'''simple docstring'''

import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__a:			List[Any]	       =       logging.getLogger(__name__)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Any						= np.argmax(UpperCAmelCase		,					axis=1					)
 return np.sum(outputs == labels					)
def       __UpperCamelCase	(       UpperCAmelCase					):
 with open(UpperCAmelCase		,					encoding='''utf_8'''					) as f:
  lowercase__    :   List[Any]						= csv.reader(UpperCAmelCase					)
  lowercase__    :   Any						= []
  next(UpperCAmelCase					)  # skip the first line
  for line in tqdm(UpperCAmelCase					):
   output.append((''' '''.join(line[1:5]					), line[5], line[6], int(line[-1]					) - 1)					)
 return output
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Dict						= []
 for dataset in encoded_datasets:
  lowercase__    :   Union[str, Any]						= len(UpperCAmelCase					)
  lowercase__    :   Optional[int]						= np.zeros((n_batch, 2, input_len)		,					dtype=np.intaa					)
  lowercase__    :   Any						= np.zeros((n_batch, 2)		,					dtype=np.intaa					)
  lowercase__    :   Dict						= np.full((n_batch, 2, input_len)		,					fill_value=-100		,					dtype=np.intaa					)
  lowercase__    :   Any						= np.zeros((n_batch,)		,					dtype=np.intaa					)
  for (
      i,
      (story, conta, conta, mc_label),
  ) in enumerate(UpperCAmelCase					):
   lowercase__    :   Dict						= [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
   lowercase__    :   List[str]						= [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
   lowercase__    :   List[Any]						= with_conta
   lowercase__    :   List[Any]						= with_conta
   lowercase__    :   Union[str, Any]						= len(UpperCAmelCase					) - 1
   lowercase__    :   int						= len(UpperCAmelCase					) - 1
   lowercase__    :   Any						= with_conta
   lowercase__    :   Union[str, Any]						= with_conta
   lowercase__    :   Tuple						= mc_label
  lowercase__    :   str						= (input_ids, mc_token_ids, lm_labels, mc_labels)
  tensor_datasets.append(tuple(torch.tensor(UpperCAmelCase					) for t in all_inputs					)					)
 return tensor_datasets
def       __UpperCamelCase	(       ):
 lowercase__    :   List[str]						= argparse.ArgumentParser()
 parser.add_argument('''--model_name'''		,					type=UpperCAmelCase		,					default='''openai-gpt'''		,					help='''pretrained model name'''					)
 parser.add_argument('''--do_train'''		,					action='''store_true'''		,					help='''Whether to run training.'''					)
 parser.add_argument('''--do_eval'''		,					action='''store_true'''		,					help='''Whether to run eval on the dev set.'''					)
 parser.add_argument(
     '''--output_dir'''		,					default=UpperCAmelCase		,					type=UpperCAmelCase		,					required=UpperCAmelCase		,					help='''The output directory where the model predictions and checkpoints will be written.'''		,					)
 parser.add_argument('''--train_dataset'''		,					type=UpperCAmelCase		,					default=''''''					)
 parser.add_argument('''--eval_dataset'''		,					type=UpperCAmelCase		,					default=''''''					)
 parser.add_argument('''--seed'''		,					type=UpperCAmelCase		,					default=42					)
 parser.add_argument('''--num_train_epochs'''		,					type=UpperCAmelCase		,					default=3					)
 parser.add_argument('''--train_batch_size'''		,					type=UpperCAmelCase		,					default=8					)
 parser.add_argument('''--eval_batch_size'''		,					type=UpperCAmelCase		,					default=16					)
 parser.add_argument('''--adam_epsilon'''		,					default=1E-8		,					type=UpperCAmelCase		,					help='''Epsilon for Adam optimizer.'''					)
 parser.add_argument('''--max_grad_norm'''		,					type=UpperCAmelCase		,					default=1					)
 parser.add_argument(
     '''--max_steps'''		,					default=-1		,					type=UpperCAmelCase		,					help=(
         '''If > 0: set total number of training                         steps to perform. Override num_train_epochs.'''
     )		,					)
 parser.add_argument(
     '''--gradient_accumulation_steps'''		,					type=UpperCAmelCase		,					default=1		,					help='''Number of updates steps to accumulate before                        performing a backward/update pass.'''		,					)
 parser.add_argument('''--learning_rate'''		,					type=UpperCAmelCase		,					default=6.25E-5					)
 parser.add_argument('''--warmup_steps'''		,					default=0		,					type=UpperCAmelCase		,					help='''Linear warmup over warmup_steps.'''					)
 parser.add_argument('''--lr_schedule'''		,					type=UpperCAmelCase		,					default='''warmup_linear'''					)
 parser.add_argument('''--weight_decay'''		,					type=UpperCAmelCase		,					default=0.0_1					)
 parser.add_argument('''--lm_coef'''		,					type=UpperCAmelCase		,					default=0.9					)
 parser.add_argument('''--n_valid'''		,					type=UpperCAmelCase		,					default=374					)

