





from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


_snake_case           =			logging.get_logger(__name__)

# General docstring
_snake_case           =			"""RegNetConfig"""

# Base docstring
_snake_case           =			"""facebook/regnet-y-040"""
_snake_case           =			[1, 1088, 7, 7]

# Image classification docstring
_snake_case           =			"""facebook/regnet-y-040"""
_snake_case           =			"""tabby, tabby cat"""

_snake_case           =			[
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]





class  lowerCAmelCase							(       nn.Module ):







  def __init__(      self  :Dict   ,							_lowercase  :int   ,							_lowercase  :int   ,							_lowercase  :int = 3   ,							_lowercase  :int = 1   ,							_lowercase  :int = 1   ,							_lowercase  :Optional[str] = "relu"   ,							):






     '''simple docstring'''





     super().__init__()
     lowercase__			       =  nn.Convad(
         _lowercase   ,							_lowercase   ,							kernel_size=_lowercase   ,							stride=_lowercase   ,							padding=kernel_size // 2   ,							groups=_lowercase   ,							bias=_lowercase   ,							)
     lowercase__			       =  nn.BatchNormad(_lowercase )
     lowercase__			       =  ACTaFN[activation] if activation is not None else nn.Identity()







  def   UpperCAmelCase					(      self  :str   ,							_lowercase  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  self.convolution(_lowercase )
     lowercase__			       =  self.normalization(_lowercase )
     lowercase__			       =  self.activation(_lowercase )
     return hidden_state





class  lowerCAmelCase							(       nn.Module ):







  def __init__(      self  :Union[str, Any]   ,							_lowercase  :RegNetConfig ):






     '''simple docstring'''





     super().__init__()
     lowercase__			       =  RegNetConvLayer(
         config.num_channels   ,							config.embedding_size   ,							kernel_size=3   ,							stride=2   ,							activation=config.hidden_act )
     lowercase__			       =  config.num_channels







  def   UpperCAmelCase					(      self  :Tuple   ,							_lowercase  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  pixel_values.shape[1]
     if num_channels != self.num_channels:
        raise ValueError(
            "Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
     lowercase__			       =  self.embedder(_lowercase )
     return hidden_state





class  lowerCAmelCase							(       nn.Module ):







  def __init__(      self  :Union[str, Any]   ,							_lowercase  :int   ,							_lowercase  :int   ,							_lowercase  :int = 2 ):






     '''simple docstring'''





     super().__init__()
     lowercase__			       =  nn.Convad(_lowercase   ,							_lowercase   ,							kernel_size=1   ,							stride=_lowercase   ,							bias=_lowercase )
     lowercase__			       =  nn.BatchNormad(_lowercase )







  def   UpperCAmelCase					(      self  :str   ,							_lowercase  :Tensor ):






     '''simple docstring'''





     lowercase__			       =  self.convolution(_lowercase )
     lowercase__			       =  self.normalization(_lowercase )
     return hidden_state





class  lowerCAmelCase							(       nn.Module ):







  def __init__(      self  :List[str]   ,							_lowercase  :int   ,							_lowercase  :int ):






     '''simple docstring'''





     super().__init__()

     lowercase__			       =  nn.AdaptiveAvgPoolad((1, 1) )
     lowercase__			       =  nn.Sequential(
         nn.Convad(_lowercase   ,							_lowercase   ,							kernel_size=1 )   ,							nn.ReLU()   ,							nn.Convad(_lowercase   ,							_lowercase   ,							kernel_size=1 )   ,							nn.Sigmoid()   ,							)







  def   UpperCAmelCase					(      self  :Any   ,							_lowercase  :Tuple ):






     '''simple docstring'''





     lowercase__			       =  self.pooler(_lowercase )
     lowercase__			       =  self.attention(_lowercase )
     lowercase__			       =  hidden_state * attention
     return hidden_state





class  lowerCAmelCase							(       nn.Module ):







  def __init__(      self  :Tuple   ,							_lowercase  :RegNetConfig   ,							_lowercase  :int   ,							_lowercase  :int   ,							_lowercase  :int = 1 ):






