



import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


_snake_case		=  logging.get_logger(__name__)






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=False  ):





							'''simple docstring'''




							try:
														import torch  # noqa: F401
							except ImportError:
														logger.error(
														    "Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
														    " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
														    " instructions."  )
														raise

							if not is_sharded:
														_lowerCAmelCase     :							List[Any]    =    os.path.abspath(_lowerCamelCase  )
														logger.info(F"Loading PyTorch weights from {pt_path}"  )

														_lowerCAmelCase     :							str    =    torch.load(_lowerCamelCase				, map_location="cpu"  )
														logger.info(F"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()  ):,} parameters."  )

														_lowerCAmelCase     :							List[Any]    =    convert_pytorch_state_dict_to_flax(_lowerCamelCase				, _lowerCamelCase  )
							else:
														# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
														_lowerCAmelCase     :							Any    =    convert_pytorch_sharded_state_dict_to_flax(_lowerCamelCase				, _lowerCamelCase  )
							return flax_state_dict






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, ):





							'''simple docstring'''




							def is_key_or_prefix_key_in_dict(_lowerCamelCase  ) -> bool:
														return len(set(_lowerCamelCase  ) & {key, (model_prefix,) + key}  ) > 0

							# layer norm
							_lowerCAmelCase     :							Tuple    =    pt_tuple_key[:-1] + ("scale",)
							if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_lowerCamelCase  ):
														return renamed_pt_tuple_key, pt_tensor

							# batch norm layer mean
							_lowerCAmelCase     :							Dict    =    pt_tuple_key[:-1] + ("mean",)
							if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_lowerCamelCase  ):
														return renamed_pt_tuple_key, pt_tensor

							# batch norm layer var
							_lowerCAmelCase     :							Optional[Any]    =    pt_tuple_key[:-1] + ("var",)
							if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_lowerCamelCase  ):
														return renamed_pt_tuple_key, pt_tensor

							# embedding
							_lowerCAmelCase     :							Any    =    pt_tuple_key[:-1] + ("embedding",)
							if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_lowerCamelCase  ):
														return renamed_pt_tuple_key, pt_tensor

							# conv layer
							_lowerCAmelCase     :							List[str]    =    pt_tuple_key[:-1] + ("kernel",)
							if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_lowerCamelCase  ):
														_lowerCAmelCase     :							Any    =    pt_tensor.transpose(2				, 3				, 1				, 0  )
														return renamed_pt_tuple_key, pt_tensor

							# linear layer
							_lowerCAmelCase     :							Any    =    pt_tuple_key[:-1] + ("kernel",)
							if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_lowerCamelCase  ):
														_lowerCAmelCase     :							Any    =    pt_tensor.T
														return renamed_pt_tuple_key, pt_tensor

							# old PyTorch layer norm weight
							_lowerCAmelCase     :							Dict    =    pt_tuple_key[:-1] + ("weight",)
							if pt_tuple_key[-1] == "gamma":
														return renamed_pt_tuple_key, pt_tensor

							# old PyTorch layer norm bias
							_lowerCAmelCase     :							Dict    =    pt_tuple_key[:-1] + ("bias",)
							if pt_tuple_key[-1] == "beta":
														return renamed_pt_tuple_key, pt_tensor

							# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
							_lowerCAmelCase     :							Tuple    =    None
							if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
														_lowerCAmelCase     :							Any    =    pt_tuple_key[-2] + "_g"
							elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
														_lowerCAmelCase     :							Any    =    pt_tuple_key[-2] + "_v"
							if name is not None:
														_lowerCAmelCase     :							List[Any]    =    pt_tuple_key[:-3] + (name,)
														return renamed_pt_tuple_key, pt_tensor

							return pt_tuple_key, pt_tensor






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[Any]    =    {k: v.numpy() for k, v in pt_state_dict.items()}

							_lowerCAmelCase     :							Optional[Any]    =    flax_model.base_model_prefix

							# use params dict if the model contains batch norm layers
							if "params" in flax_model.params:
														_lowerCAmelCase     :							List[Any]    =    flax_model.params["params"]
							else:
														_lowerCAmelCase     :							Any    =    flax_model.params
							_lowerCAmelCase     :							Union[str, Any]    =    flatten_dict(_lowerCamelCase  )

							# add batch_stats keys,values to dict
							if "batch_stats" in flax_model.params:
														_lowerCAmelCase     :							int    =    flatten_dict(flax_model.params["batch_stats"]  )
														random_flax_state_dict.update(_lowerCamelCase  )

