


'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__A   :    Optional[Any]											= logging.get_logger(__name__)







def 							UpperCamelCase_     (       A__   :  Any   ):
  '''simple docstring'''





  lowerCAmelCase_     : List[Any]     =  DPTConfig()

  if "large" in checkpoint_url:
    lowerCAmelCase_     : List[str]     =  10_24
    lowerCAmelCase_     : Optional[Any]     =  40_96
    lowerCAmelCase_     : Dict     =  24
    lowerCAmelCase_     : List[Any]     =  16
    lowerCAmelCase_     : List[str]     =  [5, 11, 17, 23]
    lowerCAmelCase_     : Tuple     =  [2_56, 5_12, 10_24, 10_24]
    lowerCAmelCase_     : Any     =  (1, 3_84, 3_84)

  if "ade" in checkpoint_url:
    lowerCAmelCase_     : Any     =  True

    lowerCAmelCase_     : List[Any]     =  1_50
    lowerCAmelCase_     : int     =  """huggingface/label-files"""
    lowerCAmelCase_     : Optional[Any]     =  """ade20k-id2label.json"""
    lowerCAmelCase_     : Dict     =  json.load(open(cached_download(hf_hub_url(A__				,       A__				,       repo_type="""dataset"""   )   )				,       """r"""   )   )
    lowerCAmelCase_     : int     =  {int(A__   ): v for k, v in idalabel.items()}
    lowerCAmelCase_     : Optional[Any]     =  idalabel
    lowerCAmelCase_     : Optional[int]     =  {v: k for k, v in idalabel.items()}
    lowerCAmelCase_     : Dict     =  [1, 1_50, 4_80, 4_80]

  return config, expected_shape







def 							UpperCamelCase_     (       A__   :  Any   ):
  '''simple docstring'''





  lowerCAmelCase_     : Union[str, Any]     =  ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
  for k in ignore_keys:
    state_dict.pop(A__				,       A__   )







