



'''simple docstring'''




from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowerCamelCase							:Optional[Any]       = logging.get_logger(__name__)  # pylint: disable=invalid-name







class      _lowerCAmelCase							(      __UpperCAmelCase				,      __UpperCAmelCase			):


	@register_to_config
	def __init__(self	,				lowercase	,				lowercase = None	,				lowercase = None							):
				super().__init__()

				A_     :						Dict     =					learnable

				if self.learnable:
							assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
							assert length is not None, "learnable=True requires `length` to be set"

							A_     :						Optional[int]     =					torch.zeros(lowercase	,				lowercase							)
				else:
							A_     :						List[Any]     =					None

				A_     :						str     =					torch.nn.Parameter(lowercase							)







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		VQModel
	__SCREAMING_SNAKE_CASE       :		CLIPTextModel
	__SCREAMING_SNAKE_CASE       :		CLIPTokenizer
	__SCREAMING_SNAKE_CASE       :		TransformeraDModel
	__SCREAMING_SNAKE_CASE       :		LearnedClassifierFreeSamplingEmbeddings
	__SCREAMING_SNAKE_CASE       :		VQDiffusionScheduler


	def __init__(self	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				):
				super().__init__()

				self.register_modules(
				    vqvae=lowercase	,				transformer=lowercase	,				text_encoder=lowercase	,				tokenizer=lowercase	,				scheduler=lowercase	,				learned_classifier_free_sampling_embeddings=lowercase	,				)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase							):
				A_     :						Tuple     =					len(lowercase							) if isinstance(lowercase	,				lowercase							) else 1

				# get prompt text embeddings
				A_     :						List[Any]     =					self.tokenizer(
				    lowercase	,				padding="""max_length"""	,				max_length=self.tokenizer.model_max_length	,				return_tensors="""pt"""	,				)
				A_     :						List[str]     =					text_inputs.input_ids

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
							A_     :						Optional[Any]     =					self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
							logger.warning(
							    """The following part of your input was truncated because CLIP can only handle sequences up to"""
							    F' {self.tokenizer.model_max_length} tokens: {removed_text}'							)
							A_     :						Optional[Any]     =					text_input_ids[:, : self.tokenizer.model_max_length]
				A_     :						List[Any]     =					self.text_encoder(text_input_ids.to(self.device							)							)[0]

				# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
				# While CLIP does normalize the pooled output of the text transformer when combining
				# the image and text embeddings, CLIP does not directly normalize the last hidden state.
				#
				# CLIP normalizing the pooled output.
				# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
				A_     :						Any     =					prompt_embeds / prompt_embeds.norm(dim=-1	,				keepdim=lowercase							)

				# duplicate text embeddings for each generation per prompt
				A_     :						Union[str, Any]     =					prompt_embeds.repeat_interleave(lowercase	,				dim=0							)

				if do_classifier_free_guidance:
							if self.learned_classifier_free_sampling_embeddings.learnable:
										A_     :						Optional[int]     =					self.learned_classifier_free_sampling_embeddings.embeddings
										A_     :						Optional[Any]     =					negative_prompt_embeds.unsqueeze(0							).repeat(lowercase	,				1	,				1							)
							else:
										A_     :						int     =					[""""""] * batch_size

										A_     :						str     =					text_input_ids.shape[-1]
										A_     :						int     =					self.tokenizer(
										    lowercase	,				padding="""max_length"""	,				max_length=lowercase	,				truncation=lowercase	,				return_tensors="""pt"""	,				)
										A_     :						Any     =					self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
										# See comment for normalizing text embeddings
										A_     :						int     =					negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1	,				keepdim=lowercase							)

							# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
							A_     :						Optional[Any]     =					negative_prompt_embeds.shape[1]
							A_     :						int     =					negative_prompt_embeds.repeat(1	,				lowercase	,				1							)
							A_     :						Union[str, Any]     =					negative_prompt_embeds.view(batch_size * num_images_per_prompt	,				lowercase	,				-1							)

							# For classifier free guidance, we need to do two forward passes.
							# Here we concatenate the unconditional and text embeddings into a single batch
							# to avoid doing two forward passes
							A_     :						List[Any]     =					torch.cat([negative_prompt_embeds, prompt_embeds]							)

				return prompt_embeds


	@torch.no_grad()
	def __call__(self	,				lowercase	,				lowercase = 100	,				lowercase = 5.0	,				lowercase = 1.0	,				lowercase = 1	,				lowercase = None	,				lowercase = None	,				lowercase = "pil"	,				lowercase = True	,				lowercase = None	,				lowercase = 1	,				):
				if isinstance(lowercase	,				lowercase							):
							A_     :						Union[str, Any]     =					1
				elif isinstance(lowercase	,				lowercase							):
							A_     :						Optional[int]     =					len(lowercase							)
				else:
							raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(lowercase							)}'							)

