






"""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   _lowerCAmelCase		(				UpperCAmelCase__							:       Any,    UpperCAmelCase__							:       List[str],    UpperCAmelCase__							:       Optional[int]   )		->Optional[Any]:
						with open(UpperCAmelCase__,    """r""",    encoding="""utf-8""",    newline="""\n"""   ) as f:
												A__    :	Any			=	f.readlines()
						# Find the start prompt.
						A__    :	List[Any]			=	0
						while not lines[start_index].startswith(UpperCAmelCase__   ):
												start_index += 1
						start_index += 1

						A__    :	str			=	start_index
						while not lines[end_index].startswith(UpperCAmelCase__   ):
												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   _lowerCAmelCase		(				UpperCAmelCase__							:       int   )		->List[Any]:
						A__    :	Optional[Any]			=	re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""",    UpperCAmelCase__   )
						return [m.group(0   ) for m in matches]




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Union[str, Any],    UpperCAmelCase__							:       Optional[Any]   )		->Optional[Any]:
						A__    :	Any			=	2 if text == """✅""" or text == """❌""" else len(UpperCAmelCase__   )
						A__    :	Tuple			=	(width - text_length) // 2
						A__    :	Union[str, Any]			=	width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent




def   _lowerCAmelCase		(				)		->Optional[Any]:
						A__    :	Dict			=	diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						A__    :	Union[str, Any]			=	{
						    name: config_mapping_names[code]
						    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
						    if code in config_mapping_names
						}
						A__    :	Optional[int]			=	{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.
						A__    :	List[str]			=	collections.defaultdict(UpperCAmelCase__   )
						A__    :	Dict			=	collections.defaultdict(UpperCAmelCase__   )
						A__    :	Any			=	collections.defaultdict(UpperCAmelCase__   )
						A__    :	Dict			=	collections.defaultdict(UpperCAmelCase__   )
						A__    :	List[str]			=	collections.defaultdict(UpperCAmelCase__   )

						# Let's lookup through all diffusers object (once).
						for attr_name in dir(UpperCAmelCase__   ):
												A__    :	Optional[int]			=	None
												if attr_name.endswith("""Tokenizer"""   ):
																		A__    :	List[Any]			=	slow_tokenizers
																		A__    :	str			=	attr_name[:-9]
												elif attr_name.endswith("""TokenizerFast"""   ):
																		A__    :	Any			=	fast_tokenizers
																		A__    :	Optional[int]			=	attr_name[:-1_3]
												elif _re_tf_models.match(UpperCAmelCase__   ) is not None:
																		A__    :	Union[str, Any]			=	tf_models
																		A__    :	Tuple			=	_re_tf_models.match(UpperCAmelCase__   ).groups()[0]
												elif _re_flax_models.match(UpperCAmelCase__   ) is not None:
																		A__    :	Optional[Any]			=	flax_models
																		A__    :	Any			=	_re_flax_models.match(UpperCAmelCase__   ).groups()[0]
												elif _re_pt_models.match(UpperCAmelCase__   ) is not None:
																		A__    :	Optional[Any]			=	pt_models
																		A__    :	Optional[int]			=	_re_pt_models.match(UpperCAmelCase__   ).groups()[0]

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

    # Let's build that table!
						A__    :	Optional[int]			=	list(model_name_to_config.keys()   )
						model_names.sort(key=str.lower   )
						A__    :	List[str]			=	["""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).
						A__    :	Union[str, Any]			=	[len(UpperCAmelCase__   ) + 2 for c in columns]
						A__    :	List[Any]			=	max([len(UpperCAmelCase__   ) for name in model_names]   ) + 2

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

						A__    :	List[Any]			=	{True: """✅""", False: """❌"""}
						for name in model_names:
												A__    :	Dict			=	model_name_to_prefix[name]
												A__    :	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(UpperCAmelCase__,    UpperCAmelCase__   ) for l, w in zip(UpperCAmelCase__,    UpperCAmelCase__   )]   ) + "|\n"
						return table




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Tuple=False   )		->Union[str, Any]:
						A__	,	A__	,	A__	,	A__    :	Dict			=	_find_text_in_file(
						    filename=os.path.join(UpperCAmelCase__,    """index.mdx"""   ),    start_prompt="""<!--This table is updated automatically from the auto modules""",    end_prompt="""<!-- End table-->""",    )
						A__    :	Optional[Any]			=	get_model_table_from_auto_modules()

						if current_table != new_table:
												if overwrite:
																		with open(os.path.join(UpperCAmelCase__,    """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
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