     '''simple docstring'''





     super().__init__()
     lowercase__			       =  in_channels != out_channels or stride != 1
     lowercase__			       =  max(1   ,							out_channels // config.groups_width )
     lowercase__			       =  (
         RegNetShortCut(_lowercase   ,							_lowercase   ,							stride=_lowercase ) if should_apply_shortcut else nn.Identity()
     )
     lowercase__			       =  nn.Sequential(
         RegNetConvLayer(_lowercase   ,							_lowercase   ,							kernel_size=1   ,							activation=config.hidden_act )   ,							RegNetConvLayer(_lowercase   ,							_lowercase   ,							stride=_lowercase   ,							groups=_lowercase   ,							activation=config.hidden_act )   ,							RegNetConvLayer(_lowercase   ,							_lowercase   ,							kernel_size=1   ,							activation=_lowercase )   ,							)
     lowercase__			       =  ACTaFN[config.hidden_act]







  def   UpperCAmelCase					(      self  :int   ,							_lowercase  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  hidden_state
     lowercase__			       =  self.layer(_lowercase )
     lowercase__			       =  self.shortcut(_lowercase )
     hidden_state += residual
     lowercase__			       =  self.activation(_lowercase )
     return hidden_state





class  lowerCAmelCase							(       nn.Module ):







  def __init__(      self  :int   ,							_lowercase  :RegNetConfig   ,							_lowercase  :int   ,							_lowercase  :int   ,							_lowercase  :int = 1 ):






     '''simple docstring'''





     super().__init__()
     lowercase__			       =  in_channels != out_channels or stride != 1
     lowercase__			       =  max(1   ,							out_channels // config.groups_width )
     lowercase__			       =  (
         RegNetShortCut(_lowercase   ,							_lowercase   ,							stride=_lowercase ) if should_apply_shortcut else nn.Identity()
     )
     lowercase__			       =  nn.Sequential(
         RegNetConvLayer(_lowercase   ,							_lowercase   ,							kernel_size=1   ,							activation=config.hidden_act )   ,							RegNetConvLayer(_lowercase   ,							_lowercase   ,							stride=_lowercase   ,							groups=_lowercase   ,							activation=config.hidden_act )   ,							RegNetSELayer(_lowercase   ,							reduced_channels=int(round(in_channels / 4 ) ) )   ,							RegNetConvLayer(_lowercase   ,							_lowercase   ,							kernel_size=1   ,							activation=_lowercase )   ,							)
     lowercase__			       =  ACTaFN[config.hidden_act]







  def   UpperCAmelCase					(      self  :List[str]   ,							_lowercase  :List[Any] ):






     '''simple docstring'''





     lowercase__			       =  hidden_state
     lowercase__			       =  self.layer(_lowercase )
     lowercase__			       =  self.shortcut(_lowercase )
     hidden_state += residual
     lowercase__			       =  self.activation(_lowercase )
     return hidden_state





class  lowerCAmelCase							(       nn.Module ):







  def __init__(      self  :Tuple   ,							_lowercase  :RegNetConfig   ,							_lowercase  :int   ,							_lowercase  :int   ,							_lowercase  :int = 2   ,							_lowercase  :int = 2   ,							):






     '''simple docstring'''





     super().__init__()

     lowercase__			       =  RegNetXLayer if config.layer_type == "x" else RegNetYLayer

     lowercase__			       =  nn.Sequential(
         # downsampling is done in the first layer with stride of 2
         layer(
             _lowercase   ,							_lowercase   ,							_lowercase   ,							stride=_lowercase   ,							)   ,							*[layer(_lowercase   ,							_lowercase   ,							_lowercase ) for _ in range(depth - 1 )]   ,							)







  def   UpperCAmelCase					(      self  :int   ,							_lowercase  :Dict ):






     '''simple docstring'''





     lowercase__			       =  self.layers(_lowercase )
     return hidden_state





class  lowerCAmelCase							(       nn.Module ):







  def __init__(      self  :List[str]   ,							_lowercase  :RegNetConfig ):






     '''simple docstring'''





     super().__init__()
     lowercase__			       =  nn.ModuleList([] )
     # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
     self.stages.append(
         RegNetStage(
             _lowercase   ,							config.embedding_size   ,							config.hidden_sizes[0]   ,							stride=2 if config.downsample_in_first_stage else 1   ,							depth=config.depths[0]   ,							) )
     lowercase__			       =  zip(config.hidden_sizes   ,							config.hidden_sizes[1:] )
     for (in_channels, out_channels), depth in zip(_lowercase   ,							config.depths[1:] ):
        self.stages.append(RegNetStage(_lowercase   ,							_lowercase   ,							_lowercase   ,							depth=_lowercase ) )







  def   UpperCAmelCase					(      self  :Optional[int]   ,							_lowercase  :Tensor   ,							_lowercase  :bool = False   ,							_lowercase  :bool = True ):






     '''simple docstring'''





     lowercase__			       =  () if output_hidden_states else None

     for stage_module in self.stages:
        if output_hidden_states:
           lowercase__			       =  hidden_states + (hidden_state,)

        lowercase__			       =  stage_module(_lowercase )

     if output_hidden_states:
        lowercase__			       =  hidden_states + (hidden_state,)

     if not return_dict:
        return tuple(v for v in [hidden_state, hidden_states] if v is not None )

     return BaseModelOutputWithNoAttention(last_hidden_state=_lowercase   ,							hidden_states=_lowercase )





class  lowerCAmelCase							(       lowercase_ ):
  __lowerCamelCase		=      RegNetConfig
  __lowerCamelCase		=      'regnet'
  __lowerCamelCase		=      'pixel_values'
  __lowerCamelCase		=      True







  def   UpperCAmelCase					(      self  :Any   ,							_lowercase  :Optional[int] ):






