


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,
)


SCREAMING_SNAKE_CASE							    =							logging.getLogger(__name__)


@dataclass
class     __UpperCAmelCase							:


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







@dataclass
class     __UpperCAmelCase							:


     """simple docstring"""
     _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=1024 ,					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=__A ,					metadata={"""help""": """Source language id for translation."""}							)
     _lowerCamelCase   =      field(default=__A ,					metadata={"""help""": """Target language id for translation."""}							)
     _lowerCamelCase   =      field(default=__A ,					metadata={"""help""": """# num_beams to use for evaluation."""}							)
     _lowerCamelCase   =      field(
         default=__A ,					metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""} ,					)







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

   __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(lowerCAmelCase__   )

   # 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"""		,				lowerCAmelCase__   )

   # 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(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
         assert hasattr(lowerCAmelCase__		,				lowerCAmelCase__   ), f'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
         setattr(lowerCAmelCase__		,				lowerCAmelCase__		,				getattr(lowerCAmelCase__		,				lowerCAmelCase__   )   )

   __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=lowerCAmelCase__		,				cache_dir=model_args.cache_dir		,				)

   # use task specific params
   use_task_specific_params(lowerCAmelCase__		,				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(lowerCAmelCase__		,				(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(lowerCAmelCase__		,				lowerCAmelCase__   ):
         __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(lowerCAmelCase__   )
   if model_args.freeze_encoder:
      freeze_params(model.get_encoder()   )
      assert_all_frozen(model.get_encoder()   )

   __a					=  SeqaSeqDataset

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

   __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"""		,				lowerCAmelCase__		,				training_args.output_dir   )
         all_metrics.update(lowerCAmelCase__   )

         # 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"""		,				lowerCAmelCase__		,				training_args.output_dir   )
         all_metrics.update(lowerCAmelCase__   )

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

      __a					=  trainer.predict(test_dataset=lowerCAmelCase__		,				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"""		,				lowerCAmelCase__		,				training_args.output_dir   )
         all_metrics.update(lowerCAmelCase__   )

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

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

   return all_metrics




def  a   (lowerCAmelCase__   ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
  main()







from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching





def 						_lowerCAmelCase							(     )    ->						Tuple:


						'''simple docstring'''






						import os as original_os
						from os import path as original_path
						from os import rename as original_rename
						from os.path import dirname as original_dirname
						from os.path import join as original_join

						assert _test_patching.os is original_os
						assert _test_patching.path is original_path
						assert _test_patching.join is original_join

						assert _test_patching.renamed_os is original_os
						assert _test_patching.renamed_path is original_path
						assert _test_patching.renamed_join is original_join

						__snake_case				=		"__test_patch_submodule_mock__"
						with patch_submodule(_test_patching   ,						"os.path.join"   ,						_lowerCAmelCase			):
												# Every way to access os.path.join must be patched, and the rest must stay untouched

												# check os.path.join
												assert isinstance(_test_patching.os   ,						_PatchedModuleObj			)
												assert isinstance(_test_patching.os.path   ,						_PatchedModuleObj			)
												assert _test_patching.os.path.join is mock

												# check path.join
												assert isinstance(_test_patching.path   ,						_PatchedModuleObj			)
												assert _test_patching.path.join is mock

												# check join
												assert _test_patching.join is mock

												# check that the other attributes are untouched
												assert _test_patching.os.rename is original_rename
												assert _test_patching.path.dirname is original_dirname
												assert _test_patching.os.path.dirname is original_dirname

												# Even renamed modules or objects must be patched

												# check renamed_os.path.join
												assert isinstance(_test_patching.renamed_os   ,						_PatchedModuleObj			)
												assert isinstance(_test_patching.renamed_os.path   ,						_PatchedModuleObj			)
												assert _test_patching.renamed_os.path.join is mock

												# check renamed_path.join
												assert isinstance(_test_patching.renamed_path   ,						_PatchedModuleObj			)
												assert _test_patching.renamed_path.join is mock

												# check renamed_join
												assert _test_patching.renamed_join is mock

												# check that the other attributes are untouched
												assert _test_patching.renamed_os.rename is original_rename
												assert _test_patching.renamed_path.dirname is original_dirname
												assert _test_patching.renamed_os.path.dirname is original_dirname

