



'''simple docstring'''


import importlib
import inspect
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowercase_      :				Any   					=    '''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
lowercase_      :				List[str]   					=    importlib.util.spec_from_file_location(
    '''transformers''',
    os.path.join(PATH_TO_TRANSFORMERS, '''__init__.py'''),
    submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
lowercase_      :				Any   					=    spec.loader.load_module()

lowercase_      :				Tuple   					=    transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
lowercase_      :				List[Any]   					=    re.compile('''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')


lowercase_      :				Any   					=    {
    '''CLIPConfigMixin''',
    '''DecisionTransformerConfigMixin''',
    '''EncoderDecoderConfigMixin''',
    '''RagConfigMixin''',
    '''SpeechEncoderDecoderConfigMixin''',
    '''VisionEncoderDecoderConfigMixin''',
    '''VisionTextDualEncoderConfigMixin''',
}







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							[]

						for config_class in list(CONFIG_MAPPING.values()						):
												lowercase        =							False

												# source code of `config_class`
												lowercase        =							inspect.getsource(lowercase_						)
												lowercase        =							_re_checkpoint.findall(lowercase_						)

												for checkpoint in checkpoints:
																		# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
																		# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
																		lowercase     ,   lowercase        =							checkpoint

																		# verify the checkpoint name corresponds to the checkpoint link
																		lowercase        =							F"""https://huggingface.co/{ckpt_name}"""
																		if ckpt_link == ckpt_link_from_name:
																								lowercase        =							True
																								break

												lowercase        =							config_class.__name__
												if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
																		configs_without_checkpoint.append(lowercase_						)

						if len(lowercase_						) > 0:
												lowercase        =							"""\n""".join(sorted(lowercase_						)						)
												raise ValueError(F"""The following configurations don't contain any valid checkpoint:\n{message}"""						)


if __name__ == "__main__":
						check_config_docstrings_have_checkpoints()







'''simple docstring'''


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowercase_      :				Tuple   					=    logging.getLogger(__name__)




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




@dataclass
class      __UpperCamelCase       :
	__A			  =					field(
	    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}			)
	__A			  =					field(
	    default='''summarization'''			,   metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''}			,   )
	__A			  =					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.'''
	        )
	    }			,   )
	__A			  =					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			  =					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			  =					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			  =					field(default=-1			,   metadata={'''help''': '''# training examples. -1 means use all.'''}			)
	__A			  =					field(default=-1			,   metadata={'''help''': '''# validation examples. -1 means use all.'''}			)
	__A			  =					field(default=-1			,   metadata={'''help''': '''# test examples. -1 means use all.'''}			)
	__A			  =					field(default=_UpperCAmelCase			,   metadata={'''help''': '''Source language id for translation.'''}			)
	__A			  =					field(default=_UpperCAmelCase			,   metadata={'''help''': '''Target language id for translation.'''}			)
	__A			  =					field(default=_UpperCAmelCase			,   metadata={'''help''': '''# num_beams to use for evaluation.'''}			)
	__A			  =					field(
	    default=_UpperCAmelCase			,   metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''}			,   )







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[Any] ,  lowercase_    :      int ,  lowercase_    :      List[Any]						):
						logger.info(F"""***** {split} metrics *****"""						)
						for key in sorted(metrics.keys()						):
												logger.info(F"""  {key} = {metrics[key]}"""						)
						save_json(lowercase_ ,  os.path.join(lowercase_ ,  F"""{split}_results.json"""						)						)







def      SCREAMING_SNAKE_CASE				(				):
						# 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(lowercase_						)

						# 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""" ,  lowercase_						)

						# 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(lowercase_ ,  lowercase_ ,  lowercase_						):
																		assert hasattr(lowercase_ ,  lowercase_						), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
																		setattr(lowercase_ ,  lowercase_ ,  getattr(lowercase_ ,  lowercase_						)						)

						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=lowercase_ ,  cache_dir=model_args.cache_dir ,  )

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

						lowercase        =							SeqaSeqDataset

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

						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""" ,  lowercase_ ,  training_args.output_dir						)
																		all_metrics.update(lowercase_						)

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

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

												lowercase        =							trainer.predict(test_dataset=lowercase_ ,  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""" ,  lowercase_ ,  training_args.output_dir						)
																		all_metrics.update(lowercase_						)

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

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

						return all_metrics







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict						):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()


