
"""simple docstring"""




import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DetaImageProcessor





class 			snake_case  (				unittest.TestCase ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=7							,							SCREAMING_SNAKE_CASE_=3							,							SCREAMING_SNAKE_CASE_=30							,							SCREAMING_SNAKE_CASE_=4_00							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]							,							SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=1 / 2_55							,							SCREAMING_SNAKE_CASE_=True							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
									SCREAMING_SNAKE_CASE_    			=			parent
									SCREAMING_SNAKE_CASE_    			=			batch_size
									SCREAMING_SNAKE_CASE_    			=			num_channels
									SCREAMING_SNAKE_CASE_    			=			min_resolution
									SCREAMING_SNAKE_CASE_    			=			max_resolution
									SCREAMING_SNAKE_CASE_    			=			do_resize
									SCREAMING_SNAKE_CASE_    			=			size
									SCREAMING_SNAKE_CASE_    			=			do_normalize
									SCREAMING_SNAKE_CASE_    			=			image_mean
									SCREAMING_SNAKE_CASE_    			=			image_std
									SCREAMING_SNAKE_CASE_    			=			do_rescale
									SCREAMING_SNAKE_CASE_    			=			rescale_factor
									SCREAMING_SNAKE_CASE_    			=			do_pad

							def _lowercase       (self     ):

									"""simple docstring"""







									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									}







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=False     ):

									"""simple docstring"""







									if not batched:
											SCREAMING_SNAKE_CASE_    			=			image_inputs[0]
											if isinstance(SCREAMING_SNAKE_CASE_							,							Image.Image     ):
													SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			image.size
											else:
													SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			image.shape[1], image.shape[2]
											if w < h:
													SCREAMING_SNAKE_CASE_    			=			int(self.size['''shortest_edge'''] * h / w     )
													SCREAMING_SNAKE_CASE_    			=			self.size['''shortest_edge''']
											elif w > h:
													SCREAMING_SNAKE_CASE_    			=			self.size['''shortest_edge''']
													SCREAMING_SNAKE_CASE_    			=			int(self.size['''shortest_edge'''] * w / h     )
											else:
													SCREAMING_SNAKE_CASE_    			=			self.size['''shortest_edge''']
													SCREAMING_SNAKE_CASE_    			=			self.size['''shortest_edge''']

									else:
											SCREAMING_SNAKE_CASE_    			=			[]
											for image in image_inputs:
													SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			self.get_expected_values([image]     )
													expected_values.append((expected_height, expected_width)     )
											SCREAMING_SNAKE_CASE_    			=			max(SCREAMING_SNAKE_CASE_							,							key=lambda SCREAMING_SNAKE_CASE_     : item[0]     )[0]
											SCREAMING_SNAKE_CASE_    			=			max(SCREAMING_SNAKE_CASE_							,							key=lambda SCREAMING_SNAKE_CASE_     : item[1]     )[1]

									return expected_height, expected_width







@require_torch
@require_vision
class 			snake_case  (				__lowercase							,      unittest.TestCase ):
							UpperCAmelCase__      =   DetaImageProcessor if is_vision_available() else None

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			DetaImageProcessingTester(self     )

							@property
							def _lowercase       (self     ):

									"""simple docstring"""







									return self.image_processor_tester.prepare_image_processor_dict()

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.image_processing_class(**self.image_processor_dict     )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,							'''image_mean'''     )     )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,							'''image_std'''     )     )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,							'''do_normalize'''     )     )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,							'''do_resize'''     )     )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,							'''do_rescale'''     )     )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,							'''do_pad'''     )     )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,							'''size'''     )     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.image_processing_class.from_dict(self.image_processor_dict     )
									self.assertEqual(image_processor.size							,							{'''shortest_edge''': 18, '''longest_edge''': 13_33}     )
									self.assertEqual(image_processor.do_pad							,							SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									pass

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.image_processing_class(**self.image_processor_dict     )
									# create random PIL images
									SCREAMING_SNAKE_CASE_    			=			prepare_image_inputs(self.image_processor_tester							,							equal_resolution=SCREAMING_SNAKE_CASE_     )
									for image in image_inputs:
											self.assertIsInstance(SCREAMING_SNAKE_CASE_							,							Image.Image     )

									# Test not batched input
									SCREAMING_SNAKE_CASE_    			=			image_processing(image_inputs[0]							,							return_tensors='''pt'''     ).pixel_values

									SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_     )

									self.assertEqual(
									    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

									# Test batched
									SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_							,							batched=SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			image_processing(SCREAMING_SNAKE_CASE_							,							return_tensors='''pt'''     ).pixel_values
									self.assertEqual(
									    encoded_images.shape							,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )							,							)

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.image_processing_class(**self.image_processor_dict     )
									# create random numpy tensors
									SCREAMING_SNAKE_CASE_    			=			prepare_image_inputs(self.image_processor_tester							,							equal_resolution=SCREAMING_SNAKE_CASE_							,							numpify=SCREAMING_SNAKE_CASE_     )
									for image in image_inputs:
											self.assertIsInstance(SCREAMING_SNAKE_CASE_							,							np.ndarray     )

									# Test not batched input
									SCREAMING_SNAKE_CASE_    			=			image_processing(image_inputs[0]							,							return_tensors='''pt'''     ).pixel_values

									SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_     )

									self.assertEqual(
									    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

									# Test batched
									SCREAMING_SNAKE_CASE_    			=			image_processing(SCREAMING_SNAKE_CASE_							,							return_tensors='''pt'''     ).pixel_values

									SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_							,							batched=SCREAMING_SNAKE_CASE_     )

									self.assertEqual(
									    encoded_images.shape							,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )							,							)

