
import argparse
import logging
import os
import re

import tensorflow as tf

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


_A     =  logging.getLogger(__name__)

_A     =  tf.data.AUTOTUNE






def      lowerCamelCase__       (       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			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=0.15					,							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."       )

			lowerCAmelCase_      			=			parser.parse_args()
			return args






def      lowerCamelCase__       (       __lowerCAmelCase						:						Tuple       ):




			"""simple docstring"""


			try:
						if args.tpu_name:
									lowerCAmelCase_      			=			tf.distribute.cluster_resolver.TPUClusterResolver(
									    args.tpu_name					,							zone=args.tpu_zone					,							project=args.gcp_project       )
						else:
									lowerCAmelCase_      			=			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						:						Any       ):




			"""simple docstring"""


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

			return num_samples






def      lowerCamelCase__       (       __lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						Optional[int]					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						List[Any]=None       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			count_samples(__lowerCAmelCase       )
			lowerCAmelCase_      			=			tf.data.Dataset.from_tensor_slices(__lowerCAmelCase       )
			if shuffle:
						lowerCAmelCase_      			=			dataset.shuffle(len(__lowerCAmelCase       )       )
			lowerCAmelCase_      			=			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
			lowerCAmelCase_      			=			dataset.apply(tf.data.experimental.assert_cardinality(__lowerCAmelCase       )       )
			lowerCAmelCase_      			=			dataset.map(__lowerCAmelCase					,							num_parallel_calls=__lowerCAmelCase       )
			if shuffle:
						assert shuffle_buffer_size is not None
						lowerCAmelCase_      			=			dataset.shuffle(args.shuffle_buffer_size       )
			lowerCAmelCase_      			=			dataset.batch(__lowerCAmelCase					,							drop_remainder=__lowerCAmelCase       )
			lowerCAmelCase_      			=			dataset.map(__lowerCAmelCase					,							num_parallel_calls=__lowerCAmelCase       )
			lowerCAmelCase_      			=			dataset.prefetch(__lowerCAmelCase       )
			return dataset






def      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[int]       ):




			"""simple docstring"""


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

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

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

			lowerCAmelCase_      			=			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}."""       )
			lowerCAmelCase_      			=			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}."""       )

			lowerCAmelCase_      			=			count_samples(__lowerCAmelCase       )

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

			with strategy.scope():
						lowerCAmelCase_      			=			TFAutoModelForMaskedLM.from_config(__lowerCAmelCase       )
						model(model.dummy_inputs       )  # Pass some dummy inputs through the model to ensure all the weights are built
						lowerCAmelCase_			,						lowerCAmelCase_      			=			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						:						str       ):
						lowerCAmelCase_      			=			{
						    "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.
			lowerCAmelCase_      			=			DataCollatorForLanguageModeling(
			    tokenizer=__lowerCAmelCase					,							mlm_probability=args.mlm_probability					,							mlm=__lowerCAmelCase					,							return_tensors="tf"       )

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

			lowerCAmelCase_      			=			args.per_replica_batch_size * strategy.num_replicas_in_sync

			lowerCAmelCase_      			=			prepare_dataset(
			    __lowerCAmelCase					,							decode_fn=__lowerCAmelCase					,							mask_fn=__lowerCAmelCase					,							batch_size=__lowerCAmelCase					,							shuffle=__lowerCAmelCase					,							shuffle_buffer_size=args.shuffle_buffer_size					,							)

			lowerCAmelCase_      			=			prepare_dataset(
			    __lowerCAmelCase					,							decode_fn=__lowerCAmelCase					,							mask_fn=__lowerCAmelCase					,							batch_size=__lowerCAmelCase					,							shuffle=__lowerCAmelCase					,							)

			lowerCAmelCase_      			=			[]
			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__":
			_A     =  parse_args()
			main(args)





import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class 			_lowerCAmelCase      (			__a      ,   __a      ,   __a      ,   unittest.TestCase ):
				_lowercase    	=StableUnCLIPPipeline
				_lowercase    	=TEXT_TO_IMAGE_PARAMS
				_lowercase    	=TEXT_TO_IMAGE_BATCH_PARAMS
				_lowercase    	=TEXT_TO_IMAGE_IMAGE_PARAMS
				_lowercase    	=TEXT_TO_IMAGE_IMAGE_PARAMS

				# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
				_lowercase    	=False




				def   __a		(      self  )       ->   Dict:
							lowerCAmelCase_      			=			32
							lowerCAmelCase_      			=			embedder_hidden_size

