



"""simple docstring"""

from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


a_       =       logging.get_logger(__name__)  # pylint: disable=invalid-name

a_       =       '''\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'''







def 				SCREAMING_SNAKE_CASE__   (		SCREAMING_SNAKE_CASE__			:	Tuple					,     SCREAMING_SNAKE_CASE__			:	Optional[int]					,     SCREAMING_SNAKE_CASE__			:	Union[str, Any]=8      ):



					"""simple docstring"""





					snake_case_       :		Any			       =       h // scale_factor**2
					if h % scale_factor**2 != 0:
										new_h += 1
					snake_case_       :		Any			       =       w // scale_factor**2
					if w % scale_factor**2 != 0:
										new_w += 1
					return new_h * scale_factor, new_w * scale_factor







class  __lowercase  (       _UpperCAmelCase):







						"""simple docstring"""







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

											self.register_modules(
											    text_encoder=SCREAMING_SNAKE_CASE_ ,		tokenizer=SCREAMING_SNAKE_CASE_ ,		unet=SCREAMING_SNAKE_CASE_ ,		scheduler=SCREAMING_SNAKE_CASE_ ,		movq=SCREAMING_SNAKE_CASE_ ,		)
											snake_case_       :		Tuple			       =       2 ** (len(self.movq.config.block_out_channels							) - 1)







						def     __UpperCamelCase				(self ,		lowercase__ ,		lowercase__ ,		lowercase__ ,		lowercase__ ,		lowercase__ ,		lowercase__							):
											if latents is None:
																snake_case_       :		Tuple			       =       randn_tensor(SCREAMING_SNAKE_CASE_ ,		generator=SCREAMING_SNAKE_CASE_ ,		device=SCREAMING_SNAKE_CASE_ ,		dtype=SCREAMING_SNAKE_CASE_							)
											else:
																if latents.shape != shape:
																					raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'							)
																snake_case_       :		Any			       =       latents.to(SCREAMING_SNAKE_CASE_							)

											snake_case_       :		Any			       =       latents * scheduler.init_noise_sigma
											return latents







						def     __UpperCamelCase				(self ,		lowercase__ ,		lowercase__ ,		lowercase__ ,		lowercase__ ,		lowercase__=None ,		):
											snake_case_       :		Union[str, Any]			       =       len(SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_							) else 1
											# get prompt text embeddings
											snake_case_       :		Optional[int]			       =       self.tokenizer(
											    SCREAMING_SNAKE_CASE_ ,		padding="""max_length""" ,		truncation=SCREAMING_SNAKE_CASE_ ,		max_length=77 ,		return_attention_mask=SCREAMING_SNAKE_CASE_ ,		add_special_tokens=SCREAMING_SNAKE_CASE_ ,		return_tensors="""pt""" ,		)

											snake_case_       :		Any			       =       text_inputs.input_ids
											snake_case_       :		Dict			       =       self.tokenizer(SCREAMING_SNAKE_CASE_ ,		padding="""longest""" ,		return_tensors="""pt"""							).input_ids

											if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_							):
																snake_case_       :		Optional[Any]			       =       self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]							)
																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}'							)

											snake_case_       :		Union[str, Any]			       =       text_input_ids.to(SCREAMING_SNAKE_CASE_							)
											snake_case_       :		Dict			       =       text_inputs.attention_mask.to(SCREAMING_SNAKE_CASE_							)

											snake_case_       ,      snake_case_       :		Optional[int]			       =       self.text_encoder(
											    input_ids=SCREAMING_SNAKE_CASE_ ,		attention_mask=SCREAMING_SNAKE_CASE_							)

											snake_case_       :		List[Any]			       =       prompt_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_ ,		dim=0							)
											snake_case_       :		Optional[Any]			       =       text_encoder_hidden_states.repeat_interleave(SCREAMING_SNAKE_CASE_ ,		dim=0							)
											snake_case_       :		str			       =       text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_ ,		dim=0							)

											if do_classifier_free_guidance:
																snake_case_       :		Dict			       =       42
																if negative_prompt is None:
																					snake_case_       :		Tuple			       =       [""""""] * batch_size
																elif type(SCREAMING_SNAKE_CASE_							) is not type(SCREAMING_SNAKE_CASE_							):
																					raise TypeError(
																					    f'`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_							)} !='
																					    f' {type(SCREAMING_SNAKE_CASE_							)}.'							)
																elif isinstance(SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_							):
																					snake_case_       :		Union[str, Any]			       =       [negative_prompt]
																elif batch_size != len(SCREAMING_SNAKE_CASE_							):
																					raise ValueError(
																					    f'`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE_							)}, but `prompt`:'
																					    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
																					    """ the batch size of `prompt`."""							)
																else:
																					snake_case_       :		List[Any]			       =       negative_prompt

