import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


SCREAMING_SNAKE_CASE__		:       Any       						=       logging.getLogger(__name__)







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		argparse.ArgumentParser(
         description='Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'			)
     parser.add_argument(
         '--dataset_name'  ,	type=lowercase__  ,	default='wikitext'  ,	help='Name of the training. Explore datasets at: hf.co/datasets.'  ,	)
     parser.add_argument(
         '--dataset_config'  ,	type=lowercase__  ,	default='wikitext-103-raw-v1'  ,	help='Configuration name of the dataset.'			)
     parser.add_argument(
         '--tokenizer_name_or_path'  ,	type=lowercase__  ,	default='sayakpaul/unigram-tokenizer-wikitext'  ,	help='Tokenizer identifier. Can be a local filepath or a Hub identifier.'  ,	)
     parser.add_argument(
         '--shard_size'  ,	type=lowercase__  ,	default=10_00  ,	help='Number of entries to go in a single shard.'  ,	)
     parser.add_argument('--split'  ,	type=lowercase__  ,	default='train'  ,	choices=['train', 'test', 'validation']			)
     parser.add_argument(
         '--limit'  ,	default=lowercase__  ,	type=lowercase__  ,	help='Limit the number of shards (used for debugging).'  ,	)
     parser.add_argument(
         '--max_length'  ,	type=lowercase__  ,	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=lowercase__  ,	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.'  ,	)

     SCREAMING_SNAKE_CASE__					:					Dict      =		parser.parse_args()
     return args







def      _a					(   lowercase__	:      Tuple			):







     '''simple docstring'''


     def fn(lowercase__	:      Optional[Any]			):
          return tokenizer(examples['text']			)

     return fn







def      _a					(   lowercase__	:      Tuple			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		[]
     for i in range(len(tokenized_data['input_ids']			)			):
          SCREAMING_SNAKE_CASE__					:					List[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]			)			),
          }
          SCREAMING_SNAKE_CASE__					:					str      =		tf.train.Features(feature=lowercase__			)
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		tf.train.Example(features=lowercase__			)
          SCREAMING_SNAKE_CASE__					:					Dict      =		example.SerializeToString()
          records.append(lowercase__			)
     return records







def      _a					(   lowercase__	:      int			):







     '''simple docstring'''


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

     if args.limit is not None:
          SCREAMING_SNAKE_CASE__					:					str      =		min(len(lowercase__			)  ,	args.limit			)
          SCREAMING_SNAKE_CASE__					:					str      =		dataset.select(range(lowercase__			)			)
          print(f'''Limiting the dataset to {args.limit} entries.'''			)

     SCREAMING_SNAKE_CASE__					:					List[str]      =		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			)
          SCREAMING_SNAKE_CASE__					:					Dict      =		os.path.join(args.output_dir  ,	args.split			)
          if not os.path.exists(lowercase__			):
               os.makedirs(lowercase__			)
     else:
          SCREAMING_SNAKE_CASE__					:					List[str]      =		os.path.join(args.output_dir  ,	args.split			)

     # Tokenize the whole dataset at once.
     SCREAMING_SNAKE_CASE__					:					Tuple      =		tokenize_function(lowercase__			)
     SCREAMING_SNAKE_CASE__					:					Tuple      =		dataset.map(lowercase__  ,	batched=lowercase__  ,	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(lowercase__	:      Any			):
          # Concatenate all texts.
          SCREAMING_SNAKE_CASE__					:					List[str]      =		{k: sum(examples[k]  ,	[]			) for k in examples.keys()}
          SCREAMING_SNAKE_CASE__					:					List[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 🫀
          SCREAMING_SNAKE_CASE__					:					str      =		(total_length // args.max_length) * args.max_length
          # Split by chunks of max_len.
          SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		{
              k: [t[i : i + args.max_length] for i in range(0  ,	lowercase__  ,	args.max_length			)]
              for k, t in concatenated_examples.items()
          }
          return result

     SCREAMING_SNAKE_CASE__					:					Dict      =		dataset_tokenized.map(lowercase__  ,	batched=lowercase__  ,	batch_size=10_00  ,	num_proc=4			)

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

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

          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=lowercase__			)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:       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()
_lowercase    	=		logging.get_logger(__name__)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Optional[Any]						)->	Union[str, Any]:
    A__					=       MobileNetVaConfig(layer_norm_eps=0.001						)

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

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

    # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
    # the usual 1000. The first class (index 0) is "background".
    A__					=       1_0_0_1
    A__					=       '''imagenet-1k-id2label.json'''
    A__					=       '''huggingface/label-files'''
    A__					=       json.load(open(hf_hub_download(UpperCamelCase_			,							UpperCamelCase_			,							repo_type='''dataset'''						)			,							'''r'''						)						)
    A__					=       {int(UpperCamelCase_						) + 1: v for k, v in idalabel.items()}
    A__					=       '''background'''
    A__					=       idalabel
    A__					=       {v: k for k, v in idalabel.items()}

    return config



def 		lowerCAmelCase__     (       )->	Tuple:
    A__					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A__					=       Image.open(requests.get(UpperCamelCase_			,							stream=UpperCamelCase_						).raw						)
    return im



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]			,							UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							int			,							UpperCamelCase_					:							List[str]=False						)->	Tuple:
    A__					=       get_mobilenet_va_config(UpperCamelCase_						)

    # Load 🤗 model
    A__					=       MobileNetVaForImageClassification(UpperCamelCase_						).eval()

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

    # Check outputs on an image, prepared by MobileNetV1ImageProcessor
    A__					=       MobileNetVaImageProcessor(
        crop_size={'''width''': config.image_size, '''height''': config.image_size}			,							size={'''shortest_edge''': config.image_size + 3_2}			,							)
    A__					=       image_processor(images=prepare_img()			,							return_tensors='''pt'''						)
    A__					=       model(**UpperCamelCase_						)
    A__					=       outputs.logits

    assert logits.shape == (1, 1_0_0_1)

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

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

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

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


if __name__ == "__main__":
   _lowercase    	=		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."
   )

   _lowercase    	=		parser.parse_args()
   convert_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )
