






import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
				import numpy as np
				import torch

				from . import (
				    AlbertForPreTraining,
				    BartForConditionalGeneration,
				    BertForPreTraining,
				    BertForQuestionAnswering,
				    BertForSequenceClassification,
				    CamembertForMaskedLM,
				    CTRLLMHeadModel,
				    DistilBertForMaskedLM,
				    DistilBertForQuestionAnswering,
				    DPRContextEncoder,
				    DPRQuestionEncoder,
				    DPRReader,
				    ElectraForPreTraining,
				    FlaubertWithLMHeadModel,
				    GPTaLMHeadModel,
				    LayoutLMForMaskedLM,
				    LxmertForPreTraining,
				    LxmertVisualFeatureEncoder,
				    OpenAIGPTLMHeadModel,
				    RobertaForMaskedLM,
				    RobertaForSequenceClassification,
				    TaForConditionalGeneration,
				    TransfoXLLMHeadModel,
				    XLMRobertaForMaskedLM,
				    XLMWithLMHeadModel,
				    XLNetLMHeadModel,
				)


logging.set_verbosity_info()

a_		:						List[Any]             =    {
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase=False							,       _UpperCAmelCase=True):
			if model_type not in MODEL_CLASSES:
						raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys())}.''')

			SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= MODEL_CLASSES[model_type]

			# Initialise TF model
			if config_file in aws_config_map:
						SCREAMING_SNAKE_CASE						= cached_file(_UpperCAmelCase							,       _UpperCAmelCase							,       force_download=not use_cached_models)
			SCREAMING_SNAKE_CASE						= config_class.from_json_file(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= True
			SCREAMING_SNAKE_CASE						= True
			print(F'''Building TensorFlow model from configuration: {config}''')
			SCREAMING_SNAKE_CASE						= model_class(_UpperCAmelCase)

			# Load weights from tf checkpoint
			if pytorch_checkpoint_path in aws_config_map.keys():
						SCREAMING_SNAKE_CASE						= cached_file(
						    _UpperCAmelCase							,       _UpperCAmelCase							,       force_download=not use_cached_models)
			# Load PyTorch checkpoint in tf2 model:
			SCREAMING_SNAKE_CASE						= load_pytorch_checkpoint_in_tfa_model(_UpperCAmelCase							,       _UpperCAmelCase)

			if compare_with_pt_model:
						SCREAMING_SNAKE_CASE						= tf_model(tf_model.dummy_inputs							,       training=_UpperCAmelCase)  # build the network

						SCREAMING_SNAKE_CASE						= torch.load(_UpperCAmelCase							,       map_location='cpu')
						SCREAMING_SNAKE_CASE						= pt_model_class.from_pretrained(
						    pretrained_model_name_or_path=_UpperCAmelCase							,       config=_UpperCAmelCase							,       state_dict=_UpperCAmelCase)

						with torch.no_grad():
									SCREAMING_SNAKE_CASE						= pt_model(**pt_model.dummy_inputs)

						SCREAMING_SNAKE_CASE						= pto[0].numpy()
						SCREAMING_SNAKE_CASE						= tfo[0].numpy()
						SCREAMING_SNAKE_CASE						= np.amax(np.abs(np_pt - np_tf))
						print(F'''Max absolute difference between models outputs {diff}''')
						assert diff <= 2e-2, F'''Error, model absolute difference is >2e-2: {diff}'''

			# Save pytorch-model
			print(F'''Save TensorFlow model to {tf_dump_path}''')
			tf_model.save_weights(_UpperCAmelCase							,       save_format='h5')






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase=None							,       _UpperCAmelCase=None							,       _UpperCAmelCase=False							,       _UpperCAmelCase=False							,       _UpperCAmelCase=False							,       _UpperCAmelCase=False							,       ):
			if args_model_type is None:
						SCREAMING_SNAKE_CASE						= list(MODEL_CLASSES.keys())
			else:
						SCREAMING_SNAKE_CASE						= [args_model_type]

			for j, model_type in enumerate(_UpperCAmelCase							,       start=1):
						print('=' * 100)
						print(F''' Converting model type {j}/{len(_UpperCAmelCase)}: {model_type}''')
						print('=' * 100)
						if model_type not in MODEL_CLASSES:
									raise ValueError(F'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys())}.''')

