							pipe.enable_attention_slicing()

							lowercase__					      =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

							lowercase__					      =		torch.manual_seed(0		)
							lowercase__					      =		pipe(
							    prompt=_UpperCAmelCase				, image=_UpperCAmelCase				, mask_image=_UpperCAmelCase				, generator=_UpperCAmelCase				, output_type="""np"""				, )
							lowercase__					      =		output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image		).max() < 5E-1


					def   lowerCamelCase__					(self   :						List[str]		)						->       Optional[Any]:




							"""simple docstring"""


							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/sd2-inpaint/init_image.png"""		)
							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""		)

							lowercase__					      =		"""stabilityai/stable-diffusion-2-inpainting"""
							lowercase__					      =		PNDMScheduler.from_pretrained(_UpperCAmelCase				, subfolder="""scheduler"""		)
							lowercase__					      =		StableDiffusionInpaintPipeline.from_pretrained(
							    _UpperCAmelCase				, safety_checker=_UpperCAmelCase				, scheduler=_UpperCAmelCase				, torch_dtype=torch.floataa				, )
							pipe.to(_UpperCAmelCase		)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase		)
							pipe.enable_attention_slicing(1		)
							pipe.enable_sequential_cpu_offload()

							lowercase__					      =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

							lowercase__					      =		torch.manual_seed(0		)
							lowercase__					      =		pipe(
							    prompt=_UpperCAmelCase				, image=_UpperCAmelCase				, mask_image=_UpperCAmelCase				, generator=_UpperCAmelCase				, num_inference_steps=2				, output_type="""np"""				, )

							lowercase__					      =		torch.cuda.max_memory_allocated()
							# make sure that less than 2.65 GB is allocated
							assert mem_bytes < 2.65 * 10**9

