
import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation




def 							a		(				snake_case__:   Optional[int]   ):


				'''simple docstring'''
				lowercase_	    =   384
				lowercase_	    =   7
				if "tiny" in model_name:
								lowercase_	    =   96
								lowercase_	    =   (2, 2, 6, 2)
								lowercase_	    =   (3, 6, 12, 24)
				elif "small" in model_name:
								lowercase_	    =   96
								lowercase_	    =   (2, 2, 18, 2)
								lowercase_	    =   (3, 6, 12, 24)
				elif "base" in model_name:
								lowercase_	    =   128
								lowercase_	    =   (2, 2, 18, 2)
								lowercase_	    =   (4, 8, 16, 32)
								lowercase_	    =   12
								lowercase_	    =   512
				elif "large" in model_name:
								lowercase_	    =   192
								lowercase_	    =   (2, 2, 18, 2)
								lowercase_	    =   (6, 12, 24, 48)
								lowercase_	    =   12
								lowercase_	    =   768

				# set label information
				lowercase_	    =   150
				lowercase_	    =   '''huggingface/label-files'''
				lowercase_	    =   '''ade20k-id2label.json'''
				lowercase_	    =   json.load(open(hf_hub_download(snake_case__		,  snake_case__		,  repo_type='''dataset'''   )		,  '''r'''   )   )
				lowercase_	    =   {int(snake_case__   ): v for k, v in idalabel.items()}
				lowercase_	    =   {v: k for k, v in idalabel.items()}

				lowercase_	    =   SwinConfig(
				    embed_dim=snake_case__		,  depths=snake_case__		,  num_heads=snake_case__		,  window_size=snake_case__		,  out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']		,  )
				lowercase_	    =   UperNetConfig(
				    backbone_config=snake_case__		,  auxiliary_in_channels=snake_case__		,  num_labels=snake_case__		,  idalabel=snake_case__		,  labelaid=snake_case__		,  )

				return config




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				lowercase_	    =   []

				# fmt: off
				# stem
				rename_keys.append(('''backbone.patch_embed.projection.weight''', '''backbone.embeddings.patch_embeddings.projection.weight''')   )
				rename_keys.append(('''backbone.patch_embed.projection.bias''', '''backbone.embeddings.patch_embeddings.projection.bias''')   )
				rename_keys.append(('''backbone.patch_embed.norm.weight''', '''backbone.embeddings.norm.weight''')   )
				rename_keys.append(('''backbone.patch_embed.norm.bias''', '''backbone.embeddings.norm.bias''')   )
				# stages
				for i in range(len(config.backbone_config.depths   )   ):
								for j in range(config.backbone_config.depths[i]   ):
												rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.norm1.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')   )
												rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.norm1.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')   )
												rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')   )
												rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')   )
												rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')   )
												rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')   )
												rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.norm2.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')   )
												rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.norm2.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')   )
												rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')   )
												rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')   )
												rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight''')   )
												rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias''')   )

								if i < 3:
												rename_keys.append((F'''backbone.stages.{i}.downsample.reduction.weight''', F'''backbone.encoder.layers.{i}.downsample.reduction.weight''')   )
												rename_keys.append((F'''backbone.stages.{i}.downsample.norm.weight''', F'''backbone.encoder.layers.{i}.downsample.norm.weight''')   )
												rename_keys.append((F'''backbone.stages.{i}.downsample.norm.bias''', F'''backbone.encoder.layers.{i}.downsample.norm.bias''')   )
								rename_keys.append((F'''backbone.norm{i}.weight''', F'''backbone.hidden_states_norms.stage{i+1}.weight''')   )
								rename_keys.append((F'''backbone.norm{i}.bias''', F'''backbone.hidden_states_norms.stage{i+1}.bias''')   )

				# decode head
				rename_keys.extend(
				    [
				        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
				        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
				        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
				        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
				    ]   )
				# fmt: on

				return rename_keys




def 							a		(				snake_case__:   Optional[Any]		,  snake_case__:   Dict		,  snake_case__:   Optional[int]   ):


				'''simple docstring'''
				lowercase_	    =   dct.pop(snake_case__   )
				lowercase_	    =   val




def 							a		(				snake_case__:   Dict		,  snake_case__:   Optional[int]   ):


				'''simple docstring'''
				lowercase_	    =   [int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
				for i in range(len(backbone_config.depths   )   ):
								lowercase_	    =   num_features[i]
								for j in range(backbone_config.depths[i]   ):
												# fmt: off
												# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
												lowercase_	    =   state_dict.pop(F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''   )
												lowercase_	    =   state_dict.pop(F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias'''   )
												# next, add query, keys and values (in that order) to the state dict
												lowercase_	    =   in_proj_weight[:dim, :]
												lowercase_	    =   in_proj_bias[: dim]
												lowercase_	    =   in_proj_weight[
												    dim : dim * 2, :
												]
												lowercase_	    =   in_proj_bias[
												    dim : dim * 2
												]
												lowercase_	    =   in_proj_weight[
												    -dim :, :
												]
												lowercase_	    =   in_proj_bias[-dim :]
												# fmt: on




def 							a		(				snake_case__:   List[Any]   ):


