
"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    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


lowerCAmelCase__				=					logging.get_logger(__name__)


if is_vision_available():
						import PIL





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   ['''pixel_values''']

							def __init__(self							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = 1 / 2_55							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = True							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			size if size is not None else {'''shortest_edge''': 2_24}
									SCREAMING_SNAKE_CASE_    			=			get_size_dict(SCREAMING_SNAKE_CASE_							,							default_to_square=SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
									SCREAMING_SNAKE_CASE_    			=			get_size_dict(SCREAMING_SNAKE_CASE_							,							default_to_square=SCREAMING_SNAKE_CASE_							,							param_name='''crop_size'''     )

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

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			get_size_dict(SCREAMING_SNAKE_CASE_							,							default_to_square=SCREAMING_SNAKE_CASE_     )
									if "shortest_edge" not in size:
											raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'     )
									SCREAMING_SNAKE_CASE_    			=			get_resize_output_image_size(SCREAMING_SNAKE_CASE_							,							size=size['''shortest_edge''']							,							default_to_square=SCREAMING_SNAKE_CASE_     )
									return resize(SCREAMING_SNAKE_CASE_							,							size=SCREAMING_SNAKE_CASE_							,							resample=SCREAMING_SNAKE_CASE_							,							data_format=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			get_size_dict(SCREAMING_SNAKE_CASE_     )
									if "height" not in size or "width" not in size:
											raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'     )
									return center_crop(SCREAMING_SNAKE_CASE_							,							size=(size['''height'''], size['''width'''])							,							data_format=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







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

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







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







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE_    			=			size if size is not None else self.size
									SCREAMING_SNAKE_CASE_    			=			get_size_dict(SCREAMING_SNAKE_CASE_							,							param_name='''size'''							,							default_to_square=SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE_    			=			do_center_crop if do_center_crop is not None else self.do_center_crop
									SCREAMING_SNAKE_CASE_    			=			crop_size if crop_size is not None else self.crop_size
									SCREAMING_SNAKE_CASE_    			=			get_size_dict(SCREAMING_SNAKE_CASE_							,							param_name='''crop_size'''							,							default_to_square=SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE_    			=			rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE_    			=			do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE_    			=			image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE_    			=			image_std if image_std is not None else self.image_std
									SCREAMING_SNAKE_CASE_    			=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									SCREAMING_SNAKE_CASE_    			=			make_list_of_images(SCREAMING_SNAKE_CASE_     )

									if not valid_images(SCREAMING_SNAKE_CASE_     ):
											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:
											raise ValueError('''Size 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.'''     )

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

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

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

									if do_center_crop:
											SCREAMING_SNAKE_CASE_    			=			[self.center_crop(image=SCREAMING_SNAKE_CASE_							,							size=SCREAMING_SNAKE_CASE_     ) for image in images]

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

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

									SCREAMING_SNAKE_CASE_    			=			[to_channel_dimension_format(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ) for image in images]

									SCREAMING_SNAKE_CASE_    			=			{'''pixel_values''': images}
									return BatchFeature(data=SCREAMING_SNAKE_CASE_							,							tensor_type=SCREAMING_SNAKE_CASE_     )

"""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,
)


lowerCAmelCase__				=					logging.getLogger(__name__)





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







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







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



def 							_lowerCamelCase			(			):
		# 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.

		SCREAMING_SNAKE_CASE_    			=			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.
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
		else:
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			parser.parse_args_into_dataclasses()

		check_output_dir(__a     )

		# 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''',				__a     )

		# 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.

		SCREAMING_SNAKE_CASE_    			=			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,				)

		SCREAMING_SNAKE_CASE_    			=			('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
		for p in extra_model_params:
				if getattr(__a,				__a,				__a     ):
						assert hasattr(__a,				__a     ), F'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
						setattr(__a,				__a,				getattr(__a,				__a     )     )

		SCREAMING_SNAKE_CASE_    			=			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,				)
		SCREAMING_SNAKE_CASE_    			=			AutoModelForSeqaSeqLM.from_pretrained(
		    model_args.model_name_or_path,				from_tf='''.ckpt''' in model_args.model_name_or_path,				config=__a,				cache_dir=model_args.cache_dir,				)

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

		# set num_beams for evaluation
		if data_args.eval_beams is None:
				SCREAMING_SNAKE_CASE_    			=			model.config.num_beams

		# set decoder_start_token_id for MBart
		if model.config.decoder_start_token_id is None and isinstance(__a,				(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(__a,				__a     ):
						SCREAMING_SNAKE_CASE_    			=			tokenizer.lang_code_to_id[data_args.tgt_lang]
				else:
						SCREAMING_SNAKE_CASE_    			=			tokenizer.convert_tokens_to_ids(data_args.tgt_lang     )

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

		SCREAMING_SNAKE_CASE_    			=			SeqaSeqDataset

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

		SCREAMING_SNAKE_CASE_    			=			{}
		# Training
		if training_args.do_train:
				logger.info('''*** Train ***'''     )

				SCREAMING_SNAKE_CASE_    			=			trainer.train(
				    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path     ) else None     )
				SCREAMING_SNAKE_CASE_    			=			train_result.metrics
				SCREAMING_SNAKE_CASE_    			=			data_args.n_train

				trainer.save_model()  # this also saves the tokenizer

				if trainer.is_world_process_zero():
						handle_metrics('''train''',				__a,				training_args.output_dir     )
						all_metrics.update(__a     )

						# 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 ***'''     )

				SCREAMING_SNAKE_CASE_    			=			trainer.evaluate(metric_key_prefix='''val'''     )
				SCREAMING_SNAKE_CASE_    			=			data_args.n_val
				SCREAMING_SNAKE_CASE_    			=			round(metrics['''val_loss'''],				4     )

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

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

				SCREAMING_SNAKE_CASE_    			=			trainer.predict(test_dataset=__a,				metric_key_prefix='''test'''     )
				SCREAMING_SNAKE_CASE_    			=			test_output.metrics
				SCREAMING_SNAKE_CASE_    			=			data_args.n_test

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

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

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

		return all_metrics



def 							_lowerCamelCase			(			__a     ):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
						main()