




import argparse
import logging
import os
import re

import tensorflow as tf

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


UpperCAmelCase_		:					List[str]          =  logging.getLogger(__name__)

UpperCAmelCase_		:					str          =  tf.data.AUTOTUNE




def 		lowerCAmelCase_ (						):
				__magic_name__						:						Tuple										=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=512						,  help="""Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"""						,  )

				parser.add_argument(
				    """--mlm_probability"""						,  type=lowerCamelCase						,  default=0.1_5						,  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.""" )

				__magic_name__						:						List[Any]										=parser.parse_args()
				return args




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

				return num_samples




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




def 		lowerCAmelCase_ (						lowerCamelCase ):
				if not args.no_tpu:
								__magic_name__						:						Optional[int]										=initialize_tpu(lowerCamelCase )
								__magic_name__						:						str										=tf.distribute.TPUStrategy(lowerCamelCase )
				else:
								__magic_name__						:						Optional[Any]										=tf.distribute.OneDeviceStrategy(device="""/gpu:0""" )

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

				__magic_name__						:						List[str]										=AutoTokenizer.from_pretrained(args.tokenizer )
				__magic_name__						:						Tuple										=AutoConfig.from_pretrained(args.pretrained_model_config )
				__magic_name__						:						Union[str, Any]										=tokenizer.vocab_size

				__magic_name__						:						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}." )
				__magic_name__						:						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}." )

				__magic_name__						:						Tuple										=count_samples(lowerCamelCase )

				__magic_name__						:						Any										=num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
				__magic_name__						:						Any										=steps_per_epoch * args.num_epochs

				with strategy.scope():
								__magic_name__						:						Optional[Any]										=TFAutoModelForMaskedLM.from_config(lowerCamelCase )
								model(model.dummy_inputs )  # Pass some dummy inputs through the model to ensure all the weights are built
								__magic_name__					,			__magic_name__						:						Dict										=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 ):
								__magic_name__						:						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.
				__magic_name__						:						Optional[Any]										=DataCollatorForLanguageModeling(
				    tokenizer=lowerCamelCase						,  mlm_probability=args.mlm_probability						,  mlm=lowerCamelCase						,  return_tensors="""tf""" )

				def mask_with_collator(lowerCamelCase ):
								# TF really needs an isin() function
								__magic_name__						:						Dict										=(
								    ~tf.cast(batch["""attention_mask"""]						,  tf.bool )
								    | (batch["""input_ids"""] == tokenizer.cls_token_id)
								    | (batch["""input_ids"""] == tokenizer.sep_token_id)
								)
								__magic_name__					,			__magic_name__						:						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

				__magic_name__						:						Optional[Any]										=args.per_replica_batch_size * strategy.num_replicas_in_sync

				__magic_name__						:						Dict										=prepare_dataset(
				    lowerCamelCase						,  decode_fn=lowerCamelCase						,  mask_fn=lowerCamelCase						,  batch_size=lowerCamelCase						,  shuffle=lowerCamelCase						,  shuffle_buffer_size=args.shuffle_buffer_size						,  )

				__magic_name__						:						List[str]										=prepare_dataset(
				    lowerCamelCase						,  decode_fn=lowerCamelCase						,  mask_fn=lowerCamelCase						,  batch_size=lowerCamelCase						,  shuffle=lowerCamelCase						,  )

				__magic_name__						:						Optional[int]										=[]
				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__":
							UpperCAmelCase_		:					Tuple          =  parse_args()
							main(args)










import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       KandinskyInpaintPipeline
						UpperCamelCase      =       ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
						UpperCamelCase      =       [
						    """prompt""",
						    """negative_prompt""",
						    """image_embeds""",
						    """negative_image_embeds""",
						    """image""",
						    """mask_image""",
						]
						UpperCamelCase      =       [
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """negative_prompt""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						UpperCamelCase      =       False

						@property
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										return self.time_input_dim

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										return self.time_input_dim * 4

						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										return 1_00

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Dict										=XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""			)
										return tokenizer

						@property
						def      A__    (		self :str			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						str										=MCLIPConfig(
										    numDims=self.cross_attention_dim     ,			transformerDimensions=self.text_embedder_hidden_size     ,			hidden_size=self.text_embedder_hidden_size     ,			intermediate_size=37     ,			num_attention_heads=4     ,			num_hidden_layers=5     ,			vocab_size=10_05     ,			)

										__magic_name__						:						Tuple										=MultilingualCLIP(__snake_case			)
										__magic_name__						:						Optional[int]										=text_encoder.eval()

										return text_encoder

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										torch.manual_seed(0			)

										__magic_name__						:						Optional[Any]										={
										    """in_channels""": 9,
										    # Out channels is double in channels because predicts mean and variance
										    """out_channels""": 8,
										    """addition_embed_type""": """text_image""",
										    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
										    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
										    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
										    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
										    """layers_per_block""": 1,
										    """encoder_hid_dim""": self.text_embedder_hidden_size,
										    """encoder_hid_dim_type""": """text_image_proj""",
										    """cross_attention_dim""": self.cross_attention_dim,
										    """attention_head_dim""": 4,
										    """resnet_time_scale_shift""": """scale_shift""",
										    """class_embed_type""": None,
										}

