
"""simple docstring"""



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


UpperCamelCase__          =	logging.get_logger(__name__)

UpperCamelCase__          =	{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__          =	{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

UpperCamelCase__          =	{
    'gpt-neox-20b': 20_48,
}





class 				a			(					lowercase      ):
      UpperCamelCase							:   Any           =     VOCAB_FILES_NAMES
      UpperCamelCase							:   Dict           =     PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase							:   Tuple           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase							:   List[Any]           =     ["""input_ids""", """attention_mask"""]



      def __init__(   self     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	UpperCamelCase_="<|endoftext|>"     ,	UpperCamelCase_="<|endoftext|>"     ,	UpperCamelCase_="<|endoftext|>"     ,	UpperCamelCase_=False     ,	**UpperCamelCase_     ,	):
          super().__init__(
              UpperCamelCase_     ,	UpperCamelCase_     ,	tokenizer_file=UpperCamelCase_     ,	unk_token=UpperCamelCase_     ,	bos_token=UpperCamelCase_     ,	eos_token=UpperCamelCase_     ,	add_prefix_space=UpperCamelCase_     ,	**UpperCamelCase_     ,	)

          UpperCAmelCase__	:      Union[str, Any]     							=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
          if pre_tok_state.get('add_prefix_space'     ,	UpperCamelCase_      ) != add_prefix_space:
              UpperCAmelCase__	:      str     							=					getattr(UpperCamelCase_     ,	pre_tok_state.pop('type'      )      )
              UpperCAmelCase__	:      Union[str, Any]     							=					add_prefix_space
              UpperCAmelCase__	:      List[Any]     							=					pre_tok_class(**UpperCamelCase_      )

          UpperCAmelCase__	:      Optional[int]     							=					add_prefix_space



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None      ):
          UpperCAmelCase__	:      List[str]     							=					self._tokenizer.model.save(UpperCamelCase_     ,	name=UpperCamelCase_      )
          return tuple(UpperCamelCase_      )



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      Tuple     							=					[]
          for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_      ) + [self.eos_token_id]      )

          if len(UpperCamelCase_      ) > self.model_max_length:
              UpperCAmelCase__	:      str     							=					input_ids[-self.model_max_length :]
          return input_ids






"""simple docstring"""







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

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase     =							logging.get_logger()

def       _lowerCamelCase				(							UpperCAmelCase_ :       int,	UpperCAmelCase_ :       str,	UpperCAmelCase_ :       LevitConfig,	UpperCAmelCase_ :       Path,	UpperCAmelCase_ :       bool = True     )	-> int:






	"""simple docstring"""





	print(F"""Converting {name}..."""     )

	with torch.no_grad():
		if hidden_sizes == 128:
			if name[-1] == "S":
				A__											=					timm.create_model("levit_128s",	pretrained=UpperCAmelCase_     )
			else:
				A__											=					timm.create_model("levit_128",	pretrained=UpperCAmelCase_     )
		if hidden_sizes == 192:
			A__											=					timm.create_model("levit_192",	pretrained=UpperCAmelCase_     )
		if hidden_sizes == 256:
			A__											=					timm.create_model("levit_256",	pretrained=UpperCAmelCase_     )
		if hidden_sizes == 384:
			A__											=					timm.create_model("levit_384",	pretrained=UpperCAmelCase_     )

		from_model.eval()
		A__											=					LevitForImageClassificationWithTeacher(UpperCAmelCase_     ).eval()
		A__											=					OrderedDict()

		A__											=					from_model.state_dict()
		A__											=					list(from_model.state_dict().keys()     )
		A__											=					list(our_model.state_dict().keys()     )
		print(len(UpperCAmelCase_     ),	len(UpperCAmelCase_     )     )
		for i in range(len(UpperCAmelCase_     )     ):
			A__											=					weights[og_keys[i]]
		our_model.load_state_dict(UpperCAmelCase_     )

		A__											=					torch.randn((2, 3, 224, 224)     )
		A__											=					from_model(UpperCAmelCase_     )
		A__											=					our_model(UpperCAmelCase_     ).logits

	assert torch.allclose(UpperCAmelCase_,	UpperCAmelCase_     ), "The model logits don't match the original one."