 parser.add_argument('''--server_ip'''		,					type=UpperCAmelCase		,					default=''''''		,					help='''Can be used for distant debugging.'''					)
 parser.add_argument('''--server_port'''		,					type=UpperCAmelCase		,					default=''''''		,					help='''Can be used for distant debugging.'''					)
 lowercase__    :   List[str]						= parser.parse_args()
 print(UpperCAmelCase					)

 if args.server_ip and args.server_port:
  # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
  import ptvsd

  print('''Waiting for debugger attach'''					)
  ptvsd.enable_attach(address=(args.server_ip, args.server_port)		,					redirect_output=UpperCAmelCase					)
  ptvsd.wait_for_attach()

 random.seed(args.seed					)
 np.random.seed(args.seed					)
 torch.manual_seed(args.seed					)
 torch.cuda.manual_seed_all(args.seed					)

 lowercase__    :   Optional[int]						= torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''					)
 lowercase__    :   List[str]						= torch.cuda.device_count()
 logger.info('''device: {}, n_gpu {}'''.format(UpperCAmelCase		,					UpperCAmelCase					)					)

 if not args.do_train and not args.do_eval:
  raise ValueError('''At least one of `do_train` or `do_eval` must be True.'''					)

 if not os.path.exists(args.output_dir					):
  os.makedirs(args.output_dir					)

 # Load tokenizer and model
 # This loading functions also add new tokens and embeddings called `special tokens`
 # These new embeddings will be fine-tuned on the RocStories dataset
 lowercase__    :   Union[str, Any]						= ['''_start_''', '''_delimiter_''', '''_classify_''']
 lowercase__    :   Tuple						= OpenAIGPTTokenizer.from_pretrained(args.model_name					)
 tokenizer.add_tokens(UpperCAmelCase					)
 lowercase__    :   Union[str, Any]						= tokenizer.convert_tokens_to_ids(UpperCAmelCase					)
 lowercase__    :   List[str]						= OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name					)
 model.resize_token_embeddings(len(UpperCAmelCase					)					)
 model.to(UpperCAmelCase					)

 # Load and encode the datasets
 def tokenize_and_encode(UpperCAmelCase					):
  if isinstance(UpperCAmelCase		,					UpperCAmelCase					):
   return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(UpperCAmelCase					)					)
  elif isinstance(UpperCAmelCase		,					UpperCAmelCase					):
   return obj
  return [tokenize_and_encode(UpperCAmelCase					) for o in obj]

 logger.info('''Encoding dataset...'''					)
 lowercase__    :   List[str]						= load_rocstories_dataset(args.train_dataset					)
 lowercase__    :   Optional[Any]						= load_rocstories_dataset(args.eval_dataset					)
 lowercase__    :   Optional[Any]						= (train_dataset, eval_dataset)
 lowercase__    :   List[str]						= tokenize_and_encode(UpperCAmelCase					)

 # Compute the max input length for the Transformer
 lowercase__    :   List[Any]						= model.config.n_positions // 2 - 2
 lowercase__    :   str						= max(
     len(story[:max_length]					) + max(len(conta[:max_length]					)		,					len(conta[:max_length]					)					) + 3
     for dataset in encoded_datasets
     for story, conta, conta, _ in dataset					)
 lowercase__    :   List[Any]						= min(UpperCAmelCase		,					model.config.n_positions					)  # Max size of input for the pre-trained model

