"""simple docstring"""
import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('SDP_ENABLED') or is_sagemaker_dp_enabled():
 lowercase__   			=      True
 lowercase__   			=      'p3dn.24xlarge'
 import smdistributed.dataparallel.tensorflow as sdp
else:
 lowercase__   			=      False







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=None  )      ->int:
  a__:	List[Any]     				=      tqdm(_SCREAMING_SNAKE_CASE  )
  for i, batch in enumerate(_SCREAMING_SNAKE_CASE  ):
    with tf.GradientTape() as tape:
      a__				,       a__:	Union[str, Any]     				=      batch
      a__:	List[str]     				=      model(_SCREAMING_SNAKE_CASE  )
      a__:	Optional[Any]     				=      loss(_SCREAMING_SNAKE_CASE						,	outputs.logits  )

    if SDP_ENABLED:
      a__:	Union[str, Any]     				=      sdp.DistributedGradientTape(_SCREAMING_SNAKE_CASE						,	sparse_as_dense=_SCREAMING_SNAKE_CASE  )

    a__:	List[str]     				=      tape.gradient(_SCREAMING_SNAKE_CASE						,	model.trainable_variables  )
    opt.apply_gradients(zip(_SCREAMING_SNAKE_CASE						,	model.trainable_variables  )  )

    pbar.set_description(F'Loss: {loss_value:.4f}'  )

    if SDP_ENABLED and i == 0:
      sdp.broadcast_variables(model.variables						,	root_rank=0  )
      sdp.broadcast_variables(opt.variables()						,	root_rank=0  )

    if max_steps and i >= max_steps:
      break

  a__:	Union[str, Any]     				=      {'loss': loss_value.numpy()}
  return train_results







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Optional[int]:
  # Load dataset
  a__				,       a__:	Tuple     				=      load_dataset('imdb'						,	split=['train', 'test']  )

  # Preprocess train dataset
  a__:	int     				=      train_dataset.map(
      lambda _SCREAMING_SNAKE_CASE  : tokenizer(e['text']						,	truncation=_SCREAMING_SNAKE_CASE						,	padding='max_length'  )						,	batched=_SCREAMING_SNAKE_CASE  )
  train_dataset.set_format(type='tensorflow'						,	columns=['input_ids', 'attention_mask', 'label']  )

  a__:	List[Any]     				=      {
      x: train_dataset[x].to_tensor(default_value=0						,	shape=[None, tokenizer.model_max_length]  )
      for x in ['input_ids', 'attention_mask']
  }
  a__:	str     				=      tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])  )

  # Preprocess test dataset
  a__:	List[Any]     				=      test_dataset.map(
      lambda _SCREAMING_SNAKE_CASE  : tokenizer(e['text']						,	truncation=_SCREAMING_SNAKE_CASE						,	padding='max_length'  )						,	batched=_SCREAMING_SNAKE_CASE  )
  test_dataset.set_format(type='tensorflow'						,	columns=['input_ids', 'attention_mask', 'label']  )

  a__:	Union[str, Any]     				=      {
      x: test_dataset[x].to_tensor(default_value=0						,	shape=[None, tokenizer.model_max_length]  )
      for x in ['input_ids', 'attention_mask']
  }
  a__:	int     				=      tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])  )

  if SDP_ENABLED:
    a__:	int     				=      tf_train_dataset.shard(sdp.size()						,	sdp.rank()  )
    a__:	Optional[int]     				=      tf_test_dataset.shard(sdp.size()						,	sdp.rank()  )
  a__:	Dict     				=      tf_train_dataset.batch(_SCREAMING_SNAKE_CASE						,	drop_remainder=_SCREAMING_SNAKE_CASE  )
  a__:	Tuple     				=      tf_test_dataset.batch(_SCREAMING_SNAKE_CASE						,	drop_remainder=_SCREAMING_SNAKE_CASE  )

  return tf_train_dataset, tf_test_dataset


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

 # Hyperparameters sent by the client are passed as command-line arguments to the script.
 parser.add_argument('--epochs', type=int, default=3)
 parser.add_argument('--per_device_train_batch_size', type=int, default=16)
 parser.add_argument('--per_device_eval_batch_size', type=int, default=8)
 parser.add_argument('--model_name_or_path', type=str)
 parser.add_argument('--learning_rate', type=str, default=5E-5)
 parser.add_argument('--do_train', type=bool, default=True)
 parser.add_argument('--do_eval', type=bool, default=True)
 parser.add_argument('--output_dir', type=str)
 parser.add_argument('--max_steps', type=int, default=None)