							_lowerCAmelCase     :							List[str]    =    {}

							_lowerCAmelCase     :							Dict    =    (model_prefix not in flax_model_params) and (
							    model_prefix in {k.split("."  )[0] for k in pt_state_dict.keys()}
							)
							_lowerCAmelCase     :							List[Any]    =    (model_prefix in flax_model_params) and (
							    model_prefix not in {k.split("."  )[0] for k in pt_state_dict.keys()}
							)

							# Need to change some parameters name to match Flax names
							for pt_key, pt_tensor in pt_state_dict.items():
														_lowerCAmelCase     :							List[str]    =    tuple(pt_key.split("."  )  )

														# remove base model prefix if necessary
														_lowerCAmelCase     :							Any    =    pt_tuple_key[0] == model_prefix
														if load_model_with_head_into_base_model and has_base_model_prefix:
																					_lowerCAmelCase     :							List[str]    =    pt_tuple_key[1:]

														# Correctly rename weight parameters
														_lowerCAmelCase   ,    _lowerCAmelCase     :							List[Any]    =    rename_key_and_reshape_tensor(
														    _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )

														# add model prefix if necessary
														_lowerCAmelCase     :							Any    =    (model_prefix,) + flax_key in random_flax_state_dict
														if load_base_model_into_model_with_head and require_base_model_prefix:
																					_lowerCAmelCase     :							Optional[int]    =    (model_prefix,) + flax_key

														if flax_key in random_flax_state_dict:
																					if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																												raise ValueError(
																												    F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
																												    F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."  )

        # add batch stats if the model contains batchnorm layers
														if "batch_stats" in flax_model.params:
																					if "mean" in flax_key[-1] or "var" in flax_key[-1]:
																												_lowerCAmelCase     :							Union[str, Any]    =    jnp.asarray(_lowerCamelCase  )
																												continue
																					# remove num_batches_tracked key
																					if "num_batches_tracked" in flax_key[-1]:
																												flax_state_dict.pop(_lowerCamelCase				, _lowerCamelCase  )
																												continue

																					# also add unexpected weight so that warning is thrown
																					_lowerCAmelCase     :							Optional[Any]    =    jnp.asarray(_lowerCamelCase  )

														else:
																					# also add unexpected weight so that warning is thrown
																					_lowerCAmelCase     :							Any    =    jnp.asarray(_lowerCamelCase  )

							return unflatten_dict(_lowerCamelCase  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							import torch

							# Load the index
							_lowerCAmelCase     :							int    =    {}
							for shard_file in shard_filenames:
														# load using msgpack utils
														_lowerCAmelCase     :							Union[str, Any]    =    torch.load(_lowerCamelCase  )
														_lowerCAmelCase     :							Dict    =    {k: v.numpy() for k, v in pt_state_dict.items()}

														_lowerCAmelCase     :							Optional[Any]    =    flax_model.base_model_prefix

														# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
														if "batch_stats" in flax_model.params:
																					_lowerCAmelCase     :							Optional[int]    =    flax_model.params["params"]

																					_lowerCAmelCase     :							Dict    =    flatten_dict(_lowerCamelCase  )
																					random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]  )  )
														else:
																					_lowerCAmelCase     :							str    =    flax_model.params
																					_lowerCAmelCase     :							Tuple    =    flatten_dict(_lowerCamelCase  )

														_lowerCAmelCase     :							Optional[int]    =    (model_prefix not in flax_model_params) and (
														    model_prefix in {k.split("."  )[0] for k in pt_state_dict.keys()}
														)
														_lowerCAmelCase     :							int    =    (model_prefix in flax_model_params) and (
														    model_prefix not in {k.split("."  )[0] for k in pt_state_dict.keys()}
														)
														# Need to change some parameters name to match Flax names
														for pt_key, pt_tensor in pt_state_dict.items():
																					_lowerCAmelCase     :							Dict    =    tuple(pt_key.split("."  )  )

																					# remove base model prefix if necessary
																					_lowerCAmelCase     :							Dict    =    pt_tuple_key[0] == model_prefix
																					if load_model_with_head_into_base_model and has_base_model_prefix:
																												_lowerCAmelCase     :							List[str]    =    pt_tuple_key[1:]

																					# Correctly rename weight parameters
																					_lowerCAmelCase   ,    _lowerCAmelCase     :							List[str]    =    rename_key_and_reshape_tensor(
																					    _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )
																					# add model prefix if necessary
																					_lowerCAmelCase     :							Any    =    (model_prefix,) + flax_key in random_flax_state_dict
																					if load_base_model_into_model_with_head and require_base_model_prefix:
																												_lowerCAmelCase     :							str    =    (model_prefix,) + flax_key

																					if flax_key in random_flax_state_dict:
																												if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																																			raise ValueError(
																																			    F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
																																			    F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."  )