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= MODEL_CLASSES[model_type]

						if model_shortcut_names_or_path is None:
									SCREAMING_SNAKE_CASE						= list(aws_model_maps.keys())
						if config_shortcut_names_or_path is None:
									SCREAMING_SNAKE_CASE						= model_shortcut_names_or_path

						for i, (model_shortcut_name, config_shortcut_name) in enumerate(
						    zip(_UpperCAmelCase							,       _UpperCAmelCase)							,       start=1):
									print('-' * 100)
									if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
												if not only_convert_finetuned_models:
															print(F'''    Skipping finetuned checkpoint {model_shortcut_name}''')
															continue
												SCREAMING_SNAKE_CASE						= model_shortcut_name
									elif only_convert_finetuned_models:
												print(F'''    Skipping not finetuned checkpoint {model_shortcut_name}''')
												continue
									print(
									    F'''    Converting checkpoint {i}/{len(_UpperCAmelCase)}: {model_shortcut_name} - model_type {model_type}''')
									print('-' * 100)

									if config_shortcut_name in aws_config_map:
												SCREAMING_SNAKE_CASE						= cached_file(_UpperCAmelCase							,       _UpperCAmelCase							,       force_download=not use_cached_models)
									else:
												SCREAMING_SNAKE_CASE						= config_shortcut_name

									if model_shortcut_name in aws_model_maps:
												SCREAMING_SNAKE_CASE						= cached_file(_UpperCAmelCase							,       _UpperCAmelCase							,       force_download=not use_cached_models)
									else:
												SCREAMING_SNAKE_CASE						= model_shortcut_name

									if os.path.isfile(_UpperCAmelCase):
												SCREAMING_SNAKE_CASE						= 'converted_model'

									convert_pt_checkpoint_to_tf(
									    model_type=_UpperCAmelCase							,       pytorch_checkpoint_path=_UpperCAmelCase							,       config_file=_UpperCAmelCase							,       tf_dump_path=os.path.join(_UpperCAmelCase							,       model_shortcut_name + '-tf_model.h5')							,       compare_with_pt_model=_UpperCAmelCase							,       )
									if remove_cached_files:
												os.remove(_UpperCAmelCase)
												os.remove(_UpperCAmelCase)


if __name__ == "__main__":
				a_		:						Any             =    argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
				)
				parser.add_argument(
				    '--model_type',
				    default=None,
				    type=str,
				    help=(
				        f"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
				        'convert all the models from AWS.'
				    ),
				)
				parser.add_argument(
				    '--pytorch_checkpoint_path',
				    default=None,
				    type=str,
				    help=(
				        'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
				        'If not given, will download and convert all the checkpoints from AWS.'
				    ),
				)
				parser.add_argument(
				    '--config_file',
				    default=None,
				    type=str,
				    help=(
				        'The config json file corresponding to the pre-trained model. \n'
				        'This specifies the model architecture. If not given and '
				        '--pytorch_checkpoint_path is not given or is a shortcut name '
				        'use the configuration associated to the shortcut name on the AWS'
				    ),
				)
				parser.add_argument(
				    '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
				)
				parser.add_argument(
				    '--use_cached_models',
				    action='store_true',
				    help='Use cached models if possible instead of updating to latest checkpoint versions.',
				)
				parser.add_argument(
				    '--remove_cached_files',
				    action='store_true',
				    help='Remove pytorch models after conversion (save memory when converting in batches).',
				)
				parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
				a_		:						Tuple             =    parser.parse_args()