def 							UpperCamelCase_     (       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  if (
      "pretrained.model" in name
      and "cls_token" not in name
      and "pos_embed" not in name
      and "patch_embed" not in name
  ):
    lowerCAmelCase_     : str     =  name.replace("""pretrained.model"""				,       """dpt.encoder"""   )
  if "pretrained.model" in name:
    lowerCAmelCase_     : str     =  name.replace("""pretrained.model"""				,       """dpt.embeddings"""   )
  if "patch_embed" in name:
    lowerCAmelCase_     : Optional[int]     =  name.replace("""patch_embed"""				,       """patch_embeddings"""   )
  if "pos_embed" in name:
    lowerCAmelCase_     : Any     =  name.replace("""pos_embed"""				,       """position_embeddings"""   )
  if "attn.proj" in name:
    lowerCAmelCase_     : Any     =  name.replace("""attn.proj"""				,       """attention.output.dense"""   )
  if "proj" in name and "project" not in name:
    lowerCAmelCase_     : Union[str, Any]     =  name.replace("""proj"""				,       """projection"""   )
  if "blocks" in name:
    lowerCAmelCase_     : List[str]     =  name.replace("""blocks"""				,       """layer"""   )
  if "mlp.fc1" in name:
    lowerCAmelCase_     : Optional[Any]     =  name.replace("""mlp.fc1"""				,       """intermediate.dense"""   )
  if "mlp.fc2" in name:
    lowerCAmelCase_     : Dict     =  name.replace("""mlp.fc2"""				,       """output.dense"""   )
  if "norm1" in name:
    lowerCAmelCase_     : Optional[int]     =  name.replace("""norm1"""				,       """layernorm_before"""   )
  if "norm2" in name:
    lowerCAmelCase_     : Optional[int]     =  name.replace("""norm2"""				,       """layernorm_after"""   )
  if "scratch.output_conv" in name:
    lowerCAmelCase_     : Tuple     =  name.replace("""scratch.output_conv"""				,       """head"""   )
  if "scratch" in name:
    lowerCAmelCase_     : int     =  name.replace("""scratch"""				,       """neck"""   )
  if "layer1_rn" in name:
    lowerCAmelCase_     : str     =  name.replace("""layer1_rn"""				,       """convs.0"""   )
  if "layer2_rn" in name:
    lowerCAmelCase_     : Tuple     =  name.replace("""layer2_rn"""				,       """convs.1"""   )
  if "layer3_rn" in name:
    lowerCAmelCase_     : Any     =  name.replace("""layer3_rn"""				,       """convs.2"""   )
  if "layer4_rn" in name:
    lowerCAmelCase_     : Optional[Any]     =  name.replace("""layer4_rn"""				,       """convs.3"""   )
  if "refinenet" in name:
    lowerCAmelCase_     : Union[str, Any]     =  int(name[len("""neck.refinenet"""   ) : len("""neck.refinenet"""   ) + 1]   )
    # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
    lowerCAmelCase_     : Optional[Any]     =  name.replace(f'refinenet{layer_idx}'				,       f'fusion_stage.layers.{abs(layer_idx-4   )}'   )
  if "out_conv" in name:
    lowerCAmelCase_     : Any     =  name.replace("""out_conv"""				,       """projection"""   )
  if "resConfUnit1" in name:
    lowerCAmelCase_     : Tuple     =  name.replace("""resConfUnit1"""				,       """residual_layer1"""   )
  if "resConfUnit2" in name:
    lowerCAmelCase_     : str     =  name.replace("""resConfUnit2"""				,       """residual_layer2"""   )
  if "conv1" in name:
    lowerCAmelCase_     : Tuple     =  name.replace("""conv1"""				,       """convolution1"""   )
  if "conv2" in name:
    lowerCAmelCase_     : Optional[int]     =  name.replace("""conv2"""				,       """convolution2"""   )
  # readout blocks
  if "pretrained.act_postprocess1.0.project.0" in name:
    lowerCAmelCase_     : Any     =  name.replace("""pretrained.act_postprocess1.0.project.0"""				,       """neck.reassemble_stage.readout_projects.0.0"""   )
  if "pretrained.act_postprocess2.0.project.0" in name:
    lowerCAmelCase_     : Tuple     =  name.replace("""pretrained.act_postprocess2.0.project.0"""				,       """neck.reassemble_stage.readout_projects.1.0"""   )
  if "pretrained.act_postprocess3.0.project.0" in name:
    lowerCAmelCase_     : Union[str, Any]     =  name.replace("""pretrained.act_postprocess3.0.project.0"""				,       """neck.reassemble_stage.readout_projects.2.0"""   )
  if "pretrained.act_postprocess4.0.project.0" in name:
    lowerCAmelCase_     : str     =  name.replace("""pretrained.act_postprocess4.0.project.0"""				,       """neck.reassemble_stage.readout_projects.3.0"""   )
  # resize blocks
  if "pretrained.act_postprocess1.3" in name:
    lowerCAmelCase_     : Any     =  name.replace("""pretrained.act_postprocess1.3"""				,       """neck.reassemble_stage.layers.0.projection"""   )
  if "pretrained.act_postprocess1.4" in name:
    lowerCAmelCase_     : Optional[Any]     =  name.replace("""pretrained.act_postprocess1.4"""				,       """neck.reassemble_stage.layers.0.resize"""   )
  if "pretrained.act_postprocess2.3" in name:
    lowerCAmelCase_     : Tuple     =  name.replace("""pretrained.act_postprocess2.3"""				,       """neck.reassemble_stage.layers.1.projection"""   )
  if "pretrained.act_postprocess2.4" in name:
    lowerCAmelCase_     : List[str]     =  name.replace("""pretrained.act_postprocess2.4"""				,       """neck.reassemble_stage.layers.1.resize"""   )
  if "pretrained.act_postprocess3.3" in name:
    lowerCAmelCase_     : List[str]     =  name.replace("""pretrained.act_postprocess3.3"""				,       """neck.reassemble_stage.layers.2.projection"""   )
  if "pretrained.act_postprocess4.3" in name:
    lowerCAmelCase_     : Dict     =  name.replace("""pretrained.act_postprocess4.3"""				,       """neck.reassemble_stage.layers.3.projection"""   )
  if "pretrained.act_postprocess4.4" in name:
    lowerCAmelCase_     : Optional[int]     =  name.replace("""pretrained.act_postprocess4.4"""				,       """neck.reassemble_stage.layers.3.resize"""   )
  if "pretrained" in name:
    lowerCAmelCase_     : List[str]     =  name.replace("""pretrained"""				,       """dpt"""   )
  if "bn" in name:
    lowerCAmelCase_     : Dict     =  name.replace("""bn"""				,       """batch_norm"""   )
  if "head" in name:
    lowerCAmelCase_     : Union[str, Any]     =  name.replace("""head"""				,       """head.head"""   )
  if "encoder.norm" in name:
    lowerCAmelCase_     : Dict     =  name.replace("""encoder.norm"""				,       """layernorm"""   )
  if "auxlayer" in name:
    lowerCAmelCase_     : Union[str, Any]     =  name.replace("""auxlayer"""				,       """auxiliary_head.head"""   )

  return name







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  Optional[Any]   ):
  '''simple docstring'''





  for i in range(config.num_hidden_layers   ):
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    lowerCAmelCase_     : Optional[int]     =  state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'   )
    lowerCAmelCase_     : List[Any]     =  state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias'   )
    # next, add query, keys and values (in that order) to the state dict
    lowerCAmelCase_     : Union[str, Any]     =  in_proj_weight[: config.hidden_size, :]
    lowerCAmelCase_     : int     =  in_proj_bias[: config.hidden_size]
    lowerCAmelCase_     : List[str]     =  in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCAmelCase_     : int     =  in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    lowerCAmelCase_     : Optional[int]     =  in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCAmelCase_     : Dict     =  in_proj_bias[-config.hidden_size :]







def 							UpperCamelCase_     (       ):
  '''simple docstring'''





  lowerCAmelCase_     : Any     =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowerCAmelCase_     : Dict     =  Image.open(requests.get(A__				,       stream=A__   ).raw   )
  return im







