





'''simple docstring'''







import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


_lowercase       						=			logging.getLogger(__name__)

def    __UpperCamelCase	(	) ->Dict:
 snake_case				=			argparse.ArgumentParser(
     description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''     )
 parser.add_argument(
     '''--dataset_name'''					,   type=a					,   default='''wikitext'''					,   help='''Name of the training. Explore datasets at: hf.co/datasets.'''					,   )
 parser.add_argument(
     '''--dataset_config'''					,   type=a					,   default='''wikitext-103-raw-v1'''					,   help='''Configuration name of the dataset.'''     )
 parser.add_argument(
     '''--tokenizer_name_or_path'''					,   type=a					,   default='''sayakpaul/unigram-tokenizer-wikitext'''					,   help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.'''					,   )
 parser.add_argument(
     '''--shard_size'''					,   type=a					,   default=1000					,   help='''Number of entries to go in a single shard.'''					,   )
 parser.add_argument('''--split'''					,   type=a					,   default='''train'''					,   choices=['''train''', '''test''', '''validation''']     )
 parser.add_argument(
     '''--limit'''					,   default=a					,   type=a					,   help='''Limit the number of shards (used for debugging).'''					,   )
 parser.add_argument(
     '''--max_length'''					,   type=a					,   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=a					,   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.'''					,   )

 snake_case				=			parser.parse_args()
 return args

def    __UpperCamelCase	(	a    : List[Any]     ) ->Tuple:
 def fn(a    : Optional[int]     ):
  return tokenizer(examples['''text''']     )

 return fn

def    __UpperCamelCase	(	a    : Any     ) ->int:
 snake_case				=			[]
 for i in range(len(tokenized_data['''input_ids''']     )     ):
  snake_case				=			{
      '''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]     )     ),
  }
  snake_case				=			tf.train.Features(feature=a     )
  snake_case				=			tf.train.Example(features=a     )
  snake_case				=			example.SerializeToString()
  records.append(a     )
 return records

def    __UpperCamelCase	(	a    : Tuple     ) ->List[str]:
 snake_case				=			datasets.load_dataset(args.dataset_name					,   args.dataset_config					,   split=args.split     )

 if args.limit is not None:
  snake_case				=			min(len(a     )					,   args.limit     )
  snake_case				=			dataset.select(range(a     )     )
  print(f"""Limiting the dataset to {args.limit} entries."""     )

 snake_case				=			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     )
  snake_case				=			os.path.join(args.output_dir					,   args.split     )
  if not os.path.exists(a     ):
   os.makedirs(a     )
 else:
  snake_case				=			os.path.join(args.output_dir					,   args.split     )

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

 snake_case				=			dataset_tokenized.map(a					,   batched=a					,   batch_size=1000					,   num_proc=4     )

 snake_case				=			0
 snake_case				=			0
 for shard in range(0					,   len(a     )					,   args.shard_size     ):
  snake_case				=			grouped_dataset[shard : shard + args.shard_size]
  snake_case				=			len(dataset_snapshot['''input_ids''']     )
  snake_case				=			os.path.join(a					,   f"""dataset-{shard_count}-{records_containing}.tfrecord"""     )
  snake_case				=			get_serialized_examples(a     )

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

  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=a     )


if __name__ == "__main__":
   _lowercase       						=			parse_args()
   main(args)








'''simple docstring'''







import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase       						=			logging.get_logger(__name__)

def    __UpperCamelCase	(	a    : Dict					,   a    : Optional[int]					,   a    : Dict					,   a    : Dict     ) ->Union[str, Any]:
 snake_case				=			original_name.split('''.'''     )[0]
 snake_case				=			key.split('''.'''     )
 snake_case				=			int(key_list[key_list.index(a     ) - 2]     )
 snake_case				=			int(key_list[key_list.index(a     ) - 1]     )
 snake_case				=			orig_block_num - offset

 snake_case				=			key.replace(f"""{orig_block_num}.{layer_num}.{original_name}"""					,   f"""block.{new_block_num}.{layer_num}.{new_name}"""     )
 return key

def    __UpperCamelCase	(	a    : Tuple     ) ->Dict:
 snake_case				=			OrderedDict()
 snake_case				,		snake_case				=			0, 0
 for key, value in state_dict.items():
  if key.startswith('''network'''     ):
   snake_case				=			key.replace('''network'''					,   '''poolformer.encoder'''     )
  if "proj" in key:
   # Works for the first embedding as well as the internal embedding layers
   if key.endswith('''bias'''     ) and "patch_embed" not in key:
    patch_emb_offset += 1
   snake_case				=			key[: key.find('''proj'''     )]
   snake_case				=			key.replace(a					,   f"""patch_embeddings.{total_embed_found}."""     )
   snake_case				=			key.replace('''proj'''					,   '''projection'''     )
   if key.endswith('''bias'''     ):
    total_embed_found += 1
  if "patch_embeddings" in key:
   snake_case				=			'''poolformer.encoder.''' + key
  if "mlp.fc1" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''mlp.fc1'''					,   '''output.conv1'''     )
  if "mlp.fc2" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''mlp.fc2'''					,   '''output.conv2'''     )
  if "norm1" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''norm1'''					,   '''before_norm'''     )
  if "norm2" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''norm2'''					,   '''after_norm'''     )
  if "layer_scale_1" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''layer_scale_1'''					,   '''layer_scale_1'''     )
  if "layer_scale_2" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''layer_scale_2'''					,   '''layer_scale_2'''     )
  if "head" in key:
   snake_case				=			key.replace('''head'''					,   '''classifier'''     )
  snake_case				=			value
 return new_state_dict