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.image_processing_class(**self.image_processor_dict     )
									# create random PyTorch tensors
									SCREAMING_SNAKE_CASE_    			=			prepare_image_inputs(self.image_processor_tester							,							equal_resolution=SCREAMING_SNAKE_CASE_							,							torchify=SCREAMING_SNAKE_CASE_     )
									for image in image_inputs:
											self.assertIsInstance(SCREAMING_SNAKE_CASE_							,							torch.Tensor     )

									# Test not batched input
									SCREAMING_SNAKE_CASE_    			=			image_processing(image_inputs[0]							,							return_tensors='''pt'''     ).pixel_values

									SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_     )

									self.assertEqual(
									    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

									# Test batched
									SCREAMING_SNAKE_CASE_    			=			image_processing(SCREAMING_SNAKE_CASE_							,							return_tensors='''pt'''     ).pixel_values

									SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_							,							batched=SCREAMING_SNAKE_CASE_     )

									self.assertEqual(
									    encoded_images.shape							,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )							,							)

							@slow
							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
									with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''							,							'''r'''     ) as f:
											SCREAMING_SNAKE_CASE_    			=			json.loads(f.read()     )

									SCREAMING_SNAKE_CASE_    			=			{'''image_id''': 3_97_69, '''annotations''': target}

									# encode them
									SCREAMING_SNAKE_CASE_    			=			DetaImageProcessor()
									SCREAMING_SNAKE_CASE_    			=			image_processing(images=SCREAMING_SNAKE_CASE_							,							annotations=SCREAMING_SNAKE_CASE_							,							return_tensors='''pt'''     )

									# verify pixel values
									SCREAMING_SNAKE_CASE_    			=			torch.Size([1, 3, 8_00, 10_66]     )
									self.assertEqual(encoding['''pixel_values'''].shape							,							SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			torch.tensor([0.27_96, 0.31_38, 0.34_81]     )
									self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,							SCREAMING_SNAKE_CASE_							,							atol=1e-4     )     )

									# verify area
									SCREAMING_SNAKE_CASE_    			=			torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]     )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']							,							SCREAMING_SNAKE_CASE_     )     )
									# verify boxes
									SCREAMING_SNAKE_CASE_    			=			torch.Size([6, 4]     )
									self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,							SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]     )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,							SCREAMING_SNAKE_CASE_							,							atol=1e-3     )     )
									# verify image_id
									SCREAMING_SNAKE_CASE_    			=			torch.tensor([3_97_69]     )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,							SCREAMING_SNAKE_CASE_     )     )
									# verify is_crowd
									SCREAMING_SNAKE_CASE_    			=			torch.tensor([0, 0, 0, 0, 0, 0]     )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,							SCREAMING_SNAKE_CASE_     )     )
									# verify class_labels
									SCREAMING_SNAKE_CASE_    			=			torch.tensor([75, 75, 63, 65, 17, 17]     )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,							SCREAMING_SNAKE_CASE_     )     )
									# verify orig_size
									SCREAMING_SNAKE_CASE_    			=			torch.tensor([4_80, 6_40]     )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,							SCREAMING_SNAKE_CASE_     )     )
									# verify size
									SCREAMING_SNAKE_CASE_    			=			torch.tensor([8_00, 10_66]     )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,							SCREAMING_SNAKE_CASE_     )     )







							@slow
							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
									with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''							,							'''r'''     ) as f:
											SCREAMING_SNAKE_CASE_    			=			json.loads(f.read()     )

									SCREAMING_SNAKE_CASE_    			=			{'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

									SCREAMING_SNAKE_CASE_    			=			pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''     )

									# encode them
									SCREAMING_SNAKE_CASE_    			=			DetaImageProcessor(format='''coco_panoptic'''     )
									SCREAMING_SNAKE_CASE_    			=			image_processing(images=SCREAMING_SNAKE_CASE_							,							annotations=SCREAMING_SNAKE_CASE_							,							masks_path=SCREAMING_SNAKE_CASE_							,							return_tensors='''pt'''     )

									# verify pixel values
									SCREAMING_SNAKE_CASE_    			=			torch.Size([1, 3, 8_00, 10_66]     )
									self.assertEqual(encoding['''pixel_values'''].shape							,							SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			torch.tensor([0.27_96, 0.31_38, 0.34_81]     )
									self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,							SCREAMING_SNAKE_CASE_							,							atol=1e-4     )     )

									# verify area
									SCREAMING_SNAKE_CASE_    			=			torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]     )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']							,							SCREAMING_SNAKE_CASE_     )     )
									# verify boxes
									SCREAMING_SNAKE_CASE_    			=			torch.Size([6, 4]     )
									self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,							SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]     )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,							SCREAMING_SNAKE_CASE_							,							atol=1e-3     )     )
									# verify image_id
									SCREAMING_SNAKE_CASE_    			=			torch.tensor([3_97_69]     )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,							SCREAMING_SNAKE_CASE_     )     )
									# verify is_crowd
									SCREAMING_SNAKE_CASE_    			=			torch.tensor([0, 0, 0, 0, 0, 0]     )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,							SCREAMING_SNAKE_CASE_     )     )
									# verify class_labels
									SCREAMING_SNAKE_CASE_    			=			torch.tensor([17, 17, 63, 75, 75, 93]     )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,							SCREAMING_SNAKE_CASE_     )     )
									# verify masks
									SCREAMING_SNAKE_CASE_    			=			82_28_73
									self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()							,							SCREAMING_SNAKE_CASE_     )
									# verify orig_size
									SCREAMING_SNAKE_CASE_    			=			torch.tensor([4_80, 6_40]     )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,							SCREAMING_SNAKE_CASE_     )     )
									# verify size
									SCREAMING_SNAKE_CASE_    			=			torch.tensor([8_00, 10_66]     )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,							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()