





"""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,
)


__magic_name__       :					Tuple						=			logging.getLogger(__name__)






@dataclass
class 							lowercase__			:

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






@dataclass
class 							lowercase__			:

			"""simple docstring"""
			__lowerCAmelCase     :				str										=  field(
			    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}	)
			__lowerCAmelCase     :				Optional[str]										=  field(
			    default="""summarization"""					,     metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}					,     )
			__lowerCAmelCase     :				Optional[int]										=  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     :				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."""
			        )
			    }					,     )
			__lowerCAmelCase     :				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``."""
			        )
			    }					,     )
			__lowerCAmelCase     :				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."""
			        )
			    }					,     )
			__lowerCAmelCase     :				Optional[int]										=  field(default=-1					,     metadata={"""help""": """# training examples. -1 means use all."""}	)
			__lowerCAmelCase     :				Optional[int]										=  field(default=-1					,     metadata={"""help""": """# validation examples. -1 means use all."""}	)
			__lowerCAmelCase     :				Optional[int]										=  field(default=-1					,     metadata={"""help""": """# test examples. -1 means use all."""}	)
			__lowerCAmelCase     :				Optional[str]										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Source language id for translation."""}	)
			__lowerCAmelCase     :				Optional[str]										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Target language id for translation."""}	)
			__lowerCAmelCase     :				Optional[int]										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """# num_beams to use for evaluation."""}	)
			__lowerCAmelCase     :				bool										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}					,     )


def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						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      UpperCamelCase							():
						# 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.

						UpperCamelCase				:		List[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.
												UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase				:		List[str]				   =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
						else:
												UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			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.

						UpperCamelCase				:		List[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							,     )

						UpperCamelCase				:		Tuple				   =			("""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     )     )

						UpperCamelCase				:		Optional[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							,     )
						UpperCamelCase				:		Any				   =			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:
												UpperCamelCase				:		Dict				   =			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     ):
																		UpperCamelCase				:		List[str]				   =			tokenizer.lang_code_to_id[data_args.tgt_lang]
												else:
																		UpperCamelCase				:		List[Any]				   =			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()     )

						UpperCamelCase				:		Optional[Any]				   =			SeqaSeqDataset

						# Get datasets
						UpperCamelCase				:		List[Any]				   =			(
						    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
						)
						UpperCamelCase				:		Optional[int]				   =			(
						    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
						)
						UpperCamelCase				:		Optional[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
						UpperCamelCase				:		Tuple				   =			(
						    build_compute_metrics_fn(data_args.task							,     SCREAMING_SNAKE_CASE     ) if training_args.predict_with_generate else None
						)
						UpperCamelCase				:		Optional[int]				   =			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							,     )

						UpperCamelCase				:		Optional[Any]				   =			{}
						# Training
						if training_args.do_train:
												logger.info("""*** Train ***"""     )

												UpperCamelCase				:		Optional[Any]				   =			trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path     ) else None     )
												UpperCamelCase				:		Dict				   =			train_result.metrics
												UpperCamelCase				:		Optional[int]				   =			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 ***"""     )

												UpperCamelCase				:		List[str]				   =			trainer.evaluate(metric_key_prefix="""val"""     )
												UpperCamelCase				:		int				   =			data_args.n_val
												UpperCamelCase				:		Any				   =			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 ***"""     )

												UpperCamelCase				:		List[Any]				   =			trainer.predict(test_dataset=SCREAMING_SNAKE_CASE							,     metric_key_prefix="""test"""     )
												UpperCamelCase				:		Union[str, Any]				   =			test_output.metrics
												UpperCamelCase				:		List[str]				   =			data_args.n_test

												if trainer.is_world_process_zero():
																		UpperCamelCase				:		Dict				   =			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:
																								UpperCamelCase				:		str				   =			tokenizer.batch_decode(
																								    test_output.predictions							,     skip_special_tokens=SCREAMING_SNAKE_CASE							,     clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE     )
																								UpperCamelCase				:		Any				   =			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      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						# For xla_spawn (TPUs)
						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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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


__lowercase        =    logging.get_logger(__name__)






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[Any]						=			['''pixel_values''']



			def __init__(  self			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = PILImageResampling.BICUBIC			,       __lowerCAmelCase = True			,       __lowerCAmelCase = 1 / 255			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)
					lowerCAmelCase     =    size if size is not None else {"""height""": 384, """width""": 384}
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       default_to_square=__lowerCAmelCase)

					lowerCAmelCase     =    do_resize
					lowerCAmelCase     =    size
					lowerCAmelCase     =    resample
					lowerCAmelCase     =    do_rescale
					lowerCAmelCase     =    rescale_factor
					lowerCAmelCase     =    do_normalize
					lowerCAmelCase     =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					lowerCAmelCase     =    image_std if image_std is not None else OPENAI_CLIP_STD
					lowerCAmelCase     =    do_convert_rgb



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = PILImageResampling.BICUBIC			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       default_to_square=__lowerCAmelCase)
					if "height" not in size or "width" not in size:
							raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
					lowerCAmelCase     =    (size["""height"""], size["""width"""])
					return resize(__lowerCAmelCase			,       size=__lowerCAmelCase			,       resample=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					return rescale(__lowerCAmelCase			,       scale=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					return normalize(__lowerCAmelCase			,       mean=__lowerCAmelCase			,       std=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = ChannelDimension.FIRST			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    do_resize if do_resize is not None else self.do_resize
					lowerCAmelCase     =    resample if resample is not None else self.resample
					lowerCAmelCase     =    do_rescale if do_rescale is not None else self.do_rescale
					lowerCAmelCase     =    rescale_factor if rescale_factor is not None else self.rescale_factor
					lowerCAmelCase     =    do_normalize if do_normalize is not None else self.do_normalize
					lowerCAmelCase     =    image_mean if image_mean is not None else self.image_mean
					lowerCAmelCase     =    image_std if image_std is not None else self.image_std
					lowerCAmelCase     =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					lowerCAmelCase     =    size if size is not None else self.size
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       default_to_square=__lowerCAmelCase)

					lowerCAmelCase     =    make_list_of_images(__lowerCAmelCase)

					if not valid_images(__lowerCAmelCase):
							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_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.""")

					# PIL RGBA images are converted to RGB
					if do_convert_rgb:
							lowerCAmelCase     =    [convert_to_rgb(__lowerCAmelCase) for image in images]

					# All transformations expect numpy arrays.
					lowerCAmelCase     =    [to_numpy_array(__lowerCAmelCase) for image in images]

					if do_resize:
							lowerCAmelCase     =    [self.resize(image=__lowerCAmelCase			,       size=__lowerCAmelCase			,       resample=__lowerCAmelCase) for image in images]

					if do_rescale:
							lowerCAmelCase     =    [self.rescale(image=__lowerCAmelCase			,       scale=__lowerCAmelCase) for image in images]

					if do_normalize:
							lowerCAmelCase     =    [self.normalize(image=__lowerCAmelCase			,       mean=__lowerCAmelCase			,       std=__lowerCAmelCase) for image in images]

					lowerCAmelCase     =    [to_channel_dimension_format(__lowerCAmelCase			,       __lowerCAmelCase) for image in images]

					lowerCAmelCase     =    BatchFeature(data={"""pixel_values""": images}			,       tensor_type=__lowerCAmelCase)

					return encoded_outputs




