






"""simple docstring"""




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


UpperCamelCase							       =    logging.getLogger(__name__)




@dataclass
class    lowercase_    :
      A__	:		str       			=				field(
          metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}     )
      A__	:		Optional[str]       			=				field(
          default=_UpperCAmelCase,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}     )
      A__	:		Optional[str]       			=				field(
          default=_UpperCAmelCase,    metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}     )
      A__	:		Optional[str]       			=				field(
          default=_UpperCAmelCase,    metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''},    )
      A__	:		bool       			=				field(default=_UpperCAmelCase,    metadata={'''help''': '''Whether tp freeze the encoder.'''}     )
      A__	:		bool       			=				field(default=_UpperCAmelCase,    metadata={'''help''': '''Whether  to freeze the embeddings.'''}     )







@dataclass
class    lowercase_    :
      A__	:		str       			=				field(
          metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}     )
      A__	:		Optional[str]       			=				field(
          default='''summarization''',    metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''},    )
      A__	:		Optional[int]       			=				field(
          default=1024,    metadata={
              '''help''': (
                  '''The maximum total input sequence length after tokenization. Sequences longer '''
                  '''than this will be truncated, sequences shorter will be padded.'''
              )
          },    )
      A__	:		Optional[int]       			=				field(
          default=128,    metadata={
              '''help''': (
                  '''The maximum total sequence length for target text after tokenization. Sequences longer '''
                  '''than this will be truncated, sequences shorter will be padded.'''
              )
          },    )
      A__	:		Optional[int]       			=				field(
          default=142,    metadata={
              '''help''': (
                  '''The maximum total sequence length for validation target text after tokenization. Sequences longer '''
                  '''than this will be truncated, sequences shorter will be padded. '''
                  '''This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '''
                  '''during ``evaluate`` and ``predict``.'''
              )
          },    )
      A__	:		Optional[int]       			=				field(
          default=142,    metadata={
              '''help''': (
                  '''The maximum total sequence length for test target text after tokenization. Sequences longer '''
                  '''than this will be truncated, sequences shorter will be padded.'''
              )
          },    )
      A__	:		Optional[int]       			=				field(default=-1,    metadata={'''help''': '''# training examples. -1 means use all.'''}     )
      A__	:		Optional[int]       			=				field(default=-1,    metadata={'''help''': '''# validation examples. -1 means use all.'''}     )
      A__	:		Optional[int]       			=				field(default=-1,    metadata={'''help''': '''# test examples. -1 means use all.'''}     )
      A__	:		Optional[str]       			=				field(default=_UpperCAmelCase,    metadata={'''help''': '''Source language id for translation.'''}     )
      A__	:		Optional[str]       			=				field(default=_UpperCAmelCase,    metadata={'''help''': '''Target language id for translation.'''}     )
      A__	:		Optional[int]       			=				field(default=_UpperCAmelCase,    metadata={'''help''': '''# num_beams to use for evaluation.'''}     )
      A__	:		bool       			=				field(
          default=_UpperCAmelCase,    metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''},    )







def        lowerCAmelCase (      UpperCamelCase_:							Tuple     , UpperCamelCase_:							Any     , UpperCamelCase_:							Union[str, Any]					)       ->							Optional[int]:







  '''simple docstring'''







  logger.info(f'''***** {split} metrics *****'''					)
  for key in sorted(metrics.keys()					):
    logger.info(f'''  {key} = {metrics[key]}'''					)
  save_json(UpperCamelCase_     , os.path.join(UpperCamelCase_     , f'''{split}_results.json'''					)					)






def        lowerCAmelCase (      )       ->							List[str]:







  '''simple docstring'''







  _a							=							HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)					)

  if len(sys.argv					) == 2 and sys.argv[1].endswith(".json"					):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    _a			,	_a			,	_a							=							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
  else:
    _a			,	_a			,	_a							=							parser.parse_args_into_dataclasses()

  check_output_dir(UpperCamelCase_					)

  # Setup logging
  logging.basicConfig(
      format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"     , datefmt="%m/%d/%Y %H:%M:%S"     , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN     , )
  logger.warning(
      "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"     , training_args.local_rank     , training_args.device     , training_args.n_gpu     , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED					)     , training_args.fpaa     , )
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank					):
    transformers.utils.logging.set_verbosity_info()
  logger.info("Training/evaluation parameters %s"     , UpperCamelCase_					)