A_     =				get_logger(__name__)




def   _lowerCAmelCase		(				UpperCAmelCase__							:       str,    UpperCAmelCase__							:       List[dict],    UpperCAmelCase__							:       list,    UpperCAmelCase__							:       list,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       GradScaler = None,    )		->Any:
						# Model states
						for i, state in enumerate(UpperCAmelCase__   ):
												A__    :	Optional[int]			=	f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
												A__    :	int			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												save(UpperCAmelCase__,    UpperCAmelCase__   )
												logger.info(f'Model weights saved in {output_model_file}'   )
						# Optimizer states
						for i, opt in enumerate(UpperCAmelCase__   ):
												A__    :	str			=	opt.state_dict()
												A__    :	Any			=	f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
												A__    :	Optional[int]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												save(UpperCAmelCase__,    UpperCAmelCase__   )
												logger.info(f'Optimizer state saved in {output_optimizer_file}'   )
						# Scheduler states
						for i, scheduler in enumerate(UpperCAmelCase__   ):
												A__    :	Union[str, Any]			=	scheduler.state_dict()
												A__    :	Union[str, Any]			=	f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
												A__    :	Tuple			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												save(UpperCAmelCase__,    UpperCAmelCase__   )
												logger.info(f'Scheduler state saved in {output_scheduler_file}'   )
						# GradScaler state
						if scaler is not None:
												A__    :	List[str]			=	scaler.state_dict()
												A__    :	Optional[Any]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												torch.save(UpperCAmelCase__,    UpperCAmelCase__   )
												logger.info(f'Gradient scaler state saved in {output_scaler_file}'   )
						# Random number generator states
						A__    :	str			=	{}
						A__    :	str			=	f'{RNG_STATE_NAME}_{process_index}.pkl'
						A__    :	Optional[Any]			=	random.getstate()
						A__    :	str			=	np.random.get_state()
						A__    :	List[str]			=	torch.get_rng_state()
						if is_xpu_available():
												A__    :	Optional[Any]			=	torch.xpu.get_rng_state_all()
						else:
												A__    :	str			=	torch.cuda.get_rng_state_all()
						if is_tpu_available():
												A__    :	Tuple			=	xm.get_rng_state()
						A__    :	str			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
						torch.save(UpperCAmelCase__,    UpperCAmelCase__   )
						logger.info(f'Random states saved in {output_states_file}'   )
						return output_dir




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Any,    UpperCAmelCase__							:       Union[str, Any],    UpperCAmelCase__							:       Union[str, Any],    UpperCAmelCase__							:       Dict,    UpperCAmelCase__							:       Tuple,    UpperCAmelCase__							:       Optional[Any]=None,    UpperCAmelCase__							:       str=None,    **UpperCAmelCase__							:       Optional[int],    )		->List[str]:
						if map_location not in [None, "cpu", "on_device"]:
												raise TypeError(
												    """Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`"""   )
						if map_location is None:
												A__    :	Union[str, Any]			=	"""cpu"""
						elif map_location == "on_device":
												A__    :	Any			=	PartialState().device
						# Model states
						for i, model in enumerate(UpperCAmelCase__   ):
												A__    :	Any			=	f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
												A__    :	List[str]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												models[i].load_state_dict(torch.load(UpperCAmelCase__,    map_location=UpperCAmelCase__   ),    **UpperCAmelCase__   )
						logger.info("""All model weights loaded successfully"""   )

						# Optimizer states
						for i, opt in enumerate(UpperCAmelCase__   ):
												A__    :	Union[str, Any]			=	f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
												A__    :	Dict			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												A__    :	List[Any]			=	torch.load(UpperCAmelCase__,    map_location=UpperCAmelCase__   )
												optimizers[i].load_state_dict(UpperCAmelCase__   )
						logger.info("""All optimizer states loaded successfully"""   )

						# Scheduler states
						for i, scheduler in enumerate(UpperCAmelCase__   ):
												A__    :	List[Any]			=	f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
												A__    :	Optional[Any]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												scheduler.load_state_dict(torch.load(UpperCAmelCase__   )   )
						logger.info("""All scheduler states loaded successfully"""   )

						# GradScaler state
						if scaler is not None:
												A__    :	List[str]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												scaler.load_state_dict(torch.load(UpperCAmelCase__   )   )
												logger.info("""GradScaler state loaded successfully"""   )

						# Random states
						try:
												A__    :	Any			=	torch.load(os.path.join(UpperCAmelCase__,    f'{RNG_STATE_NAME}_{process_index}.pkl'   )   )
												random.setstate(states["""random_state"""]   )
												np.random.set_state(states["""numpy_random_seed"""]   )
												torch.set_rng_state(states["""torch_manual_seed"""]   )
												if is_xpu_available():
																		torch.xpu.set_rng_state_all(states["""torch_xpu_manual_seed"""]   )
												else:
																		torch.cuda.set_rng_state_all(states["""torch_cuda_manual_seed"""]   )
												if is_tpu_available():
																		xm.set_rng_state(states["""xm_seed"""]   )
												logger.info("""All random states loaded successfully"""   )
						except Exception:
												logger.info("""Could not load random states"""   )




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Tuple,    UpperCAmelCase__							:       Dict,    UpperCAmelCase__							:       int = 0   )		->Dict:
						A__    :	Dict			=	Path(UpperCAmelCase__   ) / f'custom_checkpoint_{index}.pkl'
						logger.info(f'Saving the state of {get_pretty_name(UpperCAmelCase__   )} to {save_location}'   )
						torch.save(obj.state_dict(),    UpperCAmelCase__   )




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Tuple,    UpperCAmelCase__							:       str,    UpperCAmelCase__							:       int = 0   )		->str:
						A__    :	Optional[int]			=	f'{path}/custom_checkpoint_{index}.pkl'
						logger.info(f'Loading the state of {get_pretty_name(UpperCAmelCase__   )} from {load_location}'   )
						obj.load_state_dict(torch.load(UpperCAmelCase__,    map_location="""cpu"""   )   )






