
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         =   logging.getLogger(__name__)
@dataclass
class 					lowercase__:



	"""simple docstring"""




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

@dataclass
class 					lowercase__:



	"""simple docstring"""




	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 							a		(				snake_case__:   Dict		,  snake_case__:   Optional[int]		,  snake_case__:   List[str]   ):


				'''simple docstring'''
				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 							a		(				):


				'''simple docstring'''
				# 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(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.

				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(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__   )   )

				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=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:
								lowercase_	    =   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__   ):
												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(snake_case__   )
				if model_args.freeze_encoder:
								freeze_params(model.get_encoder()   )
								assert_all_frozen(model.get_encoder()   )

				lowercase_	    =   SeqaSeqDataset

				# Get datasets
				lowercase_	    =   (
				    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
				)
				lowercase_	    =   (
				    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
				)
				lowercase_	    =   (
				    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
				lowercase_	    =   (
				    build_compute_metrics_fn(data_args.task		,  snake_case__   ) if training_args.predict_with_generate else None
				)
				lowercase_	    =   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__		,  )

				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'''		,  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 ***'''   )

								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'''		,  snake_case__		,  training_args.output_dir   )
												all_metrics.update(snake_case__   )

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

								lowercase_	    =   trainer.predict(test_dataset=snake_case__		,  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'''		,  snake_case__		,  training_args.output_dir   )
												all_metrics.update(snake_case__   )

												if training_args.predict_with_generate:
																lowercase_	    =   tokenizer.batch_decode(
																    test_output.predictions		,  skip_special_tokens=snake_case__		,  clean_up_tokenization_spaces=snake_case__   )
																lowercase_	    =   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 							a		(				snake_case__:   List[str]   ):


				'''simple docstring'''
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
						main()








'''simple docstring'''







import argparse
from collections import defaultdict

import yaml


lowerCAmelCase     :  Dict											=    """docs/source/en/_toctree.yml"""






def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[int]       =							defaultdict(_A       )
       _lowerCAmelCase     :				Tuple       =							[]
       _lowerCAmelCase     :				Dict       =							[]
       for doc in doc_list:
              if "local" in doc:
                     counts[doc["local"]] += 1

              if doc["title"].lower() == "overview":
                     overview_doc.append({'local': doc['local'], 'title': doc['title']}       )
              else:
                     new_doc_list.append(_A       )

       _lowerCAmelCase     :				str       =							new_doc_list
       _lowerCAmelCase     :				List[Any]       =							[key for key, value in counts.items() if value > 1]

       _lowerCAmelCase     :				List[str]       =							[]
       for duplicate_key in duplicates:
              _lowerCAmelCase     :				Any       =							list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}       )
              if len(_A       ) > 1:
                     raise ValueError(
                         f'{duplicate_key} is present several times in the documentation table of content at '
                         '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                         'others.'       )
              # Only add this once
              new_doc.append({'local': duplicate_key, 'title': titles[0]}       )

       # Add none duplicate-keys
       new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]       )
       _lowerCAmelCase     :				Any       =							sorted(_A ,    key=lambda _A       : s["title"].lower()       )

       # "overview" gets special treatment and is always first
       if len(_A       ) > 1:
              raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'       )

       overview_doc.extend(_A       )

       # Sort
       return overview_doc






def       lowercase					(_A=False       ):







       """simple docstring"""







       with open(_A ,    encoding='utf-8'       ) as f:
              _lowerCAmelCase     :				Union[str, Any]       =							yaml.safe_load(f.read()       )

       # Get to the API doc
       _lowerCAmelCase     :				Tuple       =							0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       _lowerCAmelCase     :				str       =							content[api_idx]['sections']

       # Then to the model doc
       _lowerCAmelCase     :				Dict       =							0
       while api_doc[scheduler_idx]["title"] != "Schedulers":
              scheduler_idx += 1

       _lowerCAmelCase     :				Optional[int]       =							api_doc[scheduler_idx]['sections']
       _lowerCAmelCase     :				Any       =							clean_doc_toc(_A       )

       _lowerCAmelCase     :				int       =							False
       if new_scheduler_doc != scheduler_doc:
              _lowerCAmelCase     :				str       =							True
              if overwrite:
                     _lowerCAmelCase     :				int       =							new_scheduler_doc

       if diff:
              if overwrite:
                     _lowerCAmelCase     :				Dict       =							api_doc
                     with open(_A ,    'w' ,    encoding='utf-8'       ) as f:
                            f.write(yaml.dump(_A ,    allow_unicode=_A       )       )
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'       )






def       lowercase					(_A=False       ):







       """simple docstring"""







       with open(_A ,    encoding='utf-8'       ) as f:
              _lowerCAmelCase     :				Optional[Any]       =							yaml.safe_load(f.read()       )

       # Get to the API doc
       _lowerCAmelCase     :				Optional[Any]       =							0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       _lowerCAmelCase     :				Union[str, Any]       =							content[api_idx]['sections']

       # Then to the model doc
       _lowerCAmelCase     :				Optional[int]       =							0
       while api_doc[pipeline_idx]["title"] != "Pipelines":
              pipeline_idx += 1

       _lowerCAmelCase     :				List[Any]       =							False
       _lowerCAmelCase     :				List[Any]       =							api_doc[pipeline_idx]['sections']
       _lowerCAmelCase     :				Union[str, Any]       =							[]

       # sort sub pipeline docs
       for pipeline_doc in pipeline_docs:
              if "section" in pipeline_doc:
                     _lowerCAmelCase     :				Tuple       =							pipeline_doc['section']
                     _lowerCAmelCase     :				Optional[int]       =							clean_doc_toc(_A       )
                     if overwrite:
                            _lowerCAmelCase     :				str       =							new_sub_pipeline_doc
              new_pipeline_docs.append(_A       )

       # sort overall pipeline doc
       _lowerCAmelCase     :				str       =							clean_doc_toc(_A       )

       if new_pipeline_docs != pipeline_docs:
              _lowerCAmelCase     :				Any       =							True
              if overwrite:
                     _lowerCAmelCase     :				Optional[int]       =							new_pipeline_docs

       if diff:
              if overwrite:
                     _lowerCAmelCase     :				str       =							api_doc
                     with open(_A ,    'w' ,    encoding='utf-8'       ) as f:
                            f.write(yaml.dump(_A ,    allow_unicode=_A       )       )
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'       )


if __name__ == "__main__":
       lowerCAmelCase     :  Tuple											=    argparse.ArgumentParser()
       parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
       lowerCAmelCase     :  List[Any]											=    parser.parse_args()

       check_scheduler_doc(args.fix_and_overwrite)
       check_pipeline_doc(args.fix_and_overwrite)

