

import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
			pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_      =     logging.get_logger(__name__)

a_      =     {
    'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='align_text_model'
			def __init__(      self				:			Tuple   ,   a				:			int=3_0522   ,   a				:			int=768   ,   a				:			Optional[int]=12   ,   a				:			Union[str, Any]=12   ,   a				:			List[str]=3072   ,   a				:			str="gelu"   ,   a				:			int=0.1   ,   a				:			Tuple=0.1   ,   a				:			str=512   ,   a				:			List[str]=2   ,   a				:			Dict=0.02   ,   a				:			Optional[int]=1e-12   ,   a				:			List[Any]=0   ,   a				:			str="absolute"   ,   a				:			Optional[int]=True   ,   **a				:			Optional[int]   ,   )				->							Any:

				"""simple docstring"""

				super().__init__(**a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							vocab_size
				SCREAMING_SNAKE_CASE :   str					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   List[Any]					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   str					  =							hidden_act
				SCREAMING_SNAKE_CASE :   Dict					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   str					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Tuple					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   Tuple					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   str					  =							layer_norm_eps
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							position_embedding_type
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							use_cache
				SCREAMING_SNAKE_CASE :   Any					  =							pad_token_id






			@classmethod
			def 				__UpperCamelCase			(      cls				:			int   ,   a				:			Union[str, os.PathLike]   ,   **a				:			List[str]						)				->							"PretrainedConfig":

				"""simple docstring"""

				cls._set_token_in_kwargs(a						)

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   int					  =							cls.get_config_dict(a   ,   **a						)

				# get the text config dict if we are loading from AlignConfig
				if config_dict.get("model_type"						) == "align":
					SCREAMING_SNAKE_CASE :   Dict					  =							config_dict["text_config"]

				if "model_type" in config_dict and hasattr(cls   ,   "model_type"						) and config_dict["model_type"] != cls.model_type:
					logger.warning(
					    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
					    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."						)

				return cls.from_dict(a   ,   **a						)






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='align_vision_model'
			def __init__(      self				:			int   ,   a				:			int = 3   ,   a				:			int = 600   ,   a				:			float = 2.0   ,   a				:			float = 3.1   ,   a				:			int = 8   ,   a				:			List[int] = [3, 3, 5, 3, 5, 5, 3]   ,   a				:			List[int] = [32, 16, 24, 40, 80, 112, 192]   ,   a				:			List[int] = [16, 24, 40, 80, 112, 192, 320]   ,   a				:			List[int] = []   ,   a				:			List[int] = [1, 2, 2, 2, 1, 2, 1]   ,   a				:			List[int] = [1, 2, 2, 3, 3, 4, 1]   ,   a				:			List[int] = [1, 6, 6, 6, 6, 6, 6]   ,   a				:			float = 0.25   ,   a				:			str = "swish"   ,   a				:			int = 2560   ,   a				:			str = "mean"   ,   a				:			float = 0.02   ,   a				:			float = 0.001   ,   a				:			float = 0.99   ,   a				:			float = 0.2   ,   **a				:			List[Any]   ,   )				->							Optional[Any]:

				"""simple docstring"""

				super().__init__(**a						)

				SCREAMING_SNAKE_CASE :   Dict					  =							num_channels
				SCREAMING_SNAKE_CASE :   List[str]					  =							image_size
				SCREAMING_SNAKE_CASE :   Dict					  =							width_coefficient
				SCREAMING_SNAKE_CASE :   List[Any]					  =							depth_coefficient
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							depth_divisor
				SCREAMING_SNAKE_CASE :   Tuple					  =							kernel_sizes
				SCREAMING_SNAKE_CASE :   List[str]					  =							in_channels
				SCREAMING_SNAKE_CASE :   Any					  =							out_channels
				SCREAMING_SNAKE_CASE :   str					  =							depthwise_padding
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							strides
				SCREAMING_SNAKE_CASE :   str					  =							num_block_repeats
				SCREAMING_SNAKE_CASE :   int					  =							expand_ratios
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							squeeze_expansion_ratio
				SCREAMING_SNAKE_CASE :   int					  =							hidden_act
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_dim
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							pooling_type
				SCREAMING_SNAKE_CASE :   Dict					  =							initializer_range
				SCREAMING_SNAKE_CASE :   List[str]					  =							batch_norm_eps
				SCREAMING_SNAKE_CASE :   Tuple					  =							batch_norm_momentum
				SCREAMING_SNAKE_CASE :   Tuple					  =							drop_connect_rate
				SCREAMING_SNAKE_CASE :   List[Any]					  =							sum(a						) * 4






			@classmethod
			def 				__UpperCamelCase			(      cls				:			int   ,   a				:			Union[str, os.PathLike]   ,   **a				:			Dict						)				->							"PretrainedConfig":

				"""simple docstring"""

				cls._set_token_in_kwargs(a						)

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   str					  =							cls.get_config_dict(a   ,   **a						)

				# get the vision config dict if we are loading from AlignConfig
				if config_dict.get("model_type"						) == "align":
					SCREAMING_SNAKE_CASE :   List[Any]					  =							config_dict["vision_config"]

				if "model_type" in config_dict and hasattr(cls   ,   "model_type"						) and config_dict["model_type"] != cls.model_type:
					logger.warning(
					    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
					    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."						)

				return cls.from_dict(a   ,   **a						)






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='align'
			lowerCamelCase__							     =True
			def __init__(      self				:			Dict   ,   a				:			Optional[Any]=None   ,   a				:			List[str]=None   ,   a				:			List[str]=640   ,   a				:			Optional[Any]=1.0   ,   a				:			int=0.02   ,   **a				:			Optional[Any]   ,   )				->							Optional[Any]:

				"""simple docstring"""

				super().__init__(**a						)

				if text_config is None:
					SCREAMING_SNAKE_CASE :   int					  =							{}
					logger.info("text_config is None. Initializing the AlignTextConfig with default values."						)

				if vision_config is None:
					SCREAMING_SNAKE_CASE :   List[str]					  =							{}
					logger.info("vision_config is None. Initializing the AlignVisionConfig with default values."						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							AlignTextConfig(**a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							AlignVisionConfig(**a						)

				SCREAMING_SNAKE_CASE :   int					  =							projection_dim
				SCREAMING_SNAKE_CASE :   Dict					  =							temperature_init_value
				SCREAMING_SNAKE_CASE :   str					  =							initializer_range
			@classmethod
			def 				__UpperCamelCase			(      cls				:			Union[str, Any]   ,   a				:			AlignTextConfig   ,   a				:			AlignVisionConfig   ,   **a				:			Tuple						)				->							Any:

				"""simple docstring"""

				return cls(text_config=text_config.to_dict()   ,   vision_config=vision_config.to_dict()   ,   **a						)






			def 				__UpperCamelCase			(      self				:			Any						)				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							copy.deepcopy(self.__dict__						)
				SCREAMING_SNAKE_CASE :   Any					  =							self.text_config.to_dict()
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.vision_config.to_dict()
				SCREAMING_SNAKE_CASE :   str					  =							self.__class__.model_type
				return output

"""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()