
"""simple docstring"""




import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling



def 							_lowerCamelCase			(			__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			old_name

		if "patch_embed" in old_name:
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			old_name.split('''.'''     )

				if layer == "0":
						SCREAMING_SNAKE_CASE_    			=			old_name.replace('''0''',				'''convolution1'''     )
				elif layer == "1":
						SCREAMING_SNAKE_CASE_    			=			old_name.replace('''1''',				'''batchnorm_before'''     )
				elif layer == "3":
						SCREAMING_SNAKE_CASE_    			=			old_name.replace('''3''',				'''convolution2'''     )
				else:
						SCREAMING_SNAKE_CASE_    			=			old_name.replace('''4''',				'''batchnorm_after'''     )

		if "network" in old_name and re.search(r'''\d\.\d''',				__a     ):
				SCREAMING_SNAKE_CASE_    			=			r'''\b\d{2}\b'''
				if bool(re.search(__a,				__a     )     ):
						SCREAMING_SNAKE_CASE_    			=			re.search(r'''\d\.\d\d.''',				__a     ).group()
				else:
						SCREAMING_SNAKE_CASE_    			=			re.search(r'''\d\.\d.''',				__a     ).group()
				if int(match[0]     ) < 6:
						SCREAMING_SNAKE_CASE_    			=			old_name.replace(__a,				''''''     )
						SCREAMING_SNAKE_CASE_    			=			trimmed_name.replace('''network''',				match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]     )
						SCREAMING_SNAKE_CASE_    			=			'''intermediate_stages.''' + trimmed_name
				else:
						SCREAMING_SNAKE_CASE_    			=			old_name.replace(__a,				''''''     )
						if int(match[2]     ) < num_meta4D_last_stage:
								SCREAMING_SNAKE_CASE_    			=			trimmed_name.replace('''network''',				'''meta4D_layers.blocks.''' + match[2]     )
						else:
								SCREAMING_SNAKE_CASE_    			=			str(int(match[2]     ) - num_meta4D_last_stage     )
								SCREAMING_SNAKE_CASE_    			=			trimmed_name.replace('''network''',				'''meta3D_layers.blocks.''' + layer_index     )
								if "norm1" in old_name:
										SCREAMING_SNAKE_CASE_    			=			trimmed_name.replace('''norm1''',				'''layernorm1'''     )
								elif "norm2" in old_name:
										SCREAMING_SNAKE_CASE_    			=			trimmed_name.replace('''norm2''',				'''layernorm2'''     )
								elif "fc1" in old_name:
										SCREAMING_SNAKE_CASE_    			=			trimmed_name.replace('''fc1''',				'''linear_in'''     )
								elif "fc2" in old_name:
										SCREAMING_SNAKE_CASE_    			=			trimmed_name.replace('''fc2''',				'''linear_out'''     )

						SCREAMING_SNAKE_CASE_    			=			'''last_stage.''' + trimmed_name

		elif "network" in old_name and re.search(r'''.\d.''',				__a     ):
				SCREAMING_SNAKE_CASE_    			=			old_name.replace('''network''',				'''intermediate_stages'''     )

		if "fc" in new_name:
				SCREAMING_SNAKE_CASE_    			=			new_name.replace('''fc''',				'''convolution'''     )
		elif ("norm1" in new_name) and ("layernorm1" not in new_name):
				SCREAMING_SNAKE_CASE_    			=			new_name.replace('''norm1''',				'''batchnorm_before'''     )
		elif ("norm2" in new_name) and ("layernorm2" not in new_name):
				SCREAMING_SNAKE_CASE_    			=			new_name.replace('''norm2''',				'''batchnorm_after'''     )
		if "proj" in new_name:
				SCREAMING_SNAKE_CASE_    			=			new_name.replace('''proj''',				'''projection'''     )
		if "dist_head" in new_name:
				SCREAMING_SNAKE_CASE_    			=			new_name.replace('''dist_head''',				'''distillation_classifier'''     )
		elif "head" in new_name:
				SCREAMING_SNAKE_CASE_    			=			new_name.replace('''head''',				'''classifier'''     )
		elif "patch_embed" in new_name:
				SCREAMING_SNAKE_CASE_    			=			'''efficientformer.''' + new_name
		elif new_name == "norm.weight" or new_name == "norm.bias":
				SCREAMING_SNAKE_CASE_    			=			new_name.replace('''norm''',				'''layernorm'''     )
				SCREAMING_SNAKE_CASE_    			=			'''efficientformer.''' + new_name
		else:
				SCREAMING_SNAKE_CASE_    			=			'''efficientformer.encoder.''' + new_name

		return new_name



def 							_lowerCamelCase			(			__a,				__a     ):
		for key in checkpoint.copy().keys():
				SCREAMING_SNAKE_CASE_    			=			checkpoint.pop(__a     )
				SCREAMING_SNAKE_CASE_    			=			val

		return checkpoint



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE_    			=			Image.open(requests.get(__a,				stream=__a     ).raw     )

		return image



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			torch.load(__a,				map_location='''cpu'''     )['''model''']
		SCREAMING_SNAKE_CASE_    			=			EfficientFormerConfig.from_json_file(__a     )
		SCREAMING_SNAKE_CASE_    			=			EfficientFormerForImageClassificationWithTeacher(__a     )
		SCREAMING_SNAKE_CASE_    			=			'''_'''.join(checkpoint_path.split('''/'''     )[-1].split('''.'''     )[0].split('''_'''     )[:-1]     )

