


import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
   import bitsandbytes as bnb

from copy import deepcopy


UpperCAmelCase_      : Any  					=       logging.getLogger(__name__)







def        SCREAMING_SNAKE_CASE_						(			__A :			torch.nn.Module  ,    __A :			BnbQuantizationConfig  ,    __A :			Union[str, os.PathLike] = None  ,    __A :			Optional[Dict[str, Union[int, str, torch.device]]] = None  ,    __A :			Optional[List[str]] = None  ,    __A :			Optional[Dict[Union[int, str], Union[int, str]]] = None  ,    __A :			Optional[Union[str, os.PathLike]] = None  ,    __A :			bool = False  ,    )	->		Dict:

       """simple docstring"""

       a_					:		Optional[int]							=  bnb_quantization_config.load_in_abit
       a_					:		Any							=  bnb_quantization_config.load_in_abit

       if load_in_abit and not is_abit_bnb_available():
              raise ImportError(
                  'You have a version of `bitsandbytes` that is not compatible with 8bit quantization,'
                  ' make sure you have the latest version of `bitsandbytes` installed.'				)
       if load_in_abit and not is_abit_bnb_available():
              raise ValueError(
                  'You have a version of `bitsandbytes` that is not compatible with 4bit quantization,'
                  'make sure you have the latest version of `bitsandbytes` installed.'				)

       a_					:		List[Any]							=  []
       # custom device map
       if isinstance(__A  ,    __A				) and len(device_map.keys()				) > 1:
              a_					:		Optional[int]							=  [key for key, value in device_map.items() if value in ['disk', 'cpu']]

       # We keep some modules such as the lm_head in their original dtype for numerical stability reasons
       if bnb_quantization_config.skip_modules is None:
              a_					:		List[str]							=  get_keys_to_not_convert(__A				)

       # add cpu modules to skip modules only for 4-bit modules
       if load_in_abit:
              bnb_quantization_config.skip_modules.extend(__A				)
       a_					:		Union[str, Any]							=  bnb_quantization_config.skip_modules

       # We add the modules we want to keep in full precision
       if bnb_quantization_config.keep_in_fpaa_modules is None:
              a_					:		List[Any]							=  []
       a_					:		str							=  bnb_quantization_config.keep_in_fpaa_modules
       modules_to_not_convert.extend(__A				)

       # compatibility with peft
       a_					:		Any							=  load_in_abit
       a_					:		List[Any]							=  load_in_abit

       a_					:		List[Any]							=  get_parameter_device(__A				)
       if model_device.type != "meta":
              # quantization of an already loaded model
              logger.warning(
                  'It is not recommended to quantize a loaded model. '
                  'The model should be instantiated under the `init_empty_weights` context manager.'				)
              a_					:		Any							=  replace_with_bnb_layers(__A  ,    __A  ,    modules_to_not_convert=__A				)
              # convert param to the right dtype
              a_					:		Union[str, Any]							=  bnb_quantization_config.torch_dtype
              for name, param in model.state_dict().items():
                     if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules				):
                            param.to(torch.floataa				)
                            if param.dtype != torch.floataa:
                                   a_					:		int							=  name.replace('.weight'  ,    ''				).replace('.bias'  ,    ''				)
                                   a_					:		int							=  getattr(__A  ,    __A  ,    __A				)
                                   if param is not None:
                                          param.to(torch.floataa				)
                     elif torch.is_floating_point(__A				):
                            param.to(__A				)
              if model_device.type == "cuda":
                     # move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
                     model.cuda(torch.cuda.current_device()				)
                     torch.cuda.empty_cache()
              elif torch.cuda.is_available():
                     model.to(torch.cuda.current_device()				)
              else:
                     raise RuntimeError('No GPU found. A GPU is needed for quantization.'				)
              logger.info(
                  F"""The model device type is {model_device.type}. However, cuda is needed for quantization."""
                  'We move the model to cuda.'				)
              return model

       elif weights_location is None:
              raise RuntimeError(
                  F"""`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} """				)

       else:
              with init_empty_weights():
                     a_					:		List[Any]							=  replace_with_bnb_layers(
                         __A  ,    __A  ,    modules_to_not_convert=__A				)

