



'''simple docstring'''


from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
						import timm


if is_torch_available():
						from torch import Tensor




class      __UpperCamelCase       (_lowercase			,   _lowercase			):
	__A			  =					'''pixel_values'''
	__A			  =					False
	__A			  =					TimmBackboneConfig







	def __init__(    self ,				_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    Any:
							'''simple docstring'''






							requires_backends(self ,				"""timm"""						)
							super().__init__(A_						)
							lowercase        =							config

							if config.backbone is None:
													raise ValueError("""backbone is not set in the config. Please set it to a timm model name."""						)

							if config.backbone not in timm.list_models():
													raise ValueError(F"""backbone {config.backbone} is not supported by timm."""						)

							if hasattr(A_ ,				"""out_features"""						) and config.out_features is not None:
													raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead."""						)

							lowercase        =							getattr(A_ ,				"""use_pretrained_backbone""" ,				A_						)
							if pretrained is None:
													raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False."""						)

							# We just take the final layer by default. This matches the default for the transformers models.
							lowercase        =							config.out_indices if getattr(A_ ,				"""out_indices""" ,				A_						) is not None else (-1,)

							lowercase        =							timm.create_model(
							    config.backbone ,				pretrained=A_ ,				features_only=config.features_only ,				in_chans=config.num_channels ,				out_indices=A_ ,				**A_ ,				)
							# These are used to control the output of the model when called. If output_hidden_states is True, then
							# return_layers is modified to include all layers.
							lowercase        =							self._backbone.return_layers
							lowercase        =							{layer["""module"""]: str(A_						) for i, layer in enumerate(self._backbone.feature_info.info						)}
							super()._init_backbone(A_						)







	@classmethod
	def   _a   (    cls ,				_lowerCAmelCase ,				*_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							requires_backends(cls ,				["""vision""", """timm"""]						)
							from ...models.timm_backbone import TimmBackboneConfig

							lowercase        =							kwargs.pop("""config""" ,				TimmBackboneConfig()						)

							lowercase        =							kwargs.pop("""use_timm_backbone""" ,				A_						)
							if not use_timm:
													raise ValueError("""use_timm_backbone must be True for timm backbones"""						)

							lowercase        =							kwargs.pop("""num_channels""" ,				config.num_channels						)
							lowercase        =							kwargs.pop("""features_only""" ,				config.features_only						)
							lowercase        =							kwargs.pop("""use_pretrained_backbone""" ,				config.use_pretrained_backbone						)
							lowercase        =							kwargs.pop("""out_indices""" ,				config.out_indices						)
							lowercase        =							TimmBackboneConfig(
							    backbone=A_ ,				num_channels=A_ ,				features_only=A_ ,				use_pretrained_backbone=A_ ,				out_indices=A_ ,				)
							return super()._from_config(A_ ,				**A_						)







	def   _a   (    self ,				_lowerCAmelCase						)    ->    Any:
							'''simple docstring'''






							pass







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None ,				**_lowerCAmelCase						)    ->    Union[BackboneOutput, Tuple[Tensor, ...]]:
							'''simple docstring'''






							lowercase        =							return_dict if return_dict is not None else self.config.use_return_dict
							lowercase        =							(
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							lowercase        =							output_attentions if output_attentions is not None else self.config.output_attentions

							if output_attentions:
													raise ValueError("""Cannot output attentions for timm backbones at the moment"""						)

							if output_hidden_states:
													# We modify the return layers to include all the stages of the backbone
													lowercase        =							self._all_layers
													lowercase        =							self._backbone(A_ ,				**A_						)
													lowercase        =							self._return_layers
													lowercase        =							tuple(hidden_states[i] for i in self.out_indices						)
							else:
													lowercase        =							self._backbone(A_ ,				**A_						)
													lowercase        =							None

							lowercase        =							tuple(A_						)
							lowercase        =							tuple(A_						) if hidden_states is not None else None

							if not return_dict:
													lowercase        =							(feature_maps,)
													if output_hidden_states:
																			lowercase        =							output + (hidden_states,)
													return output

							return BackboneOutput(feature_maps=A_ ,				hidden_states=A_ ,				attentions=A_						)







'''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,
)


lowercase_      :				Tuple   					=    logging.getLogger(__name__)




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




@dataclass
class      __UpperCamelCase       :
	__A			  =					field(
	    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}			)
	__A			  =					field(
	    default='''summarization'''			,   metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''}			,   )
	__A			  =					field(
	    default=1024			,   metadata={
	        '''help''': (
	            '''The maximum total input sequence length after tokenization. Sequences longer '''
	            '''than this will be truncated, sequences shorter will be padded.'''
	        )
	    }			,   )
	__A			  =					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.'''
	        )
	    }			,   )
	__A			  =					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``.'''
	        )
	    }			,   )
	__A			  =					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.'''
	        )
	    }			,   )
	__A			  =					field(default=-1			,   metadata={'''help''': '''# training examples. -1 means use all.'''}			)
	__A			  =					field(default=-1			,   metadata={'''help''': '''# validation examples. -1 means use all.'''}			)
	__A			  =					field(default=-1			,   metadata={'''help''': '''# test examples. -1 means use all.'''}			)
	__A			  =					field(default=_UpperCAmelCase			,   metadata={'''help''': '''Source language id for translation.'''}			)
	__A			  =					field(default=_UpperCAmelCase			,   metadata={'''help''': '''Target language id for translation.'''}			)
	__A			  =					field(default=_UpperCAmelCase			,   metadata={'''help''': '''# num_beams to use for evaluation.'''}			)
	__A			  =					field(
	    default=_UpperCAmelCase			,   metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''}			,   )