		SCREAMING_SNAKE_CASE_    			=			config.depths[-1] - config.num_metaad_blocks + 1
		SCREAMING_SNAKE_CASE_    			=			convert_torch_checkpoint(__a,				__a     )

		model.load_state_dict(__a     )
		model.eval()

		SCREAMING_SNAKE_CASE_    			=			{
		    '''bilinear''': PILImageResampling.BILINEAR,
		    '''bicubic''': PILImageResampling.BICUBIC,
		    '''nearest''': PILImageResampling.NEAREST,
		}

		# prepare image
		SCREAMING_SNAKE_CASE_    			=			prepare_img()
		SCREAMING_SNAKE_CASE_    			=			256
		SCREAMING_SNAKE_CASE_    			=			224
		SCREAMING_SNAKE_CASE_    			=			EfficientFormerImageProcessor(
		    size={'''shortest_edge''': image_size},				crop_size={'''height''': crop_size, '''width''': crop_size},				resample=pillow_resamplings['''bicubic'''],				)
		SCREAMING_SNAKE_CASE_    			=			processor(images=__a,				return_tensors='''pt'''     ).pixel_values

		# original processing pipeline
		SCREAMING_SNAKE_CASE_    			=			Compose(
		    [
		        Resize(__a,				interpolation=pillow_resamplings['''bicubic''']     ),
		        CenterCrop(__a     ),
		        ToTensor(),
		        Normalize(__a,				__a     ),
		    ]     )
		SCREAMING_SNAKE_CASE_    			=			image_transforms(__a     ).unsqueeze(0     )

		assert torch.allclose(__a,				__a     )

		SCREAMING_SNAKE_CASE_    			=			model(__a     )
		SCREAMING_SNAKE_CASE_    			=			outputs.logits

		SCREAMING_SNAKE_CASE_    			=			(1, 1_000)

		if "l1" in model_name:
				SCREAMING_SNAKE_CASE_    			=			torch.Tensor(
				    [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]     )
				assert torch.allclose(logits[0, :10],				__a,				atol=1E-3     )
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				SCREAMING_SNAKE_CASE_    			=			torch.Tensor(
				    [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]     )
				assert torch.allclose(logits[0, :10],				__a,				atol=1E-3     )
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				SCREAMING_SNAKE_CASE_    			=			torch.Tensor(
				    [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]     )
				assert logits.shape == expected_shape
		else:
				raise ValueError(
				    F'Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'     )

		# Save Checkpoints
		Path(__a     ).mkdir(exist_ok=__a     )
		model.save_pretrained(__a     )
		print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'     )
		processor.save_pretrained(__a     )
		print(F'Processor successfuly saved at {pytorch_dump_path}'     )

		if push_to_hub:
				print('''Pushing model to the hub...'''     )

				model.push_to_hub(
				    repo_id=F'Bearnardd/{pytorch_dump_path}',				commit_message='''Add model''',				use_temp_dir=__a,				)
				processor.push_to_hub(
				    repo_id=F'Bearnardd/{pytorch_dump_path}',				commit_message='''Add image processor''',				use_temp_dir=__a,				)


if __name__ == "__main__":
						lowerCAmelCase__				=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--pytorch_model_path',
						    default=None,
						    type=str,
						    required=True,
						    help='Path to EfficientFormer pytorch checkpoint.',
						)
						parser.add_argument(
						    '--config_file',
						    default=None,
						    type=str,
						    required=True,
						    help='The json file for EfficientFormer model config.',
						)
						parser.add_argument(
						    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
						)

						parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
						parser.add_argument(
						    '--no-push_to_hub',
						    dest='push_to_hub',
						    action='store_false',
						    help='Do not push model and image processor to the hub',
						)
						parser.set_defaults(push_to_hub=True)

						lowerCAmelCase__				=					parser.parse_args()
						convert_efficientformer_checkpoint(
						    checkpoint_path=args.pytorch_model_path,
						    efficientformer_config_file=args.config_file,
						    pytorch_dump_path=args.pytorch_dump_path,
						    push_to_hub=args.push_to_hub,
						)

"""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()