





import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def   __lowerCamelCase   (				__a  :Union[str, Any]       )						->							Dict:






		"""simple docstring"""

		A__     =  SwinConfig()
		A__     =  swin_name.split("""_"""       )

		A__     =  name_split[1]
		A__     =  int(name_split[4]       )
		A__     =  int(name_split[3][-1]       )

		if model_size == "tiny":
				A__     =  9_6
				A__     =  (2, 2, 6, 2)
				A__     =  (3, 6, 1_2, 2_4)
		elif model_size == "small":
				A__     =  9_6
				A__     =  (2, 2, 1_8, 2)
				A__     =  (3, 6, 1_2, 2_4)
		elif model_size == "base":
				A__     =  1_2_8
				A__     =  (2, 2, 1_8, 2)
				A__     =  (4, 8, 1_6, 3_2)
		else:
				A__     =  1_9_2
				A__     =  (2, 2, 1_8, 2)
				A__     =  (6, 1_2, 2_4, 4_8)

		if "in22k" in swin_name:
				A__     =  2_1_8_4_1
		else:
				A__     =  1_0_0_0
				A__     =  """huggingface/label-files"""
				A__     =  """imagenet-1k-id2label.json"""
				A__     =  json.load(open(hf_hub_download(__a       ,	__a       ,	repo_type="""dataset"""       )       ,	"""r"""       )       )
				A__     =  {int(__a       ): v for k, v in idalabel.items()}
				A__     =  idalabel
				A__     =  {v: k for k, v in idalabel.items()}

		A__     =  img_size
		A__     =  num_classes
		A__     =  embed_dim
		A__     =  depths
		A__     =  num_heads
		A__     =  window_size

		return config







def   __lowerCamelCase   (				__a  :int       )						->							List[str]:






		"""simple docstring"""

		if "patch_embed.proj" in name:
				A__     =  name.replace("""patch_embed.proj"""       ,	"""embeddings.patch_embeddings.projection"""       )
		if "patch_embed.norm" in name:
				A__     =  name.replace("""patch_embed.norm"""       ,	"""embeddings.norm"""       )
		if "layers" in name:
				A__     =  """encoder.""" + name
		if "attn.proj" in name:
				A__     =  name.replace("""attn.proj"""       ,	"""attention.output.dense"""       )
		if "attn" in name:
				A__     =  name.replace("""attn"""       ,	"""attention.self"""       )
		if "norm1" in name:
				A__     =  name.replace("""norm1"""       ,	"""layernorm_before"""       )
		if "norm2" in name:
				A__     =  name.replace("""norm2"""       ,	"""layernorm_after"""       )
		if "mlp.fc1" in name:
				A__     =  name.replace("""mlp.fc1"""       ,	"""intermediate.dense"""       )
		if "mlp.fc2" in name:
				A__     =  name.replace("""mlp.fc2"""       ,	"""output.dense"""       )

		if name == "norm.weight":
				A__     =  """layernorm.weight"""
		if name == "norm.bias":
				A__     =  """layernorm.bias"""

		if "head" in name:
				A__     =  name.replace("""head"""       ,	"""classifier"""       )
		else:
				A__     =  """swin.""" + name

		return name







def   __lowerCamelCase   (				__a  :Optional[int]       ,	__a  :str       )						->							Dict:






		"""simple docstring"""

		for key in orig_state_dict.copy().keys():
				A__     =  orig_state_dict.pop(__a       )

				if "mask" in key:
						continue
				elif "qkv" in key:
						A__     =  key.split("""."""       )
						A__     =  int(key_split[1]       )
						A__     =  int(key_split[3]       )
						A__     =  model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

						if "weight" in key:
								A__     =  val[:dim, :]
								A__     =  val[
								    dim : dim * 2, :
								]
								A__     =  val[-dim:, :]
						else:
								A__     =  val[
								    :dim
								]
								A__     =  val[
								    dim : dim * 2
								]
								A__     =  val[
								    -dim:
								]
				else:
						A__     =  val

		return orig_state_dict







def   __lowerCamelCase   (				__a  :List[Any]       ,	__a  :Optional[Any]       )						->							Optional[int]:






		"""simple docstring"""

		A__     =  timm.create_model(__a       ,	pretrained=__a       )
		timm_model.eval()

		A__     =  get_swin_config(__a       )
		A__     =  SwinForImageClassification(__a       )
		model.eval()

		A__     =  convert_state_dict(timm_model.state_dict()       ,	__a       )
		model.load_state_dict(__a       )

		A__     =  """http://images.cocodataset.org/val2017/000000039769.jpg"""

		A__     =  AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""       ,	"""-"""       )       )       )
		A__     =  Image.open(requests.get(__a       ,	stream=__a       ).raw       )
		A__     =  image_processor(images=__a       ,	return_tensors="""pt"""       )

		A__     =  timm_model(inputs["""pixel_values"""]       )
		A__     =  model(**__a       ).logits

		assert torch.allclose(__a       ,	__a       ,	atol=1E-3       )

		print(F'Saving model {swin_name} to {pytorch_dump_folder_path}'       )
		model.save_pretrained(__a       )

		print(F'Saving image processor to {pytorch_dump_folder_path}'       )
		image_processor.save_pretrained(__a       )


if __name__ == "__main__":
					A       :   Optional[Any]					= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--swin_name''',
					    default='''swin_tiny_patch4_window7_224''',
					    type=str,
					    help='''Name of the Swin timm model you\'d like to convert.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
					)

					A       :   List[str]					= parser.parse_args()
					convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)










import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def   __lowerCamelCase   (				__a  :Optional[int]       )						->							str:






		"""simple docstring"""

		A__     =  {}
		A__     =  tokenizer(example["""content"""]       ,	truncation=__a       )["""input_ids"""]
		A__     =  len(example["""content"""]       ) / len(output["""input_ids"""]       )
		return output


A       :   Union[str, Any]					= HfArgumentParser(PretokenizationArguments)
A       :   str					= parser.parse_args()
if args.num_workers is None:
					A       :   Any					= multiprocessing.cpu_count()
A       :   int					= AutoTokenizer.from_pretrained(args.tokenizer_dir)

A       :   Optional[int]					= time.time()
A       :   int					= load_dataset(args.dataset_name, split='''train''')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

A       :   Optional[Any]					= time.time()
A       :   int					= ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        '''repo_name''',
        '''path''',
        '''copies''',
        '''size''',
        '''content''',
        '''license''',
        '''hash''',
        '''line_mean''',
        '''line_max''',
        '''alpha_frac''',
        '''autogenerated''',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

A       :   Tuple					= time.time()
ds.push_to_hub(args.tokenized_data_repo)






print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')



