





"""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,
)


_UpperCamelCase							=      logging.getLogger(__name__)
@dataclass
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""
				__snake_case   :       str	 =		field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}   )
				__snake_case   :       Optional[str]	 =		field(
				    default=snake_case__       ,  metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
				__snake_case   :       Optional[str]	 =		field(
				    default=snake_case__       ,  metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
				__snake_case   :       Optional[str]	 =		field(
				    default=snake_case__       ,  metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}       ,  )
				__snake_case   :       bool	 =		field(default=snake_case__       ,  metadata={"""help""": """Whether tp freeze the encoder."""}   )
				__snake_case   :       bool	 =		field(default=snake_case__       ,  metadata={"""help""": """Whether  to freeze the embeddings."""}   )

@dataclass
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""
				__snake_case   :       str	 =		field(
				    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}   )
				__snake_case   :       Optional[str]	 =		field(
				    default="""summarization"""       ,  metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}       ,  )
				__snake_case   :       Optional[int]	 =		field(
				    default=10_24       ,  metadata={
				        """help""": (
				            """The maximum total input sequence length after tokenization. Sequences longer """
				            """than this will be truncated, sequences shorter will be padded."""
				        )
				    }       ,  )
				__snake_case   :       Optional[int]	 =		field(
				    default=1_28       ,  metadata={
				        """help""": (
				            """The maximum total sequence length for target text after tokenization. Sequences longer """
				            """than this will be truncated, sequences shorter will be padded."""
				        )
				    }       ,  )
				__snake_case   :       Optional[int]	 =		field(
				    default=1_42       ,  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``."""
				        )
				    }       ,  )
				__snake_case   :       Optional[int]	 =		field(
				    default=1_42       ,  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."""
				        )
				    }       ,  )
				__snake_case   :       Optional[int]	 =		field(default=-1       ,  metadata={"""help""": """# training examples. -1 means use all."""}   )
				__snake_case   :       Optional[int]	 =		field(default=-1       ,  metadata={"""help""": """# validation examples. -1 means use all."""}   )
				__snake_case   :       Optional[int]	 =		field(default=-1       ,  metadata={"""help""": """# test examples. -1 means use all."""}   )
				__snake_case   :       Optional[str]	 =		field(default=snake_case__       ,  metadata={"""help""": """Source language id for translation."""}   )
				__snake_case   :       Optional[str]	 =		field(default=snake_case__       ,  metadata={"""help""": """Target language id for translation."""}   )
				__snake_case   :       Optional[int]	 =		field(default=snake_case__       ,  metadata={"""help""": """# num_beams to use for evaluation."""}   )
				__snake_case   :       bool	 =		field(
				    default=snake_case__       ,  metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}       ,  )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[Any]    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							Any   ):


							'''simple docstring'''







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



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[int]         =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.
														__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase      :      Optional[int]         =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
							else:
														__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase      :      Tuple         =parser.parse_args_into_dataclasses()

							check_output_dir(SCREAMING_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'''    ,						SCREAMING_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.

							__lowerCamelCase      :      Optional[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    ,						)

							__lowerCamelCase      :      Union[str, Any]         =('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
							for p in extra_model_params:
														if getattr(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
																					assert hasattr(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ), F'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
																					setattr(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						getattr(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )   )

							__lowerCamelCase      :      Union[str, Any]         =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    ,						)
							__lowerCamelCase      :      List[str]         =AutoModelForSeqaSeqLM.from_pretrained(
							    model_args.model_name_or_path    ,						from_tf='''.ckpt''' in model_args.model_name_or_path    ,						config=SCREAMING_SNAKE_CASE    ,						cache_dir=model_args.cache_dir    ,						)

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

							# set num_beams for evaluation
							if data_args.eval_beams is None:
														__lowerCamelCase      :      List[str]         =model.config.num_beams

							# set decoder_start_token_id for MBart
							if model.config.decoder_start_token_id is None and isinstance(SCREAMING_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(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      Union[str, Any]         =tokenizer.lang_code_to_id[data_args.tgt_lang]
														else:
																					__lowerCamelCase      :      Optional[int]         =tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

