



"""simple docstring"""
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'''


						snake_case_ :Optional[int]						=			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."""	)

						snake_case_ :Dict						=			parser.parse_args()
						return args
def    A_  (			_lowercase	):







						'''simple docstring'''


						try:
												if args.tpu_name:
																		snake_case_ :List[str]						=			tf.distribute.cluster_resolver.TPUClusterResolver(
																		    args.tpu_name,					zone=args.tpu_zone,					project=args.gcp_project	)
												else:
																		snake_case_ :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(_lowercase	)
						tf.tpu.experimental.initialize_tpu_system(_lowercase	)

						return tpu
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Optional[int]						=			0
						for file in file_list:
												snake_case_ :List[Any]						=			file.split("""/"""	)[-1]
												snake_case_ :Union[str, Any]						=			re.search(r"""-\d+-(\d+)\.tfrecord""",					_lowercase	).group(1	)
												snake_case_ :List[str]						=			int(_lowercase	)
												num_samples += sample_count

						return num_samples
def    A_  (			_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase=None	):







						'''simple docstring'''


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







						'''simple docstring'''


						if not args.no_tpu:
												snake_case_ :Optional[Any]						=			initialize_tpu(_lowercase	)
												snake_case_ :Dict						=			tf.distribute.TPUStrategy(_lowercase	)
						else:
												snake_case_ :Tuple						=			tf.distribute.OneDeviceStrategy(device="""/gpu:0"""	)

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

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

						snake_case_ :Optional[int]						=			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}."""	)
						snake_case_ :Union[str, Any]						=			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}."""	)

						snake_case_ :Optional[Any]						=			count_samples(_lowercase	)

						snake_case_ :Union[str, Any]						=			num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
						snake_case_ :List[str]						=			steps_per_epoch * args.num_epochs

						with strategy.scope():
												snake_case_ :List[str]						=			TFAutoModelForMaskedLM.from_config(_lowercase	)
												model(model.dummy_inputs	)  # Pass some dummy inputs through the model to ensure all the weights are built
												snake_case_,       snake_case_ :Optional[Any]						=			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	):
												snake_case_ :Tuple						=			{
												    """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.
						snake_case_ :Any						=			DataCollatorForLanguageModeling(
						    tokenizer=_lowercase,					mlm_probability=args.mlm_probability,					mlm=_lowercase,					return_tensors="""tf"""	)

						def mask_with_collator(_lowercase	):
												# TF really needs an isin() function
												snake_case_ :List[str]						=			(
												    ~tf.cast(batch["""attention_mask"""],					tf.bool	)
												    | (batch["""input_ids"""] == tokenizer.cls_token_id)
												    | (batch["""input_ids"""] == tokenizer.sep_token_id)
												)
												snake_case_,       snake_case_ :Union[str, Any]						=			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

						snake_case_ :Optional[Any]						=			args.per_replica_batch_size * strategy.num_replicas_in_sync

						snake_case_ :Union[str, Any]						=			prepare_dataset(
						    _lowercase,					decode_fn=_lowercase,					mask_fn=_lowercase,					batch_size=_lowercase,					shuffle=_lowercase,					shuffle_buffer_size=args.shuffle_buffer_size,					)

						snake_case_ :Optional[Any]						=			prepare_dataset(
						    _lowercase,					decode_fn=_lowercase,					mask_fn=_lowercase,					batch_size=_lowercase,					shuffle=_lowercase,					)

						snake_case_ :List[Any]						=			[]
						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__":
						__a      =				parse_args()
						main(args)