																snake_case_       :		Tuple			       =       self.tokenizer(
																    SCREAMING_SNAKE_CASE_ ,		padding="""max_length""" ,		max_length=77 ,		truncation=SCREAMING_SNAKE_CASE_ ,		return_attention_mask=SCREAMING_SNAKE_CASE_ ,		add_special_tokens=SCREAMING_SNAKE_CASE_ ,		return_tensors="""pt""" ,		)
																snake_case_       :		int			       =       uncond_input.input_ids.to(SCREAMING_SNAKE_CASE_							)
																snake_case_       :		Optional[Any]			       =       uncond_input.attention_mask.to(SCREAMING_SNAKE_CASE_							)

																snake_case_       ,      snake_case_       :		List[Any]			       =       self.text_encoder(
																    input_ids=SCREAMING_SNAKE_CASE_ ,		attention_mask=SCREAMING_SNAKE_CASE_							)

																# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

																snake_case_       :		int			       =       negative_prompt_embeds.shape[1]
																snake_case_       :		int			       =       negative_prompt_embeds.repeat(1 ,		SCREAMING_SNAKE_CASE_							)
																snake_case_       :		Union[str, Any]			       =       negative_prompt_embeds.view(batch_size * num_images_per_prompt ,		SCREAMING_SNAKE_CASE_							)

																snake_case_       :		List[Any]			       =       uncond_text_encoder_hidden_states.shape[1]
																snake_case_       :		str			       =       uncond_text_encoder_hidden_states.repeat(1 ,		SCREAMING_SNAKE_CASE_ ,		1							)
																snake_case_       :		int			       =       uncond_text_encoder_hidden_states.view(
																    batch_size * num_images_per_prompt ,		SCREAMING_SNAKE_CASE_ ,		-1							)
																snake_case_       :		List[str]			       =       uncond_text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_ ,		dim=0							)

																# done duplicates

																# 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
																snake_case_       :		List[Any]			       =       torch.cat([negative_prompt_embeds, prompt_embeds]							)
																snake_case_       :		Union[str, Any]			       =       torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]							)

																snake_case_       :		str			       =       torch.cat([uncond_text_mask, text_mask]							)

											return prompt_embeds, text_encoder_hidden_states, text_mask







						def     __UpperCamelCase				(self ,		lowercase__=0							):
											if is_accelerate_available():
																from accelerate import cpu_offload
											else:
																raise ImportError("""Please install accelerate via `pip install accelerate`"""							)

											snake_case_       :		Union[str, Any]			       =       torch.device(f'cuda:{gpu_id}'							)

											snake_case_       :		Tuple			       =       [
											    self.unet,
											    self.text_encoder,
											    self.movq,
											]
											for cpu_offloaded_model in models:
																if cpu_offloaded_model is not None:
																					cpu_offload(SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_							)







						def     __UpperCamelCase				(self ,		lowercase__=0							):
											if is_accelerate_available() and is_accelerate_version(""">=""" ,		"""0.17.0.dev0"""							):
																from accelerate import cpu_offload_with_hook
											else:
																raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""							)

											snake_case_       :		Optional[int]			       =       torch.device(f'cuda:{gpu_id}'							)

											if self.device.type != "cpu":
																self.to("""cpu""" ,		silence_dtype_warnings=SCREAMING_SNAKE_CASE_							)
																torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

											snake_case_       :		Any			       =       None
											for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
																snake_case_       ,      snake_case_       :		List[str]			       =       cpu_offload_with_hook(SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		prev_module_hook=SCREAMING_SNAKE_CASE_							)

											if self.safety_checker is not None:
																snake_case_       ,      snake_case_       :		Tuple			       =       cpu_offload_with_hook(self.safety_checker ,		SCREAMING_SNAKE_CASE_ ,		prev_module_hook=SCREAMING_SNAKE_CASE_							)