            # add batch stats if the model contains batchnorm layers
																					if "batch_stats" in flax_model.params:
																												if "mean" in flax_key[-1]:
																																			_lowerCAmelCase     :							Any    =    jnp.asarray(_lowerCamelCase  )
																																			continue
																												if "var" in flax_key[-1]:
																																			_lowerCAmelCase     :							Tuple    =    jnp.asarray(_lowerCamelCase  )
																																			continue
																												# remove num_batches_tracked key
																												if "num_batches_tracked" in flax_key[-1]:
																																			flax_state_dict.pop(_lowerCamelCase				, _lowerCamelCase  )
																																			continue

																												# also add unexpected weight so that warning is thrown
																												_lowerCAmelCase     :							Optional[int]    =    jnp.asarray(_lowerCamelCase  )

																					else:
																												# also add unexpected weight so that warning is thrown
																												_lowerCAmelCase     :							Tuple    =    jnp.asarray(_lowerCamelCase  )
							return unflatten_dict(_lowerCamelCase  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[Any]    =    os.path.abspath(_lowerCamelCase  )
							logger.info(F"Loading Flax weights from {flax_checkpoint_path}"  )

							# import correct flax class
							_lowerCAmelCase     :							Dict    =    getattr(_lowerCamelCase				, "Flax" + model.__class__.__name__  )

							# load flax weight dict
							with open(_lowerCamelCase				, "rb"  ) as state_f:
														try:
																					_lowerCAmelCase     :							Optional[Any]    =    from_bytes(_lowerCamelCase				, state_f.read()  )
														except UnpicklingError:
																					raise EnvironmentError(F"Unable to convert {flax_checkpoint_path} to Flax deserializable object. "  )

							return load_flax_weights_in_pytorch_model(_lowerCamelCase				, _lowerCamelCase  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							try:
														import torch  # noqa: F401
							except ImportError:
														logger.error(
														    "Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
														    " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
														    " instructions."  )
														raise

							# check if we have bf16 weights
							_lowerCAmelCase     :							List[Any]    =    flatten_dict(jax.tree_util.tree_map(lambda _lowerCamelCase  : x.dtype == jnp.bfloataa				, _lowerCamelCase  )  ).values()
							if any(_lowerCamelCase  ):
														# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
														# and bf16 is not fully supported in PT yet.
														logger.warning(
														    "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
														    "before loading those in PyTorch model."  )
														_lowerCAmelCase     :							Tuple    =    jax.tree_util.tree_map(
														    lambda _lowerCamelCase  : params.astype(np.floataa  ) if params.dtype == jnp.bfloataa else params				, _lowerCamelCase  )

							_lowerCAmelCase     :							Optional[int]    =    flatten_dict(_lowerCamelCase  )
							_lowerCAmelCase     :							Any    =    pt_model.state_dict()

							_lowerCAmelCase     :							Tuple    =    (pt_model.base_model_prefix in flax_state) and (
							    pt_model.base_model_prefix not in {k.split("."  )[0] for k in pt_model_dict.keys()}
							)
							_lowerCAmelCase     :							Optional[Any]    =    (pt_model.base_model_prefix not in flax_state) and (
							    pt_model.base_model_prefix in {k.split("."  )[0] for k in pt_model_dict.keys()}
							)

							# keep track of unexpected & missing keys
							_lowerCAmelCase     :							Dict    =    []
							_lowerCAmelCase     :							Optional[Any]    =    set(pt_model_dict.keys()  )

							for flax_key_tuple, flax_tensor in flax_state_dict.items():
														_lowerCAmelCase     :							int    =    flax_key_tuple[0] == pt_model.base_model_prefix
														_lowerCAmelCase     :							List[str]    =    ".".join((pt_model.base_model_prefix,) + flax_key_tuple  ) in pt_model_dict

														# adapt flax_key to prepare for loading from/to base model only
														if load_model_with_head_into_base_model and has_base_model_prefix:
																					_lowerCAmelCase     :							Dict    =    flax_key_tuple[1:]
														elif load_base_model_into_model_with_head and require_base_model_prefix:
																					_lowerCAmelCase     :							int    =    (pt_model.base_model_prefix,) + flax_key_tuple

