
"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase__				=					logging.get_logger(__name__)

lowerCAmelCase__				=					{
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a,				__a     ):
		for attribute in key.split('''.'''     ):
				SCREAMING_SNAKE_CASE_    			=			getattr(__a,				__a     )

		if weight_type is not None:
				SCREAMING_SNAKE_CASE_    			=			getattr(__a,				__a     ).shape
		else:
				SCREAMING_SNAKE_CASE_    			=			hf_pointer.shape

		assert hf_shape == value.shape, (
		    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
		    F' {value.shape} for {full_name}'
		)

		if weight_type == "weight":
				SCREAMING_SNAKE_CASE_    			=			value
		elif weight_type == "weight_g":
				SCREAMING_SNAKE_CASE_    			=			value
		elif weight_type == "weight_v":
				SCREAMING_SNAKE_CASE_    			=			value
		elif weight_type == "bias":
				SCREAMING_SNAKE_CASE_    			=			value
		else:
				SCREAMING_SNAKE_CASE_    			=			value

		logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'     )



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			[]
		SCREAMING_SNAKE_CASE_    			=			fairseq_model.state_dict()

		SCREAMING_SNAKE_CASE_    			=			hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				SCREAMING_SNAKE_CASE_    			=			False
				if "conv_layers" in name:
						load_conv_layer(
						    __a,				__a,				__a,				__a,				hf_model.config.feat_extract_norm == '''group''',				)
						SCREAMING_SNAKE_CASE_    			=			True
				else:
						for key, mapped_key in MAPPING.items():
								SCREAMING_SNAKE_CASE_    			=			'''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

								if key in name or key.split('''w2v_model.'''     )[-1] == name.split('''.'''     )[0]:
										SCREAMING_SNAKE_CASE_    			=			True
										if "*" in mapped_key:
												SCREAMING_SNAKE_CASE_    			=			name.split(__a     )[0].split('''.'''     )[-2]
												SCREAMING_SNAKE_CASE_    			=			mapped_key.replace('''*''',				__a     )
										if "weight_g" in name:
												SCREAMING_SNAKE_CASE_    			=			'''weight_g'''
										elif "weight_v" in name:
												SCREAMING_SNAKE_CASE_    			=			'''weight_v'''
										elif "weight" in name:
												SCREAMING_SNAKE_CASE_    			=			'''weight'''
										elif "bias" in name:
												SCREAMING_SNAKE_CASE_    			=			'''bias'''
										else:
												SCREAMING_SNAKE_CASE_    			=			None
										set_recursively(__a,				__a,				__a,				__a,				__a     )
								continue
				if not is_used:
						unused_weights.append(__a     )

		logger.warning(F'Unused weights: {unused_weights}'     )



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			full_name.split('''conv_layers.'''     )[-1]
		SCREAMING_SNAKE_CASE_    			=			name.split('''.'''     )
		SCREAMING_SNAKE_CASE_    			=			int(items[0]     )
		SCREAMING_SNAKE_CASE_    			=			int(items[1]     )

		if type_id == 0:
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
						    F'{full_name} has size {value.shape}, but'
						    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
						)
						SCREAMING_SNAKE_CASE_    			=			value
						logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'     )
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
						    F'{full_name} has size {value.shape}, but'
						    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
						)
						SCREAMING_SNAKE_CASE_    			=			value
						logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'     )
		elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
						    F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
						    " found."
						)
						SCREAMING_SNAKE_CASE_    			=			value
						logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
						    F'{full_name} has size {value.shape}, but'
						    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
						)
						SCREAMING_SNAKE_CASE_    			=			value
						logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
		else:
				unused_weights.append(__a     )



def 							_lowerCamelCase			(			__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			SEWConfig()
		if is_finetuned:
				SCREAMING_SNAKE_CASE_    			=			model.wav_encoder.wav_model.cfg
		else:
				SCREAMING_SNAKE_CASE_    			=			model.cfg

		SCREAMING_SNAKE_CASE_    			=			fs_config.conv_bias
		SCREAMING_SNAKE_CASE_    			=			eval(fs_config.conv_feature_layers     )
		SCREAMING_SNAKE_CASE_    			=			[x[0] for x in conv_layers]
		SCREAMING_SNAKE_CASE_    			=			[x[1] for x in conv_layers]
		SCREAMING_SNAKE_CASE_    			=			[x[2] for x in conv_layers]
		SCREAMING_SNAKE_CASE_    			=			'''gelu'''
		SCREAMING_SNAKE_CASE_    			=			'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
		SCREAMING_SNAKE_CASE_    			=			0.0
		SCREAMING_SNAKE_CASE_    			=			fs_config.activation_fn.name
		SCREAMING_SNAKE_CASE_    			=			fs_config.encoder_embed_dim
		SCREAMING_SNAKE_CASE_    			=			0.0_2
		SCREAMING_SNAKE_CASE_    			=			fs_config.encoder_ffn_embed_dim
		SCREAMING_SNAKE_CASE_    			=			1E-5
		SCREAMING_SNAKE_CASE_    			=			fs_config.encoder_layerdrop
		SCREAMING_SNAKE_CASE_    			=			fs_config.encoder_attention_heads
		SCREAMING_SNAKE_CASE_    			=			fs_config.conv_pos_groups
		SCREAMING_SNAKE_CASE_    			=			fs_config.conv_pos
		SCREAMING_SNAKE_CASE_    			=			len(__a     )
		SCREAMING_SNAKE_CASE_    			=			fs_config.encoder_layers
		SCREAMING_SNAKE_CASE_    			=			fs_config.squeeze_factor