 # Data, model, and output directories
 parser.add_argument('--output_data_dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
 parser.add_argument('--model_dir', type=str, default=os.environ['SM_MODEL_DIR'])
 parser.add_argument('--n_gpus', type=str, default=os.environ['SM_NUM_GPUS'])

 lowercase__			,   lowercase__   			=      parser.parse_known_args()

 # Set up logging
 lowercase__   			=      logging.getLogger(__name__)

 logging.basicConfig(
     level=logging.getLevelName('INFO'),
     handlers=[logging.StreamHandler(sys.stdout)],
     format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
 )

 if SDP_ENABLED:
  sdp.init()

  lowercase__   			=      tf.config.experimental.list_physical_devices('GPU')
  for gpu in gpus:
   tf.config.experimental.set_memory_growth(gpu, True)
  if gpus:
   tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], 'GPU')

    # Load model and tokenizer
 lowercase__   			=      TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
 lowercase__   			=      AutoTokenizer.from_pretrained(args.model_name_or_path)

 # get datasets
 lowercase__			,   lowercase__   			=      get_datasets(
     tokenizer=tokenizer,
     train_batch_size=args.per_device_train_batch_size,
     eval_batch_size=args.per_device_eval_batch_size,
 )

 # fine optimizer and loss
 lowercase__   			=      tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
 lowercase__   			=      tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
 lowercase__   			=      [tf.keras.metrics.SparseCategoricalAccuracy()]
 model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

 # Training
 if args.do_train:
  # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
  lowercase__   			=      time.time()
  lowercase__   			=      fit(
      model,
      loss,
      optimizer,
      tf_train_dataset,
      args.epochs,
      args.per_device_train_batch_size,
      max_steps=args.max_steps,
  )
  lowercase__   			=      time.time() - start_train_time
  logger.info('*** Train ***')
  logger.info(f"train_runtime = {end_train_time}")

  lowercase__   			=      os.path.join(args.output_dir, 'train_results.txt')

  if not SDP_ENABLED or sdp.rank() == 0:
   with open(output_eval_file, 'w') as writer:
    logger.info('***** Train results *****')
    logger.info(train_results)
    for key, value in train_results.items():
     logger.info(f"  {key} = {value}")
     writer.write(f"{key} = {value}\n")

    # Evaluation
 if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
  lowercase__   			=      model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
  logger.info('*** Evaluate ***')

  lowercase__   			=      os.path.join(args.output_dir, 'eval_results.txt')

  with open(output_eval_file, 'w') as writer:
   logger.info('***** Eval results *****')
   logger.info(result)
   for key, value in result.items():
    logger.info(f"  {key} = {value}")
    writer.write(f"{key} = {value}\n")

    # Save result
 if SDP_ENABLED:
  if sdp.rank() == 0:
   model.save_pretrained(args.output_dir)
   tokenizer.save_pretrained(args.output_dir)
 else:
  model.save_pretrained(args.output_dir)
  tokenizer.save_pretrained(args.output_dir)





"""simple docstring"""





# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Union[str, Any]   ,				UpperCAmelCase  :				Tuple   ,				UpperCAmelCase  :				Tuple=0					):


					'''simple docstring'''






					if name is None:
										UpperCamelCase__		:     Any								=None
					else:
										UpperCamelCase__		:     str								='''.''' * max(0   ,				spaces - 2					) + '''# {:''' + str(50 - spaces					) + '''s}'''
										UpperCamelCase__		:     int								=fmt.format(UpperCAmelCase					)