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

							__lowerCamelCase      :      Tuple         =SeqaSeqDataset

							# Get datasets
							__lowerCamelCase      :      List[str]         =(
							    dataset_class(
							        SCREAMING_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
							)
							__lowerCamelCase      :      Optional[Any]         =(
							    dataset_class(
							        SCREAMING_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
							)
							__lowerCamelCase      :      Any         =(
							    dataset_class(
							        SCREAMING_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
							__lowerCamelCase      :      str         =(
							    build_compute_metrics_fn(data_args.task    ,						SCREAMING_SNAKE_CASE   ) if training_args.predict_with_generate else None
							)
							__lowerCamelCase      :      Dict         =SeqaSeqTrainer(
							    model=SCREAMING_SNAKE_CASE    ,						args=SCREAMING_SNAKE_CASE    ,						data_args=SCREAMING_SNAKE_CASE    ,						train_dataset=SCREAMING_SNAKE_CASE    ,						eval_dataset=SCREAMING_SNAKE_CASE    ,						data_collator=SeqaSeqDataCollator(
							        SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						model.config.decoder_start_token_id    ,						training_args.tpu_num_cores   )    ,						compute_metrics=SCREAMING_SNAKE_CASE    ,						tokenizer=SCREAMING_SNAKE_CASE    ,						)

							__lowerCamelCase      :      Union[str, Any]         ={}
							# Training
							if training_args.do_train:
														logger.info('''*** Train ***'''   )

														__lowerCamelCase      :      Dict         =trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
														__lowerCamelCase      :      Dict         =train_result.metrics
														__lowerCamelCase      :      Optional[Any]         =data_args.n_train

														trainer.save_model()  # this also saves the tokenizer

														if trainer.is_world_process_zero():
																					handle_metrics('''train'''    ,						SCREAMING_SNAKE_CASE    ,						training_args.output_dir   )
																					all_metrics.update(SCREAMING_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 ***'''   )

														__lowerCamelCase      :      Any         =trainer.evaluate(metric_key_prefix='''val'''   )
														__lowerCamelCase      :      int         =data_args.n_val
														__lowerCamelCase      :      Tuple         =round(metrics['''val_loss''']    ,						4   )

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

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

														__lowerCamelCase      :      Tuple         =trainer.predict(test_dataset=SCREAMING_SNAKE_CASE    ,						metric_key_prefix='''test'''   )
														__lowerCamelCase      :      Optional[int]         =test_output.metrics
														__lowerCamelCase      :      List[Any]         =data_args.n_test

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

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

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

							return all_metrics



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Tuple   ):


							'''simple docstring'''







							main()


if __name__ == "__main__":
							main()











"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


_UpperCamelCase							=      logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       List[str]	 =		["""pixel_values"""]




				def __init__(     self  :List[str]						,    __lowercase  :bool = True						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :PILImageResampling = PIL.Image.BICUBIC						,    __lowercase  :bool = True						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :Union[int, float] = 1 / 255						,    __lowercase  :bool = True						,    __lowercase  :bool = True						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    **__lowercase  :Union[str, Any]						,    ):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      Dict         =size if size is not None else {'''height''': 256, '''width''': 256}
											__lowerCamelCase      :      Union[str, Any]         =get_size_dict(__lowercase			)
											__lowerCamelCase      :      Tuple         =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
											__lowerCamelCase      :      int         =get_size_dict(__lowercase						,    param_name='''crop_size'''			)

											__lowerCamelCase      :      Tuple         =do_resize
											__lowerCamelCase      :      int         =size
											__lowerCamelCase      :      Union[str, Any]         =resample
											__lowerCamelCase      :      Dict         =do_center_crop
											__lowerCamelCase      :      Any         =crop_size
											__lowerCamelCase      :      Dict         =do_rescale
											__lowerCamelCase      :      List[Any]         =rescale_factor
											__lowerCamelCase      :      Any         =do_normalize
											__lowerCamelCase      :      int         =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCamelCase      :      List[str]         =image_std if image_std is not None else IMAGENET_STANDARD_STD