											# We'll offload the last model manually.
											snake_case_       :		Optional[Any]			       =       hook







						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def     __UpperCamelCase				(self							):
											if not hasattr(self.unet ,		"""_hf_hook"""							):
																return self.device
											for module in self.unet.modules():
																if (
																    hasattr(SCREAMING_SNAKE_CASE_ ,		"""_hf_hook"""							)
																    and hasattr(module._hf_hook ,		"""execution_device"""							)
																    and module._hf_hook.execution_device is not None
																):
																					return torch.device(module._hf_hook.execution_device							)
											return self.device







						@torch.no_grad()
						@replace_example_docstring(SCREAMING_SNAKE_CASE_							)
						def __call__(self ,		lowercase__ ,		lowercase__ ,		lowercase__ ,		lowercase__ = None ,		lowercase__ = 5_12 ,		lowercase__ = 5_12 ,		lowercase__ = 1_00 ,		lowercase__ = 4.0 ,		lowercase__ = 1 ,		lowercase__ = None ,		lowercase__ = None ,		lowercase__ = "pil" ,		lowercase__ = True ,		):

											if isinstance(SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_							):
																snake_case_       :		Dict			       =       1
											elif isinstance(SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_							):
																snake_case_       :		Union[str, Any]			       =       len(SCREAMING_SNAKE_CASE_							)
											else:
																raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_							)}'							)

											snake_case_       :		Tuple			       =       self._execution_device

											snake_case_       :		Dict			       =       batch_size * num_images_per_prompt
											snake_case_       :		Any			       =       guidance_scale > 1.0

											snake_case_       ,      snake_case_       ,      snake_case_       :		str			       =       self._encode_prompt(
											    SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_							)

											if isinstance(SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_							):
																snake_case_       :		Optional[Any]			       =       torch.cat(SCREAMING_SNAKE_CASE_ ,		dim=0							)
											if isinstance(SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_							):
																snake_case_       :		int			       =       torch.cat(SCREAMING_SNAKE_CASE_ ,		dim=0							)

											if do_classifier_free_guidance:
																snake_case_       :		Any			       =       image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_ ,		dim=0							)
																snake_case_       :		Optional[int]			       =       negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_ ,		dim=0							)

											snake_case_       :		Optional[int]			       =       torch.cat([negative_image_embeds, image_embeds] ,		dim=0							).to(
											    dtype=prompt_embeds.dtype ,		device=SCREAMING_SNAKE_CASE_							)

											self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ ,		device=SCREAMING_SNAKE_CASE_							)
											snake_case_       :		str			       =       self.scheduler.timesteps

											snake_case_       :		Optional[int]			       =       self.unet.config.in_channels

											snake_case_       ,      snake_case_       :		List[Any]			       =       get_new_h_w(SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		self.movq_scale_factor							)

											# create initial latent
											snake_case_       :		List[Any]			       =       self.prepare_latents(
											    (batch_size, num_channels_latents, height, width) ,		text_encoder_hidden_states.dtype ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		self.scheduler ,		)

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

																snake_case_       :		Optional[int]			       =       {"""text_embeds""": prompt_embeds, """image_embeds""": image_embeds}
																snake_case_       :		List[str]			       =       self.unet(
																    sample=SCREAMING_SNAKE_CASE_ ,		timestep=SCREAMING_SNAKE_CASE_ ,		encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,		added_cond_kwargs=SCREAMING_SNAKE_CASE_ ,		return_dict=SCREAMING_SNAKE_CASE_ ,		)[0]

																if do_classifier_free_guidance:
																					snake_case_       ,      snake_case_       :		Any			       =       noise_pred.split(latents.shape[1] ,		dim=1							)
																					snake_case_       ,      snake_case_       :		Optional[int]			       =       noise_pred.chunk(2							)
																					snake_case_       ,      snake_case_       :		str			       =       variance_pred.chunk(2							)
																					snake_case_       :		Union[str, Any]			       =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																					snake_case_       :		Optional[Any]			       =       torch.cat([noise_pred, variance_pred_text] ,		dim=1							)

																if not (
																    hasattr(self.scheduler.config ,		"""variance_type"""							)
																    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																):
																					snake_case_       ,      snake_case_       :		Optional[Any]			       =       noise_pred.split(latents.shape[1] ,		dim=1							)

