






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

A        =			[
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

A        =			[
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

A        =			[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
				# loop over downblocks/upblocks

				for j in range(2):
								# loop over resnets/attentions for downblocks
								A        =			f"""down_blocks.{i}.resnets.{j}."""
								A        =			f"""input_blocks.{3*i + j + 1}.0."""
								unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

								if i < 3:
												# no attention layers in down_blocks.3
												A        =			f"""down_blocks.{i}.attentions.{j}."""
												A        =			f"""input_blocks.{3*i + j + 1}.1."""
												unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

				for j in range(3):
								# loop over resnets/attentions for upblocks
								A        =			f"""up_blocks.{i}.resnets.{j}."""
								A        =			f"""output_blocks.{3*i + j}.0."""
								unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

								if i > 0:
												# no attention layers in up_blocks.0
												A        =			f"""up_blocks.{i}.attentions.{j}."""
												A        =			f"""output_blocks.{3*i + j}.1."""
												unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

				if i < 3:
								# no downsample in down_blocks.3
								A        =			f"""down_blocks.{i}.downsamplers.0.conv."""
								A        =			f"""input_blocks.{3*(i+1)}.0.op."""
								unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

								# no upsample in up_blocks.3
								A        =			f"""up_blocks.{i}.upsamplers.0."""
								A        =			f"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
								unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

A        =			'mid_block.attentions.0.'
A        =			'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
				A        =			f"""mid_block.resnets.{j}."""
				A        =			f"""middle_block.{2*j}."""
				unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))



def 					_lowerCamelCase  (			lowerCAmelCase__				:		Union[str, Any]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {k: k for k in unet_state_dict.keys()}
				for sd_name, hf_name in unet_conversion_map:
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   sd_name
				for k, v in mapping.items():
								if "resnets" in k:
												for sd_part, hf_part in unet_conversion_map_resnet:
																SCREAMING_SNAKE_CASE_   :	int													=   v.replace(lowerCAmelCase__    ,    lowerCAmelCase__      )
												SCREAMING_SNAKE_CASE_   :	List[str]													=   v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												SCREAMING_SNAKE_CASE_   :	str													=   v.replace(lowerCAmelCase__    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Tuple													=   v
				SCREAMING_SNAKE_CASE_   :	str													=   {v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


# ================#
# VAE Conversion #
# ================#

A        =			[
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
				# down_blocks have two resnets
				for j in range(2):
								A        =			f"""encoder.down_blocks.{i}.resnets.{j}."""
								A        =			f"""encoder.down.{i}.block.{j}."""
								vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

				if i < 3:
								A        =			f"""down_blocks.{i}.downsamplers.0."""
								A        =			f"""down.{i}.downsample."""
								vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

								A        =			f"""up_blocks.{i}.upsamplers.0."""
								A        =			f"""up.{3-i}.upsample."""
								vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

				# up_blocks have three resnets
				# also, up blocks in hf are numbered in reverse from sd
				for j in range(3):
								A        =			f"""decoder.up_blocks.{i}.resnets.{j}."""
								A        =			f"""decoder.up.{3-i}.block.{j}."""
								vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
				A        =			f"""mid_block.resnets.{i}."""
				A        =			f"""mid.block_{i+1}."""
				vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


A        =			[
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]



def 					_lowerCamelCase  (			lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				return w.reshape(*w.shape    ,    1    ,    1      )



def 					_lowerCamelCase  (			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Dict													=   {k: k for k in vae_state_dict.keys()}
				for k, v in mapping.items():
								for sd_part, hf_part in vae_conversion_map:
												SCREAMING_SNAKE_CASE_   :	Any													=   v.replace(lowerCAmelCase__    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	int													=   v
				for k, v in mapping.items():
								if "attentions" in k:
												for sd_part, hf_part in vae_conversion_map_attn:
																SCREAMING_SNAKE_CASE_   :	Dict													=   v.replace(lowerCAmelCase__    ,    lowerCAmelCase__      )
												SCREAMING_SNAKE_CASE_   :	Tuple													=   v
				SCREAMING_SNAKE_CASE_   :	Dict													=   {v: vae_state_dict[k] for k, v in mapping.items()}
				SCREAMING_SNAKE_CASE_   :	int													=   ['q', 'k', 'v', 'proj_out']
				for k, v in new_state_dict.items():
								for weight_name in weights_to_convert:
												if F'''mid.attn_1.{weight_name}.weight''' in k:
																print(F'''Reshaping {k} for SD format'''      )
																SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   reshape_weight_for_sd(lowerCAmelCase__      )
				return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