  # Set seed
  set_seed(training_args.seed					)

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  _a							=							AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path     , cache_dir=model_args.cache_dir     , )

  _a							=							("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
  for p in extra_model_params:
    if getattr(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					):
      assert hasattr(UpperCamelCase_     , UpperCamelCase_					), f'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
      setattr(UpperCamelCase_     , UpperCamelCase_     , getattr(UpperCamelCase_     , UpperCamelCase_					)					)

  _a							=							AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     , cache_dir=model_args.cache_dir     , )
  _a							=							AutoModelForSeqaSeqLM.from_pretrained(
      model_args.model_name_or_path     , from_tf=".ckpt" in model_args.model_name_or_path     , config=UpperCamelCase_     , cache_dir=model_args.cache_dir     , )

  # use task specific params
  use_task_specific_params(UpperCamelCase_     , data_args.task					)

  # set num_beams for evaluation
  if data_args.eval_beams is None:
    _a							=							model.config.num_beams

  # set decoder_start_token_id for MBart
  if model.config.decoder_start_token_id is None and isinstance(UpperCamelCase_     , (MBartTokenizer, MBartTokenizerFast)					):
    assert (
        data_args.tgt_lang is not None and data_args.src_lang is not None
    ), "mBart requires --tgt_lang and --src_lang"
    if isinstance(UpperCamelCase_     , UpperCamelCase_					):
      _a							=							tokenizer.lang_code_to_id[data_args.tgt_lang]
    else:
      _a							=							tokenizer.convert_tokens_to_ids(data_args.tgt_lang					)

  if model_args.freeze_embeds:
    freeze_embeds(UpperCamelCase_					)
  if model_args.freeze_encoder:
    freeze_params(model.get_encoder()					)
    assert_all_frozen(model.get_encoder()					)

  _a							=							SeqaSeqDataset

  # Get datasets
  _a							=							(
      dataset_class(
          UpperCamelCase_     , type_path="train"     , data_dir=data_args.data_dir     , n_obs=data_args.n_train     , max_target_length=data_args.max_target_length     , max_source_length=data_args.max_source_length     , prefix=model.config.prefix or ""     , )
      if training_args.do_train
      else None
  )
  _a							=							(
      dataset_class(
          UpperCamelCase_     , type_path="val"     , data_dir=data_args.data_dir     , n_obs=data_args.n_val     , max_target_length=data_args.val_max_target_length     , max_source_length=data_args.max_source_length     , prefix=model.config.prefix or ""     , )
      if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
      else None
  )
  _a							=							(
      dataset_class(
          UpperCamelCase_     , type_path="test"     , data_dir=data_args.data_dir     , n_obs=data_args.n_test     , max_target_length=data_args.test_max_target_length     , max_source_length=data_args.max_source_length     , prefix=model.config.prefix or ""     , )
      if training_args.do_predict
      else None
  )

  # Initialize our Trainer
  _a							=							(
      build_compute_metrics_fn(data_args.task     , UpperCamelCase_					) if training_args.predict_with_generate else None
  )
  _a							=							SeqaSeqTrainer(
      model=UpperCamelCase_     , args=UpperCamelCase_     , data_args=UpperCamelCase_     , train_dataset=UpperCamelCase_     , eval_dataset=UpperCamelCase_     , data_collator=SeqaSeqDataCollator(
          UpperCamelCase_     , UpperCamelCase_     , model.config.decoder_start_token_id     , training_args.tpu_num_cores					)     , compute_metrics=UpperCamelCase_     , tokenizer=UpperCamelCase_     , )

  _a							=							{}
  # Training
  if training_args.do_train:
    logger.info("*** Train ***"					)

    _a							=							trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
    _a							=							train_result.metrics
    _a							=							data_args.n_train

    trainer.save_model()  # this also saves the tokenizer

    if trainer.is_world_process_zero():
      handle_metrics("train"     , UpperCamelCase_     , training_args.output_dir					)
      all_metrics.update(UpperCamelCase_					)

      # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
      trainer.state.save_to_json(os.path.join(training_args.output_dir     , "trainer_state.json"					)					)

      # For convenience, we also re-save the tokenizer to the same directory,
      # so that you can share your model easily on huggingface.co/models =)
      tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
  if training_args.do_eval:
    logger.info("*** Evaluate ***"					)

    _a							=							trainer.evaluate(metric_key_prefix="val"					)
    _a							=							data_args.n_val
    _a							=							round(metrics["val_loss"]     , 4					)

    if trainer.is_world_process_zero():
      handle_metrics("val"     , UpperCamelCase_     , training_args.output_dir					)
      all_metrics.update(UpperCamelCase_					)

  if training_args.do_predict:
    logger.info("*** Predict ***"					)

    _a							=							trainer.predict(test_dataset=UpperCamelCase_     , metric_key_prefix="test"					)
    _a							=							test_output.metrics
    _a							=							data_args.n_test

    if trainer.is_world_process_zero():
      _a							=							round(metrics["test_loss"]     , 4					)
      handle_metrics("test"     , UpperCamelCase_     , training_args.output_dir					)
      all_metrics.update(UpperCamelCase_					)

      if training_args.predict_with_generate:
        _a							=							tokenizer.batch_decode(
            test_output.predictions     , skip_special_tokens=UpperCamelCase_     , clean_up_tokenization_spaces=UpperCamelCase_					)
        _a							=							lmap(str.strip     , UpperCamelCase_					)
        write_txt_file(UpperCamelCase_     , os.path.join(training_args.output_dir     , "test_generations.txt"					)					)

  if trainer.is_world_process_zero():
    save_json(UpperCamelCase_     , os.path.join(training_args.output_dir     , "all_results.json"					)					)

  return all_metrics






def        lowerCAmelCase (      UpperCamelCase_:							List[Any]					)       ->							Dict:







  '''simple docstring'''







  main()


if __name__ == "__main__":
     main()










"""simple docstring"""




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def        lowerCAmelCase (      UpperCamelCase_:							Dict					)       ->							Any:







  '''simple docstring'''