																# compute the previous noisy sample x_t -> x_t-1
																snake_case_       :		List[str]			       =       self.scheduler.step(
																    SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		generator=SCREAMING_SNAKE_CASE_ ,		).prev_sample
											# post-processing
											snake_case_       :		List[Any]			       =       self.movq.decode(SCREAMING_SNAKE_CASE_ ,		force_not_quantize=SCREAMING_SNAKE_CASE_							)["""sample"""]

											if output_type not in ["pt", "np", "pil"]:
																raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'							)

											if output_type in ["np", "pil"]:
																snake_case_       :		Tuple			       =       image * 0.5 + 0.5
																snake_case_       :		List[Any]			       =       image.clamp(0 ,		1							)
																snake_case_       :		List[str]			       =       image.cpu().permute(0 ,		2 ,		3 ,		1							).float().numpy()

											if output_type == "pil":
																snake_case_       :		Optional[Any]			       =       self.numpy_to_pil(SCREAMING_SNAKE_CASE_							)

											if not return_dict:
																return (image,)

											return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_							)






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 							a		(				):


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

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

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

				parser.add_argument(
				    '''--learning_rate'''		,  type=snake_case__		,  default=1e-4		,  help='''Learning rate to use for training.'''		,  )

				parser.add_argument(
				    '''--weight_decay_rate'''		,  type=snake_case__		,  default=1e-3		,  help='''Weight decay rate to use for training.'''		,  )

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

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

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

				lowercase_	    =   parser.parse_args()
				return args




def 							a		(				snake_case__:   int   ):


				'''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(snake_case__   )
				tf.tpu.experimental.initialize_tpu_system(snake_case__   )

				return tpu




def 							a		(				snake_case__:   Optional[int]   ):


				'''simple docstring'''
				lowercase_	    =   0
				for file in file_list:
								lowercase_	    =   file.split('''/'''   )[-1]
								lowercase_	    =   re.search(r'''-\d+-(\d+)\.tfrecord'''		,  snake_case__   ).group(1   )
								lowercase_	    =   int(snake_case__   )
								num_samples += sample_count

				return num_samples




def 							a		(				snake_case__:   Dict		,  snake_case__:   Dict		,  snake_case__:   Union[str, Any]		,  snake_case__:   Optional[int]		,  snake_case__:   Optional[int]		,  snake_case__:   Dict=None   ):


				'''simple docstring'''
				lowercase_	    =   count_samples(snake_case__   )
				lowercase_	    =   tf.data.Dataset.from_tensor_slices(snake_case__   )
				if shuffle:
								lowercase_	    =   dataset.shuffle(len(snake_case__   )   )
				lowercase_	    =   tf.data.TFRecordDataset(snake_case__		,  num_parallel_reads=snake_case__   )
				# 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(snake_case__   )   )
				lowercase_	    =   dataset.map(snake_case__		,  num_parallel_calls=snake_case__   )
				if shuffle:
								assert shuffle_buffer_size is not None
								lowercase_	    =   dataset.shuffle(args.shuffle_buffer_size   )
				lowercase_	    =   dataset.batch(snake_case__		,  drop_remainder=snake_case__   )
				lowercase_	    =   dataset.map(snake_case__		,  num_parallel_calls=snake_case__   )
				lowercase_	    =   dataset.prefetch(snake_case__   )
				return dataset




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				if not args.no_tpu:
								lowercase_	    =   initialize_tpu(snake_case__   )
								lowercase_	    =   tf.distribute.TPUStrategy(snake_case__   )
				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(snake_case__   )

				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(snake_case__   )
								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=snake_case__		,  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=snake_case__		,  metrics=['''accuracy''']   )

				def decode_fn(snake_case__:   Dict   ):
								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(snake_case__		,  snake_case__   )

				# 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=snake_case__		,  mlm_probability=args.mlm_probability		,  mlm=snake_case__		,  return_tensors='''tf'''   )

				def mask_with_collator(snake_case__:   str   ):
								# 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(snake_case__   )		,  mask_token_id=tokenizer.mask_token_id		,  special_tokens_mask=snake_case__		,  )
								return batch

				lowercase_	    =   args.per_replica_batch_size * strategy.num_replicas_in_sync

				lowercase_	    =   prepare_dataset(
				    snake_case__		,  decode_fn=snake_case__		,  mask_fn=snake_case__		,  batch_size=snake_case__		,  shuffle=snake_case__		,  shuffle_buffer_size=args.shuffle_buffer_size		,  )

				lowercase_	    =   prepare_dataset(
				    snake_case__		,  decode_fn=snake_case__		,  mask_fn=snake_case__		,  batch_size=snake_case__		,  shuffle=snake_case__		,  )

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

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

				model.save_pretrained(args.output_dir   )


if __name__ == "__main__":
						__a         =   parse_args()
						main(args)