A        =			[
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
A        =			{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
A        =			re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
A        =			{'q': 0, 'k': 1, 'v': 2}



def 					_lowerCamelCase  (			lowerCAmelCase__				:		List[Any]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   {}
				SCREAMING_SNAKE_CASE_   :	List[str]													=   {}
				SCREAMING_SNAKE_CASE_   :	Any													=   {}
				for k, v in text_enc_dict.items():
								if (
								    k.endswith('.self_attn.q_proj.weight'      )
								    or k.endswith('.self_attn.k_proj.weight'      )
								    or k.endswith('.self_attn.v_proj.weight'      )
								):
												SCREAMING_SNAKE_CASE_   :	int													=   k[: -len('.q_proj.weight'      )]
												SCREAMING_SNAKE_CASE_   :	List[str]													=   k[-len('q_proj.weight'      )]
												if k_pre not in capture_qkv_weight:
																SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [None, None, None]
												SCREAMING_SNAKE_CASE_   :	int													=   v
												continue

								if (
								    k.endswith('.self_attn.q_proj.bias'      )
								    or k.endswith('.self_attn.k_proj.bias'      )
								    or k.endswith('.self_attn.v_proj.bias'      )
								):
												SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   k[: -len('.q_proj.bias'      )]
												SCREAMING_SNAKE_CASE_   :	str													=   k[-len('q_proj.bias'      )]
												if k_pre not in capture_qkv_bias:
																SCREAMING_SNAKE_CASE_   :	Tuple													=   [None, None, None]
												SCREAMING_SNAKE_CASE_   :	Any													=   v
												continue

								SCREAMING_SNAKE_CASE_   :	Tuple													=   textenc_pattern.sub(lambda lowerCAmelCase__      : protected[re.escape(m.group(0      )      )]    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Any													=   v

				for k_pre, tensors in capture_qkv_weight.items():
								if None in tensors:
												raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'      )
								SCREAMING_SNAKE_CASE_   :	Dict													=   textenc_pattern.sub(lambda lowerCAmelCase__      : protected[re.escape(m.group(0      )      )]    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.cat(lowerCAmelCase__      )

				for k_pre, tensors in capture_qkv_bias.items():
								if None in tensors:
												raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'      )
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   textenc_pattern.sub(lambda lowerCAmelCase__      : protected[re.escape(m.group(0      )      )]    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.cat(lowerCAmelCase__      )

				return new_state_dict



def 					_lowerCamelCase  (			lowerCAmelCase__				:		Optional[Any]      ):





				'''simple docstring'''







				return text_enc_dict


if __name__ == "__main__":
				A        =			argparse.ArgumentParser()

				parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
				parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
				parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
				parser.add_argument(
				    '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
				)

				A        =			parser.parse_args()

				assert args.model_path is not None, "Must provide a model path!"

				assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

				# Path for safetensors
				A        =			osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
				A        =			osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
				A        =			osp.join(args.model_path, 'text_encoder', 'model.safetensors')

				# Load models from safetensors if it exists, if it doesn't pytorch
				if osp.exists(unet_path):
								A        =			load_file(unet_path, device='cpu')
				else:
								A        =			osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
								A        =			torch.load(unet_path, map_location='cpu')

				if osp.exists(vae_path):
								A        =			load_file(vae_path, device='cpu')
				else:
								A        =			osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
								A        =			torch.load(vae_path, map_location='cpu')

				if osp.exists(text_enc_path):
								A        =			load_file(text_enc_path, device='cpu')
				else:
								A        =			osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
								A        =			torch.load(text_enc_path, map_location='cpu')

				# Convert the UNet model
				A        =			convert_unet_state_dict(unet_state_dict)
				A        =			{'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

				# Convert the VAE model
				A        =			convert_vae_state_dict(vae_state_dict)
				A        =			{'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

				# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
				A        =			'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

				if is_vaa_model:
								# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
								A        =			{'transformer.' + k: v for k, v in text_enc_dict.items()}
								A        =			convert_text_enc_state_dict_vaa(text_enc_dict)
								A        =			{'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
				else:
								A        =			convert_text_enc_state_dict(text_enc_dict)
								A        =			{'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

				# Put together new checkpoint
				A        =			{**unet_state_dict, **vae_state_dict, **text_enc_dict}
				if args.half:
								A        =			{k: v.half() for k, v in state_dict.items()}

				if args.use_safetensors:
								save_file(state_dict, args.checkpoint_path)
				else:
								A        =			{'state_dict': state_dict}
								torch.save(state_dict, args.checkpoint_path)







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 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	List[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=0.15    ,    help='Fraction of tokens to mask during training.'    ,    )

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

				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   parser.parse_args()
				return args