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[Any] ,  lowercase_    :      int ,  lowercase_    :      List[Any]						):
						logger.info(F"""***** {split} metrics *****"""						)
						for key in sorted(metrics.keys()						):
												logger.info(F"""  {key} = {metrics[key]}"""						)
						save_json(lowercase_ ,  os.path.join(lowercase_ ,  F"""{split}_results.json"""						)						)







def      SCREAMING_SNAKE_CASE				(				):
						# 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.

						lowercase        =							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.
												lowercase     ,   lowercase     ,   lowercase        =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
						else:
												lowercase     ,   lowercase     ,   lowercase        =							parser.parse_args_into_dataclasses()

						check_output_dir(lowercase_						)

						# 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""" ,  lowercase_						)

						# 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.

						lowercase        =							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 ,  )

						lowercase        =							("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
						for p in extra_model_params:
												if getattr(lowercase_ ,  lowercase_ ,  lowercase_						):
																		assert hasattr(lowercase_ ,  lowercase_						), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
																		setattr(lowercase_ ,  lowercase_ ,  getattr(lowercase_ ,  lowercase_						)						)

						lowercase        =							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 ,  )
						lowercase        =							AutoModelForSeqaSeqLM.from_pretrained(
						    model_args.model_name_or_path ,  from_tf=""".ckpt""" in model_args.model_name_or_path ,  config=lowercase_ ,  cache_dir=model_args.cache_dir ,  )

						# use task specific params
						use_task_specific_params(lowercase_ ,  data_args.task						)

						# set num_beams for evaluation
						if data_args.eval_beams is None:
												lowercase        =							model.config.num_beams

						# set decoder_start_token_id for MBart
						if model.config.decoder_start_token_id is None and isinstance(lowercase_ ,  (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(lowercase_ ,  lowercase_						):
																		lowercase        =							tokenizer.lang_code_to_id[data_args.tgt_lang]
												else:
																		lowercase        =							tokenizer.convert_tokens_to_ids(data_args.tgt_lang						)

						if model_args.freeze_embeds:
												freeze_embeds(lowercase_						)
						if model_args.freeze_encoder:
												freeze_params(model.get_encoder()						)
												assert_all_frozen(model.get_encoder()						)

						lowercase        =							SeqaSeqDataset

						# Get datasets
						lowercase        =							(
						    dataset_class(
						        lowercase_ ,  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
						)
						lowercase        =							(
						    dataset_class(
						        lowercase_ ,  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
						)
						lowercase        =							(
						    dataset_class(
						        lowercase_ ,  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
						lowercase        =							(
						    build_compute_metrics_fn(data_args.task ,  lowercase_						) if training_args.predict_with_generate else None
						)
						lowercase        =							SeqaSeqTrainer(
						    model=lowercase_ ,  args=lowercase_ ,  data_args=lowercase_ ,  train_dataset=lowercase_ ,  eval_dataset=lowercase_ ,  data_collator=SeqaSeqDataCollator(
						        lowercase_ ,  lowercase_ ,  model.config.decoder_start_token_id ,  training_args.tpu_num_cores						) ,  compute_metrics=lowercase_ ,  tokenizer=lowercase_ ,  )

						lowercase        =							{}
						# Training
						if training_args.do_train:
												logger.info("""*** Train ***"""						)

												lowercase        =							trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
												lowercase        =							train_result.metrics
												lowercase        =							data_args.n_train

												trainer.save_model()  # this also saves the tokenizer

												if trainer.is_world_process_zero():
																		handle_metrics("""train""" ,  lowercase_ ,  training_args.output_dir						)
																		all_metrics.update(lowercase_						)

																		# 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 ***"""						)

												lowercase        =							trainer.evaluate(metric_key_prefix="""val"""						)
												lowercase        =							data_args.n_val
												lowercase        =							round(metrics["""val_loss"""] ,  4						)

												if trainer.is_world_process_zero():
																		handle_metrics("""val""" ,  lowercase_ ,  training_args.output_dir						)
																		all_metrics.update(lowercase_						)

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

												lowercase        =							trainer.predict(test_dataset=lowercase_ ,  metric_key_prefix="""test"""						)
												lowercase        =							test_output.metrics
												lowercase        =							data_args.n_test

												if trainer.is_world_process_zero():
																		lowercase        =							round(metrics["""test_loss"""] ,  4						)
																		handle_metrics("""test""" ,  lowercase_ ,  training_args.output_dir						)
																		all_metrics.update(lowercase_						)

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

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

						return all_metrics







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict						):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()