"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			os.path.join(args.tf_model_dir,					"""parameters.json"""	)
						snake_case_ :Any						=			json.loads(open(_lowercase	).read()	)
						if not params:
												raise ValueError(
												    f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""	)
						if not args.output.endswith(""".pt"""	):
												snake_case_ :Optional[int]						=			args.output + """.pt"""
						snake_case_ :List[str]						=			OrderedDict()
						with tf.device("""/CPU:0"""	):
												snake_case_ :Dict						=			tf.train.load_checkpoint(args.tf_model_dir	)
												snake_case_ :str						=			reader.get_variable_to_shape_map()
												for key_name in shapes.keys():
																		snake_case_ :List[Any]						=			reader.get_tensor(_lowercase	).astype(np.floataa	)
																		if key_name.endswith("""/adam_m"""	) or key_name.endswith("""/adam_v"""	):
																								continue
																		if key_name.startswith("""pasts/"""	):
																								if key_name.startswith("""pasts/mlp"""	):
																														snake_case_ :Any						=			int(key_name[9]	)
																								elif key_name.startswith("""pasts/out"""	):
																														snake_case_ :Optional[int]						=			8
																								snake_case_ :List[str]						=			"""model.sqout.%d.weight""" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																								snake_case_ :Optional[Any]						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																								snake_case_ :List[str]						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/moe"""	):
																								snake_case_ :Tuple						=			int(key_name[9:].split("""/"""	)[0]	)
																								if key_name.endswith("""/switch_gating/kernel"""	):
																														snake_case_ :Union[str, Any]						=			"""model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
																														snake_case_ :Optional[Any]						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/softmlp/kernel"""	):
																														snake_case_ :List[Any]						=			"""model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
																														snake_case_ :Optional[int]						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/wo/kernel"""	) or key_name.endswith("""/wi/kernel"""	):
																														snake_case_ :Dict						=			key_name[-9:-7]
																														for i in range(16	):
																																				snake_case_ :str						=			"""model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
																																				snake_case_ :Tuple						=			(
																																				    vnp[i].transpose([1, 0]	).copy()
																																				)  # In Mesh-Tensorflow, it is one array, so it is divided
																																				snake_case_ :Optional[int]						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/mlp"""	):
																								snake_case_ :Optional[int]						=			int(key_name[9:].split("""/"""	)[0]	)
																								if key_name.endswith("""/p1/kernel"""	):
																														snake_case_ :Union[str, Any]						=			"""model.blocks.%d.feed_forward.mlp.wi.weight""" % player
																														snake_case_ :Dict						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/p1/bias"""	):
																														snake_case_ :List[Any]						=			"""model.blocks.%d.feed_forward.mlp.wi.bias""" % player
																														snake_case_ :str						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/p2/kernel"""	):
																														snake_case_ :Union[str, Any]						=			"""model.blocks.%d.feed_forward.mlp.wo.weight""" % player
																														snake_case_ :Dict						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Tuple						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/p2/bias"""	):
																														snake_case_ :Dict						=			"""model.blocks.%d.feed_forward.mlp.wo.bias""" % player
																														snake_case_ :Any						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Optional[int]						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/ln"""	):
																								snake_case_ :Union[str, Any]						=			int(key_name[8:].split("""/"""	)[0]	)
																								if key_name.endswith("""/b"""	):
																														snake_case_ :str						=			"""model.blocks.%d.feed_forward.norm.bias""" % player
																														snake_case_ :Dict						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :int						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/g"""	):
																														snake_case_ :Dict						=			"""model.blocks.%d.feed_forward.norm.weight""" % player
																														snake_case_ :Dict						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Tuple						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/att"""	):
																								snake_case_ :List[str]						=			int(key_name[9:].split("""/"""	)[0]	)
																								if key_name.endswith("""/qkv/kernel"""	):
																														snake_case_ :Optional[int]						=			vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																														snake_case_ :Dict						=			state[:, 0, :, :]
																														snake_case_ :int						=			state[:, 1, :, :]
																														snake_case_ :List[str]						=			state[:, 2, :, :]
																														snake_case_ :str						=			(
																														    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]	)
																														    .transpose([1, 0]	)
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Any						=			(
																														    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]	)
																														    .transpose([1, 0]	)
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Optional[int]						=			(
																														    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]	)
																														    .transpose([1, 0]	)
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :int						=			"""model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
																														snake_case_ :int						=			torch.tensor(_lowercase	)
																														snake_case_ :Optional[Any]						=			"""model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
																														snake_case_ :Dict						=			torch.tensor(_lowercase	)
																														snake_case_ :Dict						=			"""model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/o/kernel"""	):
																														snake_case_ :str						=			"""model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
																														snake_case_ :str						=			(
																														    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]	).transpose([1, 0]	).copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Any						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/an"""	):
																								snake_case_ :Optional[int]						=			int(key_name[8:].split("""/"""	)[0]	)
																								if key_name.endswith("""/b"""	):
																														snake_case_ :Any						=			"""model.blocks.%d.self_attn.norm.bias""" % player
																														snake_case_ :Optional[int]						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Tuple						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/g"""	):
																														snake_case_ :Union[str, Any]						=			"""model.blocks.%d.self_attn.norm.weight""" % player
																														snake_case_ :Dict						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Optional[int]						=			torch.tensor(_lowercase	)
																		elif (
																		    key_name.startswith("""model/wte"""	)
																		    or key_name.startswith("""model/wpe"""	)
																		    or key_name.startswith("""model/ete"""	)
																		):
																								snake_case_ :List[Any]						=			{"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
																								    key_name[-3:]
																								]
																								snake_case_ :Optional[Any]						=			"""model.%s.weight""" % nlayer
																								snake_case_ :Any						=			vnp.copy()  # same in embedded
																								snake_case_ :List[Any]						=			torch.tensor(_lowercase	)
																								if key_name.startswith("""model/wte"""	):
																														snake_case_ :Tuple						=			"""lm_head.weight"""
																														snake_case_ :List[str]						=			vnp.copy()  # same in embedded
																														snake_case_ :List[Any]						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/wob"""	):
																								snake_case_ :str						=			"""final_logits_bias"""
																								snake_case_ :Any						=			vnp.copy()  # same in embedded
																								snake_case_ :List[Any]						=			state.reshape((1, -1)	)
																								snake_case_ :Union[str, Any]						=			torch.tensor(_lowercase	)
																		elif key_name == "model/dense/kernel":
																								snake_case_ :str						=			"""model.last_project.weight"""
																								snake_case_ :Dict						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																								snake_case_ :int						=			torch.tensor(_lowercase	)
																		elif key_name == "model/dense_1/bias":
																								snake_case_ :Optional[int]						=			"""model.last_project.bias"""
																								snake_case_ :Tuple						=			vnp.copy()  # same because it is one dimensional
																								snake_case_ :Any						=			torch.tensor(_lowercase	)
						torch.save(_lowercase,					args.output	)


if __name__ == "__main__":
						__a      =				argparse.ArgumentParser(
						    description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
						)
						parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
						parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
						__a      =				parser.parse_args()
						convert_tf_gptsan_to_pt(args)