				'''simple docstring'''
				lowercase_    ,      lowercase_	    =   x.shape
				lowercase_	    =   x.reshape(snake_case__		,  4		,  in_channel // 4   )
				lowercase_	    =   x[:, [0, 2, 1, 3], :].transpose(1		,  2   ).reshape(snake_case__		,  snake_case__   )
				return x




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				lowercase_    ,      lowercase_	    =   x.shape
				lowercase_	    =   x.reshape(snake_case__		,  in_channel // 4		,  4   )
				lowercase_	    =   x[:, :, [0, 2, 1, 3]].transpose(1		,  2   ).reshape(snake_case__		,  snake_case__   )

				return x




def 							a		(				snake_case__:   List[Any]   ):


				'''simple docstring'''
				lowercase_	    =   x.shape[0]
				lowercase_	    =   x.reshape(4		,  in_channel // 4   )
				lowercase_	    =   x[[0, 2, 1, 3], :].transpose(0		,  1   ).reshape(snake_case__   )
				return x




def 							a		(				snake_case__:   Any   ):


				'''simple docstring'''
				lowercase_	    =   x.shape[0]
				lowercase_	    =   x.reshape(in_channel // 4		,  4   )
				lowercase_	    =   x[:, [0, 2, 1, 3]].transpose(0		,  1   ).reshape(snake_case__   )
				return x




def 							a		(				snake_case__:   Dict		,  snake_case__:   Dict		,  snake_case__:   str   ):


				'''simple docstring'''
				lowercase_	    =   {
				    '''upernet-swin-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth''',
				    '''upernet-swin-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth''',
				    '''upernet-swin-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth''',
				    '''upernet-swin-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth''',
				}
				lowercase_	    =   model_name_to_url[model_name]
				lowercase_	    =   torch.hub.load_state_dict_from_url(snake_case__		,  map_location='''cpu'''		,  file_name=snake_case__   )[
				    '''state_dict'''
				]

				for name, param in state_dict.items():
								print(snake_case__		,  param.shape   )

				lowercase_	    =   get_upernet_config(snake_case__   )
				lowercase_	    =   UperNetForSemanticSegmentation(snake_case__   )
				model.eval()

				# replace "bn" => "batch_norm"
				for key in state_dict.copy().keys():
								lowercase_	    =   state_dict.pop(snake_case__   )
								if "bn" in key:
												lowercase_	    =   key.replace('''bn'''		,  '''batch_norm'''   )
								lowercase_	    =   val

				# rename keys
				lowercase_	    =   create_rename_keys(snake_case__   )
				for src, dest in rename_keys:
								rename_key(snake_case__		,  snake_case__		,  snake_case__   )
				read_in_q_k_v(snake_case__		,  config.backbone_config   )

				# fix downsample parameters
				for key, value in state_dict.items():
								if "downsample" in key:
												if "reduction" in key:
																lowercase_	    =   reverse_correct_unfold_reduction_order(snake_case__   )
												if "norm" in key:
																lowercase_	    =   reverse_correct_unfold_norm_order(snake_case__   )

				model.load_state_dict(snake_case__   )

				# verify on image
				lowercase_	    =   '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
				lowercase_	    =   Image.open(requests.get(snake_case__		,  stream=snake_case__   ).raw   ).convert('''RGB'''   )

				lowercase_	    =   SegformerImageProcessor()
				lowercase_	    =   processor(snake_case__		,  return_tensors='''pt'''   ).pixel_values

				with torch.no_grad():
								lowercase_	    =   model(snake_case__   )
								lowercase_	    =   outputs.logits

				print(logits.shape   )
				print('''First values of logits:'''		,  logits[0, 0, :3, :3]   )
				# assert values
				if model_name == "upernet-swin-tiny":
								lowercase_	    =   torch.tensor(
								    [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]]   )
				elif model_name == "upernet-swin-small":
								lowercase_	    =   torch.tensor(
								    [[-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.0_9_0_8, -7.0_9_0_8, -6.8_5_3_4]]   )
				elif model_name == "upernet-swin-base":
								lowercase_	    =   torch.tensor(
								    [[-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.4_7_6_3, -6.4_7_6_3, -6.3_2_5_4]]   )
				elif model_name == "upernet-swin-large":
								lowercase_	    =   torch.tensor(
								    [[-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.4_0_4_4, -7.4_0_4_4, -7.2_5_8_6]]   )
				print('''Logits:'''		,  outputs.logits[0, 0, :3, :3]   )
				assert torch.allclose(outputs.logits[0, 0, :3, :3]		,  snake_case__		,  atol=1e-4   )
				print('''Looks ok!'''   )

				if pytorch_dump_folder_path is not None:
								print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''   )
								model.save_pretrained(snake_case__   )
								print(F'''Saving processor to {pytorch_dump_folder_path}'''   )
								processor.save_pretrained(snake_case__   )

				if push_to_hub:
								print(F'''Pushing model and processor for {model_name} to hub'''   )
								model.push_to_hub(F'''openmmlab/{model_name}'''   )
								processor.push_to_hub(F'''openmmlab/{model_name}'''   )


if __name__ == "__main__":
						__a         =   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='upernet-swin-tiny',
						    type=str,
						    choices=[f"upernet-swin-{size}" for size in ['tiny', 'small', 'base', 'large']],
						    help='Name of the Swin + UperNet model you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
						)
						parser.add_argument(
						    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
						)

						__a         =   parser.parse_args()
						convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






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)




