



"""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
snake_case_								=			"""src/diffusers"""
snake_case_								=			"""docs/source/en"""
snake_case_								=			"""."""

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

						UpperCAmelCase			 =					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 |
snake_case_								=			"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
snake_case_								=			re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
snake_case_								=			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.
snake_case_								=			re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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

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

def 					_lowerCAmelCase (  lowercase_						,      lowercase_		):
						UpperCAmelCase			 =					2 if text == '✅' or text == '❌' else len(lowercase_		)
						UpperCAmelCase			 =					(width - text_length) // 2
						UpperCAmelCase			 =					width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent

def 					_lowerCAmelCase (  ):
						UpperCAmelCase			 =					diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						UpperCAmelCase			 =					{
						    name: config_mapping_names[code]
						    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
						    if code in config_mapping_names
						}
						UpperCAmelCase			 =					{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.
						UpperCAmelCase			 =					collections.defaultdict(lowercase_		)
						UpperCAmelCase			 =					collections.defaultdict(lowercase_		)
						UpperCAmelCase			 =					collections.defaultdict(lowercase_		)
						UpperCAmelCase			 =					collections.defaultdict(lowercase_		)
						UpperCAmelCase			 =					collections.defaultdict(lowercase_		)

						# Let's lookup through all diffusers object (once).
						for attr_name in dir(lowercase_		):
												UpperCAmelCase			 =					None
												if attr_name.endswith('Tokenizer'		):
																		UpperCAmelCase			 =					slow_tokenizers
																		UpperCAmelCase			 =					attr_name[:-9]
												elif attr_name.endswith('TokenizerFast'		):
																		UpperCAmelCase			 =					fast_tokenizers
																		UpperCAmelCase			 =					attr_name[:-13]
												elif _re_tf_models.match(lowercase_		) is not None:
																		UpperCAmelCase			 =					tf_models
																		UpperCAmelCase			 =					_re_tf_models.match(lowercase_		).groups()[0]
												elif _re_flax_models.match(lowercase_		) is not None:
																		UpperCAmelCase			 =					flax_models
																		UpperCAmelCase			 =					_re_flax_models.match(lowercase_		).groups()[0]
												elif _re_pt_models.match(lowercase_		) is not None:
																		UpperCAmelCase			 =					pt_models
																		UpperCAmelCase			 =					_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():
																														UpperCAmelCase			 =					True
																														break
																								# Try again after removing the last word in the name
																								UpperCAmelCase			 =					''.join(camel_case_split(lowercase_		)[:-1]		)

    # Let's build that table!
						UpperCAmelCase			 =					list(model_name_to_config.keys()		)
						model_names.sort(key=str.lower		)
						UpperCAmelCase			 =					['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).
						UpperCAmelCase			 =					[len(lowercase_		) + 2 for c in columns]
						UpperCAmelCase			 =					max([len(lowercase_		) for name in model_names]		) + 2

						# Build the table per se
						UpperCAmelCase			 =					'|' + '|'.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"

						UpperCAmelCase			 =					{True: '✅', False: '❌'}
						for name in model_names:
												UpperCAmelCase			 =					model_name_to_prefix[name]
												UpperCAmelCase			 =					[
												    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 					_lowerCAmelCase (  lowercase_=False		):
						UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					_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-->'						,      )
						UpperCAmelCase			 =					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__":
				snake_case_								=			argparse.ArgumentParser()
				parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
				snake_case_								=			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)