						# check that everthing is back to normal when the patch is over

						assert _test_patching.os is original_os
						assert _test_patching.path is original_path
						assert _test_patching.join is original_join

						assert _test_patching.renamed_os is original_os
						assert _test_patching.renamed_path is original_path
						assert _test_patching.renamed_join is original_join





def 						_lowerCAmelCase							(     )    ->						int:


						'''simple docstring'''






						assert _test_patching.open is open

						__snake_case				=		"__test_patch_submodule_builtin_mock__"
						# _test_patching has "open" in its globals
						assert _test_patching.open is open
						with patch_submodule(_test_patching   ,						"open"   ,						_lowerCAmelCase			):
												assert _test_patching.open is mock

						# check that everthing is back to normal when the patch is over

						assert _test_patching.open is open





def 						_lowerCAmelCase							(     )    ->						Optional[Any]:


						'''simple docstring'''






						__snake_case				=		"__test_patch_submodule_missing_mock__"
						with patch_submodule(_test_patching   ,						"pandas.read_csv"   ,						_lowerCAmelCase			):
												pass





def 						_lowerCAmelCase							(     )    ->						Optional[Any]:


						'''simple docstring'''






						__snake_case				=		"__test_patch_submodule_missing_builtin_mock__"
						# _test_patching doesn't have "len" in its globals
						assert getattr(_test_patching   ,						"len"   ,						_lowerCAmelCase			) is None
						with patch_submodule(_test_patching   ,						"len"   ,						_lowerCAmelCase			):
												assert _test_patching.len is mock
						assert _test_patching.len is len





def 						_lowerCAmelCase							(     )    ->						Optional[Any]:


						'''simple docstring'''






						__snake_case				=		"__test_patch_submodule_start_and_stop_mock__"
						__snake_case				=		patch_submodule(_test_patching   ,						"open"   ,						_lowerCAmelCase			)
						assert _test_patching.open is open
						patch.start()
						assert _test_patching.open is mock
						patch.stop()
						assert _test_patching.open is open





def 						_lowerCAmelCase							(     )    ->						Any:


						'''simple docstring'''






						from os import rename as original_rename
						from os.path import dirname as original_dirname
						from os.path import join as original_join

						__snake_case				=		"__test_patch_submodule_successive_join__"
						__snake_case				=		"__test_patch_submodule_successive_dirname__"
						__snake_case				=		"__test_patch_submodule_successive_rename__"
						assert _test_patching.os.path.join is original_join
						assert _test_patching.os.path.dirname is original_dirname
						assert _test_patching.os.rename is original_rename

						with patch_submodule(_test_patching   ,						"os.path.join"   ,						_lowerCAmelCase			):
												with patch_submodule(_test_patching   ,						"os.rename"   ,						_lowerCAmelCase			):
																		with patch_submodule(_test_patching   ,						"os.path.dirname"   ,						_lowerCAmelCase			):
																								assert _test_patching.os.path.join is mock_join
																								assert _test_patching.os.path.dirname is mock_dirname
																								assert _test_patching.os.rename is mock_rename

    # try another order
						with patch_submodule(_test_patching   ,						"os.rename"   ,						_lowerCAmelCase			):
												with patch_submodule(_test_patching   ,						"os.path.join"   ,						_lowerCAmelCase			):
																		with patch_submodule(_test_patching   ,						"os.path.dirname"   ,						_lowerCAmelCase			):
																								assert _test_patching.os.path.join is mock_join
																								assert _test_patching.os.path.dirname is mock_dirname
																								assert _test_patching.os.rename is mock_rename

						assert _test_patching.os.path.join is original_join
						assert _test_patching.os.path.dirname is original_dirname
						assert _test_patching.os.rename is original_rename





def 						_lowerCAmelCase							(     )    ->						str:


						'''simple docstring'''






						__snake_case				=		"__test_patch_submodule_doesnt_exist_mock__"
						with patch_submodule(_test_patching   ,						"__module_that_doesn_exist__.__attribute_that_doesn_exist__"   ,						_lowerCAmelCase			):
												pass
						with patch_submodule(_test_patching   ,						"os.__attribute_that_doesn_exist__"   ,						_lowerCAmelCase			):
												pass