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				try:
								if args.tpu_name:
												SCREAMING_SNAKE_CASE_   :	Dict													=   tf.distribute.cluster_resolver.TPUClusterResolver(
												    args.tpu_name    ,    zone=args.tpu_zone    ,    project=args.gcp_project      )
								else:
												SCREAMING_SNAKE_CASE_   :	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__				:		List[Any]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   0
				for file in file_list:
								SCREAMING_SNAKE_CASE_   :	Dict													=   file.split('/'      )[-1]
								SCREAMING_SNAKE_CASE_   :	Any													=   re.search(R'-\d+-(\d+)\.tfrecord'    ,    lowerCAmelCase__      ).group(1      )
								SCREAMING_SNAKE_CASE_   :	Dict													=   int(lowerCAmelCase__      )
								num_samples += sample_count

				return num_samples



def 					_lowerCamelCase(			lowerCAmelCase__				:		Tuple    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Any    ,    lowerCAmelCase__				:		Dict    ,    lowerCAmelCase__				:		List[str]=None      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   count_samples(lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	int													=   tf.data.Dataset.from_tensor_slices(lowerCAmelCase__      )
				if shuffle:
								SCREAMING_SNAKE_CASE_   :	List[str]													=   dataset.shuffle(len(lowerCAmelCase__      )      )
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   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
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   dataset.apply(tf.data.experimental.assert_cardinality(lowerCAmelCase__      )      )
				SCREAMING_SNAKE_CASE_   :	List[str]													=   dataset.map(lowerCAmelCase__    ,    num_parallel_calls=lowerCAmelCase__      )
				if shuffle:
								assert shuffle_buffer_size is not None
								SCREAMING_SNAKE_CASE_   :	Any													=   dataset.shuffle(args.shuffle_buffer_size      )
				SCREAMING_SNAKE_CASE_   :	str													=   dataset.batch(lowerCAmelCase__    ,    drop_remainder=lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	Any													=   dataset.map(lowerCAmelCase__    ,    num_parallel_calls=lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	int													=   dataset.prefetch(lowerCAmelCase__      )
				return dataset



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]      ):





				'''simple docstring'''







				if not args.no_tpu:
								SCREAMING_SNAKE_CASE_   :	Tuple													=   initialize_tpu(lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	str													=   tf.distribute.TPUStrategy(lowerCAmelCase__      )
				else:
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   tf.distribute.OneDeviceStrategy(device='/gpu:0'      )

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

				SCREAMING_SNAKE_CASE_   :	str													=   AutoTokenizer.from_pretrained(args.tokenizer      )
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   AutoConfig.from_pretrained(args.pretrained_model_config      )
				SCREAMING_SNAKE_CASE_   :	Any													=   tokenizer.vocab_size

				SCREAMING_SNAKE_CASE_   :	str													=   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}.'''      )
				SCREAMING_SNAKE_CASE_   :	str													=   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}.'''      )

				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   count_samples(lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	int													=   num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   steps_per_epoch * args.num_epochs

				with strategy.scope():
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   TFAutoModelForMaskedLM.from_config(lowerCAmelCase__      )
								model(model.dummy_inputs      )  # Pass some dummy inputs through the model to ensure all the weights are built
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	str													=   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__				:		Optional[int]      ):
								SCREAMING_SNAKE_CASE_   :	int													=   {
								    'input_ids': tf.io.FixedLenFeature(dtype=tf.intaa    ,    shape=(args.max_length,)      ),
								    'attention_mask': tf.io.FixedLenFeature(dtype=tf.intaa    ,    shape=(args.max_length,)      ),
								}
								return tf.io.parse_single_example(lowerCAmelCase__    ,    lowerCAmelCase__      )

				# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
				# use their methods in our data pipeline.
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   DataCollatorForLanguageModeling(
				    tokenizer=lowerCAmelCase__    ,    mlm_probability=args.mlm_probability    ,    mlm=lowerCAmelCase__    ,    return_tensors='tf'      )

				def mask_with_collator(lowerCAmelCase__				:		Optional[Any]      ):
								# TF really needs an isin() function
								SCREAMING_SNAKE_CASE_   :	Tuple													=   (
								    ~tf.cast(batch['attention_mask']    ,    tf.bool      )
								    | (batch['input_ids'] == tokenizer.cls_token_id)
								    | (batch['input_ids'] == tokenizer.sep_token_id)
								)
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[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

				SCREAMING_SNAKE_CASE_   :	Any													=   args.per_replica_batch_size * strategy.num_replicas_in_sync

				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   prepare_dataset(
				    lowerCAmelCase__    ,    decode_fn=lowerCAmelCase__    ,    mask_fn=lowerCAmelCase__    ,    batch_size=lowerCAmelCase__    ,    shuffle=lowerCAmelCase__    ,    shuffle_buffer_size=args.shuffle_buffer_size    ,    )

				SCREAMING_SNAKE_CASE_   :	List[Any]													=   prepare_dataset(
				    lowerCAmelCase__    ,    decode_fn=lowerCAmelCase__    ,    mask_fn=lowerCAmelCase__    ,    batch_size=lowerCAmelCase__    ,    shuffle=lowerCAmelCase__    ,    )

				SCREAMING_SNAKE_CASE_   :	str													=   []
				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__":
				A        =			parse_args()
				main(args)