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :np.ndarray						,    __lowercase  :Dict[str, int]						,    __lowercase  :PILImageResampling = PIL.Image.BICUBIC						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :Optional[int]						,    ):
											__lowerCamelCase      :      Optional[int]         =get_size_dict(__lowercase			)
											if "height" not in size or "width" not in size:
																		raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'			)
											return resize(
											    __lowercase						,    size=(size['''height'''], size['''width'''])						,    resample=__lowercase						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :np.ndarray						,    __lowercase  :Dict[str, int]						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :Optional[Any]						,    ):
											__lowerCamelCase      :      Dict         =get_size_dict(__lowercase			)
											if "height" not in size or "width" not in size:
																		raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'			)
											return center_crop(__lowercase						,    size=(size['''height'''], size['''width'''])						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :int						,    __lowercase  :np.ndarray						,    __lowercase  :Union[int, float]						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :List[Any]						,    ):
											return rescale(__lowercase						,    scale=__lowercase						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Dict						,    __lowercase  :np.ndarray						,    __lowercase  :Union[float, List[float]]						,    __lowercase  :Union[float, List[float]]						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :int						,    ):
											return normalize(__lowercase						,    mean=__lowercase						,    std=__lowercase						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :ImageInput						,    __lowercase  :bool = None						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :Optional[Any]=None						,    __lowercase  :bool = None						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :bool = None						,    __lowercase  :float = None						,    __lowercase  :bool = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[str, TensorType]] = None						,    __lowercase  :ChannelDimension = ChannelDimension.FIRST						,    **__lowercase  :Any						,    ):
											__lowerCamelCase      :      Optional[Any]         =do_resize if do_resize is not None else self.do_resize
											__lowerCamelCase      :      List[Any]         =resample if resample is not None else self.resample
											__lowerCamelCase      :      Optional[int]         =do_center_crop if do_center_crop is not None else self.do_center_crop
											__lowerCamelCase      :      List[str]         =do_rescale if do_rescale is not None else self.do_rescale
											__lowerCamelCase      :      Optional[Any]         =rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCamelCase      :      Optional[int]         =do_normalize if do_normalize is not None else self.do_normalize
											__lowerCamelCase      :      Dict         =image_mean if image_mean is not None else self.image_mean
											__lowerCamelCase      :      Union[str, Any]         =image_std if image_std is not None else self.image_std

											__lowerCamelCase      :      Dict         =size if size is not None else self.size
											__lowerCamelCase      :      int         =get_size_dict(__lowercase			)
											__lowerCamelCase      :      List[Any]         =crop_size if crop_size is not None else self.crop_size
											__lowerCamelCase      :      Optional[int]         =get_size_dict(__lowercase						,    param_name='''crop_size'''			)

											__lowerCamelCase      :      int         =make_list_of_images(__lowercase			)

											if not valid_images(__lowercase			):
																		raise ValueError(
																		    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																		    '''torch.Tensor, tf.Tensor or jax.ndarray.'''			)

											if do_resize and size is None or resample is None:
																		raise ValueError('''Size and resample must be specified if do_resize is True.'''			)

											if do_center_crop and crop_size is None:
																		raise ValueError('''Crop size must be specified if do_center_crop is True.'''			)

											if do_rescale and rescale_factor is None:
																		raise ValueError('''Rescale factor must be specified if do_rescale is True.'''			)

											if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError('''Image mean and std must be specified if do_normalize is True.'''			)

											# All transformations expect numpy arrays.
											__lowerCamelCase      :      str         =[to_numpy_array(__lowercase			) for image in images]

											if do_resize:
																		__lowerCamelCase      :      Union[str, Any]         =[self.resize(image=__lowercase						,    size=__lowercase						,    resample=__lowercase			) for image in images]

											if do_center_crop:
																		__lowerCamelCase      :      Optional[Any]         =[self.center_crop(image=__lowercase						,    size=__lowercase			) for image in images]

											if do_rescale:
																		__lowerCamelCase      :      str         =[self.rescale(image=__lowercase						,    scale=__lowercase			) for image in images]

											if do_normalize:
																		__lowerCamelCase      :      int         =[self.normalize(image=__lowercase						,    mean=__lowercase						,    std=__lowercase			) for image in images]

											__lowerCamelCase      :      Tuple         =[to_channel_dimension_format(__lowercase						,    __lowercase			) for image in images]

											__lowerCamelCase      :      Any         ={'''pixel_values''': images}
											return BatchFeature(data=__lowercase						,    tensor_type=__lowercase			)




