



'''simple docstring'''






import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


UpperCamelCase__:     List[Any]										=    get_logger(__name__)


UpperCamelCase__:     Any										=    Path(__file__).parent / "model_card_template.md"
UpperCamelCase__:     List[Any]										=    uuida().hex
UpperCamelCase__:     int										=    os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
UpperCamelCase__:     Dict										=    os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
UpperCamelCase__:     Optional[int]										=    HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"


def 				snake_case_  (      _lowerCAmelCase						:		Union[Dict, str, None] = None						)   ->     str:
							UpperCAmelCase				: Any											=	f"""diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"""
							if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
														return ua + "; telemetry/off"
							if is_torch_available():
														ua += f"""; torch/{_torch_version}"""
							if is_flax_available():
														ua += f"""; jax/{_jax_version}"""
														ua += f"""; flax/{_flax_version}"""
							if is_onnx_available():
														ua += f"""; onnxruntime/{_onnxruntime_version}"""
							# CI will set this value to True
							if os.environ.get('''DIFFUSERS_IS_CI'''     ,   ''''''						).upper() in ENV_VARS_TRUE_VALUES:
														ua += "; is_ci/true"
							if isinstance(_lowerCAmelCase     ,   _lowerCAmelCase						):
														ua += "; " + "; ".join(f"""{k}/{v}""" for k, v in user_agent.items()						)
							elif isinstance(_lowerCAmelCase     ,   _lowerCAmelCase						):
														ua += "; " + user_agent
							return ua


def 				snake_case_  (      _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Optional[str] = None     ,   _lowerCAmelCase						:		Optional[str] = None						)   ->     Dict:
							if token is None:
														UpperCAmelCase				: Optional[int]											=	HfFolder.get_token()
							if organization is None:
														UpperCAmelCase				: str											=	whoami(_lowerCAmelCase						)['''name''']
														return f"""{username}/{model_id}"""
							else:
														return f"""{organization}/{model_id}"""


def 				snake_case_  (      _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		Optional[Any]						)   ->     List[Any]:
							if not is_jinja_available():
														raise ValueError(
														    '''Modelcard rendering is based on Jinja templates.'''
														    ''' Please make sure to have `jinja` installed before using `create_model_card`.'''
														    ''' To install it, please run `pip install Jinja2`.'''						)

							if hasattr(_lowerCAmelCase     ,   '''local_rank'''						) and args.local_rank not in [-1, 0]:
														return

							UpperCAmelCase				: Optional[int]											=	args.hub_token if hasattr(_lowerCAmelCase     ,   '''hub_token'''						) else None
							UpperCAmelCase				: Tuple											=	get_full_repo_name(_lowerCAmelCase     ,   token=_lowerCAmelCase						)

							UpperCAmelCase				: Tuple											=	ModelCard.from_template(
							    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
							        language='''en'''     ,   license='''apache-2.0'''     ,   library_name='''diffusers'''     ,   tags=[]     ,   datasets=args.dataset_name     ,   metrics=[]     ,   )     ,   template_path=_lowerCAmelCase     ,   model_name=_lowerCAmelCase     ,   repo_name=_lowerCAmelCase     ,   dataset_name=args.dataset_name if hasattr(_lowerCAmelCase     ,   '''dataset_name'''						) else None     ,   learning_rate=args.learning_rate     ,   train_batch_size=args.train_batch_size     ,   eval_batch_size=args.eval_batch_size     ,   gradient_accumulation_steps=(
							        args.gradient_accumulation_steps if hasattr(_lowerCAmelCase     ,   '''gradient_accumulation_steps'''						) else None
							    )     ,   adam_betaa=args.adam_betaa if hasattr(_lowerCAmelCase     ,   '''adam_beta1'''						) else None     ,   adam_betaa=args.adam_betaa if hasattr(_lowerCAmelCase     ,   '''adam_beta2'''						) else None     ,   adam_weight_decay=args.adam_weight_decay if hasattr(_lowerCAmelCase     ,   '''adam_weight_decay'''						) else None     ,   adam_epsilon=args.adam_epsilon if hasattr(_lowerCAmelCase     ,   '''adam_epsilon'''						) else None     ,   lr_scheduler=args.lr_scheduler if hasattr(_lowerCAmelCase     ,   '''lr_scheduler'''						) else None     ,   lr_warmup_steps=args.lr_warmup_steps if hasattr(_lowerCAmelCase     ,   '''lr_warmup_steps'''						) else None     ,   ema_inv_gamma=args.ema_inv_gamma if hasattr(_lowerCAmelCase     ,   '''ema_inv_gamma'''						) else None     ,   ema_power=args.ema_power if hasattr(_lowerCAmelCase     ,   '''ema_power'''						) else None     ,   ema_max_decay=args.ema_max_decay if hasattr(_lowerCAmelCase     ,   '''ema_max_decay'''						) else None     ,   mixed_precision=args.mixed_precision     ,   )

