




"""simple docstring"""




import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


a_            =   logging.get_logger(__name__)

a_            =   OrderedDict(
    [
        ('align', 'AlignProcessor'),
        ('altclip', 'AltCLIPProcessor'),
        ('bark', 'BarkProcessor'),
        ('blip', 'BlipProcessor'),
        ('blip-2', 'Blip2Processor'),
        ('bridgetower', 'BridgeTowerProcessor'),
        ('chinese_clip', 'ChineseCLIPProcessor'),
        ('clap', 'ClapProcessor'),
        ('clip', 'CLIPProcessor'),
        ('clipseg', 'CLIPSegProcessor'),
        ('flava', 'FlavaProcessor'),
        ('git', 'GitProcessor'),
        ('groupvit', 'CLIPProcessor'),
        ('hubert', 'Wav2Vec2Processor'),
        ('instructblip', 'InstructBlipProcessor'),
        ('layoutlmv2', 'LayoutLMv2Processor'),
        ('layoutlmv3', 'LayoutLMv3Processor'),
        ('markuplm', 'MarkupLMProcessor'),
        ('mctct', 'MCTCTProcessor'),
        ('mgp-str', 'MgpstrProcessor'),
        ('oneformer', 'OneFormerProcessor'),
        ('owlvit', 'OwlViTProcessor'),
        ('pix2struct', 'Pix2StructProcessor'),
        ('sam', 'SamProcessor'),
        ('sew', 'Wav2Vec2Processor'),
        ('sew-d', 'Wav2Vec2Processor'),
        ('speech_to_text', 'Speech2TextProcessor'),
        ('speech_to_text_2', 'Speech2Text2Processor'),
        ('speecht5', 'SpeechT5Processor'),
        ('trocr', 'TrOCRProcessor'),
        ('tvlt', 'TvltProcessor'),
        ('unispeech', 'Wav2Vec2Processor'),
        ('unispeech-sat', 'Wav2Vec2Processor'),
        ('vilt', 'ViltProcessor'),
        ('vision-text-dual-encoder', 'VisionTextDualEncoderProcessor'),
        ('wav2vec2', 'Wav2Vec2Processor'),
        ('wav2vec2-conformer', 'Wav2Vec2Processor'),
        ('wavlm', 'Wav2Vec2Processor'),
        ('whisper', 'WhisperProcessor'),
        ('xclip', 'XCLIPProcessor'),
    ]
)

a_            =   _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)







def       __UpperCAmelCase      (				__UpperCamelCase					):







			for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
						if class_name in processors:
									__lowercase	:       str				=   model_type_to_module_name(__UpperCamelCase					)

									__lowercase	:       Any				=   importlib.import_module(f""".{module_name}"""					,							'''transformers.models'''					)
									try:
												return getattr(__UpperCamelCase					,							__UpperCamelCase					)
									except AttributeError:
												continue

			for processor in PROCESSOR_MAPPING._extra_content.values():
						if getattr(__UpperCamelCase					,							'''__name__'''					,							__UpperCamelCase					) == class_name:
									return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
			__lowercase	:       List[str]				=   importlib.import_module('''transformers'''					)
			if hasattr(__UpperCamelCase					,							__UpperCamelCase					):
						return getattr(__UpperCamelCase					,							__UpperCamelCase					)

			return None


class        UpperCAmelCase_  :
		def __init__(		self   )		->   Union[str, Any]:
					raise EnvironmentError(
					    '''AutoProcessor is designed to be instantiated '''
					    '''using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''   )
		@classmethod
		@replace_list_option_in_docstrings(UpperCamelCase_   )
		def 					_lowerCamelCase    (		cls							,		UpperCamelCase_							,		**UpperCamelCase_   )		->   Optional[int]:
					__lowercase	:       Any				=   kwargs.pop('''config'''							,		UpperCamelCase_   )
					__lowercase	:       Union[str, Any]				=   kwargs.pop('''trust_remote_code'''							,		UpperCamelCase_   )
					__lowercase	:       Tuple				=   True