  _a							=	os.path.join(args.tf_model_dir     , "parameters.json"					)
  _a							=	json.loads(open(UpperCamelCase_					).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"					):
    _a							=	args.output + ".pt"
  _a							=	OrderedDict()
  with tf.device("/CPU:0"					):
    _a							=	tf.train.load_checkpoint(args.tf_model_dir					)
    _a							=	reader.get_variable_to_shape_map()
    for key_name in shapes.keys():
      _a							=	reader.get_tensor(UpperCamelCase_					).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"					):
          _a							=	int(key_name[9]					)
        elif key_name.startswith("pasts/out"					):
          _a							=	8
        _a							=	"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
        _a							=	vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
        _a							=	torch.tensor(UpperCamelCase_					)
      elif key_name.startswith("model/moe"					):
        _a							=	int(key_name[9:].split("/"					)[0]					)
        if key_name.endswith("/switch_gating/kernel"					):
          _a							=	"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
          _a							=	vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
          _a							=	torch.tensor(UpperCamelCase_					)
        elif key_name.endswith("/softmlp/kernel"					):
          _a							=	"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
          _a							=	vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
          _a							=	torch.tensor(UpperCamelCase_					)
        elif key_name.endswith("/wo/kernel"					) or key_name.endswith("/wi/kernel"					):
          _a							=	key_name[-9:-7]
          for i in range(16					):
            _a							=	"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
            _a							=	(
                vnp[i].transpose([1, 0]					).copy()
            )  # In Mesh-Tensorflow, it is one array, so it is divided
            _a							=	torch.tensor(UpperCamelCase_					)
      elif key_name.startswith("model/mlp"					):
        _a							=	int(key_name[9:].split("/"					)[0]					)
        if key_name.endswith("/p1/kernel"					):
          _a							=	"model.blocks.%d.feed_forward.mlp.wi.weight" % player
          _a							=	vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
          _a							=	torch.tensor(UpperCamelCase_					)
        elif key_name.endswith("/p1/bias"					):
          _a							=	"model.blocks.%d.feed_forward.mlp.wi.bias" % player
          _a							=	vnp.copy()  # same because it is one dimensional
          _a							=	torch.tensor(UpperCamelCase_					)
        elif key_name.endswith("/p2/kernel"					):
          _a							=	"model.blocks.%d.feed_forward.mlp.wo.weight" % player
          _a							=	vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
          _a							=	torch.tensor(UpperCamelCase_					)
        elif key_name.endswith("/p2/bias"					):
          _a							=	"model.blocks.%d.feed_forward.mlp.wo.bias" % player
          _a							=	vnp.copy()  # same because it is one dimensional
          _a							=	torch.tensor(UpperCamelCase_					)
      elif key_name.startswith("model/ln"					):
        _a							=	int(key_name[8:].split("/"					)[0]					)
        if key_name.endswith("/b"					):
          _a							=	"model.blocks.%d.feed_forward.norm.bias" % player
          _a							=	vnp.copy()  # same because it is one dimensional
          _a							=	torch.tensor(UpperCamelCase_					)
        elif key_name.endswith("/g"					):
          _a							=	"model.blocks.%d.feed_forward.norm.weight" % player
          _a							=	vnp.copy()  # same because it is one dimensional
          _a							=	torch.tensor(UpperCamelCase_					)
      elif key_name.startswith("model/att"					):
        _a							=	int(key_name[9:].split("/"					)[0]					)
        if key_name.endswith("/qkv/kernel"					):
          _a							=	vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
          _a							=	state[:, 0, :, :]
          _a							=	state[:, 1, :, :]
          _a							=	state[:, 2, :, :]
          _a							=	(
              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
          _a							=	(
              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
          _a							=	(
              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
          _a							=	"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
          _a							=	torch.tensor(UpperCamelCase_					)
          _a							=	"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
          _a							=	torch.tensor(UpperCamelCase_					)
          _a							=	"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
          _a							=	torch.tensor(UpperCamelCase_					)
        elif key_name.endswith("/o/kernel"					):
          _a							=	"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
          _a							=	(
              vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]					).transpose([1, 0]					).copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          _a							=	torch.tensor(UpperCamelCase_					)
      elif key_name.startswith("model/an"					):
        _a							=	int(key_name[8:].split("/"					)[0]					)
        if key_name.endswith("/b"					):
          _a							=	"model.blocks.%d.self_attn.norm.bias" % player
          _a							=	vnp.copy()  # same because it is one dimensional
          _a							=	torch.tensor(UpperCamelCase_					)
        elif key_name.endswith("/g"					):
          _a							=	"model.blocks.%d.self_attn.norm.weight" % player
          _a							=	vnp.copy()  # same because it is one dimensional
          _a							=	torch.tensor(UpperCamelCase_					)
      elif (
          key_name.startswith("model/wte"					)
          or key_name.startswith("model/wpe"					)
          or key_name.startswith("model/ete"					)
      ):
        _a							=	{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
            key_name[-3:]
        ]
        _a							=	"model.%s.weight" % nlayer
        _a							=	vnp.copy()  # same in embedded
        _a							=	torch.tensor(UpperCamelCase_					)
        if key_name.startswith("model/wte"					):
          _a							=	"lm_head.weight"
          _a							=	vnp.copy()  # same in embedded
          _a							=	torch.tensor(UpperCamelCase_					)
      elif key_name.startswith("model/wob"					):
        _a							=	"final_logits_bias"
        _a							=	vnp.copy()  # same in embedded
        _a							=	state.reshape((1, -1)					)
        _a							=	torch.tensor(UpperCamelCase_					)
      elif key_name == "model/dense/kernel":
        _a							=	"model.last_project.weight"
        _a							=	vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
        _a							=	torch.tensor(UpperCamelCase_					)
      elif key_name == "model/dense_1/bias":
        _a							=	"model.last_project.bias"
        _a							=	vnp.copy()  # same because it is one dimensional
        _a							=	torch.tensor(UpperCamelCase_					)
  torch.save(UpperCamelCase_     , args.output					)


if __name__ == "__main__":
     UpperCamelCase							       =    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""")
     UpperCamelCase							       =    parser.parse_args()
     convert_tf_gptsan_to_pt(args)