														# rename flax weights to PyTorch format
														if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_lowerCamelCase  ) not in pt_model_dict:
																					# conv layer
																					_lowerCAmelCase     :							Optional[Any]    =    flax_key_tuple[:-1] + ("weight",)
																					_lowerCAmelCase     :							List[Any]    =    jnp.transpose(_lowerCamelCase				, (3, 2, 0, 1)  )
														elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCamelCase  ) not in pt_model_dict:
																					# linear layer
																					_lowerCAmelCase     :							Any    =    flax_key_tuple[:-1] + ("weight",)
																					_lowerCAmelCase     :							Any    =    flax_tensor.T
														elif flax_key_tuple[-1] in ["scale", "embedding"]:
																					_lowerCAmelCase     :							Optional[int]    =    flax_key_tuple[:-1] + ("weight",)

														# adding batch stats from flax batch norm to pt
														elif "mean" in flax_key_tuple[-1]:
																					_lowerCAmelCase     :							Union[str, Any]    =    flax_key_tuple[:-1] + ("running_mean",)
														elif "var" in flax_key_tuple[-1]:
																					_lowerCAmelCase     :							Dict    =    flax_key_tuple[:-1] + ("running_var",)

														if "batch_stats" in flax_state:
																					_lowerCAmelCase     :							str    =    ".".join(flax_key_tuple[1:]  )  # Remove the params/batch_stats header
														else:
																					_lowerCAmelCase     :							Optional[int]    =    ".".join(_lowerCamelCase  )

														# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
														_lowerCAmelCase     :							Dict    =    {}
														# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
														for key in pt_model_dict:
																					_lowerCAmelCase     :							Dict    =    key.split("."  )
																					_lowerCAmelCase     :							List[Any]    =    None
																					if key_components[-3::2] == ["parametrizations", "original0"]:
																												_lowerCAmelCase     :							List[Any]    =    key_components[-2] + "_g"
																					elif key_components[-3::2] == ["parametrizations", "original1"]:
																												_lowerCAmelCase     :							str    =    key_components[-2] + "_v"
																					if name is not None:
																												_lowerCAmelCase     :							Any    =    key_components[:-3] + [name]
																												_lowerCAmelCase     :							Union[str, Any]    =    ".".join(_lowerCamelCase  )
																												_lowerCAmelCase     :							Dict    =    key

														if flax_key in special_pt_names:
																					_lowerCAmelCase     :							Optional[int]    =    special_pt_names[flax_key]

														if flax_key in pt_model_dict:
																					if flax_tensor.shape != pt_model_dict[flax_key].shape:
																												raise ValueError(
																												    F"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
																												    F"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."  )
																					else:
																												# add weight to pytorch dict
																												_lowerCAmelCase     :							str    =    np.asarray(_lowerCamelCase  ) if not isinstance(_lowerCamelCase				, np.ndarray  ) else flax_tensor
																												_lowerCAmelCase     :							Union[str, Any]    =    torch.from_numpy(_lowerCamelCase  )
																												# remove from missing keys
																												missing_keys.remove(_lowerCamelCase  )
														else:
																					# weight is not expected by PyTorch model
																					unexpected_keys.append(_lowerCamelCase  )

							pt_model.load_state_dict(_lowerCamelCase  )

							# re-transform missing_keys to list
							_lowerCAmelCase     :							Dict    =    list(_lowerCamelCase  )

							if len(_lowerCamelCase  ) > 0:
														logger.warning(
														    "Some weights of the Flax model were not used when initializing the PyTorch model"
														    F" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
														    F" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
														    " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
														    F" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
														    " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
														    " FlaxBertForSequenceClassification model)."  )
							else:
														logger.warning(F"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"  )
							if len(_lowerCamelCase  ) > 0:
														logger.warning(
														    F"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
														    F" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
														    " use it for predictions and inference."  )
							else:
														logger.warning(
														    F"All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"
														    "If your task is similar to the task the model of the checkpoint was trained on, "
														    F"you can already use {pt_model.__class__.__name__} for predictions without further training."  )

							return pt_model





import argparse
from collections import defaultdict

import yaml


_snake_case		=  "docs/source/en/_toctree.yml"






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    defaultdict(_lowerCamelCase  )
							_lowerCAmelCase     :							Any    =    []
							_lowerCAmelCase     :							List[str]    =    []
							for doc in doc_list:
														if "local" in doc:
																					counts[doc["local"]] += 1

														if doc["title"].lower() == "overview":
																					overview_doc.append({"local": doc["local"], "title": doc["title"]}  )
														else:
																					new_doc_list.append(_lowerCamelCase  )

							_lowerCAmelCase     :							Optional[Any]    =    new_doc_list
							_lowerCAmelCase     :							List[Any]    =    [key for key, value in counts.items() if value > 1]