def    __UpperCamelCase	(	) ->Optional[int]:
 snake_case				=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
 snake_case				=			Image.open(requests.get(a					,   stream=a     ).raw     )

 return image

@torch.no_grad()
def    __UpperCamelCase	(	a    : Dict					,   a    : Optional[Any]					,   a    : Tuple     ) ->List[str]:
 snake_case				=			PoolFormerConfig()

 # set attributes based on model_name
 snake_case				=			'''huggingface/label-files'''
 snake_case				=			model_name[-3:]
 snake_case				=			1000
 snake_case				=			'''imagenet-1k-id2label.json'''
 snake_case				=			(1, 1000)

 # set config attributes
 snake_case				=			json.load(open(hf_hub_download(a					,   a					,   repo_type='''dataset'''     )					,   '''r'''     )     )
 snake_case				=			{int(a     ): v for k, v in idalabel.items()}
 snake_case				=			idalabel
 snake_case				=			{v: k for k, v in idalabel.items()}
 if size == "s12":
  snake_case				=			[2, 2, 6, 2]
  snake_case				=			[64, 128, 320, 512]
  snake_case				=			4.0
  snake_case				=			0.9
 elif size == "s24":
  snake_case				=			[4, 4, 12, 4]
  snake_case				=			[64, 128, 320, 512]
  snake_case				=			4.0
  snake_case				=			0.9
 elif size == "s36":
  snake_case				=			[6, 6, 18, 6]
  snake_case				=			[64, 128, 320, 512]
  snake_case				=			4.0
  snake_case				=			1e-6
  snake_case				=			0.9
 elif size == "m36":
  snake_case				=			[6, 6, 18, 6]
  snake_case				=			[96, 192, 384, 768]
  snake_case				=			4.0
  snake_case				=			1e-6
  snake_case				=			0.95
 elif size == "m48":
  snake_case				=			[8, 8, 24, 8]
  snake_case				=			[96, 192, 384, 768]
  snake_case				=			4.0
  snake_case				=			1e-6
  snake_case				=			0.95
 else:
  raise ValueError(f"""Size {size} not supported"""     )

 # load image processor
 snake_case				=			PoolFormerImageProcessor(crop_pct=a     )

 # Prepare image
 snake_case				=			prepare_img()
 snake_case				=			image_processor(images=a					,   return_tensors='''pt'''     ).pixel_values

 logger.info(f"""Converting model {model_name}..."""     )

 # load original state dict
 snake_case				=			torch.load(a					,   map_location=torch.device('''cpu'''     )     )

 # rename keys
 snake_case				=			rename_keys(a     )

 # create HuggingFace model and load state dict
 snake_case				=			PoolFormerForImageClassification(a     )
 model.load_state_dict(a     )
 model.eval()

 # Define image processor
 snake_case				=			PoolFormerImageProcessor(crop_pct=a     )
 snake_case				=			image_processor(images=prepare_img()					,   return_tensors='''pt'''     ).pixel_values

 # forward pass
 snake_case				=			model(a     )
 snake_case				=			outputs.logits

 # define expected logit slices for different models
 if size == "s12":
  snake_case				=			torch.tensor([-0.3045, -0.6758, -0.4869]     )
 elif size == "s24":
  snake_case				=			torch.tensor([0.4402, -0.1374, -0.8045]     )
 elif size == "s36":
  snake_case				=			torch.tensor([-0.6080, -0.5133, -0.5898]     )
 elif size == "m36":
  snake_case				=			torch.tensor([0.3952, 0.2263, -1.2668]     )
 elif size == "m48":
  snake_case				=			torch.tensor([0.1167, -0.0656, -0.3423]     )
 else:
  raise ValueError(f"""Size {size} not supported"""     )

 # verify logits
 assert logits.shape == expected_shape
 assert torch.allclose(logits[0, :3]					,   a					,   atol=1e-2     )

 # finally, save model and image processor
 logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""     )
 Path(a     ).mkdir(exist_ok=a     )
 model.save_pretrained(a     )
 print(f"""Saving image processor to {pytorch_dump_folder_path}"""     )
 image_processor.save_pretrained(a     )


if __name__ == "__main__":
   _lowercase       						=			argparse.ArgumentParser()

   parser.add_argument(
       '--model_name',
       default='poolformer_s12',
       type=str,
       help='Name of the model you\'d like to convert.',
   )
   parser.add_argument(
       '--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
   )
   _lowercase       						=			parser.parse_args()
   convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)