										__magic_name__						:						Union[str, Any]										=UNetaDConditionModel(**__snake_case			)
										return model

						@property
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										return {
										    "block_out_channels": [32, 64],
										    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
										    "in_channels": 3,
										    "latent_channels": 4,
										    "layers_per_block": 1,
										    "norm_num_groups": 8,
										    "norm_type": "spatial",
										    "num_vq_embeddings": 12,
										    "out_channels": 3,
										    "up_block_types": [
										        "AttnUpDecoderBlock2D",
										        "UpDecoderBlock2D",
										    ],
										    "vq_embed_dim": 4,
										}

						@property
						def      A__    (		self :Tuple			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						Dict										=VQModel(**self.dummy_movq_kwargs			)
										return model

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=self.dummy_text_encoder
										__magic_name__						:						Optional[Any]										=self.dummy_tokenizer
										__magic_name__						:						Optional[Any]										=self.dummy_unet
										__magic_name__						:						Tuple										=self.dummy_movq

										__magic_name__						:						List[str]										=DDIMScheduler(
										    num_train_timesteps=10_00     ,			beta_schedule="""linear"""     ,			beta_start=0.00085     ,			beta_end=0.012     ,			clip_sample=__snake_case     ,			set_alpha_to_one=__snake_case     ,			steps_offset=1     ,			prediction_type="""epsilon"""     ,			thresholding=__snake_case     ,			)

										__magic_name__						:						str										={
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """movq""": movq,
										}

										return components

						def      A__    (		self :str     ,			__snake_case :Optional[Any]     ,			__snake_case :int=0			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(__snake_case			)			).to(__snake_case			)
										__magic_name__						:						Dict										=floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(seed + 1			)			).to(__snake_case			)
										# create init_image
										__magic_name__						:						str										=floats_tensor((1, 3, 64, 64)     ,			rng=random.Random(__snake_case			)			).to(__snake_case			)
										__magic_name__						:						int										=image.cpu().permute(0     ,			2     ,			3     ,			1			)[0]
										__magic_name__						:						str										=Image.fromarray(np.uinta(__snake_case			)			).convert("""RGB"""			).resize((2_56, 2_56)			)
										# create mask
										__magic_name__						:						Dict										=np.ones((64, 64)     ,			dtype=np.floataa			)
										__magic_name__						:						Any										=0

										if str(__snake_case			).startswith("""mps"""			):
														__magic_name__						:						Dict										=torch.manual_seed(__snake_case			)
										else:
														__magic_name__						:						Tuple										=torch.Generator(device=__snake_case			).manual_seed(__snake_case			)
										__magic_name__						:						List[Any]										={
										    """prompt""": """horse""",
										    """image""": init_image,
										    """mask_image""": mask,
										    """image_embeds""": image_embeds,
										    """negative_image_embeds""": negative_image_embeds,
										    """generator""": generator,
										    """height""": 64,
										    """width""": 64,
										    """num_inference_steps""": 2,
										    """guidance_scale""": 4.0,
										    """output_type""": """np""",
										}
										return inputs

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										="""cpu"""

										__magic_name__						:						List[Any]										=self.get_dummy_components()

										__magic_name__						:						Union[str, Any]										=self.pipeline_class(**__snake_case			)
										__magic_name__						:						Tuple										=pipe.to(__snake_case			)

										pipe.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Tuple										=pipe(**self.get_dummy_inputs(__snake_case			)			)
										__magic_name__						:						List[Any]										=output.images

										__magic_name__						:						Any										=pipe(
										    **self.get_dummy_inputs(__snake_case			)     ,			return_dict=__snake_case     ,			)[0]

										__magic_name__						:						int										=image[0, -3:, -3:, -1]
										__magic_name__						:						str										=image_from_tuple[0, -3:, -3:, -1]

										print(f"image.shape {image.shape}"			)

										assert image.shape == (1, 64, 64, 3)

										__magic_name__						:						Optional[Any]										=np.array(
										    [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]			)

										assert (
										    np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
										), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
										assert (
										    np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2
										), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"


						def      A__    (		self :Dict			):
										'''simple docstring'''

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




@slow
@require_torch_gpu
class 		__A   (      unittest.TestCase      ):

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

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


						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""			)

										__magic_name__						:						int										=load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""			)
										__magic_name__						:						List[Any]										=np.ones((7_68, 7_68)     ,			dtype=np.floataa			)
										__magic_name__						:						Any										=0

										__magic_name__						:						int										="""a hat"""

										__magic_name__						:						int										=KandinskyPriorPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-1-prior"""     ,			torch_dtype=torch.floataa			)
										pipe_prior.to(__snake_case			)

										__magic_name__						:						Dict										=KandinskyInpaintPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-1-inpaint"""     ,			torch_dtype=torch.floataa			)
										__magic_name__						:						int										=pipeline.to(__snake_case			)
										pipeline.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Union[str, Any]										=torch.Generator(device="""cpu"""			).manual_seed(0			)
										__magic_name__					,			__magic_name__						:						Dict										=pipe_prior(
										    __snake_case     ,			generator=__snake_case     ,			num_inference_steps=5     ,			negative_prompt=""""""     ,			).to_tuple()

										__magic_name__						:						Optional[Any]										=pipeline(
										    __snake_case     ,			image=__snake_case     ,			mask_image=__snake_case     ,			image_embeds=__snake_case     ,			negative_image_embeds=__snake_case     ,			generator=__snake_case     ,			num_inference_steps=1_00     ,			height=7_68     ,			width=7_68     ,			output_type="""np"""     ,			)

										__magic_name__						:						Optional[int]										=output.images[0]

										assert image.shape == (7_68, 7_68, 3)

										assert_mean_pixel_difference(__snake_case     ,			__snake_case			)