							_lowerCAmelCase     :							str    =    []
							for duplicate_key in duplicates:
														_lowerCAmelCase     :							List[str]    =    list({doc["title"] for doc in doc_list if doc["local"] == duplicate_key}  )
														if len(_lowerCamelCase  ) > 1:
																					raise ValueError(
																					    F"{duplicate_key} is present several times in the documentation table of content at "
																					    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
																					    "others."  )
														# Only add this once
														new_doc.append({"local": duplicate_key, "title": titles[0]}  )

							# Add none duplicate-keys
							new_doc.extend([doc for doc in doc_list if "local" not in counts or counts[doc["local"]] == 1]  )
							_lowerCAmelCase     :							Optional[Any]    =    sorted(_lowerCamelCase				, key=lambda _lowerCamelCase  : s["title"].lower()  )

							# "overview" gets special treatment and is always first
							if len(_lowerCamelCase  ) > 1:
														raise ValueError("{doc_list} has two 'overview' docs which is not allowed."  )

							overview_doc.extend(_lowerCamelCase  )

							# Sort
							return overview_doc






def 				A				(					_lowerCamelCase=False  ):





							'''simple docstring'''




							with open(_lowerCamelCase				, encoding="utf-8"  ) as f:
														_lowerCAmelCase     :							int    =    yaml.safe_load(f.read()  )

							# Get to the API doc
							_lowerCAmelCase     :							Optional[Any]    =    0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							_lowerCAmelCase     :							List[str]    =    content[api_idx]["sections"]

							# Then to the model doc
							_lowerCAmelCase     :							Union[str, Any]    =    0
							while api_doc[scheduler_idx]["title"] != "Schedulers":
														scheduler_idx += 1

							_lowerCAmelCase     :							Optional[Any]    =    api_doc[scheduler_idx]["sections"]
							_lowerCAmelCase     :							Optional[Any]    =    clean_doc_toc(_lowerCamelCase  )

							_lowerCAmelCase     :							int    =    False
							if new_scheduler_doc != scheduler_doc:
														_lowerCAmelCase     :							List[Any]    =    True
														if overwrite:
																					_lowerCAmelCase     :							Dict    =    new_scheduler_doc

							if diff:
														if overwrite:
																					_lowerCAmelCase     :							Tuple    =    api_doc
																					with open(_lowerCamelCase				, "w"				, encoding="utf-8"  ) as f:
																												f.write(yaml.dump(_lowerCamelCase				, allow_unicode=_lowerCamelCase  )  )
														else:
																					raise ValueError(
																					    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."  )






def 				A				(					_lowerCamelCase=False  ):





							'''simple docstring'''




							with open(_lowerCamelCase				, encoding="utf-8"  ) as f:
														_lowerCAmelCase     :							Tuple    =    yaml.safe_load(f.read()  )

							# Get to the API doc
							_lowerCAmelCase     :							Optional[int]    =    0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							_lowerCAmelCase     :							int    =    content[api_idx]["sections"]

							# Then to the model doc
							_lowerCAmelCase     :							List[str]    =    0
							while api_doc[pipeline_idx]["title"] != "Pipelines":
														pipeline_idx += 1

							_lowerCAmelCase     :							Dict    =    False
							_lowerCAmelCase     :							Optional[int]    =    api_doc[pipeline_idx]["sections"]
							_lowerCAmelCase     :							Tuple    =    []

							# sort sub pipeline docs
							for pipeline_doc in pipeline_docs:
														if "section" in pipeline_doc:
																					_lowerCAmelCase     :							List[Any]    =    pipeline_doc["section"]
																					_lowerCAmelCase     :							Union[str, Any]    =    clean_doc_toc(_lowerCamelCase  )
																					if overwrite:
																												_lowerCAmelCase     :							Optional[Any]    =    new_sub_pipeline_doc
														new_pipeline_docs.append(_lowerCamelCase  )

							# sort overall pipeline doc
							_lowerCAmelCase     :							Union[str, Any]    =    clean_doc_toc(_lowerCamelCase  )

							if new_pipeline_docs != pipeline_docs:
														_lowerCAmelCase     :							Dict    =    True
														if overwrite:
																					_lowerCAmelCase     :							Optional[int]    =    new_pipeline_docs

							if diff:
														if overwrite:
																					_lowerCAmelCase     :							Optional[int]    =    api_doc
																					with open(_lowerCamelCase				, "w"				, encoding="utf-8"  ) as f:
																												f.write(yaml.dump(_lowerCamelCase				, allow_unicode=_lowerCamelCase  )  )
														else:
																					raise ValueError(
																					    "The model doc part of the table of content is not properly sorted, run `make style` 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_scheduler_doc(args.fix_and_overwrite)
						check_pipeline_doc(args.fix_and_overwrite)