     '''simple docstring'''





     if isinstance(_lowercase   ,							nn.Convad ):
        nn.init.kaiming_normal_(module.weight   ,							mode="fan_out"   ,							nonlinearity="relu" )
     elif isinstance(_lowercase   ,							(nn.BatchNormad, nn.GroupNorm) ):
        nn.init.constant_(module.weight   ,							1 )
        nn.init.constant_(module.bias   ,							0 )







  def   UpperCAmelCase					(      self  :Union[str, Any]   ,							_lowercase  :Optional[Any]   ,							_lowercase  :List[str]=False ):






     '''simple docstring'''





     if isinstance(_lowercase   ,							_lowercase ):
        lowercase__			       =  value
_snake_case           =			R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

_snake_case           =			R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""





@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.'							,							lowercase_							,							)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class  lowerCAmelCase							(       lowercase_ ):







  def __init__(      self  :List[Any]   ,							_lowercase  :Any ):






     '''simple docstring'''





     super().__init__(_lowercase )
     lowercase__			       =  config
     lowercase__			       =  RegNetEmbeddings(_lowercase )
     lowercase__			       =  RegNetEncoder(_lowercase )
     lowercase__			       =  nn.AdaptiveAvgPoolad((1, 1) )
     # Initialize weights and apply final processing
     self.post_init()







  @add_start_docstrings_to_model_forward(_lowercase )
  @add_code_sample_docstrings(
      checkpoint=_CHECKPOINT_FOR_DOC   ,							output_type=_lowercase   ,							config_class=_CONFIG_FOR_DOC   ,							modality="vision"   ,							expected_output=_EXPECTED_OUTPUT_SHAPE   ,							)
  def   UpperCAmelCase					(      self  :Tuple   ,							_lowercase  :Tensor   ,							_lowercase  :Optional[bool] = None   ,							_lowercase  :Optional[bool] = None ):






     '''simple docstring'''





     lowercase__			       =  (
         output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
     )
     lowercase__			       =  return_dict if return_dict is not None else self.config.use_return_dict

     lowercase__			       =  self.embedder(_lowercase )

     lowercase__			       =  self.encoder(
         _lowercase   ,							output_hidden_states=_lowercase   ,							return_dict=_lowercase )

     lowercase__			       =  encoder_outputs[0]

     lowercase__			       =  self.pooler(_lowercase )

     if not return_dict:
        return (last_hidden_state, pooled_output) + encoder_outputs[1:]

     return BaseModelOutputWithPoolingAndNoAttention(
         last_hidden_state=_lowercase   ,							pooler_output=_lowercase   ,							hidden_states=encoder_outputs.hidden_states   ,							)





@add_start_docstrings(
    '\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '							,							lowercase_							,							)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class  lowerCAmelCase							(       lowercase_ ):







  def __init__(      self  :List[Any]   ,							_lowercase  :List[Any] ):






     '''simple docstring'''





     super().__init__(_lowercase )
     lowercase__			       =  config.num_labels
     lowercase__			       =  RegNetModel(_lowercase )
     # classification head
     lowercase__			       =  nn.Sequential(
         nn.Flatten()   ,							nn.Linear(config.hidden_sizes[-1]   ,							config.num_labels ) if config.num_labels > 0 else nn.Identity()   ,							)
     # initialize weights and apply final processing
     self.post_init()







  @add_start_docstrings_to_model_forward(_lowercase )
  @add_code_sample_docstrings(
      checkpoint=_IMAGE_CLASS_CHECKPOINT   ,							output_type=_lowercase   ,							config_class=_CONFIG_FOR_DOC   ,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT   ,							)
  def   UpperCAmelCase					(      self  :int   ,							_lowercase  :Optional[torch.FloatTensor] = None   ,							_lowercase  :Optional[torch.LongTensor] = None   ,							_lowercase  :Optional[bool] = None   ,							_lowercase  :Optional[bool] = None   ,							):