@torch.no_grad()
def 							UpperCamelCase_     (       A__   :  Optional[Any]				,       A__   :  List[str]				,       A__   :  Tuple				,       A__   :  Optional[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_,				lowerCAmelCase_     : Any     =  get_dpt_config(A__   )
  # load original state_dict from URL
  lowerCAmelCase_     : List[Any]     =  torch.hub.load_state_dict_from_url(A__				,       map_location="""cpu"""   )
  # remove certain keys
  remove_ignore_keys_(A__   )
  # rename keys
  for key in state_dict.copy().keys():
    lowerCAmelCase_     : List[str]     =  state_dict.pop(A__   )
    lowerCAmelCase_     : List[Any]     =  val
  # read in qkv matrices
  read_in_q_k_v(A__				,       A__   )

  # load HuggingFace model
  lowerCAmelCase_     : List[str]     =  DPTForSemanticSegmentation(A__   ) if """ade""" in checkpoint_url else DPTForDepthEstimation(A__   )
  model.load_state_dict(A__   )
  model.eval()

  # Check outputs on an image
  lowerCAmelCase_     : Union[str, Any]     =  4_80 if """ade""" in checkpoint_url else 3_84
  lowerCAmelCase_     : Optional[Any]     =  DPTImageProcessor(size=A__   )

  lowerCAmelCase_     : Dict     =  prepare_img()
  lowerCAmelCase_     : Dict     =  image_processor(A__				,       return_tensors="""pt"""   )

  # forward pass
  lowerCAmelCase_     : str     =  model(**A__   ).logits if """ade""" in checkpoint_url else model(**A__   ).predicted_depth

  # Assert logits
  lowerCAmelCase_     : Dict     =  torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]   )
  if "ade" in checkpoint_url:
    lowerCAmelCase_     : Optional[Any]     =  torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]   )
  assert outputs.shape == torch.Size(A__   )
  assert (
      torch.allclose(outputs[0, 0, :3, :3]				,       A__				,       atol=1E-4   )
      if "ade" in checkpoint_url
      else torch.allclose(outputs[0, :3, :3]				,       A__   )
  )

  Path(A__   ).mkdir(exist_ok=A__   )
  print(f'Saving model to {pytorch_dump_folder_path}'   )
  model.save_pretrained(A__   )
  print(f'Saving image processor to {pytorch_dump_folder_path}'   )
  image_processor.save_pretrained(A__   )

  if push_to_hub:
    print("""Pushing model to hub..."""   )
    model.push_to_hub(
        repo_path_or_name=Path(A__				,       A__   )				,       organization="""nielsr"""				,       commit_message="""Add model"""				,       use_temp_dir=A__				,       )
    image_processor.push_to_hub(
        repo_path_or_name=Path(A__				,       A__   )				,       organization="""nielsr"""				,       commit_message="""Add image processor"""				,       use_temp_dir=A__				,       )


if __name__ == "__main__":
  __A   :    List[str]											= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--checkpoint_url",
      default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
      type=str,
      help="URL of the original DPT checkpoint you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default=None,
      type=str,
      required=True,
      help="Path to the output PyTorch model directory.",
  )
  parser.add_argument(
      "--push_to_hub",
      action="store_true",
  )
  parser.add_argument(
      "--model_name",
      default="dpt-large",
      type=str,
      help="Name of the model, in case you're pushing to the hub.",
  )

  __A   :    Union[str, Any]											= parser.parse_args()
  convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







'''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,
)


__A   :    str											= logging.getLogger(__name__)






@dataclass
class   __snake_case			:







    """simple docstring"""



    lowercase  					=   field(
        metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
    lowercase  					=   field(
        default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Pretrained config name or path if not the same as model_name'})
    lowercase  					=   field(
        default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'})
    lowercase  					=   field(
        default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}      ,)
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Whether tp freeze the encoder.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Whether  to freeze the embeddings.'})







@dataclass
class   __snake_case			:







