'''simple docstring'''





import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
							import onnxruntime as ort





class 		__SCREAMING_SNAKE_CASE       ( UpperCamelCase_	,       unittest.TestCase			):
				lowerCamelCase_							=						'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'







				def    lowerCamelCase_  (					self    :		List[str]		,				UpperCAmelCase__    :		Tuple=0      ):
										'''simple docstring'''
										lowercase   :       str								=floats_tensor((1, 3, 128, 128)		,				rng=random.Random(UpperCAmelCase__      )      )
										lowercase   :       Optional[int]								=np.random.RandomState(UpperCAmelCase__      )
										lowercase   :       Optional[int]								={
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''image''': image,
										    '''generator''': generator,
										    '''num_inference_steps''': 3,
										    '''strength''': 0.75,
										    '''guidance_scale''': 7.5,
										    '''output_type''': '''numpy''',
										}
										return inputs







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,				provider='''CPUExecutionProvider'''      )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__      )

										lowercase   :       int								=self.get_dummy_inputs()
										lowercase   :       str								=pipe(**UpperCAmelCase__      ).images
										lowercase   :       Dict								=image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 128, 128, 3)
										lowercase   :       Dict								=np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87]      )
										assert np.abs(image_slice - expected_slice      ).max() < 1E-1







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       List[str]								=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,				provider='''CPUExecutionProvider'''      )
										lowercase   :       int								=PNDMScheduler.from_config(pipe.scheduler.config		,				skip_prk_steps=UpperCAmelCase__      )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__      )

										lowercase   :       Union[str, Any]								=self.get_dummy_inputs()
										lowercase   :       str								=pipe(**UpperCAmelCase__      ).images
										lowercase   :       Optional[Any]								=image[0, -3:, -3:, -1]

										assert image.shape == (1, 128, 128, 3)
										lowercase   :       Optional[int]								=np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54]      )

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







				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       str								=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,				provider='''CPUExecutionProvider'''      )
										lowercase   :       List[Any]								=LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__      )

										# warmup pass to apply optimizations
										lowercase   :       Union[str, Any]								=pipe(**self.get_dummy_inputs()      )

										lowercase   :       Union[str, Any]								=self.get_dummy_inputs()
										lowercase   :       str								=pipe(**UpperCAmelCase__      ).images
										lowercase   :       Optional[int]								=image[0, -3:, -3:, -1]

										assert image.shape == (1, 128, 128, 3)
										lowercase   :       List[str]								=np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03]      )

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







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,				provider='''CPUExecutionProvider'''      )
										lowercase   :       List[Any]								=EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__      )

										lowercase   :       Optional[int]								=self.get_dummy_inputs()
										lowercase   :       List[str]								=pipe(**UpperCAmelCase__      ).images
										lowercase   :       Optional[Any]								=image[0, -3:, -3:, -1]

										assert image.shape == (1, 128, 128, 3)
										lowercase   :       Any								=np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]      )

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







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       List[str]								=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,				provider='''CPUExecutionProvider'''      )
										lowercase   :       int								=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__      )

										lowercase   :       Union[str, Any]								=self.get_dummy_inputs()
										lowercase   :       Any								=pipe(**UpperCAmelCase__      ).images
										lowercase   :       Union[str, Any]								=image[0, -3:, -3:, -1]

										assert image.shape == (1, 128, 128, 3)
										lowercase   :       List[Any]								=np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]      )

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







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										lowercase   :       str								=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,				provider='''CPUExecutionProvider'''      )
										lowercase   :       List[str]								=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__      )

										lowercase   :       Tuple								=self.get_dummy_inputs()
										lowercase   :       Optional[int]								=pipe(**UpperCAmelCase__      ).images
										lowercase   :       Union[str, Any]								=image[0, -3:, -3:, -1]

										assert image.shape == (1, 128, 128, 3)
										lowercase   :       Optional[Any]								=np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52]      )

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





@nightly
@require_onnxruntime
@require_torch_gpu
class 		__SCREAMING_SNAKE_CASE       ( unittest.TestCase			):