              a_					:		Any							=  get_quantized_model_device_map(
                  __A  ,    __A  ,    __A  ,    max_memory=__A  ,    no_split_module_classes=__A  ,    )
              if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
                     a_					:		Dict							=  True

              a_					:		Any							=  any(x in list(device_map.values()				) for x in ['cpu', 'disk']				)

              load_checkpoint_in_model(
                  __A  ,    __A  ,    __A  ,    dtype=bnb_quantization_config.torch_dtype  ,    offload_folder=__A  ,    offload_state_dict=__A  ,    keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules  ,    offload_abit_bnb=load_in_abit and offload  ,    )
              return dispatch_model(__A  ,    device_map=__A  ,    offload_dir=__A				)







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]  ,    __A :			Dict  ,    __A :			List[Any]=None  ,    __A :			Union[str, Any]=None  ,    __A :			Optional[Any]=None				)	->		str:

       """simple docstring"""

       if device_map is None:
              if torch.cuda.is_available():
                     a_					:		Dict							=  {'': torch.cuda.current_device()}
              else:
                     raise RuntimeError('No GPU found. A GPU is needed for quantization.'				)
              logger.info('The device_map was not initialized.' 'Setting device_map to `{\'\':torch.cuda.current_device()}`.'				)

       if isinstance(__A  ,    __A				):
              if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
                     raise ValueError(
                         'If passing a string for `device_map`, please choose \'auto\', \'balanced\', \'balanced_low_0\' or '
                         '\'sequential\'.'				)

              a_					:		Any							=  {}
              special_dtypes.update(
                  {
                      name: bnb_quantization_config.torch_dtype
                      for name, _ in model.named_parameters()
                      if any(m in name for m in bnb_quantization_config.skip_modules				)
                  }				)
              special_dtypes.update(
                  {
                      name: torch.floataa
                      for name, _ in model.named_parameters()
                      if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules				)
                  }				)

              a_					:		Optional[Any]							=  {}
              a_					:		Union[str, Any]							=  special_dtypes
              a_					:		Optional[int]							=  no_split_module_classes
              a_					:		str							=  bnb_quantization_config.target_dtype

              # get max_memory for each device.
              if device_map != "sequential":
                     a_					:		Optional[Any]							=  get_balanced_memory(
                         __A  ,    low_zero=(device_map == 'balanced_low_0')  ,    max_memory=__A  ,    **__A  ,    )

              a_					:		int							=  max_memory
              a_					:		Optional[int]							=  infer_auto_device_map(__A  ,    **__A				)

       if isinstance(__A  ,    __A				):
              # check if don't have any quantized module on the cpu
              a_					:		str							=  bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

              a_					:		Optional[Any]							=  {
                  key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
              }
              for device in ["cpu", "disk"]:
                     if device in device_map_without_some_modules.values():
                            if bnb_quantization_config.load_in_abit:
                                   raise ValueError(
                                       '\n                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n                        these modules in `torch_dtype`, you need to pass a custom `device_map` to\n                        `load_and_quantize_model`. Check\n                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n                        for more details.\n                        '				)
                            else:
                                   logger.info(
                                       'Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit'				)
              del device_map_without_some_modules
       return device_map







def        SCREAMING_SNAKE_CASE_						(			__A :			Tuple  ,    __A :			List[str]  ,    __A :			List[Any]=None  ,    __A :			List[Any]=None				)	->		Any:

       """simple docstring"""

       if modules_to_not_convert is None:
              a_					:		Union[str, Any]							=  []

       a_							,		a_					:		List[str]							=  _replace_with_bnb_layers(
           __A  ,    __A  ,    __A  ,    __A				)
       if not has_been_replaced:
              logger.warning(
                  'You are loading your model in 8bit or 4bit but no linear modules were found in your model.'
                  ' this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers.'
                  ' Please double check your model architecture, or submit an issue on github if you think this is'
                  ' a bug.'				)
       return model







def        SCREAMING_SNAKE_CASE_						(			__A :			int  ,    __A :			int  ,    __A :			Tuple=None  ,    __A :			Any=None  ,    )	->		Any:

       """simple docstring"""

       a_					:		int							=  False
       for name, module in model.named_children():
              if current_key_name is None:
                     a_					:		List[str]							=  []
              current_key_name.append(__A				)
              if isinstance(__A  ,    nn.Linear				) and name not in modules_to_not_convert:
                     # Check if the current key is not in the `modules_to_not_convert`
                     a_					:		Union[str, Any]							=  '.'.join(__A				)
                     a_					:		Any							=  True
                     for key in modules_to_not_convert:
                            if (
                                (key in current_key_name_str) and (key + "." in current_key_name_str)
                            ) or key == current_key_name_str:
                                   a_					:		int							=  False
                                   break
                     if proceed:
                            # Load bnb module with empty weight and replace ``nn.Linear` module
                            if bnb_quantization_config.load_in_abit:
                                   a_					:		Union[str, Any]							=  bnb.nn.LinearabitLt(
                                       module.in_features  ,    module.out_features  ,    module.bias is not None  ,    has_fpaa_weights=__A  ,    threshold=bnb_quantization_config.llm_inta_threshold  ,    )
                            elif bnb_quantization_config.load_in_abit:
                                   a_					:		Optional[int]							=  bnb.nn.Linearabit(
                                       module.in_features  ,    module.out_features  ,    module.bias is not None  ,    bnb_quantization_config.bnb_abit_compute_dtype  ,    compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant  ,    quant_type=bnb_quantization_config.bnb_abit_quant_type  ,    )
                            else:
                                   raise ValueError('load_in_8bit and load_in_4bit can\'t be both False'				)
                            a_					:		Union[str, Any]							=  module.weight.data
                            if module.bias is not None:
                                   a_					:		str							=  module.bias.data
                            bnb_module.requires_grad_(__A				)
                            setattr(__A  ,    __A  ,    __A				)
                            a_					:		int							=  True
              if len(list(module.children()				)				) > 0:
                     a_							,		a_					:		List[str]							=  _replace_with_bnb_layers(
                         __A  ,    __A  ,    __A  ,    __A				)
                     a_					:		List[str]							=  has_been_replaced | _has_been_replaced
              # Remove the last key for recursion
              current_key_name.pop(-1				)
       return model, has_been_replaced







def        SCREAMING_SNAKE_CASE_						(			__A :			Tuple				)	->		Optional[int]:

       """simple docstring"""

       with init_empty_weights():
              a_					:		List[str]							=  deepcopy(__A				)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

       a_					:		List[str]							=  find_tied_parameters(__A				)
       # For compatibility with Accelerate < 0.18
       if isinstance(__A  ,    __A				):
              a_					:		Union[str, Any]							=  sum(list(tied_params.values()				)  ,    []				) + list(tied_params.keys()				)
       else:
              a_					:		Any							=  sum(__A  ,    []				)
       a_					:		Optional[int]							=  len(__A				) > 0

       # Check if it is a base model
       a_					:		Union[str, Any]							=  False
       if hasattr(__A  ,    'base_model_prefix'				):
              a_					:		Union[str, Any]							=  not hasattr(__A  ,    model.base_model_prefix				)

       # Ignore this for base models (BertModel, GPT2Model, etc.)
       if (not has_tied_params) and is_base_model:
              return []

       # otherwise they have an attached head
       a_					:		Tuple							=  list(model.named_children()				)
       a_					:		str							=  [list_modules[-1][0]]

       # add last module together with tied weights
       a_					:		List[str]							=  set(__A				) - set(__A				)
       a_					:		Dict							=  list(set(__A				)				) + list(__A				)

       # remove ".weight" from the keys
       a_					:		List[str]							=  ['.weight', '.bias']
       a_					:		List[Any]							=  []
       for name in list_untouched:
              for name_to_remove in names_to_remove:
                     if name_to_remove in name:
                            a_					:		Dict							=  name.replace(__A  ,    ''				)
              filtered_module_names.append(__A				)

       return filtered_module_names







def        SCREAMING_SNAKE_CASE_						(			__A :			Any				)	->		Any:

       """simple docstring"""

       for m in model.modules():
              if isinstance(__A  ,    bnb.nn.Linearabit				):
                     return True
       return False







def        SCREAMING_SNAKE_CASE_						(			__A :			nn.Module				)	->		Union[str, Any]:

       """simple docstring"""

       return next(parameter.parameters()				).device







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[int]  ,    __A :			int  ,    __A :			Optional[Any]  ,    __A :			List[str]  ,    __A :			Dict  ,    __A :			Tuple  ,    __A :			Optional[Any]				)	->		Dict:

       """simple docstring"""

       if fpaa_statistics is None:
              set_module_tensor_to_device(__A  ,    __A  ,    0  ,    dtype=__A  ,    value=__A				)
              a_					:		Optional[int]							=  param_name
              a_					:		List[str]							=  model
              if "." in tensor_name:
                     a_					:		int							=  tensor_name.split('.'				)
                     for split in splits[:-1]:
                            a_					:		int							=  getattr(__A  ,    __A				)
                            if new_module is None:
                                   raise ValueError(F"""{module} has no attribute {split}."""				)
                            a_					:		Optional[Any]							=  new_module
                     a_					:		List[str]							=  splits[-1]
              # offload weights
              a_					:		Union[str, Any]							=  False
              offload_weight(module._parameters[tensor_name]  ,    __A  ,    __A  ,    index=__A				)
              if hasattr(module._parameters[tensor_name]  ,    'SCB'				):
                     offload_weight(
                         module._parameters[tensor_name].SCB  ,    param_name.replace('weight'  ,    'SCB'				)  ,    __A  ,    index=__A  ,    )
       else:
              offload_weight(__A  ,    __A  ,    __A  ,    index=__A				)
              offload_weight(__A  ,    param_name.replace('weight'  ,    'SCB'				)  ,    __A  ,    index=__A				)

       set_module_tensor_to_device(__A  ,    __A  ,    'meta'  ,    dtype=__A  ,    value=torch.empty(*param.size()				)				)









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 		SCREAMING_SNAKE_CASE__				:
      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 		SCREAMING_SNAKE_CASE__				:
      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=1_024						,      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__    (							snake_case_	:  List[Any]       ,      snake_case_	:  List[str]       ,      snake_case_	:  Dict       )      ->				str:
   logger.info(f"""***** {split} metrics *****"""       )
   for key in sorted(metrics.keys()       ):
      logger.info(f"""  {key} = {metrics[key]}"""       )
   save_json(snake_case_       ,      os.path.join(snake_case_       ,      f"""{split}_results.json"""       )       )






def lowerCamelCase__    (							)      ->				Optional[Any]:
   # 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.

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

   check_output_dir(snake_case_       )

   # 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'''       ,      snake_case_       )

   # 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.

   __snake_case							       =   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       ,      )

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

   __snake_case							       =   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       ,      )
   __snake_case							       =   AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path       ,      from_tf='''.ckpt''' in model_args.model_name_or_path       ,      config=snake_case_       ,      cache_dir=model_args.cache_dir       ,      )

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

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

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

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

   __snake_case							       =   SeqaSeqDataset

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

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

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

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics('''train'''       ,      snake_case_       ,      training_args.output_dir       )
         all_metrics.update(snake_case_       )

         # 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 ***'''       )

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

      if trainer.is_world_process_zero():
         handle_metrics('''val'''       ,      snake_case_       ,      training_args.output_dir       )
         all_metrics.update(snake_case_       )

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

      __snake_case							       =   trainer.predict(test_dataset=snake_case_       ,      metric_key_prefix='''test'''       )
      __snake_case							       =   test_output.metrics
      __snake_case							       =   data_args.n_test

      if trainer.is_world_process_zero():
         __snake_case							       =   round(metrics['''test_loss''']       ,      4       )
         handle_metrics('''test'''       ,      snake_case_       ,      training_args.output_dir       )
         all_metrics.update(snake_case_       )

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

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

   return all_metrics






def lowerCamelCase__    (							snake_case_	:  Optional[Any]       )      ->				Tuple:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
    main()