							UpperCAmelCase				: Tuple											=	os.path.join(args.output_dir     ,   '''README.md'''						)
							model_card.save(_lowerCAmelCase						)


def 				snake_case_  (      _lowerCAmelCase						:		Optional[str]     ,   _lowerCAmelCase						:		Optional[str] = None						)   ->     Dict:
							if resolved_file is None or commit_hash is not None:
														return commit_hash
							UpperCAmelCase				: List[str]											=	str(Path(_lowerCAmelCase						).as_posix()						)
							UpperCAmelCase				: List[str]											=	re.search(R'''snapshots/([^/]+)/'''     ,   _lowerCAmelCase						)
							if search is None:
														return None
							UpperCAmelCase				: Optional[int]											=	search.groups()[0]
							return commit_hash if REGEX_COMMIT_HASH.match(_lowerCAmelCase						) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
UpperCamelCase__:     List[Any]										=    os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
UpperCamelCase__:     Optional[int]										=    os.path.join(hf_cache_home, "diffusers")


def 				snake_case_  (      _lowerCAmelCase						:		Optional[str] = None     ,   _lowerCAmelCase						:		Optional[str] = None						)   ->     None:
							if new_cache_dir is None:
														UpperCAmelCase				: Tuple											=	DIFFUSERS_CACHE
							if old_cache_dir is None:
														UpperCAmelCase				: Tuple											=	old_diffusers_cache

							UpperCAmelCase				: Optional[int]											=	Path(_lowerCAmelCase						).expanduser()
							UpperCAmelCase				: Dict											=	Path(_lowerCAmelCase						).expanduser()
							for old_blob_path in old_cache_dir.glob('''**/blobs/*'''						):
														if old_blob_path.is_file() and not old_blob_path.is_symlink():
																					UpperCAmelCase				: str											=	new_cache_dir / old_blob_path.relative_to(_lowerCAmelCase						)
																					new_blob_path.parent.mkdir(parents=_lowerCAmelCase     ,   exist_ok=_lowerCAmelCase						)
																					os.replace(_lowerCAmelCase     ,   _lowerCAmelCase						)
																					try:
																												os.symlink(_lowerCAmelCase     ,   _lowerCAmelCase						)
																					except OSError:
																												logger.warning(
																												    '''Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.'''						)
							# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


UpperCamelCase__:     Any										=    os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
						UpperCamelCase__:     int										=    0
else:
						with open(cache_version_file) as f:
												try:
																		UpperCamelCase__:     Any										=    int(f.read())
												except ValueError:
																		UpperCamelCase__:     Dict										=    0

if cache_version < 1:
						UpperCamelCase__:     Optional[Any]										=    os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
						if old_cache_is_not_empty:
												logger.warning(
												    "The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
												    "existing cached models. This is a one-time operation, you can interrupt it or run it "
												    "later by calling `diffusers.utils.hub_utils.move_cache()`."
												)
												try:
																		move_cache()
												except Exception as e:
																		UpperCamelCase__:     List[str]										=    "\n".join(traceback.format_tb(e.__traceback__))
																		logger.error(
																		    F"There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease "
																		    "file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
																		    "message and we will do our best to help."
																		)