 # Prepare inputs tensors and dataloaders
 lowercase__    :   Optional[int]						= pre_process_datasets(UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					*UpperCAmelCase					)
 lowercase__			,							lowercase__    :   str						= tensor_datasets[0], tensor_datasets[1]

 lowercase__    :   List[Any]						= TensorDataset(*UpperCAmelCase					)
 lowercase__    :   Any						= RandomSampler(UpperCAmelCase					)
 lowercase__    :   Optional[Any]						= DataLoader(UpperCAmelCase		,					sampler=UpperCAmelCase		,					batch_size=args.train_batch_size					)

 lowercase__    :   List[Any]						= TensorDataset(*UpperCAmelCase					)
 lowercase__    :   Optional[Any]						= SequentialSampler(UpperCAmelCase					)
 lowercase__    :   List[Any]						= DataLoader(UpperCAmelCase		,					sampler=UpperCAmelCase		,					batch_size=args.eval_batch_size					)

 # Prepare optimizer
 if args.do_train:
  if args.max_steps > 0:
   lowercase__    :   Any						= args.max_steps
   lowercase__    :   Union[str, Any]						= args.max_steps // (len(UpperCAmelCase					) // args.gradient_accumulation_steps) + 1
  else:
   lowercase__    :   int						= len(UpperCAmelCase					) // args.gradient_accumulation_steps * args.num_train_epochs

  lowercase__    :   List[str]						= list(model.named_parameters()					)
  lowercase__    :   Optional[int]						= ['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
  lowercase__    :   str						= [
      {
          '''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay					)],
          '''weight_decay''': args.weight_decay,
      },
      {'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay					)], '''weight_decay''': 0.0},
  ]
  lowercase__    :   Any						= AdamW(UpperCAmelCase		,					lr=args.learning_rate		,					eps=args.adam_epsilon					)
  lowercase__    :   Any						= get_linear_schedule_with_warmup(
      UpperCAmelCase		,					num_warmup_steps=args.warmup_steps		,					num_training_steps=UpperCAmelCase					)

 if args.do_train:
  lowercase__			,							lowercase__			,							lowercase__    :   Tuple						= 0, 0, None
  model.train()
  for _ in trange(int(args.num_train_epochs					)		,					desc='''Epoch'''					):
   lowercase__    :   Optional[Any]						= 0
   lowercase__    :   List[str]						= 0
   lowercase__    :   Dict						= tqdm(UpperCAmelCase		,					desc='''Training'''					)
   for step, batch in enumerate(UpperCAmelCase					):
    lowercase__    :   List[str]						= tuple(t.to(UpperCAmelCase					) for t in batch					)
    lowercase__			,							lowercase__			,							lowercase__			,							lowercase__    :   Optional[int]						= batch
    lowercase__    :   Dict						= model(UpperCAmelCase		,					mc_token_ids=UpperCAmelCase		,					lm_labels=UpperCAmelCase		,					mc_labels=UpperCAmelCase					)
    lowercase__    :   Any						= args.lm_coef * losses[0] + losses[1]
    loss.backward()
    optimizer.step()
    scheduler.step()
    optimizer.zero_grad()
    tr_loss += loss.item()
    lowercase__    :   Optional[Any]						= (
        loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
    )
    nb_tr_steps += 1
    lowercase__    :   Dict						= '''Training loss: {:.2e} lr: {:.2e}'''.format(UpperCAmelCase		,					scheduler.get_lr()[0]					)

    # Save a trained model
 if args.do_train:
  # Save a trained model, configuration and tokenizer
  lowercase__    :   Tuple						= model.module if hasattr(UpperCAmelCase		,					'''module'''					) else model  # Only save the model itself

  # If we save using the predefined names, we can load using `from_pretrained`
  lowercase__    :   Any						= os.path.join(args.output_dir		,					UpperCAmelCase					)
  lowercase__    :   Any						= os.path.join(args.output_dir		,					UpperCAmelCase					)

  torch.save(model_to_save.state_dict()		,					UpperCAmelCase					)
  model_to_save.config.to_json_file(UpperCAmelCase					)
  tokenizer.save_vocabulary(args.output_dir					)

  # Load a trained model and vocabulary that you have fine-tuned
  lowercase__    :   Any						= OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir					)
  lowercase__    :   Dict						= OpenAIGPTTokenizer.from_pretrained(args.output_dir					)
  model.to(UpperCAmelCase					)

 if args.do_eval:
  model.eval()
  lowercase__			,							lowercase__    :   str						= 0, 0
  lowercase__			,							lowercase__    :   Dict						= 0, 0
  for batch in tqdm(UpperCAmelCase		,					desc='''Evaluating'''					):
   lowercase__    :   List[Any]						= tuple(t.to(UpperCAmelCase					) for t in batch					)
   lowercase__			,							lowercase__			,							lowercase__			,							lowercase__    :   int						= batch
   with torch.no_grad():
    lowercase__			,							lowercase__			,							lowercase__			,							lowercase__    :   List[str]						= model(
        UpperCAmelCase		,					mc_token_ids=UpperCAmelCase		,					lm_labels=UpperCAmelCase		,					mc_labels=UpperCAmelCase					)

