




import argparse
import logging
import os
import re

import tensorflow as tf

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


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

UpperCAmelCase_		:				int									=     tf.data.AUTOTUNE




def 		lowerCAmelCase_   (	):
				__magic_name__	:	Optional[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=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__	:	int								=parser.parse_args()
				return args




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				try:
								if args.tpu_name:
												__magic_name__	:	Tuple								=tf.distribute.cluster_resolver.TPUClusterResolver(
												    args.tpu_name	,     zone=args.tpu_zone	,     project=args.gcp_project   )
								else:
												__magic_name__	:	List[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 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Tuple								=0
				for file in file_list:
								__magic_name__	:	Any								=file.split("""/"""   )[-1]
								__magic_name__	:	Union[str, Any]								=re.search(R"""-\d+-(\d+)\.tfrecord"""	,     lowerCamelCase   ).group(1   )
								__magic_name__	:	Any								=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__	:	str								=tf.data.Dataset.from_tensor_slices(lowerCamelCase   )
				if shuffle:
								__magic_name__	:	Optional[Any]								=dataset.shuffle(len(lowerCamelCase   )   )
				__magic_name__	:	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__	:	List[str]								=dataset.apply(tf.data.experimental.assert_cardinality(lowerCamelCase   )   )
				__magic_name__	:	Union[str, Any]								=dataset.map(lowerCamelCase	,     num_parallel_calls=lowerCamelCase   )
				if shuffle:
								assert shuffle_buffer_size is not None
								__magic_name__	:	int								=dataset.shuffle(args.shuffle_buffer_size   )
				__magic_name__	:	int								=dataset.batch(lowerCamelCase	,     drop_remainder=lowerCamelCase   )
				__magic_name__	:	List[Any]								=dataset.map(lowerCamelCase	,     num_parallel_calls=lowerCamelCase   )
				__magic_name__	:	Tuple								=dataset.prefetch(lowerCamelCase   )
				return dataset




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

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

				__magic_name__	:	Optional[int]								=AutoTokenizer.from_pretrained(args.tokenizer   )
				__magic_name__	:	Optional[int]								=AutoConfig.from_pretrained(args.pretrained_model_config   )
				__magic_name__	:	Tuple								=tokenizer.vocab_size

				__magic_name__	:	Optional[Any]								=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__	:	Optional[Any]								=count_samples(lowerCamelCase   )

				__magic_name__	:	List[str]								=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__	:	str								=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[Any]								={
								    """input_ids""": tf.io.FixedLenFeature(dtype=tf.intaa	,     shape=(args.max_length,)   ),
								    """attention_mask""": tf.io.FixedLenFeature(dtype=tf.intaa	,     shape=(args.max_length,)   ),
								}
								return tf.io.parse_single_example(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__	:	Union[str, 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__	:	Optional[Any]								=(
								    ~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__	:	int								=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__	:	Dict								=args.per_replica_batch_size * strategy.num_replicas_in_sync

				__magic_name__	:	List[Any]								=prepare_dataset(
				    lowerCamelCase	,     decode_fn=lowerCamelCase	,     mask_fn=lowerCamelCase	,     batch_size=lowerCamelCase	,     shuffle=lowerCamelCase	,     shuffle_buffer_size=args.shuffle_buffer_size	,     )

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

				__magic_name__	:	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_		:				List[str]									=     parse_args()
							main(args)










import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_		:				Union[str, Any]									=     logging.get_logger(__name__)

UpperCAmelCase_		:				Dict									=     {
    "google/pix2struct-textcaps-base": (
        "https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"
    ),
}




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""pix2struct_text_model"""
						UpperCamelCase     						=					["""past_key_values"""]
						UpperCamelCase     						=					{
						    """hidden_size""": """hidden_size""",
						    """num_attention_heads""": """num_heads""",
						    """num_hidden_layers""": """num_layers""",
						}

						def __init__(		self   :List[Any]    , __snake_case   :Dict=5_02_44    , __snake_case   :Dict=7_68    , __snake_case   :Union[str, Any]=64    , __snake_case   :Union[str, Any]=20_48    , __snake_case   :List[Any]=12    , __snake_case   :Any=12    , __snake_case   :str=32    , __snake_case   :Dict=1_28    , __snake_case   :Optional[int]=0.1    , __snake_case   :Any=1E-6    , __snake_case   :Optional[int]=1.0    , __snake_case   :List[Any]="gelu_new"    , __snake_case   :Any=0    , __snake_case   :Tuple=False    , __snake_case   :Any=0    , __snake_case   :int=1    , __snake_case   :Dict=False    , __snake_case   :Optional[Any]=True    , **__snake_case   :Dict    , ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=vocab_size
										__magic_name__	:	List[str]								=hidden_size
										__magic_name__	:	Optional[int]								=d_kv
										__magic_name__	:	Dict								=d_ff
										__magic_name__	:	Tuple								=num_layers
										__magic_name__	:	str								=num_heads
										__magic_name__	:	Optional[Any]								=relative_attention_num_buckets
										__magic_name__	:	List[Any]								=relative_attention_max_distance
										__magic_name__	:	str								=dropout_rate
										__magic_name__	:	Optional[Any]								=layer_norm_epsilon
										__magic_name__	:	Union[str, Any]								=initializer_factor
										__magic_name__	:	Tuple								=use_cache

										__magic_name__	:	Any								=eos_token_id
										__magic_name__	:	int								=decoder_start_token_id

										# for backwards compatibility
										__magic_name__	:	Union[str, Any]								=dense_act_fn

										super().__init__(
										    pad_token_id=__snake_case    , eos_token_id=__snake_case    , decoder_start_token_id=__snake_case    , tie_word_embeddings=__snake_case    , is_decoder=__snake_case    , **__snake_case    , )