				# if args.pytorch_checkpoint_path is not None:
				#     convert_pt_checkpoint_to_tf(args.model_type.lower(),
				#                                 args.pytorch_checkpoint_path,
				#                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
				#                                 args.tf_dump_path,
				#                                 compare_with_pt_model=args.compare_with_pt_model,
				#                                 use_cached_models=args.use_cached_models)
				# else:
				convert_all_pt_checkpoints_to_tf(
				    args.model_type.lower() if args.model_type is not None else None,
				    args.tf_dump_path,
				    model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
				    if args.pytorch_checkpoint_path is not None
				    else None,
				    config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
				    compare_with_pt_model=args.compare_with_pt_model,
				    use_cached_models=args.use_cached_models,
				    remove_cached_files=args.remove_cached_files,
				    only_convert_finetuned_models=args.only_convert_finetuned_models,
				)












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,
)


a_		:						Optional[Any]             =    logging.getLogger(__name__)

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



@dataclass
class 		_snake_case  :
			_lowercase		:    str			     =							field(
			    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default='''summarization'''   ,    metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''}   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=10_24   ,    metadata={
			        '''help''': (
			            '''The maximum total input sequence length after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=1_28   ,    metadata={
			        '''help''': (
			            '''The maximum total sequence length for target text after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=1_42   ,    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``.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=1_42   ,    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.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(default=-1   ,    metadata={'''help''': '''# training examples. -1 means use all.'''}					)
			_lowercase		:    Optional[int]			     =							field(default=-1   ,    metadata={'''help''': '''# validation examples. -1 means use all.'''}					)
			_lowercase		:    Optional[int]			     =							field(default=-1   ,    metadata={'''help''': '''# test examples. -1 means use all.'''}					)
			_lowercase		:    Optional[str]			     =							field(default=A__   ,    metadata={'''help''': '''Source language id for translation.'''}					)
			_lowercase		:    Optional[str]			     =							field(default=A__   ,    metadata={'''help''': '''Target language id for translation.'''}					)
			_lowercase		:    Optional[int]			     =							field(default=A__   ,    metadata={'''help''': '''# num_beams to use for evaluation.'''}					)
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''}   ,    )






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

			# 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'							,       _UpperCAmelCase)

			# 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(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
									assert hasattr(_UpperCAmelCase							,       _UpperCAmelCase), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
									setattr(_UpperCAmelCase							,       _UpperCAmelCase							,       getattr(_UpperCAmelCase							,       _UpperCAmelCase))

			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=_UpperCAmelCase							,       cache_dir=model_args.cache_dir							,       )

			# use task specific params
			use_task_specific_params(_UpperCAmelCase							,       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(_UpperCAmelCase							,       (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(_UpperCAmelCase							,       _UpperCAmelCase):
									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(_UpperCAmelCase)
			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(
			        _UpperCAmelCase							,       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(
			        _UpperCAmelCase							,       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(
			        _UpperCAmelCase							,       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							,       _UpperCAmelCase) if training_args.predict_with_generate else None
			)
			SCREAMING_SNAKE_CASE						= SeqaSeqTrainer(
			    model=_UpperCAmelCase							,       args=_UpperCAmelCase							,       data_args=_UpperCAmelCase							,       train_dataset=_UpperCAmelCase							,       eval_dataset=_UpperCAmelCase							,       data_collator=SeqaSeqDataCollator(
			        _UpperCAmelCase							,       _UpperCAmelCase							,       model.config.decoder_start_token_id							,       training_args.tpu_num_cores)							,       compute_metrics=_UpperCAmelCase							,       tokenizer=_UpperCAmelCase							,       )

			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'							,       _UpperCAmelCase							,       training_args.output_dir)
									all_metrics.update(_UpperCAmelCase)

									# 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'							,       _UpperCAmelCase							,       training_args.output_dir)
									all_metrics.update(_UpperCAmelCase)

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

						SCREAMING_SNAKE_CASE						= trainer.predict(test_dataset=_UpperCAmelCase							,       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'							,       _UpperCAmelCase							,       training_args.output_dir)
									all_metrics.update(_UpperCAmelCase)

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

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

			return all_metrics






def 				lowerCamelCase__				(_UpperCAmelCase):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
				main()