				@property
				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										return (
										    "CUDAExecutionProvider",
										    {
										        "gpu_mem_limit": "15000000000",  # 15GB
										        "arena_extend_strategy": "kSameAsRequested",
										    },
										)







				@property
				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       int								=ort.SessionOptions()
										lowercase   :       Union[str, Any]								=False
										return options







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       int								=load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/img2img/sketch-mountains-input.jpg'''      )
										lowercase   :       Optional[Any]								=init_image.resize((768, 512)      )
										# using the PNDM scheduler by default
										lowercase   :       List[str]								=OnnxStableDiffusionImgaImgPipeline.from_pretrained(
										    '''CompVis/stable-diffusion-v1-4'''		,				revision='''onnx'''		,				safety_checker=UpperCAmelCase__		,				feature_extractor=UpperCAmelCase__		,				provider=self.gpu_provider		,				sess_options=self.gpu_options		,				)
										pipe.set_progress_bar_config(disable=UpperCAmelCase__      )

										lowercase   :       List[str]								='''A fantasy landscape, trending on artstation'''

										lowercase   :       str								=np.random.RandomState(0      )
										lowercase   :       Dict								=pipe(
										    prompt=UpperCAmelCase__		,				image=UpperCAmelCase__		,				strength=0.75		,				guidance_scale=7.5		,				num_inference_steps=10		,				generator=UpperCAmelCase__		,				output_type='''np'''		,				)
										lowercase   :       int								=output.images
										lowercase   :       Optional[Any]								=images[0, 255:258, 383:386, -1]

										assert images.shape == (1, 512, 768, 3)
										lowercase   :       Any								=np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19]      )
										# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       List[str]								=load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/img2img/sketch-mountains-input.jpg'''      )
										lowercase   :       Any								=init_image.resize((768, 512)      )
										lowercase   :       Any								=LMSDiscreteScheduler.from_pretrained(
										    '''runwayml/stable-diffusion-v1-5'''		,				subfolder='''scheduler'''		,				revision='''onnx'''      )
										lowercase   :       Optional[int]								=OnnxStableDiffusionImgaImgPipeline.from_pretrained(
										    '''runwayml/stable-diffusion-v1-5'''		,				revision='''onnx'''		,				scheduler=UpperCAmelCase__		,				safety_checker=UpperCAmelCase__		,				feature_extractor=UpperCAmelCase__		,				provider=self.gpu_provider		,				sess_options=self.gpu_options		,				)
										pipe.set_progress_bar_config(disable=UpperCAmelCase__      )

										lowercase   :       str								='''A fantasy landscape, trending on artstation'''

										lowercase   :       Tuple								=np.random.RandomState(0      )
										lowercase   :       List[Any]								=pipe(
										    prompt=UpperCAmelCase__		,				image=UpperCAmelCase__		,				strength=0.75		,				guidance_scale=7.5		,				num_inference_steps=20		,				generator=UpperCAmelCase__		,				output_type='''np'''		,				)
										lowercase   :       List[Any]								=output.images
										lowercase   :       Union[str, Any]								=images[0, 255:258, 383:386, -1]

										assert images.shape == (1, 512, 768, 3)
										lowercase   :       Any								=np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31]      )
										# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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








'''simple docstring'''





import argparse
import logging
import os
import re

import tensorflow as tf

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


UpperCamelCase_							=					logging.getLogger(__name__)

UpperCamelCase_							=					tf.data.AUTOTUNE






def 							_lowerCAmelCase (      )							->     Any:
						lowercase   :       Dict								=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   :       Union[str, Any]								=parser.parse_args()
						return args






def 							_lowerCAmelCase (      __magic_name__	:    List[str]	)							->     List[Any]:
						try:
												if args.tpu_name:
																		lowercase   :       Dict								=tf.distribute.cluster_resolver.TPUClusterResolver(
																		    args.tpu_name      ,   zone=args.tpu_zone      ,   project=args.gcp_project	)
												else:
																		lowercase   :       Optional[int]								=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 							_lowerCAmelCase (      __magic_name__	:    Tuple	)							->     Union[str, Any]:
						lowercase   :       str								=0
						for file in file_list:
												lowercase   :       List[str]								=file.split('''/'''	)[-1]
												lowercase   :       Union[str, Any]								=re.search(R'''-\d+-(\d+)\.tfrecord'''      ,   __magic_name__	).group(1	)
												lowercase   :       int								=int(__magic_name__	)
												num_samples += sample_count