						@classmethod
						def     A__						(		cls   :Union[str, Any]    , __snake_case   :Union[str, os.PathLike]    , **__snake_case   :Any ):
										'''simple docstring'''

										cls._set_token_in_kwargs(__snake_case )

										__magic_name__				, __magic_name__	:	Optional[Any]								=cls.get_config_dict(__snake_case    , **__snake_case )

										# get the text config dict if we are loading from Pix2StructConfig
										if config_dict.get("""model_type""" ) == "pix2struct":
														__magic_name__	:	Tuple								=config_dict["""text_config"""]

										if "model_type" in config_dict and hasattr(cls    , """model_type""" ) and config_dict["model_type"] != cls.model_type:
														logger.warning(
														    f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
														    f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )

										return cls.from_dict(__snake_case    , **__snake_case )




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""pix2struct_vision_model"""

						def __init__(		self   :Union[str, Any]    , __snake_case   :Tuple=7_68    , __snake_case   :Any=7_68    , __snake_case   :Tuple=20_48    , __snake_case   :Dict=64    , __snake_case   :List[str]=12    , __snake_case   :str=12    , __snake_case   :str="gelu_new"    , __snake_case   :Optional[int]=1E-6    , __snake_case   :Optional[int]=0.0    , __snake_case   :Any=0.0    , __snake_case   :Any=1E-10    , __snake_case   :List[str]=1.0    , __snake_case   :Tuple=40_96    , __snake_case   :Optional[int]=32    , __snake_case   :Union[str, Any]=1_28    , **__snake_case   :Optional[int]    , ):
										'''simple docstring'''

										super().__init__(**__snake_case )

										__magic_name__	:	Union[str, Any]								=hidden_size
										__magic_name__	:	Dict								=patch_embed_hidden_size
										__magic_name__	:	Tuple								=d_ff
										__magic_name__	:	str								=dropout_rate
										__magic_name__	:	str								=num_hidden_layers
										__magic_name__	:	Dict								=num_attention_heads
										__magic_name__	:	List[str]								=initializer_range
										__magic_name__	:	Optional[int]								=initializer_factor
										__magic_name__	:	str								=attention_dropout
										__magic_name__	:	Union[str, Any]								=layer_norm_eps
										__magic_name__	:	List[str]								=dense_act_fn
										__magic_name__	:	List[str]								=seq_len
										__magic_name__	:	str								=relative_attention_num_buckets
										__magic_name__	:	Optional[int]								=relative_attention_max_distance
										__magic_name__	:	List[str]								=d_kv


						@classmethod
						def     A__						(		cls   :Optional[Any]    , __snake_case   :Union[str, os.PathLike]    , **__snake_case   :Any ):
										'''simple docstring'''

										cls._set_token_in_kwargs(__snake_case )

										__magic_name__				, __magic_name__	:	Optional[int]								=cls.get_config_dict(__snake_case    , **__snake_case )

										# get the vision config dict if we are loading from Pix2StructConfig
										if config_dict.get("""model_type""" ) == "pix2struct":
														__magic_name__	:	int								=config_dict["""vision_config"""]

										if "model_type" in config_dict and hasattr(cls    , """model_type""" ) and config_dict["model_type"] != cls.model_type:
														logger.warning(
														    f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
														    f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )

										return cls.from_dict(__snake_case    , **__snake_case )




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""pix2struct"""
						UpperCamelCase     						=					True

						def __init__(		self   :Union[str, Any]    , __snake_case   :Optional[int]=None    , __snake_case   :List[Any]=None    , __snake_case   :Optional[Any]=1.0    , __snake_case   :Tuple=0.02    , __snake_case   :str=False    , __snake_case   :List[str]=False    , __snake_case   :str=True    , **__snake_case   :List[str]    , ):
										'''simple docstring'''

										super().__init__(tie_word_embeddings=__snake_case    , is_encoder_decoder=__snake_case    , **__snake_case )

										if text_config is None:
														__magic_name__	:	int								={}
														logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values.""" )

										if vision_config is None:
														__magic_name__	:	str								={}
														logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values.""" )

										__magic_name__	:	Union[str, Any]								=PixaStructTextConfig(**__snake_case )
										__magic_name__	:	str								=PixaStructVisionConfig(**__snake_case )

										__magic_name__	:	int								=self.text_config.decoder_start_token_id
										__magic_name__	:	Optional[Any]								=self.text_config.pad_token_id
										__magic_name__	:	str								=self.text_config.eos_token_id

										__magic_name__	:	int								=initializer_factor
										__magic_name__	:	List[Any]								=initializer_range

										__magic_name__	:	Dict								=self.initializer_range
										__magic_name__	:	Union[str, Any]								=self.initializer_range

										__magic_name__	:	Tuple								=is_vqa

						@classmethod
						def     A__						(		cls   :Optional[int]    , __snake_case   :PixaStructTextConfig    , __snake_case   :PixaStructVisionConfig    , **__snake_case   :List[str] ):
										'''simple docstring'''

										return cls(text_config=text_config.to_dict()    , vision_config=vision_config.to_dict()    , **__snake_case )


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

										__magic_name__	:	Union[str, Any]								=copy.deepcopy(self.__dict__ )
										__magic_name__	:	Any								=self.text_config.to_dict()
										__magic_name__	:	List[str]								=self.vision_config.to_dict()
										__magic_name__	:	Optional[int]								=self.__class__.model_type
										return output