					# Print and recurse (if needed).
					if isinstance(UpperCAmelCase   ,				UpperCAmelCase					):
										if msg is not None:
															print(UpperCAmelCase					)
										for k in val.keys():
															recursive_print(UpperCAmelCase   ,				val[k]   ,				spaces + 2					)
					elif isinstance(UpperCAmelCase   ,				torch.Tensor					):
										print(UpperCAmelCase   ,				''':'''   ,				val.size()					)
					else:
										print(UpperCAmelCase   ,				''':'''   ,				UpperCAmelCase					)



def 							_lowerCAmelCase   (    UpperCAmelCase  :				List[str]   ,				UpperCAmelCase  :				Dict   ,				UpperCAmelCase  :				Union[str, Any]   ,				UpperCAmelCase  :				List[str]   ,				UpperCAmelCase  :				List[Any]					):


					'''simple docstring'''






					UpperCamelCase__		:     Any								=param.size()
					if checkpoint_version == 1.0:
										# version 1.0 stores [num_heads * hidden_size * num_splits, :]
										UpperCamelCase__		:     Tuple								=(num_heads, hidden_size, num_splits) + input_shape[1:]
										UpperCamelCase__		:     List[Any]								=param.view(*UpperCAmelCase					)
										UpperCamelCase__		:     List[Any]								=param.transpose(0   ,				2					)
										UpperCamelCase__		:     int								=param.transpose(1   ,				2					).contiguous()
					elif checkpoint_version >= 2.0:
										# other versions store [num_heads * num_splits * hidden_size, :]
										UpperCamelCase__		:     int								=(num_heads, num_splits, hidden_size) + input_shape[1:]
										UpperCamelCase__		:     Any								=param.view(*UpperCAmelCase					)
										UpperCamelCase__		:     Any								=param.transpose(0   ,				1					).contiguous()
					UpperCamelCase__		:     List[str]								=param.view(*UpperCAmelCase					)
					return param



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Optional[int]   ,				UpperCAmelCase  :				Tuple   ,				UpperCAmelCase  :				int					):


					'''simple docstring'''






					UpperCamelCase__		:     int								={}

					# old versions did not store training args
					UpperCamelCase__		:     Optional[int]								=input_state_dict.get('''args'''   ,				UpperCAmelCase					)
					if ds_args is not None:
										# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
										# from pprint import pprint
										# pprint(vars(ds_args))

										UpperCamelCase__		:     List[str]								=ds_args.tokenizer_type
										UpperCamelCase__		:     str								=ds_args.padded_vocab_size
										UpperCamelCase__		:     List[str]								=ds_args.max_position_embeddings
										UpperCamelCase__		:     Any								=ds_args.hidden_size
										UpperCamelCase__		:     Optional[Any]								=ds_args.num_layers
										UpperCamelCase__		:     Optional[Any]								=ds_args.num_attention_heads
										UpperCamelCase__		:     Optional[Any]								=ds_args.ffn_hidden_size if '''ffn_hidden_size''' in ds_args else 4 * ds_args.hidden_size
										# pprint(config)

					# The number of heads.
					UpperCamelCase__		:     List[str]								=config.num_attention_heads
					# The hidden_size per head.
					UpperCamelCase__		:     Union[str, Any]								=config.hidden_size // heads
					# Megatron-LM checkpoint version
					if "checkpoint_version" in input_state_dict.keys():
										UpperCamelCase__		:     Optional[Any]								=input_state_dict['''checkpoint_version''']
					else:
										UpperCamelCase__		:     int								=0.0

					# The model.
					UpperCamelCase__		:     Optional[int]								=input_state_dict['''model''']
					# The language model.
					UpperCamelCase__		:     int								=model['''language_model''']
					# The embeddings.
					UpperCamelCase__		:     Any								=lm['''embedding''']

					# The word embeddings.
					UpperCamelCase__		:     Union[str, Any]								=embeddings['''word_embeddings''']['''weight''']
					# Truncate the embedding table to vocab_size rows.
					UpperCamelCase__		:     Optional[Any]								=word_embeddings[: config.vocab_size, :]
					# Store the word embeddings.
					UpperCamelCase__		:     List[str]								=word_embeddings

					# The position embeddings.
					UpperCamelCase__		:     Dict								=embeddings['''position_embeddings''']['''weight''']
					assert pos_embeddings.size(0					) == config.max_position_embeddings and pos_embeddings.size(1					) == config.hidden_size
					# Store the position embeddings.
					UpperCamelCase__		:     List[str]								=pos_embeddings

