
"""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,
)


_A     :			List[Any]  						=			logging.getLogger(__name__)

@dataclass
class 						a__		:
   __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 						a__		:
   __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     __magic_name__      ( __snake_case							:			str	,					__snake_case							:			Dict	,					__snake_case							:			Tuple    )					->							Any:
       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     __magic_name__      ( )					->							List[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.

       lowercase       :							Any									=				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       :							Dict									=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
       else:
              lowercase						,       lowercase						,       lowercase       :							Optional[int]									=				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       :							Any									=				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       :							List[str]									=				("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       :							List[str]									=				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       :							Tuple									=				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       :							Optional[int]									=				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       :							int									=				tokenizer.lang_code_to_id[data_args.tgt_lang]
              else:
                     lowercase       :							int									=				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       :							Union[str, Any]									=				SeqaSeqDataset

       # Get datasets
       lowercase       :							List[str]									=				(
           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       :							Tuple									=				(
           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       :							Optional[Any]									=				(
           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       :							Union[str, Any]									=				(
           build_compute_metrics_fn(data_args.task	,					__snake_case    ) if training_args.predict_with_generate else None
       )
       lowercase       :							Tuple									=				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       :							Tuple									=				{}
       # Training
       if training_args.do_train:
              logger.info("*** Train ***"    )

              lowercase       :							str									=				trainer.train(
                  model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
              lowercase       :							Dict									=				train_result.metrics
              lowercase       :							str									=				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       :							Optional[int]									=				trainer.evaluate(metric_key_prefix="val"    )
              lowercase       :							Tuple									=				data_args.n_val
              lowercase       :							Tuple									=				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       :							List[str]									=				trainer.predict(test_dataset=__snake_case	,					metric_key_prefix="test"    )
              lowercase       :							Optional[int]									=				test_output.metrics
              lowercase       :							List[Any]									=				data_args.n_test

              if trainer.is_world_process_zero():
                     lowercase       :							Tuple									=				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       :							Optional[Any]									=				tokenizer.batch_decode(
                                test_output.predictions	,					skip_special_tokens=__snake_case	,					clean_up_tokenization_spaces=__snake_case    )
                            lowercase       :							int									=				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     __magic_name__      ( __snake_case							:			List[Any]    )					->							Union[str, Any]:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
   main()



'''simple docstring'''

from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_     :						Tuple    							=logging.get_logger(__name__)

A_     :						Dict    							={
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}


class     __UpperCAmelCase       ( __a		):
  __A :				Union[str, Any]											= 'efficientnet'





  def __init__(	self    ,     _lowerCamelCase = 3    ,     _lowerCamelCase = 600    ,     _lowerCamelCase = 2.0    ,     _lowerCamelCase = 3.1    ,     _lowerCamelCase = 8    ,     _lowerCamelCase = [3, 3, 5, 3, 5, 5, 3]    ,     _lowerCamelCase = [32, 16, 24, 40, 80, 112, 192]    ,     _lowerCamelCase = [16, 24, 40, 80, 112, 192, 320]    ,     _lowerCamelCase = []    ,     _lowerCamelCase = [1, 2, 2, 2, 1, 2, 1]    ,     _lowerCamelCase = [1, 2, 2, 3, 3, 4, 1]    ,     _lowerCamelCase = [1, 6, 6, 6, 6, 6, 6]    ,     _lowerCamelCase = 0.25    ,     _lowerCamelCase = "swish"    ,     _lowerCamelCase = 2560    ,     _lowerCamelCase = "mean"    ,     _lowerCamelCase = 0.02    ,     _lowerCamelCase = 0.0_01    ,     _lowerCamelCase = 0.99    ,     _lowerCamelCase = 0.5    ,     _lowerCamelCase = 0.2    ,     **_lowerCamelCase    ,     ):
     super().__init__(**_lowerCamelCase )

     lowerCAmelCase_			=      num_channels
     lowerCAmelCase_			=      image_size
     lowerCAmelCase_			=      width_coefficient
     lowerCAmelCase_			=      depth_coefficient
     lowerCAmelCase_			=      depth_divisor
     lowerCAmelCase_			=      kernel_sizes
     lowerCAmelCase_			=      in_channels
     lowerCAmelCase_			=      out_channels
     lowerCAmelCase_			=      depthwise_padding
     lowerCAmelCase_			=      strides
     lowerCAmelCase_			=      num_block_repeats
     lowerCAmelCase_			=      expand_ratios
     lowerCAmelCase_			=      squeeze_expansion_ratio
     lowerCAmelCase_			=      hidden_act
     lowerCAmelCase_			=      hidden_dim
     lowerCAmelCase_			=      pooling_type
     lowerCAmelCase_			=      initializer_range
     lowerCAmelCase_			=      batch_norm_eps
     lowerCAmelCase_			=      batch_norm_momentum
     lowerCAmelCase_			=      dropout_rate
     lowerCAmelCase_			=      drop_connect_rate
     lowerCAmelCase_			=      sum(_lowerCamelCase ) * 4






class     __UpperCAmelCase       ( __a		):
  __A :				str											= version.parse('1.11'		)





  @property
  def 						UpperCAmelCase_	(	self ):
     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
         ] )







  @property
  def 						UpperCAmelCase_	(	self ):
     return 1E-5





