



'''simple docstring'''


import argparse
import logging
import os
import re

import tensorflow as tf

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


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

lowercase_      :				Optional[Any]   					=    tf.data.AUTOTUNE







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

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

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

						parser.add_argument(
						    """--learning_rate""" ,  type=lowercase_ ,  default=1E-4 ,  help="""Learning rate to use for training.""" ,  )

						parser.add_argument(
						    """--weight_decay_rate""" ,  type=lowercase_ ,  default=1E-3 ,  help="""Weight decay rate to use for training.""" ,  )

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

						parser.add_argument(
						    """--mlm_probability""" ,  type=lowercase_ ,  default=0.15 ,  help="""Fraction of tokens to mask during training.""" ,  )

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

						lowercase        =							parser.parse_args()
						return args







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict						):
						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(lowercase_						)
						tf.tpu.experimental.initialize_tpu_system(lowercase_						)

						return tpu







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[Any]						):
						lowercase        =							0
						for file in file_list:
												lowercase        =							file.split("""/"""						)[-1]
												lowercase        =							re.search(R"""-\d+-(\d+)\.tfrecord""" ,  lowercase_						).group(1						)
												lowercase        =							int(lowercase_						)
												num_samples += sample_count

						return num_samples







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Union[str, Any] ,  lowercase_    :      Dict ,  lowercase_    :      Optional[Any] ,  lowercase_    :      Any ,  lowercase_    :      Union[str, Any] ,  lowercase_    :      Dict=None						):
						lowercase        =							count_samples(lowercase_						)
						lowercase        =							tf.data.Dataset.from_tensor_slices(lowercase_						)
						if shuffle:
												lowercase        =							dataset.shuffle(len(lowercase_						)						)
						lowercase        =							tf.data.TFRecordDataset(lowercase_ ,  num_parallel_reads=lowercase_						)
						# 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(lowercase_						)						)
						lowercase        =							dataset.map(lowercase_ ,  num_parallel_calls=lowercase_						)
						if shuffle:
												assert shuffle_buffer_size is not None
												lowercase        =							dataset.shuffle(args.shuffle_buffer_size						)
						lowercase        =							dataset.batch(lowercase_ ,  drop_remainder=lowercase_						)
						lowercase        =							dataset.map(lowercase_ ,  num_parallel_calls=lowercase_						)
						lowercase        =							dataset.prefetch(lowercase_						)
						return dataset







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[int]						):
						if not args.no_tpu:
												lowercase        =							initialize_tpu(lowercase_						)
												lowercase        =							tf.distribute.TPUStrategy(lowercase_						)
						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(lowercase_						)

						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(lowercase_						)
												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=lowercase_ ,  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=lowercase_ ,  metrics=["""accuracy"""]						)

						def decode_fn(lowercase_    :      List[Any]						):
												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(lowercase_ ,  lowercase_						)

						# 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=lowercase_ ,  mlm_probability=args.mlm_probability ,  mlm=lowercase_ ,  return_tensors="""tf"""						)

						def mask_with_collator(lowercase_    :      List[Any]						):
												# 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(lowercase_						) ,  mask_token_id=tokenizer.mask_token_id ,  special_tokens_mask=lowercase_ ,  )
												return batch

						lowercase        =							args.per_replica_batch_size * strategy.num_replicas_in_sync

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

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

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

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

						model.save_pretrained(args.output_dir						)


if __name__ == "__main__":
						lowercase_      :				List[str]   					=    parse_args()
						main(args)







'''simple docstring'''


import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import SwiftFormerForImageClassification, SwiftFormerModel
						from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor




class      __UpperCamelCase       :







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase=13 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				_lowerCAmelCase=True ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=224 ,				_lowerCAmelCase=1000 ,				_lowerCAmelCase=[3, 3, 6, 4] ,				_lowerCAmelCase=[48, 56, 112, 220] ,				)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							parent
							lowercase        =							batch_size
							lowercase        =							num_channels
							lowercase        =							is_training
							lowercase        =							use_labels
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							num_labels
							lowercase        =							image_size
							lowercase        =							layer_depths
							lowercase        =							embed_dims







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

							lowercase        =							None
							if self.use_labels:
													lowercase        =							ids_tensor([self.batch_size] ,				self.num_labels						)

							lowercase        =							self.get_config()

							return config, pixel_values, labels







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							return SwiftFormerConfig(
							    depths=self.layer_depths ,				embed_dims=self.embed_dims ,				mlp_ratio=4 ,				downsamples=[True, True, True, True] ,				hidden_act="""gelu""" ,				num_labels=self.num_labels ,				down_patch_size=3 ,				down_stride=2 ,				down_pad=1 ,				drop_rate=0.0 ,				drop_path_rate=0.0 ,				use_layer_scale=_lowerCAmelCase ,				layer_scale_init_value=1E-5 ,				)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							SwiftFormerModel(config=_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							lowercase        =							model(_lowerCAmelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.embed_dims[-1], 7, 7)						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							self.num_labels
							lowercase        =							SwiftFormerForImageClassification(_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							lowercase        =							model(_lowerCAmelCase ,				labels=_lowerCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)						)

							lowercase        =							SwiftFormerForImageClassification(_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()

							lowercase        =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)
							lowercase        =							model(_lowerCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)						)