					# The token-type embeddings.
					UpperCamelCase__		:     str								=embeddings['''tokentype_embeddings''']['''weight''']
					# Store the position embeddings.
					UpperCamelCase__		:     List[str]								=tokentype_embeddings

					# The transformer.
					UpperCamelCase__		:     Optional[int]								=lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

					# The regex to extract layer names.
					UpperCamelCase__		:     int								=re.compile(r'''layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'''					)

					# The simple map of names for "automated" rules.
					UpperCamelCase__		:     List[Any]								={
					    '''attention.dense''': '''.attention.output.dense.''',
					    '''self_attention.dense''': '''.attention.output.dense.''',
					    '''mlp.dense_h_to_4h''': '''.intermediate.dense.''',
					    '''mlp.dense_4h_to_h''': '''.output.dense.''',
					}

					# Keep track of the attention/query/value tensor.
					UpperCamelCase__		:     Tuple								=None

					# Extract the layers.
					for key, val in transformer.items():
										# Match the name.
										UpperCamelCase__		:     Union[str, Any]								=layer_re.match(UpperCAmelCase					)

										# Stop if that's not a layer
										if m is None:
															break

										# The index of the layer.
										UpperCamelCase__		:     int								=int(m.group(1					)					)
										# The name of the operation.
										UpperCamelCase__		:     List[str]								=m.group(2					)
										# Is it a weight or a bias?
										UpperCamelCase__		:     int								=m.group(3					)

										# The name of the layer.
										UpperCamelCase__		:     str								=F'''bert.encoder.layer.{layer_idx}'''

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith('''layernorm'''					):
															UpperCamelCase__		:     int								='''attention.ln''' if op_name.startswith('''input'''					) else '''ln'''
															UpperCamelCase__		:     Tuple								=val

										# Transpose the QKV matrix.
										elif (
										    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
										) and weight_or_bias == "weight":
															# Make sure the QKV pointer is nil.
															assert attention_qkv_weight is None, ""

															UpperCamelCase__		:     Any								=fix_query_key_value_ordering(UpperCAmelCase   ,				UpperCAmelCase   ,				3   ,				UpperCAmelCase   ,				UpperCAmelCase					)
															# Store the tensor as we need the bias as well to interleave QKV and biases.
															UpperCamelCase__		:     Tuple								=out_val

										# Transpose the bias.
										elif (
										    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
										) and weight_or_bias == "bias":
															# Make sure we read the weight tensor.
															assert attention_qkv_weight is not None, ""

															# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
															UpperCamelCase__		:     Tuple								=attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
															UpperCamelCase__		:     Tuple								=attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
															UpperCamelCase__		:     str								=attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

															UpperCamelCase__		:     int								=fix_query_key_value_ordering(UpperCAmelCase   ,				UpperCAmelCase   ,				3   ,				UpperCAmelCase   ,				UpperCAmelCase					)
															# Split the bias.
															UpperCamelCase__		:     Any								=out_val[0 * config.hidden_size : 1 * config.hidden_size]
															UpperCamelCase__		:     Any								=out_val[1 * config.hidden_size : 2 * config.hidden_size]
															UpperCamelCase__		:     List[Any]								=out_val[2 * config.hidden_size : 3 * config.hidden_size]

															# Store.
															UpperCamelCase__		:     int								=q
															UpperCamelCase__		:     Dict								=q_bias
															UpperCamelCase__		:     List[Any]								=k
															UpperCamelCase__		:     Optional[int]								=k_bias
															UpperCamelCase__		:     Tuple								=v
															UpperCamelCase__		:     Optional[Any]								=v_bias

															# Clear the stored tensor.
															UpperCamelCase__		:     Optional[Any]								=None

										# Copy weights and biases as is.
										elif weight_or_bias in ["weight", "bias"]:
															UpperCamelCase__		:     str								=megatron_to_transformers[op_name]
															UpperCamelCase__		:     Any								=val

    # The final layernorm.
					UpperCamelCase__		:     Dict								=transformer['''final_layernorm.weight''']
					UpperCamelCase__		:     List[str]								=transformer['''final_layernorm.bias''']

