

"""simple docstring"""
import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
A									=						"""src/diffusers"""
A									=						"""docs/source/en"""
A									=						"""."""







def       UpperCamelCase_					(     lowerCamelCase							:					Optional[Any]     , lowerCamelCase							:					int     , lowerCamelCase							:					Tuple   )						->     List[str]:



       """simple docstring"""







       with open(lowercase_     , '''r'''     , encoding='''utf-8'''     , newline='''\n'''   ) as f:
              __magic_name__			:		Optional[Any]        =     f.readlines()
       # Find the start prompt.
       __magic_name__			:		List[Any]        =     0
       while not lines[start_index].startswith(lowercase_   ):
              start_index += 1
       start_index += 1

       __magic_name__			:		List[Any]        =     start_index
       while not lines[end_index].startswith(lowercase_   ):
              end_index += 1
       end_index -= 1

       while len(lines[start_index]   ) <= 1:
              start_index += 1
       while len(lines[end_index]   ) <= 1:
              end_index -= 1
       end_index += 1
       return "".join(lines[start_index:end_index]   ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
A									=						"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
A									=						re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
A									=						re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
A									=						re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
A									=						importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
A									=						spec.loader.load_module()







def       UpperCamelCase_					(     lowerCamelCase							:					Dict   )						->     Optional[int]:



       """simple docstring"""







       __magic_name__			:		int        =     re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''     , lowercase_   )
       return [m.group(0   ) for m in matches]







def       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					Optional[int]   )						->     List[str]:



       """simple docstring"""







       __magic_name__			:		Tuple        =     2 if text == '''✅''' or text == '''❌''' else len(lowercase_   )
       __magic_name__			:		Optional[Any]        =     (width - text_length) // 2
       __magic_name__			:		str        =     width - text_length - left_indent
       return " " * left_indent + text + " " * right_indent







def       UpperCamelCase_					(     )						->     List[Any]:



       """simple docstring"""







       __magic_name__			:		str        =     diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       __magic_name__			:		int        =     {
           name: config_mapping_names[code]
           for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
           if code in config_mapping_names
       }
       __magic_name__			:		Dict        =     {name: config.replace('''ConfigMixin'''     , ''''''   ) for name, config in model_name_to_config.items()}

       # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
       __magic_name__			:		int        =     collections.defaultdict(lowercase_   )
       __magic_name__			:		str        =     collections.defaultdict(lowercase_   )
       __magic_name__			:		int        =     collections.defaultdict(lowercase_   )
       __magic_name__			:		Optional[Any]        =     collections.defaultdict(lowercase_   )
       __magic_name__			:		int        =     collections.defaultdict(lowercase_   )

       # Let's lookup through all diffusers object (once).
       for attr_name in dir(lowercase_   ):
              __magic_name__			:		List[Any]        =     None
              if attr_name.endswith('''Tokenizer'''   ):
                     __magic_name__			:		int        =     slow_tokenizers
                     __magic_name__			:		Tuple        =     attr_name[:-9]
              elif attr_name.endswith('''TokenizerFast'''   ):
                     __magic_name__			:		Optional[int]        =     fast_tokenizers
                     __magic_name__			:		Any        =     attr_name[:-13]
              elif _re_tf_models.match(lowercase_   ) is not None:
                     __magic_name__			:		Optional[Any]        =     tf_models
                     __magic_name__			:		Tuple        =     _re_tf_models.match(lowercase_   ).groups()[0]
              elif _re_flax_models.match(lowercase_   ) is not None:
                     __magic_name__			:		int        =     flax_models
                     __magic_name__			:		int        =     _re_flax_models.match(lowercase_   ).groups()[0]
              elif _re_pt_models.match(lowercase_   ) is not None:
                     __magic_name__			:		str        =     pt_models
                     __magic_name__			:		int        =     _re_pt_models.match(lowercase_   ).groups()[0]

              if lookup_dict is not None:
                     while len(lowercase_   ) > 0:
                            if attr_name in model_name_to_prefix.values():
                                   __magic_name__			:		Tuple        =     True
                                   break
                            # Try again after removing the last word in the name
                            __magic_name__			:		Optional[Any]        =     ''''''.join(camel_case_split(lowercase_   )[:-1]   )

    # Let's build that table!
       __magic_name__			:		int        =     list(model_name_to_config.keys()   )
       model_names.sort(key=str.lower   )
       __magic_name__			:		Optional[int]        =     ['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
       # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
       __magic_name__			:		Any        =     [len(lowercase_   ) + 2 for c in columns]
       __magic_name__			:		Union[str, Any]        =     max([len(lowercase_   ) for name in model_names]   ) + 2

       # Build the table per se
       __magic_name__			:		Tuple        =     '''|''' + '''|'''.join([_center_text(lowercase_     , lowercase_   ) for c, w in zip(lowercase_     , lowercase_   )]   ) + '''|\n'''
       # Use ":-----:" format to center-aligned table cell texts
       table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]   ) + "|\n"

       __magic_name__			:		Union[str, Any]        =     {True: '''✅''', False: '''❌'''}
       for name in model_names:
              __magic_name__			:		List[Any]        =     model_name_to_prefix[name]
              __magic_name__			:		str        =     [
                  name,
                  check[slow_tokenizers[prefix]],
                  check[fast_tokenizers[prefix]],
                  check[pt_models[prefix]],
                  check[tf_models[prefix]],
                  check[flax_models[prefix]],
              ]
              table += "|" + "|".join([_center_text(lowercase_     , lowercase_   ) for l, w in zip(lowercase_     , lowercase_   )]   ) + "|\n"
       return table







def       UpperCamelCase_					(     lowerCamelCase							:					int=False   )						->     List[Any]:



       """simple docstring"""







       __magic_name__       ,							__magic_name__       ,							__magic_name__       ,							__magic_name__			:		Optional[int]        =     _find_text_in_file(
           filename=os.path.join(lowercase_     , '''index.mdx'''   )     , start_prompt='''<!--This table is updated automatically from the auto modules'''     , end_prompt='''<!-- End table-->'''     , )
       __magic_name__			:		Union[str, Any]        =     get_model_table_from_auto_modules()

       if current_table != new_table:
              if overwrite:
                     with open(os.path.join(lowercase_     , '''index.mdx'''   )     , '''w'''     , encoding='''utf-8'''     , newline='''\n'''   ) as f:
                            f.writelines(lines[:start_index] + [new_table] + lines[end_index:]   )
              else:
                     raise ValueError(
                         '''The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this.'''   )


if __name__ == "__main__":
 A									=						argparse.ArgumentParser()
 parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
 A									=						parser.parse_args()

 check_model_table(args.fix_and_overwrite)





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

				snake_case_								=			load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
				snake_case_								=			load_dataset("""json""", data_files=args.val_data_path)["""train"""]

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

				if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
								snake_case_								=			tr_dataset.shuffle().select(range(8_0000))
								snake_case_								=			val_dataset.shuffle().select(range(8000))

				print(tr_dataset)
				print(val_dataset)

				snake_case_								=			FlaxBigBirdForNaturalQuestions.from_pretrained(
				    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
				)
				snake_case_								=			BigBirdTokenizerFast.from_pretrained(args.model_id)
				snake_case_								=			DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

				snake_case_								=			{
				    """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,
				}
				snake_case_	,  snake_case_								=			build_tx(**tx_args)

				snake_case_								=			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,
				)

				snake_case_								=			None
				snake_case_								=			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`""")
				snake_case_								=			jax_utils.unreplicate(state.params)
				model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)