							# prior components

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTextModelWithProjection(
							    CLIPTextConfig(
							        bos_token_id=0       ,       eos_token_id=2       ,       hidden_size=_UpperCamelCase       ,       projection_dim=_UpperCamelCase       ,       intermediate_size=37       ,       layer_norm_eps=1e-0_5       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       pad_token_id=1       ,       vocab_size=1_000       ,       )  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			PriorTransformer(
							    num_attention_heads=2       ,       attention_head_dim=12       ,       embedding_dim=_UpperCamelCase       ,       num_layers=1       ,       )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			DDPMScheduler(
							    variance_type="fixed_small_log"       ,       prediction_type="sample"       ,       num_train_timesteps=1_000       ,       clip_sample=_UpperCamelCase       ,       clip_sample_range=5.0       ,       beta_schedule="squaredcos_cap_v2"       ,       )

							# regular denoising components

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			StableUnCLIPImageNormalizer(embedding_dim=_UpperCamelCase  )
							lowerCAmelCase_      			=			DDPMScheduler(beta_schedule="squaredcos_cap_v2"  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0       ,       eos_token_id=2       ,       hidden_size=_UpperCamelCase       ,       projection_dim=32       ,       intermediate_size=37       ,       layer_norm_eps=1e-0_5       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       pad_token_id=1       ,       vocab_size=1_000       ,       )  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			UNetaDConditionModel(
							    sample_size=32       ,       in_channels=4       ,       out_channels=4       ,       down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")       ,       up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")       ,       block_out_channels=(32, 64)       ,       attention_head_dim=(2, 4)       ,       class_embed_type="projection"       ,       projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,       cross_attention_dim=_UpperCamelCase       ,       layers_per_block=1       ,       upcast_attention=_UpperCamelCase       ,       use_linear_projection=_UpperCamelCase       ,       )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			DDIMScheduler(
							    beta_schedule="scaled_linear"       ,       beta_start=0.00085       ,       beta_end=0.012       ,       prediction_type="v_prediction"       ,       set_alpha_to_one=_UpperCamelCase       ,       steps_offset=1       ,       )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			AutoencoderKL()

							lowerCAmelCase_      			=			{
							    # prior components
							    "prior_tokenizer": prior_tokenizer,
							    "prior_text_encoder": prior_text_encoder,
							    "prior": prior,
							    "prior_scheduler": prior_scheduler,
							    # image noising components
							    "image_normalizer": image_normalizer,
							    "image_noising_scheduler": image_noising_scheduler,
							    # regular denoising components
							    "tokenizer": tokenizer,
							    "text_encoder": text_encoder,
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							}

							return components




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=0  )       ->   Tuple:
							if str(_UpperCamelCase  ).startswith("mps"  ):
										lowerCAmelCase_      			=			torch.manual_seed(_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			torch.Generator(device=_UpperCamelCase  ).manual_seed(_UpperCamelCase  )
							lowerCAmelCase_      			=			{
							    "prompt": "A painting of a squirrel eating a burger",
							    "generator": generator,
							    "num_inference_steps": 2,
							    "prior_num_inference_steps": 2,
							    "output_type": "numpy",
							}
							return inputs




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			torch_device == "cpu"

							self._test_attention_slicing_forward_pass(test_max_difference=_UpperCamelCase  )




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			torch_device in ["cpu", "mps"]

							self._test_inference_batch_single_identical(test_max_difference=_UpperCamelCase  )





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




				def   __a		(      self  )       ->   List[str]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




				def   __a		(      self  )       ->   Optional[int]:
							lowerCAmelCase_      			=			load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"  )

							lowerCAmelCase_      			=			StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"       ,       torch_dtype=torch.floataa  )
							pipe.to(_UpperCamelCase  )
							pipe.set_progress_bar_config(disable=_UpperCamelCase  )
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase_      			=			torch.Generator(device="cpu"  ).manual_seed(0  )
							lowerCAmelCase_      			=			pipe("anime turle"       ,       generator=_UpperCamelCase       ,       output_type="np"  )

							lowerCAmelCase_      			=			output.images[0]

							assert image.shape == (768, 768, 3)

							assert_mean_pixel_difference(_UpperCamelCase       ,       _UpperCamelCase  )




				def   __a		(      self  )       ->   Optional[Any]:
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowerCAmelCase_      			=			StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"       ,       torch_dtype=torch.floataa  )
							lowerCAmelCase_      			=			pipe.to(_UpperCamelCase  )
							pipe.set_progress_bar_config(disable=_UpperCamelCase  )
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase_      			=			pipe(
							    "anime turtle"       ,       prior_num_inference_steps=2       ,       num_inference_steps=2       ,       output_type="np"       ,       )

							lowerCAmelCase_      			=			torch.cuda.max_memory_allocated()
							# make sure that less than 7 GB is allocated
							assert mem_bytes < 7 * 10**9



