




"""simple docstring"""







import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


__A :	int       =   logging.getLogger(__name__)

def     snake_case__							(       )      ->Optional[Any]:






		"""simple docstring"""


		__lowercase					:		Any        =					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=10_00,	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=5_12,	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.",	)

		__lowercase					:		Optional[Any]        =					parser.parse_args()
		return args

def     snake_case__							(       _lowerCamelCase					)      ->Union[str, Any]:






		"""simple docstring"""


		def fn(_lowerCamelCase					):
				return tokenizer(examples["text"]					)

		return fn

def     snake_case__							(       _lowerCamelCase					)      ->List[Any]:






		"""simple docstring"""


		__lowercase					:		List[Any]        =					[]
		for i in range(len(tokenized_data["input_ids"]					)					):
				__lowercase					:		int        =					{
				    "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]					)					),
				}
				__lowercase					:		List[str]        =					tf.train.Features(feature=_lowerCamelCase					)
				__lowercase					:		List[str]        =					tf.train.Example(features=_lowerCamelCase					)
				__lowercase					:		Dict        =					example.SerializeToString()
				records.append(_lowerCamelCase					)
		return records

def     snake_case__							(       _lowerCamelCase					)      ->int:






		"""simple docstring"""


		__lowercase					:		Optional[Any]        =					datasets.load_dataset(args.dataset_name,	args.dataset_config,	split=args.split					)

		if args.limit is not None:
				__lowercase					:		List[str]        =					min(len(_lowerCamelCase					),	args.limit					)
				__lowercase					:		Optional[int]        =					dataset.select(range(_lowerCamelCase					)					)
				print(F'Limiting the dataset to {args.limit} entries.'					)

		__lowercase					:		Optional[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					)
				__lowercase					:		Optional[int]        =					os.path.join(args.output_dir,	args.split					)
				if not os.path.exists(_lowerCamelCase					):
						os.makedirs(_lowerCamelCase					)
		else:
				__lowercase					:		List[str]        =					os.path.join(args.output_dir,	args.split					)

		# Tokenize the whole dataset at once.
		__lowercase					:		Union[str, Any]        =					tokenize_function(_lowerCamelCase					)
		__lowercase					:		Union[str, Any]        =					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					):
				# Concatenate all texts.
				__lowercase					:		Optional[Any]        =					{k: sum(examples[k],	[]					) for k in examples.keys()}
				__lowercase					:		Optional[int]        =					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 🫀
				__lowercase					:		Union[str, Any]        =					(total_length // args.max_length) * args.max_length
				# Split by chunks of max_len.
				__lowercase					:		Dict        =					{
				    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

		__lowercase					:		Optional[int]        =					dataset_tokenized.map(_lowerCamelCase,	batched=_lowerCamelCase,	batch_size=10_00,	num_proc=4					)

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

				with tf.io.TFRecordWriter(_lowerCamelCase					) as out_file:
						for i in range(len(_lowerCamelCase					)					):
								__lowercase					:		List[str]        =					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__":
						__A :	List[str]       =   parse_args()
						main(args)







import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_   :      str			=   logging.get_logger(__name__)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		__lowercase											=     MobileNetVaConfig(layer_norm_eps=0.001							)

		if "_quant" in model_name:
				raise ValueError('Quantized models are not supported.'							)

		__lowercase											=     re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'						,   snake_case							)
		if matches:
				__lowercase											=     float(matches[1]							)
				__lowercase											=     int(matches[2]							)

		# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
		# the usual 1000. The first class (index 0) is "background".
		__lowercase											=     1_001
		__lowercase											=     'imagenet-1k-id2label.json'
		__lowercase											=     'huggingface/label-files'
		__lowercase											=     json.load(open(hf_hub_download(snake_case						,   snake_case						,   repo_type='dataset'							)						,   'r'							)							)
		__lowercase											=     {int(snake_case							) + 1: v for k, v in idalabel.items()}
		__lowercase											=     'background'
		__lowercase											=     idalabel
		__lowercase											=     {v: k for k, v in idalabel.items()}

		return config



def  SCREAMING_SNAKE_CASE				(      )		->    List[Any]:
		__lowercase											=     'http://images.cocodataset.org/val2017/000000039769.jpg'
		__lowercase											=     Image.open(requests.get(snake_case						,   stream=snake_case							).raw							)
		return im



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case=False							)		->    Union[str, Any]:
		__lowercase											=     get_mobilenet_va_config(snake_case							)

		# Load 🤗 model
		__lowercase											=     MobileNetVaForImageClassification(snake_case							).eval()

		# Load weights from TensorFlow checkpoint
		load_tf_weights_in_mobilenet_va(snake_case						,   snake_case						,   snake_case							)

		# Check outputs on an image, prepared by MobileNetV1ImageProcessor
		__lowercase											=     MobileNetVaImageProcessor(
		    crop_size={'width': config.image_size, 'height': config.image_size}						,   size={'shortest_edge': config.image_size + 32}						,   )
		__lowercase											=     image_processor(images=prepare_img()						,   return_tensors='pt'							)
		__lowercase											=     model(**snake_case							)
		__lowercase											=     outputs.logits

		assert logits.shape == (1, 1_001)

		if model_name == "mobilenet_v1_1.0_224":
				__lowercase											=     torch.tensor([-4.1739, -1.1233, 3.1205]							)
		elif model_name == "mobilenet_v1_0.75_192":
				__lowercase											=     torch.tensor([-3.9440, -2.3141, -0.3333]							)
		else:
				__lowercase											=     None

		if expected_logits is not None:
				assert torch.allclose(logits[0, :3]						,   snake_case						,   atol=1E-4							)

		Path(snake_case							).mkdir(exist_ok=snake_case							)
		print(F"Saving model {model_name} to {pytorch_dump_folder_path}"							)
		model.save_pretrained(snake_case							)
		print(F"Saving image processor to {pytorch_dump_folder_path}"							)
		image_processor.save_pretrained(snake_case							)

		if push_to_hub:
				print('Pushing to the hub...'							)
				__lowercase											=     'google/' + model_name
				image_processor.push_to_hub(snake_case							)
				model.push_to_hub(snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''mobilenet_v1_1.0_224''',
		    type=str,
		    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
		)
		parser.add_argument(
		    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
		)

		SCREAMING_SNAKE_CASE_   :      int			=   parser.parse_args()
		convert_movilevit_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)