if cache_version < 1:
						try:
												os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
												with open(cache_version_file, "w") as f:
																		f.write("1")
						except Exception:
												logger.warning(
												    F"There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure "
												    "the directory exists and can be written to."
												)


def 				snake_case_  (      _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Optional[str] = None						)   ->     str:
							if variant is not None:
														UpperCAmelCase				: Union[str, Any]											=	weights_name.split('''.'''						)
														UpperCAmelCase				: List[str]											=	splits[:-1] + [variant] + splits[-1:]
														UpperCAmelCase				: str											=	'''.'''.join(_lowerCAmelCase						)

							return weights_name




def 				snake_case_  (      _lowerCAmelCase						:		int     ,   *,
    _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		Dict     ,   _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		List[Any]     ,   _lowerCAmelCase						:		List[Any]     ,   _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Optional[Any]=None     ,   )   ->     Optional[Any]:
							UpperCAmelCase				: Any											=	str(_lowerCAmelCase						)
							if os.path.isfile(_lowerCAmelCase						):
														return pretrained_model_name_or_path
							elif os.path.isdir(_lowerCAmelCase						):
														if os.path.isfile(os.path.join(_lowerCAmelCase     ,   _lowerCAmelCase						)						):
																					# Load from a PyTorch checkpoint
																					UpperCAmelCase				: Optional[Any]											=	os.path.join(_lowerCAmelCase     ,   _lowerCAmelCase						)
																					return model_file
														elif subfolder is not None and os.path.isfile(
														    os.path.join(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						)						):
																					UpperCAmelCase				: List[str]											=	os.path.join(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						)
																					return model_file
														else:
																					raise EnvironmentError(
																					    f"""Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."""						)
							else:
														# 1. First check if deprecated way of loading from branches is used
														if (
														    revision in DEPRECATED_REVISION_ARGS
														    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
														    and version.parse(version.parse(_lowerCAmelCase						).base_version						) >= version.parse('''0.20.0'''						)
														):
																					try:
																												UpperCAmelCase				: Dict											=	hf_hub_download(
																												    _lowerCAmelCase     ,   filename=_add_variant(_lowerCAmelCase     ,   _lowerCAmelCase						)     ,   cache_dir=_lowerCAmelCase     ,   force_download=_lowerCAmelCase     ,   proxies=_lowerCAmelCase     ,   resume_download=_lowerCAmelCase     ,   local_files_only=_lowerCAmelCase     ,   use_auth_token=_lowerCAmelCase     ,   user_agent=_lowerCAmelCase     ,   subfolder=_lowerCAmelCase     ,   revision=revision or commit_hash     ,   )
																												warnings.warn(
																												    f"""Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead."""     ,   _lowerCAmelCase     ,   )
																												return model_file
																					except:  # noqa: E722
																												warnings.warn(
																												    f"""You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(_lowerCAmelCase     ,   _lowerCAmelCase						)} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(_lowerCAmelCase     ,   _lowerCAmelCase						)}' so that the correct variant file can be added."""     ,   _lowerCAmelCase     ,   )
														try:
																					# 2. Load model file as usual
																					UpperCAmelCase				: Optional[Any]											=	hf_hub_download(
																					    _lowerCAmelCase     ,   filename=_lowerCAmelCase     ,   cache_dir=_lowerCAmelCase     ,   force_download=_lowerCAmelCase     ,   proxies=_lowerCAmelCase     ,   resume_download=_lowerCAmelCase     ,   local_files_only=_lowerCAmelCase     ,   use_auth_token=_lowerCAmelCase     ,   user_agent=_lowerCAmelCase     ,   subfolder=_lowerCAmelCase     ,   revision=revision or commit_hash     ,   )
																					return model_file

														except RepositoryNotFoundError:
																					raise EnvironmentError(
																					    f"""{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier """
																					    '''listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '''
																					    '''token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '''
																					    '''login`.'''						)
														except RevisionNotFoundError:
																					raise EnvironmentError(
																					    f"""{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for """
																					    '''this model name. Check the model page at '''
																					    f"""'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."""						)
														except EntryNotFoundError:
																					raise EnvironmentError(
																					    f"""{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."""						)
														except HTTPError as err:
																					raise EnvironmentError(
																					    f"""There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"""						)
														except ValueError:
																					raise EnvironmentError(
																					    f"""We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"""
																					    f""" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"""
																					    f""" directory containing a file named {weights_name} or"""
																					    ''' \nCheckout your internet connection or see how to run the library in'''
																					    ''' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.'''						)
														except EnvironmentError:
																					raise EnvironmentError(
																					    f"""Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from """
																					    '''\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '''
																					    f"""Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory """
																					    f"""containing a file named {weights_name}"""						)