				A_     :						Any     =					batch_size * num_images_per_prompt

				A_     :						Optional[Any]     =					guidance_scale > 1.0

				A_     :						List[Any]     =					self._encode_prompt(lowercase	,				lowercase	,				lowercase							)

				if (callback_steps is None) or (
				    callback_steps is not None and (not isinstance(lowercase	,				lowercase							) or callback_steps <= 0)
				):
							raise ValueError(
							    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
							    F' {type(lowercase							)}.'							)

				# get the initial completely masked latents unless the user supplied it

				A_     :						Any     =					(batch_size, self.transformer.num_latent_pixels)
				if latents is None:
							A_     :						Optional[int]     =					self.transformer.num_vector_embeds - 1
							A_     :						Optional[Any]     =					torch.full(lowercase	,				lowercase							).to(self.device							)
				else:
							if latents.shape != latents_shape:
										raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'							)
							if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
										raise ValueError(
										    """Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"""
										    F' {self.transformer.num_vector_embeds - 1} (inclusive).'							)
							A_     :						Optional[Any]     =					latents.to(self.device							)

				# set timesteps
				self.scheduler.set_timesteps(lowercase	,				device=self.device							)

				A_     :						int     =					self.scheduler.timesteps.to(self.device							)

				A_     :						Any     =					latents

				for i, t in enumerate(self.progress_bar(lowercase							)							):
							# expand the sample if we are doing classifier free guidance
							A_     :						Optional[Any]     =					torch.cat([sample] * 2							) if do_classifier_free_guidance else sample

							# predict the un-noised image
							# model_output == `log_p_x_0`
							A_     :						Optional[int]     =					self.transformer(lowercase	,				encoder_hidden_states=lowercase	,				timestep=lowercase							).sample

							if do_classifier_free_guidance:
										A_     :						int     =					model_output.chunk(2							)
										A_     :						str     =					model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
										model_output -= torch.logsumexp(lowercase	,				dim=1	,				keepdim=lowercase							)

							A_     :						int     =					self.truncate(lowercase	,				lowercase							)

							# remove `log(0)`'s (`-inf`s)
							A_     :						Any     =					model_output.clamp(-70							)

							# compute the previous noisy sample x_t -> x_t-1
							A_     :						Optional[Any]     =					self.scheduler.step(lowercase	,				timestep=lowercase	,				sample=lowercase	,				generator=lowercase							).prev_sample

							# call the callback, if provided
							if callback is not None and i % callback_steps == 0:
										callback(lowercase	,				lowercase	,				lowercase							)

				A_     :						List[str]     =					self.vqvae.config.vq_embed_dim
				A_     :						Optional[int]     =					(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
				A_     :						str     =					self.vqvae.quantize.get_codebook_entry(lowercase	,				shape=lowercase							)
				A_     :						Any     =					self.vqvae.decode(lowercase	,				force_not_quantize=lowercase							).sample

				A_     :						List[Any]     =					(image / 2 + 0.5).clamp(0	,				1							)
				A_     :						Any     =					image.cpu().permute(0	,				2	,				3	,				1							).numpy()

				if output_type == "pil":
							A_     :						str     =					self.numpy_to_pil(lowercase							)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=lowercase							)


	def    _a     (self	,				lowercase	,				lowercase							):
				A_     :						List[Any]     =					torch.sort(lowercase	,				1	,				descending=lowercase							)
				A_     :						List[Any]     =					torch.exp(lowercase							)
				A_     :						Optional[Any]     =					sorted_p_x_0.cumsum(dim=1							) < truncation_rate

				# Ensure that at least the largest probability is not zeroed out
				A_     :						List[Any]     =					torch.full_like(keep_mask[:, 0:1, :]	,				lowercase							)
				A_     :						Optional[int]     =					torch.cat((all_true, keep_mask)	,				dim=1							)
				A_     :						int     =					keep_mask[:, :-1, :]

				A_     :						Any     =					keep_mask.gather(1	,				indices.argsort(1							)							)

				A_     :						Tuple     =					log_p_x_0.clone()

				A_     :						Optional[Any]     =					-torch.inf  # -inf = log(0)

				return rv




'''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)