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							((lowercase)     ,   (lowercase)     ,   (lowercase))        =							self.prepare_config_and_inputs()
							lowercase        =							{"""pixel_values""": pixel_values}
							return config, inputs_dict




@require_torch
class      __UpperCamelCase       (_UpperCAmelCase			,   _UpperCAmelCase			,   unittest.TestCase			):
	__A			  =					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	__A			  =					(
	    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__A			  =					False
	__A			  =					False
	__A			  =					False
	__A			  =					False
	__A			  =					False







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							SwiftFormerModelTester(self						)
							lowercase        =							ConfigTester(
							    self ,				config_class=_lowerCAmelCase ,				has_text_modality=_lowerCAmelCase ,				hidden_size=37 ,				num_attention_heads=12 ,				num_hidden_layers=12 ,				)







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							self.config_tester.run_common_tests()







	@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""						)
	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							pass







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase        =							model_class(_lowerCAmelCase						)
													lowercase        =							model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(_lowerCAmelCase ,				nn.Linear						)						)







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase        =							model_class(_lowerCAmelCase						)
													lowercase        =							inspect.signature(model.forward						)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowercase        =							[*signature.parameters.keys()]

													lowercase        =							["""pixel_values"""]
													self.assertListEqual(arg_names[:1] ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_lowerCAmelCase						)







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase						)







	@slow
	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase        =							SwiftFormerModel.from_pretrained(_lowerCAmelCase						)
													self.assertIsNotNone(_lowerCAmelCase						)







	@unittest.skip(reason="""SwiftFormer does not output attentions"""						)
	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							pass







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							def check_hidden_states_output(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						):
													lowercase        =							model_class(_lowerCAmelCase						)
													model.to(_lowerCAmelCase						)
													model.eval()

													with torch.no_grad():
																			lowercase        =							model(**self._prepare_for_class(_lowerCAmelCase ,				_lowerCAmelCase						)						)

													lowercase        =							outputs.hidden_states

													lowercase        =							8
													self.assertEqual(len(_lowerCAmelCase						) ,				_lowerCAmelCase						)  # TODO

													# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
													# with the width and height being successively divided by 2, after every 2 blocks
													for i in range(len(_lowerCAmelCase						)						):
																			self.assertEqual(
																			    hidden_states[i].shape ,				torch.Size(
																			        [
																			            self.model_tester.batch_size,
																			            self.model_tester.embed_dims[i // 2],
																			            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																			            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																			        ]						) ,				)

							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase        =							True
													check_hidden_states_output(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowercase        =							True

													check_hidden_states_output(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							def _config_zero_init(_lowerCAmelCase						):
													lowercase        =							copy.deepcopy(_lowerCAmelCase						)
													for key in configs_no_init.__dict__.keys():
																			if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																									setattr(_lowerCAmelCase ,				_lowerCAmelCase ,				1E-10						)
																			if isinstance(getattr(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						) ,				_lowerCAmelCase						):
																									lowercase        =							_config_zero_init(getattr(_lowerCAmelCase ,				_lowerCAmelCase						)						)
																									setattr(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)
													return configs_no_init

							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

							lowercase        =							_config_zero_init(_lowerCAmelCase						)
							for model_class in self.all_model_classes:
													lowercase        =							model_class(config=_lowerCAmelCase						)
													for name, param in model.named_parameters():
																			if param.requires_grad:
																									self.assertIn(
																									    ((param.data.mean() * 1E9) / 1E9).round().item() ,				[0.0, 1.0] ,				msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" ,				)







	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""						)
	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							pass







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
						return image




@require_torch
@require_vision
class      __UpperCamelCase       (unittest.TestCase			):







	@cached_property
	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""						) if is_vision_available() else None







	@slow
	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							lowercase        =							SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""						).to(_lowerCAmelCase						)

							lowercase        =							self.default_image_processor
							lowercase        =							prepare_img()
							lowercase        =							image_processor(images=_lowerCAmelCase ,				return_tensors="""pt"""						).to(_lowerCAmelCase						)

							# forward pass
							with torch.no_grad():
													lowercase        =							model(**_lowerCAmelCase						)

							# verify the logits
							lowercase        =							torch.Size((1, 1000)						)
							self.assertEqual(outputs.logits.shape ,				_lowerCAmelCase						)

							lowercase        =							torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]]						).to(_lowerCAmelCase						)
							self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				_lowerCAmelCase ,				atol=1E-4						)						)