'''simple docstring'''






import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


UpperCamelCase__:     List[Any]										=    logging.getLogger(__name__)


def 				snake_case_  (      )   ->     int:
							UpperCAmelCase				: int											=	argparse.ArgumentParser(
							    description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''						)
							parser.add_argument(
							    '''--dataset_name'''     ,   type=_lowerCAmelCase     ,   default='''wikitext'''     ,   help='''Name of the training. Explore datasets at: hf.co/datasets.'''     ,   )
							parser.add_argument(
							    '''--dataset_config'''     ,   type=_lowerCAmelCase     ,   default='''wikitext-103-raw-v1'''     ,   help='''Configuration name of the dataset.'''						)
							parser.add_argument(
							    '''--tokenizer_name_or_path'''     ,   type=_lowerCAmelCase     ,   default='''sayakpaul/unigram-tokenizer-wikitext'''     ,   help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.'''     ,   )
							parser.add_argument(
							    '''--shard_size'''     ,   type=_lowerCAmelCase     ,   default=1000     ,   help='''Number of entries to go in a single shard.'''     ,   )
							parser.add_argument('''--split'''     ,   type=_lowerCAmelCase     ,   default='''train'''     ,   choices=['''train''', '''test''', '''validation''']						)
							parser.add_argument(
							    '''--limit'''     ,   default=_lowerCAmelCase     ,   type=_lowerCAmelCase     ,   help='''Limit the number of shards (used for debugging).'''     ,   )
							parser.add_argument(
							    '''--max_length'''     ,   type=_lowerCAmelCase     ,   default=512     ,   help='''Maximum sequence length. For training on TPUs, it helps to have a maximum'''
							    ''' sequence length that is a multiple of 8.'''     ,   )
							parser.add_argument(
							    '''--output_dir'''     ,   default='''tf-tpu'''     ,   type=_lowerCAmelCase     ,   help='''Output directory where the TFRecord shards will be saved. If the'''
							    ''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'''
							    ''' shards will be directly saved to a Google Cloud Storage bucket.'''     ,   )

							UpperCAmelCase				: List[str]											=	parser.parse_args()
							return args


def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]						)   ->     Optional[int]:
							def fn(_lowerCAmelCase						:		Tuple						):
														return tokenizer(examples['''text''']						)

							return fn


def 				snake_case_  (      _lowerCAmelCase						:		int						)   ->     Dict:
							UpperCAmelCase				: Optional[Any]											=	[]
							for i in range(len(tokenized_data['''input_ids''']						)						):
														UpperCAmelCase				: Optional[Any]											=	{
														    '''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i]						)						),
														    '''attention_mask''': tf.train.Feature(
														        intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i]						)						),
														}
														UpperCAmelCase				: Optional[Any]											=	tf.train.Features(feature=_lowerCAmelCase						)
														UpperCAmelCase				: Union[str, Any]											=	tf.train.Example(features=_lowerCAmelCase						)
														UpperCAmelCase				: Any											=	example.SerializeToString()
														records.append(_lowerCAmelCase						)
							return records


def 				snake_case_  (      _lowerCAmelCase						:		List[str]						)   ->     List[str]:
							UpperCAmelCase				: List[str]											=	datasets.load_dataset(args.dataset_name     ,   args.dataset_config     ,   split=args.split						)

							if args.limit is not None:
														UpperCAmelCase				: List[Any]											=	min(len(_lowerCAmelCase						)     ,   args.limit						)
														UpperCAmelCase				: Dict											=	dataset.select(range(_lowerCAmelCase						)						)
														print(f"""Limiting the dataset to {args.limit} entries."""						)