						return num_samples






def 							_lowerCAmelCase (      __magic_name__	:    List[str]      ,   __magic_name__	:    Union[str, Any]      ,   __magic_name__	:    int      ,   __magic_name__	:    Optional[Any]      ,   __magic_name__	:    Optional[int]      ,   __magic_name__	:    Optional[int]=None	)							->     str:
						lowercase   :       int								=count_samples(__magic_name__	)
						lowercase   :       Union[str, Any]								=tf.data.Dataset.from_tensor_slices(__magic_name__	)
						if shuffle:
												lowercase   :       Union[str, Any]								=dataset.shuffle(len(__magic_name__	)	)
						lowercase   :       Any								=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   :       Optional[int]								=dataset.apply(tf.data.experimental.assert_cardinality(__magic_name__	)	)
						lowercase   :       str								=dataset.map(__magic_name__      ,   num_parallel_calls=__magic_name__	)
						if shuffle:
												assert shuffle_buffer_size is not None
												lowercase   :       int								=dataset.shuffle(args.shuffle_buffer_size	)
						lowercase   :       Optional[int]								=dataset.batch(__magic_name__      ,   drop_remainder=__magic_name__	)
						lowercase   :       int								=dataset.map(__magic_name__      ,   num_parallel_calls=__magic_name__	)
						lowercase   :       Union[str, Any]								=dataset.prefetch(__magic_name__	)
						return dataset






def 							_lowerCAmelCase (      __magic_name__	:    Any	)							->     str:
						if not args.no_tpu:
												lowercase   :       Optional[Any]								=initialize_tpu(__magic_name__	)
												lowercase   :       Any								=tf.distribute.TPUStrategy(__magic_name__	)
						else:
												lowercase   :       Optional[Any]								=tf.distribute.OneDeviceStrategy(device='''/gpu:0'''	)

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

						lowercase   :       Any								=AutoTokenizer.from_pretrained(args.tokenizer	)
						lowercase   :       Union[str, Any]								=AutoConfig.from_pretrained(args.pretrained_model_config	)
						lowercase   :       Optional[Any]								=tokenizer.vocab_size

						lowercase   :       str								=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   :       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}.'''	)

						lowercase   :       Any								=count_samples(__magic_name__	)

						lowercase   :       str								=num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
						lowercase   :       Union[str, Any]								=steps_per_epoch * args.num_epochs

						with strategy.scope():
												lowercase   :       List[Any]								=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   :       Dict								=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__	:    Optional[Any]	):
												lowercase   :       Union[str, Any]								={
												    '''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   :       str								=DataCollatorForLanguageModeling(
						    tokenizer=__magic_name__      ,   mlm_probability=args.mlm_probability      ,   mlm=__magic_name__      ,   return_tensors='''tf'''	)

						def mask_with_collator(__magic_name__	:    Dict	):
												# TF really needs an isin() function
												lowercase   :       int								=(
												    ~tf.cast(batch['''attention_mask''']      ,   tf.bool	)
												    | (batch['''input_ids'''] == tokenizer.cls_token_id)
												    | (batch['''input_ids'''] == tokenizer.sep_token_id)
												)
												lowercase		,  lowercase   :       Union[str, Any]								=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   :       List[str]								=args.per_replica_batch_size * strategy.num_replicas_in_sync

						lowercase   :       Dict								=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   :       Union[str, Any]								=prepare_dataset(
						    __magic_name__      ,   decode_fn=__magic_name__      ,   mask_fn=__magic_name__      ,   batch_size=__magic_name__      ,   shuffle=__magic_name__      ,   )

						lowercase   :       Tuple								=[]
						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__":
							UpperCamelCase_							=					parse_args()
							main(args)