					__lowercase	:       int				=   None
					__lowercase	:       int				=   None

					# First, let's see if we have a preprocessor config.
					# Filter the kwargs for `get_file_from_repo`.
					__lowercase	:       Optional[Any]				=   {
					    key: kwargs[key] for key in inspect.signature(UpperCamelCase_   ).parameters.keys() if key in kwargs
					}
					# Let's start by checking whether the processor class is saved in an image processor
					__lowercase	:       Union[str, Any]				=   get_file_from_repo(
					    UpperCamelCase_							,		UpperCamelCase_							,		**UpperCamelCase_   )
					if preprocessor_config_file is not None:
								__lowercase	,__lowercase	:       List[str]				=   ImageProcessingMixin.get_image_processor_dict(UpperCamelCase_							,		**UpperCamelCase_   )
								__lowercase	:       Optional[Any]				=   config_dict.get('''processor_class'''							,		UpperCamelCase_   )
								if "AutoProcessor" in config_dict.get('''auto_map'''							,		{}   ):
											__lowercase	:       Optional[int]				=   config_dict['''auto_map''']['''AutoProcessor''']

        # If not found, let's check whether the processor class is saved in a feature extractor config
					if preprocessor_config_file is not None and processor_class is None:
								__lowercase	,__lowercase	:       List[str]				=   FeatureExtractionMixin.get_feature_extractor_dict(UpperCamelCase_							,		**UpperCamelCase_   )
								__lowercase	:       List[str]				=   config_dict.get('''processor_class'''							,		UpperCamelCase_   )
								if "AutoProcessor" in config_dict.get('''auto_map'''							,		{}   ):
											__lowercase	:       Optional[int]				=   config_dict['''auto_map''']['''AutoProcessor''']

					if processor_class is None:
								# Next, let's check whether the processor class is saved in a tokenizer
								__lowercase	:       str				=   get_file_from_repo(
								    UpperCamelCase_							,		UpperCamelCase_							,		**UpperCamelCase_   )
								if tokenizer_config_file is not None:
											with open(UpperCamelCase_							,		encoding='''utf-8'''   ) as reader:
														__lowercase	:       Tuple				=   json.load(UpperCamelCase_   )

											__lowercase	:       Optional[int]				=   config_dict.get('''processor_class'''							,		UpperCamelCase_   )
											if "AutoProcessor" in config_dict.get('''auto_map'''							,		{}   ):
														__lowercase	:       str				=   config_dict['''auto_map''']['''AutoProcessor''']

					if processor_class is None:
								# Otherwise, load config, if it can be loaded.
								if not isinstance(UpperCamelCase_							,		UpperCamelCase_   ):
											__lowercase	:       Union[str, Any]				=   AutoConfig.from_pretrained(
											    UpperCamelCase_							,		trust_remote_code=UpperCamelCase_							,		**UpperCamelCase_   )

								# And check if the config contains the processor class.
								__lowercase	:       int				=   getattr(UpperCamelCase_							,		'''processor_class'''							,		UpperCamelCase_   )
								if hasattr(UpperCamelCase_							,		'''auto_map'''   ) and "AutoProcessor" in config.auto_map:
											__lowercase	:       Optional[Any]				=   config.auto_map['''AutoProcessor''']

					if processor_class is not None:
								__lowercase	:       int				=   processor_class_from_name(UpperCamelCase_   )

					__lowercase	:       Union[str, Any]				=   processor_auto_map is not None
					__lowercase	:       Any				=   processor_class is not None or type(UpperCamelCase_   ) in PROCESSOR_MAPPING
					__lowercase	:       Optional[Any]				=   resolve_trust_remote_code(
					    UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )

					if has_remote_code and trust_remote_code:
								__lowercase	:       Optional[int]				=   get_class_from_dynamic_module(
								    UpperCamelCase_							,		UpperCamelCase_							,		**UpperCamelCase_   )
								__lowercase	:       int				=   kwargs.pop('''code_revision'''							,		UpperCamelCase_   )
								if os.path.isdir(UpperCamelCase_   ):
											processor_class.register_for_auto_class()
								return processor_class.from_pretrained(
								    UpperCamelCase_							,		trust_remote_code=UpperCamelCase_							,		**UpperCamelCase_   )
					elif processor_class is not None:
								return processor_class.from_pretrained(
								    UpperCamelCase_							,		trust_remote_code=UpperCamelCase_							,		**UpperCamelCase_   )
					# Last try: we use the PROCESSOR_MAPPING.
					elif type(UpperCamelCase_   ) in PROCESSOR_MAPPING:
								return PROCESSOR_MAPPING[type(UpperCamelCase_   )].from_pretrained(UpperCamelCase_							,		**UpperCamelCase_   )