		# take care of any params that are overridden by the Wav2VecCtc model
		if is_finetuned:
				SCREAMING_SNAKE_CASE_    			=			model.cfg
				SCREAMING_SNAKE_CASE_    			=			fs_config.final_dropout
				SCREAMING_SNAKE_CASE_    			=			fs_config.layerdrop
		SCREAMING_SNAKE_CASE_    			=			fs_config.activation_dropout
		SCREAMING_SNAKE_CASE_    			=			fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
		SCREAMING_SNAKE_CASE_    			=			fs_config.attention_dropout
		SCREAMING_SNAKE_CASE_    			=			fs_config.dropout_input
		SCREAMING_SNAKE_CASE_    			=			fs_config.dropout
		SCREAMING_SNAKE_CASE_    			=			fs_config.mask_channel_length
		SCREAMING_SNAKE_CASE_    			=			fs_config.mask_channel_prob
		SCREAMING_SNAKE_CASE_    			=			fs_config.mask_length
		SCREAMING_SNAKE_CASE_    			=			fs_config.mask_prob

		SCREAMING_SNAKE_CASE_    			=			'''Wav2Vec2FeatureExtractor'''
		SCREAMING_SNAKE_CASE_    			=			'''Wav2Vec2CTCTokenizer'''

		return config



@torch.no_grad()
def 							_lowerCamelCase			(			__a,				__a,				__a=None,				__a=None,				__a=True     ):

		if is_finetuned:
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path],				arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''     )[:-1]     )}     )
		else:
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

		if config_path is not None:
				SCREAMING_SNAKE_CASE_    			=			SEWConfig.from_pretrained(__a     )
		else:
				SCREAMING_SNAKE_CASE_    			=			convert_config(model[0],				__a     )
		SCREAMING_SNAKE_CASE_    			=			model[0].eval()

		SCREAMING_SNAKE_CASE_    			=			True if config.feat_extract_norm == '''layer''' else False
		SCREAMING_SNAKE_CASE_    			=			WavaVecaFeatureExtractor(
		    feature_size=1,				sampling_rate=16_000,				padding_value=0,				do_normalize=__a,				return_attention_mask=__a,				)

		if is_finetuned:
				if dict_path:
						SCREAMING_SNAKE_CASE_    			=			Dictionary.load(__a     )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						SCREAMING_SNAKE_CASE_    			=			target_dict.pad_index
						SCREAMING_SNAKE_CASE_    			=			target_dict.bos_index
						SCREAMING_SNAKE_CASE_    			=			target_dict.pad_index
						SCREAMING_SNAKE_CASE_    			=			target_dict.bos_index
						SCREAMING_SNAKE_CASE_    			=			target_dict.eos_index
						SCREAMING_SNAKE_CASE_    			=			len(target_dict.symbols     )
						SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				'''vocab.json'''     )
						if not os.path.isdir(__a     ):
								logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__a     )     )
								return
						os.makedirs(__a,				exist_ok=__a     )
						with open(__a,				'''w''',				encoding='''utf-8'''     ) as vocab_handle:
								json.dump(target_dict.indices,				__a     )
						SCREAMING_SNAKE_CASE_    			=			WavaVecaCTCTokenizer(
						    __a,				unk_token=target_dict.unk_word,				pad_token=target_dict.pad_word,				bos_token=target_dict.bos_word,				eos_token=target_dict.eos_word,				word_delimiter_token='''|''',				do_lower_case=__a,				)
						SCREAMING_SNAKE_CASE_    			=			WavaVecaProcessor(feature_extractor=__a,				tokenizer=__a     )
						processor.save_pretrained(__a     )

				SCREAMING_SNAKE_CASE_    			=			SEWForCTC(__a     )
		else:
				SCREAMING_SNAKE_CASE_    			=			SEWModel(__a     )
				feature_extractor.save_pretrained(__a     )

		recursively_load_weights(__a,				__a,				__a     )

		hf_model.save_pretrained(__a     )


if __name__ == "__main__":
						lowerCAmelCase__				=					argparse.ArgumentParser()
						parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
						parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
						parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
						parser.add_argument(
						    '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
						)
						lowerCAmelCase__				=					parser.parse_args()
						convert_sew_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
						)

"""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()