     '''simple docstring'''





     lowercase__			       =  return_dict if return_dict is not None else self.config.use_return_dict

     lowercase__			       =  self.regnet(_lowercase   ,							output_hidden_states=_lowercase   ,							return_dict=_lowercase )

     lowercase__			       =  outputs.pooler_output if return_dict else outputs[1]

     lowercase__			       =  self.classifier(_lowercase )

     lowercase__			       =  None

     if labels is not None:
        if self.config.problem_type is None:
           if self.num_labels == 1:
              lowercase__			       =  "regression"
           elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
              lowercase__			       =  "single_label_classification"
           else:
              lowercase__			       =  "multi_label_classification"
        if self.config.problem_type == "regression":
           lowercase__			       =  MSELoss()
           if self.num_labels == 1:
              lowercase__			       =  loss_fct(logits.squeeze()   ,							labels.squeeze() )
           else:
              lowercase__			       =  loss_fct(_lowercase   ,							_lowercase )
        elif self.config.problem_type == "single_label_classification":
           lowercase__			       =  CrossEntropyLoss()
           lowercase__			       =  loss_fct(logits.view(-1   ,							self.num_labels )   ,							labels.view(-1 ) )
        elif self.config.problem_type == "multi_label_classification":
           lowercase__			       =  BCEWithLogitsLoss()
           lowercase__			       =  loss_fct(_lowercase   ,							_lowercase )

     if not return_dict:
        lowercase__			       =  (logits,) + outputs[2:]
        return (loss,) + output if loss is not None else output

     return ImageClassifierOutputWithNoAttention(loss=_lowercase   ,							logits=_lowercase   ,							hidden_states=outputs.hidden_states )













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,
)


_snake_case           =			logging.getLogger(__name__)





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





@dataclass
class  lowerCAmelCase							:
  __lowerCamelCase		=      field(
      metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
  __lowerCamelCase		=      field(
      default='summarization'							,							metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}							,							)
  __lowerCamelCase		=      field(
      default=1_024							,							metadata={
          'help': (
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )
      }							,							)
  __lowerCamelCase		=      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.'
          )
      }							,							)
  __lowerCamelCase		=      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``.'
          )
      }							,							)
  __lowerCamelCase		=      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.'
          )
      }							,							)
  __lowerCamelCase		=      field(default=-1							,							metadata={'help': '# training examples. -1 means use all.'} )
  __lowerCamelCase		=      field(default=-1							,							metadata={'help': '# validation examples. -1 means use all.'} )
  __lowerCamelCase		=      field(default=-1							,							metadata={'help': '# test examples. -1 means use all.'} )
  __lowerCamelCase		=      field(default=lowercase_							,							metadata={'help': 'Source language id for translation.'} )
  __lowerCamelCase		=      field(default=lowercase_							,							metadata={'help': 'Target language id for translation.'} )
  __lowerCamelCase		=      field(default=lowercase_							,							metadata={'help': '# num_beams to use for evaluation.'} )
  __lowerCamelCase		=      field(
      default=lowercase_							,							metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}							,							)


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


def _A       (   ):
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   lowercase__			       =  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.
      lowercase__       ,							lowercase__       ,							lowercase__			       =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
   else:
      lowercase__       ,							lowercase__       ,							lowercase__			       =  parser.parse_args_into_dataclasses()

   check_output_dir(__magic_name__	)

   # 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"  , __magic_name__	)

   # 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.

   lowercase__			       =  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  , )

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

   lowercase__			       =  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  , )
   lowercase__			       =  AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path  , from_tf=".ckpt" in model_args.model_name_or_path  , config=__magic_name__  , cache_dir=model_args.cache_dir  , )

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

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

   # set decoder_start_token_id for MBart
   if model.config.decoder_start_token_id is None and isinstance(__magic_name__  , (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(__magic_name__  , __magic_name__	):
         lowercase__			       =  tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         lowercase__			       =  tokenizer.convert_tokens_to_ids(data_args.tgt_lang	)

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

   lowercase__			       =  SeqaSeqDataset

   # Get datasets
   lowercase__			       =  (
       dataset_class(
           __magic_name__  , 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
   )
   lowercase__			       =  (
       dataset_class(
           __magic_name__  , 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
   )
   lowercase__			       =  (
       dataset_class(
           __magic_name__  , 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
   lowercase__			       =  (
       build_compute_metrics_fn(data_args.task  , __magic_name__	) if training_args.predict_with_generate else None
   )
   lowercase__			       =  SeqaSeqTrainer(
       model=__magic_name__  , args=__magic_name__  , data_args=__magic_name__  , train_dataset=__magic_name__  , eval_dataset=__magic_name__  , data_collator=SeqaSeqDataCollator(
           __magic_name__  , __magic_name__  , model.config.decoder_start_token_id  , training_args.tpu_num_cores	)  , compute_metrics=__magic_name__  , tokenizer=__magic_name__  , )

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

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

      trainer.save_model()  # this also saves the tokenizer

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

         # 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 ***"	)

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

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

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

      lowercase__			       =  trainer.predict(test_dataset=__magic_name__  , metric_key_prefix="test"	)
      lowercase__			       =  test_output.metrics
      lowercase__			       =  data_args.n_test

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

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

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

   return all_metrics


def _A       (   __magic_name__	):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
  main()