							UpperCAmelCase				: List[Any]											=	AutoTokenizer.from_pretrained(args.tokenizer_name_or_path						)

							# Handle output directory creation.
							# For serializing into a Google Cloud Storage Bucket, one needs to first
							# create a bucket.
							if "gs" not in args.output_dir:
														if not os.path.exists(args.output_dir						):
																					os.makedirs(args.output_dir						)
														UpperCAmelCase				: List[Any]											=	os.path.join(args.output_dir     ,   args.split						)
														if not os.path.exists(_lowerCAmelCase						):
																					os.makedirs(_lowerCAmelCase						)
							else:
														UpperCAmelCase				: Tuple											=	os.path.join(args.output_dir     ,   args.split						)

							# Tokenize the whole dataset at once.
							UpperCAmelCase				: Optional[Any]											=	tokenize_function(_lowerCAmelCase						)
							UpperCAmelCase				: int											=	dataset.map(_lowerCAmelCase     ,   batched=_lowerCAmelCase     ,   num_proc=4     ,   remove_columns=['''text''']						)

							# We need to concatenate all our texts together, and then split the result
							# into chunks of a fixed size, which we will call block_size. To do this, we
							# will use the map method again, with the option batched=True. When we use batched=True,
							# the function we pass to map() will be passed multiple inputs at once, allowing us
							# to group them into more or fewer examples than we had in the input.
							# This allows us to create our new fixed-length samples. The advantage of this
							# method is that we don't lose a whole lot of content from the dataset compared to the
							# case where we simply tokenize with a pre-defined max_length.

							def group_texts(_lowerCAmelCase						:		List[str]						):
														# Concatenate all texts.
														UpperCAmelCase				: Optional[Any]											=	{k: sum(examples[k]     ,   []						) for k in examples.keys()}
														UpperCAmelCase				: Optional[Any]											=	len(concatenated_examples[list(examples.keys()						)[0]]						)
														# We drop the small remainder, though you could add padding instead if the model supports it
														# In this, as in all things, we advise you to follow your heart 🫀
														UpperCAmelCase				: Any											=	(total_length // args.max_length) * args.max_length
														# Split by chunks of max_len.
														UpperCAmelCase				: List[str]											=	{
														    k: [t[i : i + args.max_length] for i in range(0     ,   _lowerCAmelCase     ,   args.max_length						)]
														    for k, t in concatenated_examples.items()
														}
														return result

							UpperCAmelCase				: str											=	dataset_tokenized.map(_lowerCAmelCase     ,   batched=_lowerCAmelCase     ,   batch_size=1000     ,   num_proc=4						)

							UpperCAmelCase				: Union[str, Any]											=	0
							UpperCAmelCase				: Dict											=	0
							for shard in range(0     ,   len(_lowerCAmelCase						)     ,   args.shard_size						):
														UpperCAmelCase				: Union[str, Any]											=	grouped_dataset[shard : shard + args.shard_size]
														UpperCAmelCase				: Optional[int]											=	len(dataset_snapshot['''input_ids''']						)
														UpperCAmelCase				: Dict											=	os.path.join(_lowerCAmelCase     ,   f"""dataset-{shard_count}-{records_containing}.tfrecord"""						)
														UpperCAmelCase				: List[Any]											=	get_serialized_examples(_lowerCAmelCase						)

														with tf.io.TFRecordWriter(_lowerCAmelCase						) as out_file:
																					for i in range(len(_lowerCAmelCase						)						):
																												UpperCAmelCase				: Any											=	serialized_examples[i]
																												out_file.write(_lowerCAmelCase						)
																					print('''Wrote file {} containing {} records'''.format(_lowerCAmelCase     ,   _lowerCAmelCase						)						)

														shard_count += 1
														total_records += records_containing

							with open(f"""split-{args.split}-records-count.txt"""     ,   '''w'''						) as f:
														print(f"""Total {args.split} records: {total_records}"""     ,   file=_lowerCAmelCase						)


if __name__ == "__main__":
						UpperCamelCase__:     List[Any]										=    parse_args()
						main(args)