					# The pooler.
					UpperCamelCase__		:     str								=lm['''pooler''']

					# Store the matrix and the bias.
					UpperCamelCase__		:     Optional[Any]								=pooler['''dense.weight''']
					UpperCamelCase__		:     Tuple								=pooler['''dense.bias''']

					# The LM head from Megatron (for RACE).
					UpperCamelCase__		:     Tuple								=model['''lm_head''']

					# The transform matrix.
					UpperCamelCase__		:     Optional[Any]								=lm_head['''dense.weight''']
					UpperCamelCase__		:     Union[str, Any]								=lm_head['''dense.bias''']

					# The transform LN.
					UpperCamelCase__		:     Optional[int]								=lm_head['''layernorm.weight''']
					UpperCamelCase__		:     Dict								=lm_head['''layernorm.bias''']

					# For the decoder, we replicate the weights.
					UpperCamelCase__		:     Any								=word_embeddings
					UpperCamelCase__		:     Optional[Any]								=lm_head['''bias''']

					# The classifier from Megatron (for MLNI).
					UpperCamelCase__		:     Union[str, Any]								=model['''binary_head''']

					# Store the classifier.
					UpperCamelCase__		:     Union[str, Any]								=binary_head['''weight''']
					UpperCamelCase__		:     int								=binary_head['''bias''']

					# It should be done!
					return output_state_dict



def 							_lowerCAmelCase   (    ):


					'''simple docstring'''






					UpperCamelCase__		:     Dict								=argparse.ArgumentParser()
					parser.add_argument('''--print-checkpoint-structure'''   ,				action='''store_true'''					)
					parser.add_argument('''path_to_checkpoint'''   ,				type=UpperCAmelCase   ,				help='''Path to the ZIP file containing the checkpoint'''					)
					parser.add_argument(
					    '''--config_file'''   ,				default=''''''   ,				type=UpperCAmelCase   ,				help='''An optional config json file describing the pre-trained model.'''   ,				)
					UpperCamelCase__		:     Dict								=parser.parse_args()

					# Extract the basename.
					UpperCamelCase__		:     Optional[int]								=os.path.dirname(args.path_to_checkpoint					)

					# Load the model.
					# the .zip is very optional, let's keep it for backward compatibility
					print(F'''Extracting PyTorch state dictionary from "{args.path_to_checkpoint}"'''					)
					if args.path_to_checkpoint.endswith('''.zip'''					):
										with zipfile.ZipFile(args.path_to_checkpoint   ,				'''r'''					) as checkpoint:
															with checkpoint.open('''release/mp_rank_00/model_optim_rng.pt'''					) as pytorch_dict:
																				UpperCamelCase__		:     Optional[int]								=torch.load(UpperCAmelCase   ,				map_location='''cpu'''					)
					else:
										UpperCamelCase__		:     Optional[Any]								=torch.load(args.path_to_checkpoint   ,				map_location='''cpu'''					)

					if args.config_file == "":
										# Default config of megatron-bert 345m
										UpperCamelCase__		:     Union[str, Any]								=MegatronBertConfig()

										# different megatron-bert-*-345m models have different vocab sizes, so override the default
										# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
										UpperCamelCase__		:     Tuple								=input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
					else:
										UpperCamelCase__		:     Any								=MegatronBertConfig.from_json_file(args.config_file					)

					# Convert.
					print('''Converting'''					)
					UpperCamelCase__		:     str								=convert_megatron_checkpoint(UpperCAmelCase   ,				UpperCAmelCase   ,				UpperCAmelCase					)

					# Print the structure of converted state dict.
					if args.print_checkpoint_structure:
										recursive_print(UpperCAmelCase   ,				UpperCAmelCase					)

					# Store the config to file.
					print('''Saving config'''					)
					config.save_pretrained(UpperCAmelCase					)

					# Store the state_dict to file.
					UpperCamelCase__		:     Union[str, Any]								=os.path.join(UpperCAmelCase   ,				'''pytorch_model.bin'''					)
					print(F'''Saving checkpoint to "{output_checkpoint_file}"'''					)
					torch.save(UpperCAmelCase   ,				UpperCAmelCase					)


####################################################################################################

if __name__ == "__main__":
				main()

####################################################################################################