					# At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
					# tokenizer.
					try:
								return AutoTokenizer.from_pretrained(
								    UpperCamelCase_							,		trust_remote_code=UpperCamelCase_							,		**UpperCamelCase_   )
					except Exception:
								try:
											return AutoImageProcessor.from_pretrained(
											    UpperCamelCase_							,		trust_remote_code=UpperCamelCase_							,		**UpperCamelCase_   )
								except Exception:
											pass

								try:
											return AutoFeatureExtractor.from_pretrained(
											    UpperCamelCase_							,		trust_remote_code=UpperCamelCase_							,		**UpperCamelCase_   )
								except Exception:
											pass

					raise ValueError(
					    F"""Unrecognized processing class in {pretrained_model_name_or_path}. Can't instantiate a processor, a """
					    '''tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'''
					    '''the files of at least one of those processing classes.'''   )
		@staticmethod
		def 					_lowerCamelCase    (		UpperCamelCase_							,		UpperCamelCase_   )		->   List[Any]:
					PROCESSOR_MAPPING.register(UpperCamelCase_							,		UpperCamelCase_   )









"""simple docstring"""




import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
			print('#################### AVAILABLE DEVICES ####################')
			print(jax.devices())
			print('###########################################################')

			# setup for wandb sweep
			a_            =   Args()
			a_            =   wandb.init(project='bigbird-natural-questions', config=args.__dict__)
			a_            =   dict(logger.config)
			del wandb_args["batch_size"]
			a_            =   replace(args, **wandb_args)
			a_            =   args.base_dir + '-' + wandb.run.id
			a_            =   replace(args, base_dir=base_dir)
			print(args)

			a_            =   load_dataset('json', data_files=args.tr_data_path)['train']
			a_            =   load_dataset('json', data_files=args.val_data_path)['train']

			# drop extra batch for now
			a_            =   range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
			a_            =   tr_dataset.shuffle().select(indices)
			a_            =   range(len(val_dataset) - len(val_dataset) % args.batch_size)
			a_            =   val_dataset.shuffle().select(indices)

			if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
						a_            =   tr_dataset.shuffle().select(range(8_0_0_0_0))
						a_            =   val_dataset.shuffle().select(range(8_0_0_0))

			print(tr_dataset)
			print(val_dataset)

			a_            =   FlaxBigBirdForNaturalQuestions.from_pretrained(
			    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
			)
			a_            =   BigBirdTokenizerFast.from_pretrained(args.model_id)
			a_            =   DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

			a_            =   {
			    'lr': args.lr,
			    'init_lr': args.init_lr,
			    'warmup_steps': args.warmup_steps,
			    'num_train_steps': args.max_epochs * (len(tr_dataset) // args.batch_size),
			    'weight_decay': args.weight_decay,
			}
			a_       ,	a_            =   build_tx(**tx_args)

			a_            =   Trainer(
			    args=args,
			    data_collator=data_collator,
			    model_save_fn=model.save_pretrained,
			    train_step_fn=train_step,
			    val_step_fn=val_step,
			    logger=logger,
			    scheduler_fn=lr,
			)

			a_            =   None
			a_            =   trainer.create_state(model, tx, num_train_steps=tx_args['num_train_steps'], ckpt_dir=ckpt_dir)
			try:
						trainer.train(state, tr_dataset, val_dataset)
			except KeyboardInterrupt:
						print('Oooops; TRAINING STOPPED UNFORTUNATELY')

			print('SAVING WEIGHTS IN `final-weights`')
			a_            =   jax_utils.unreplicate(state.params)
			model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)