    """simple docstring"""



    lowercase  					=   field(
        metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
    lowercase  					=   field(
        default='summarization'      ,metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}      ,)
    lowercase  					=   field(
        default=10_24      ,metadata={
            'help': (
                'The maximum total input sequence length after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }      ,)
    lowercase  					=   field(
        default=1_28      ,metadata={
            'help': (
                'The maximum total sequence length for target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }      ,)
    lowercase  					=   field(
        default=1_42      ,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``.'
            )
        }      ,)
    lowercase  					=   field(
        default=1_42      ,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.'
            )
        }      ,)
    lowercase  					=   field(default=-1      ,metadata={'help': '# training examples. -1 means use all.'})
    lowercase  					=   field(default=-1      ,metadata={'help': '# validation examples. -1 means use all.'})
    lowercase  					=   field(default=-1      ,metadata={'help': '# test examples. -1 means use all.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Source language id for translation.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Target language id for translation.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': '# num_beams to use for evaluation.'})
    lowercase  					=   field(
        default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}      ,)







def 							UpperCamelCase_     (       A__   :  Tuple				,       A__   :  List[str]				,       A__   :  str   ):
  '''simple docstring'''





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







def 							UpperCamelCase_     (       ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  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.
    lowerCAmelCase_,				lowerCAmelCase_,				lowerCAmelCase_     : Optional[int]     =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
  else:
    lowerCAmelCase_,				lowerCAmelCase_,				lowerCAmelCase_     : Union[str, Any]     =  parser.parse_args_into_dataclasses()

  check_output_dir(A__   )

  # 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"""				,       A__   )

  # 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.

  lowerCAmelCase_     : List[Any]     =  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				,       )

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

  lowerCAmelCase_     : Any     =  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				,       )
  lowerCAmelCase_     : Union[str, Any]     =  AutoModelForSeqaSeqLM.from_pretrained(
      model_args.model_name_or_path				,       from_tf=""".ckpt""" in model_args.model_name_or_path				,       config=A__				,       cache_dir=model_args.cache_dir				,       )

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

  # set num_beams for evaluation
  if data_args.eval_beams is None:
    lowerCAmelCase_     : List[Any]     =  model.config.num_beams

  # set decoder_start_token_id for MBart
  if model.config.decoder_start_token_id is None and isinstance(A__				,       (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(A__				,       A__   ):
      lowerCAmelCase_     : List[str]     =  tokenizer.lang_code_to_id[data_args.tgt_lang]
    else:
      lowerCAmelCase_     : int     =  tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

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

  lowerCAmelCase_     : Any     =  SeqaSeqDataset

  # Get datasets
  lowerCAmelCase_     : List[str]     =  (
      dataset_class(
          A__				,       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
  )
  lowerCAmelCase_     : List[Any]     =  (
      dataset_class(
          A__				,       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
  )
  lowerCAmelCase_     : List[Any]     =  (
      dataset_class(
          A__				,       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
  lowerCAmelCase_     : Dict     =  (
      build_compute_metrics_fn(data_args.task				,       A__   ) if training_args.predict_with_generate else None
  )
  lowerCAmelCase_     : str     =  SeqaSeqTrainer(
      model=A__				,       args=A__				,       data_args=A__				,       train_dataset=A__				,       eval_dataset=A__				,       data_collator=SeqaSeqDataCollator(
          A__				,       A__				,       model.config.decoder_start_token_id				,       training_args.tpu_num_cores   )				,       compute_metrics=A__				,       tokenizer=A__				,       )

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

    lowerCAmelCase_     : Optional[int]     =  trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
    lowerCAmelCase_     : Union[str, Any]     =  train_result.metrics
    lowerCAmelCase_     : Optional[int]     =  data_args.n_train

    trainer.save_model()  # this also saves the tokenizer

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

      # 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 ***"""   )

    lowerCAmelCase_     : Any     =  trainer.evaluate(metric_key_prefix="""val"""   )
    lowerCAmelCase_     : Optional[int]     =  data_args.n_val
    lowerCAmelCase_     : Any     =  round(metrics["""val_loss"""]				,       4   )

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

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

    lowerCAmelCase_     : Union[str, Any]     =  trainer.predict(test_dataset=A__				,       metric_key_prefix="""test"""   )
    lowerCAmelCase_     : Optional[int]     =  test_output.metrics
    lowerCAmelCase_     : List[Any]     =  data_args.n_test

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

      if training_args.predict_with_generate:
        lowerCAmelCase_     : int     =  tokenizer.batch_decode(
            test_output.predictions				,       skip_special_tokens=A__				,       clean_up_tokenization_spaces=A__   )
        lowerCAmelCase_     : List[Any]     =  lmap(str.strip				,       A__   )
        write_txt_file(A__				,       os.path.join(training_args.output_dir				,       """test_generations.txt"""   )   )

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

  return all_metrics







def 							UpperCamelCase_     (       A__   :  Optional[int]   ):
  '''simple docstring'''





  main()


if __name__ == "__main__":
  main()