	A__											=					name
	print(UpperCAmelCase_     )

	if push_to_hub:
		our_model.save_pretrained(save_directory / checkpoint_name     )
		A__											=					LevitImageProcessor()
		image_processor.save_pretrained(save_directory / checkpoint_name     )

		print(F"""Pushed {checkpoint_name}"""     )

def       _lowerCamelCase				(							UpperCAmelCase_ :       Path,	UpperCAmelCase_ :       str = None,	UpperCAmelCase_ :       bool = True     )	-> Union[str, Any]:






	"""simple docstring"""





	A__											=					"imagenet-1k-id2label.json"
	A__											=					1000
	A__											=					(1, num_labels)

	A__											=					"huggingface/label-files"
	A__											=					num_labels
	A__											=					json.load(open(hf_hub_download(UpperCAmelCase_,	UpperCAmelCase_,	repo_type="dataset"     ),	"r"     )     )
	A__											=					{int(UpperCAmelCase_     ): v for k, v in idalabel.items()}

	A__											=					idalabel
	A__											=					{v: k for k, v in idalabel.items()}

	A__											=					partial(UpperCAmelCase_,	num_labels=UpperCAmelCase_,	idalabel=UpperCAmelCase_,	labelaid=UpperCAmelCase_     )

	A__											=					{
	    "levit-128S": 128,
	    "levit-128": 128,
	    "levit-192": 192,
	    "levit-256": 256,
	    "levit-384": 384,
	}

	A__											=					{
	    "levit-128S": ImageNetPreTrainedConfig(
	        hidden_sizes=[128, 256, 384],	num_attention_heads=[4, 6, 8],	depths=[2, 3, 4],	key_dim=[16, 16, 16],	drop_path_rate=0,	),
	    "levit-128": ImageNetPreTrainedConfig(
	        hidden_sizes=[128, 256, 384],	num_attention_heads=[4, 8, 12],	depths=[4, 4, 4],	key_dim=[16, 16, 16],	drop_path_rate=0,	),
	    "levit-192": ImageNetPreTrainedConfig(
	        hidden_sizes=[192, 288, 384],	num_attention_heads=[3, 5, 6],	depths=[4, 4, 4],	key_dim=[32, 32, 32],	drop_path_rate=0,	),
	    "levit-256": ImageNetPreTrainedConfig(
	        hidden_sizes=[256, 384, 512],	num_attention_heads=[4, 6, 8],	depths=[4, 4, 4],	key_dim=[32, 32, 32],	drop_path_rate=0,	),
	    "levit-384": ImageNetPreTrainedConfig(
	        hidden_sizes=[384, 512, 768],	num_attention_heads=[6, 9, 12],	depths=[4, 4, 4],	key_dim=[32, 32, 32],	drop_path_rate=0.1,	),
	}

	if model_name:
		convert_weight_and_push(
		    names_to_hidden_sizes[model_name],	UpperCAmelCase_,	names_to_config[model_name],	UpperCAmelCase_,	UpperCAmelCase_     )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(names_to_hidden_sizes[model_name],	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_     )
	return config, expected_shape


if __name__ == "__main__":
			UpperCamelCase     =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--model_name""",
			    default=None,
			    type=str,
			    help="""The name of the model you wish to convert, it must be one of the supported Levit* architecture,""",
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""",
			    default="""levit-dump-folder/""",
			    type=Path,
			    required=False,
			    help="""Path to the output PyTorch model directory.""",
			)
			parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
			parser.add_argument(
			    """--no-push_to_hub""",
			    dest="""push_to_hub""",
			    action="""store_false""",
			    help="""Do not push model and image processor to the hub""",
			)

			UpperCamelCase     =							parser.parse_args()
			UpperCamelCase     =							args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