   lowercase__    :   Dict						= mc_logits.detach().cpu().numpy()
   lowercase__    :   Any						= mc_labels.to('''cpu'''					).numpy()
   lowercase__    :   Any						= accuracy(UpperCAmelCase		,					UpperCAmelCase					)

   eval_loss += mc_loss.mean().item()
   eval_accuracy += tmp_eval_accuracy

   nb_eval_examples += input_ids.size(0					)
   nb_eval_steps += 1

  lowercase__    :   Union[str, Any]						= eval_loss / nb_eval_steps
  lowercase__    :   int						= eval_accuracy / nb_eval_examples
  lowercase__    :   Dict						= tr_loss / nb_tr_steps if args.do_train else None
  lowercase__    :   str						= {'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}

  lowercase__    :   str						= os.path.join(args.output_dir		,					'''eval_results.txt'''					)
  with open(UpperCAmelCase		,					'''w'''					) as writer:
   logger.info('''***** Eval results *****'''					)
   for key in sorted(result.keys()					):
    logger.info('''  %s = %s'''		,					UpperCAmelCase		,					str(result[key]					)					)
    writer.write('''%s = %s\n''' % (key, str(result[key]					))					)


if __name__ == "__main__":
  main()


'''simple docstring'''

import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase="attention"					):
 lowercase__    :   Dict						= params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
 lowercase__    :   Dict						= params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
 lowercase__    :   int						= params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
 lowercase__    :   Any						= params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
 return k, o, q, v
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=False					):
 if split_mlp_wi:
  lowercase__    :   Tuple						= params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
  lowercase__    :   Tuple						= params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
  lowercase__    :   Dict						= (wi_a, wi_a)
 else:
  lowercase__    :   List[Any]						= params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

 lowercase__    :   int						= params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
 return wi, wo
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]
def       __UpperCamelCase	(       UpperCAmelCase		,					*, UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Union[str, Any]						= traverse_util.flatten_dict(variables['''target''']					)
 lowercase__    :   Any						= {'''/'''.join(UpperCAmelCase					): v for k, v in old.items()}

 # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
 lowercase__    :   List[str]						= '''encoder/layers_0/mlp/wi_0/kernel''' in old
 print('''Split MLP:'''		,					UpperCAmelCase					)

 lowercase__    :   Union[str, Any]						= collections.OrderedDict()

 # Shared embeddings.
 lowercase__    :   str						= old['''token_embedder/embedding''']

 # Encoder.
 for i in range(UpperCAmelCase					):
  # Block i, layer 0 (Self Attention).
  lowercase__    :   List[Any]						= tax_layer_norm_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''encoder'''		,					'''pre_attention_layer_norm'''					)
  lowercase__			,							lowercase__			,							lowercase__			,							lowercase__    :   Tuple						= tax_attention_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''encoder'''		,					'''attention'''					)
  lowercase__    :   Dict						= layer_norm
  lowercase__    :   Any						= k.T
  lowercase__    :   Optional[int]						= o.T
  lowercase__    :   Optional[int]						= q.T
  lowercase__    :   Any						= v.T

  # Block i, layer 1 (MLP).
  lowercase__    :   List[str]						= tax_layer_norm_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''encoder'''		,					'''pre_mlp_layer_norm'''					)
  lowercase__			,							lowercase__    :   List[Any]						= tax_mlp_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''encoder'''		,					UpperCAmelCase					)
  lowercase__    :   int						= layer_norm
  if split_mlp_wi:
   lowercase__    :   Union[str, Any]						= wi[0].T
   lowercase__    :   List[str]						= wi[1].T
  else:
   lowercase__    :   int						= wi.T
  lowercase__    :   List[str]						= wo.T

 lowercase__    :   Dict						= old[
     '''encoder/relpos_bias/rel_embedding'''
 ].T
 lowercase__    :   str						= old['''encoder/encoder_norm/scale''']

 if not is_encoder_only:
  # Decoder.
  for i in range(UpperCAmelCase					):
   # Block i, layer 0 (Self Attention).
   lowercase__    :   Optional[Any]						= tax_layer_norm_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''decoder'''		,					'''pre_self_attention_layer_norm'''					)
   lowercase__			,							lowercase__			,							lowercase__			,							lowercase__    :   Optional[Any]						= tax_attention_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''decoder'''		,					'''self_attention'''					)
   lowercase__    :   List[str]						= layer_norm
   lowercase__    :   List[Any]						= k.T
   lowercase__    :   List[Any]						= o.T
   lowercase__    :   List[str]						= q.T
   lowercase__    :   Optional[Any]						= v.T

   # Block i, layer 1 (Cross Attention).
   lowercase__    :   List[Any]						= tax_layer_norm_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''decoder'''		,					'''pre_cross_attention_layer_norm'''					)
   lowercase__			,							lowercase__			,							lowercase__			,							lowercase__    :   Any						= tax_attention_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''decoder'''		,					'''encoder_decoder_attention'''					)
   lowercase__    :   Any						= layer_norm
   lowercase__    :   Optional[int]						= k.T
   lowercase__    :   Optional[Any]						= o.T
   lowercase__    :   Tuple						= q.T
   lowercase__    :   List[Any]						= v.T

   # Block i, layer 2 (MLP).
   lowercase__    :   List[str]						= tax_layer_norm_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''decoder'''		,					'''pre_mlp_layer_norm'''					)
   lowercase__			,							lowercase__    :   List[Any]						= tax_mlp_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''decoder'''		,					UpperCAmelCase					)
   lowercase__    :   Optional[int]						= layer_norm
   if split_mlp_wi:
    lowercase__    :   List[str]						= wi[0].T
    lowercase__    :   str						= wi[1].T
   else:
    lowercase__    :   str						= wi.T
   lowercase__    :   Optional[Any]						= wo.T

  lowercase__    :   Any						= old['''decoder/decoder_norm/scale''']
  lowercase__    :   Optional[Any]						= old[
      '''decoder/relpos_bias/rel_embedding'''
  ].T

  # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
  if "decoder/logits_dense/kernel" in old:
   lowercase__    :   Dict						= old['''decoder/logits_dense/kernel'''].T

 return new
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   int						= collections.OrderedDict([(k, torch.from_numpy(v.copy()					)) for (k, v) in converted_params.items()]					)

 # Add what is missing.
 if "encoder.embed_tokens.weight" not in state_dict:
  lowercase__    :   Optional[int]						= state_dict['''shared.weight''']

 if not is_encoder_only:
  if "decoder.embed_tokens.weight" not in state_dict:
   lowercase__    :   List[Any]						= state_dict['''shared.weight''']

  if "lm_head.weight" not in state_dict:  # For old 1.0 models.
   print('''Using shared word embeddings as lm_head.'''					)
   lowercase__    :   Union[str, Any]						= state_dict['''shared.weight''']

 return state_dict
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   List[str]						= checkpoints.load_tax_checkpoint(UpperCAmelCase					)
 lowercase__    :   str						= convert_tax_to_pytorch(UpperCAmelCase		,					num_layers=config.num_layers		,					is_encoder_only=UpperCAmelCase					)
 lowercase__    :   List[Any]						= make_state_dict(UpperCAmelCase		,					UpperCAmelCase					)
 model.load_state_dict(UpperCAmelCase		,					strict=UpperCAmelCase					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = False					):
 lowercase__    :   Dict						= TaConfig.from_json_file(UpperCAmelCase					)
 print(F"""Building PyTorch model from configuration: {config}"""					)
 # Non-v1.1 checkpoints could also use T5Model, but this works for all.
 # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
 if is_encoder_only:
  lowercase__    :   List[str]						= TaEncoderModel(UpperCAmelCase					)
 else:
  lowercase__    :   Tuple						= TaForConditionalGeneration(UpperCAmelCase					)

 # Load weights from tf checkpoint
 load_tax_weights_in_ta(UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					)

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
 model.save_pretrained(UpperCAmelCase					)

 # Verify that we can load the checkpoint.
 model.from_pretrained(UpperCAmelCase					)
 print('''Done'''					)


if __name__ == "__main__":
  __a:			Optional[Any]	       =       argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
  # Required parameters
  parser.add_argument(
      """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  parser.add_argument(
      """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
  )
  __a:			Tuple	       =       parser.parse_args()
  convert_tax_checkpoint_to_pytorch(
      args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
  )

