

'''simple docstring'''

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE							:				int     	=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:				int     	=			{"vocab_file": "spm_char.model"}

SCREAMING_SNAKE_CASE							:				Tuple     	=			{
    "vocab_file": {
        "microsoft/speecht5_asr": "https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model",
        "microsoft/speecht5_tts": "https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model",
        "microsoft/speecht5_vc": "https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model",
    }
}

SCREAMING_SNAKE_CASE							:				Dict     	=			{
    "microsoft/speecht5_asr": 1024,
    "microsoft/speecht5_tts": 1024,
    "microsoft/speecht5_vc": 1024,
}






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     VOCAB_FILES_NAMES
				_a    	=     PRETRAINED_VOCAB_FILES_MAP
				_a    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_a    	=     ["""input_ids""", """attention_mask"""]



				def __init__(    self,     _lowercase,     _lowercase="<s>",     _lowercase="</s>",     _lowercase="<unk>",     _lowercase="<pad>",     _lowercase = None,     **_lowercase,     )     ->							None:
											SCREAMING_SNAKE_CASE_				=				{} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    bos_token=_lowercase,     eos_token=_lowercase,     unk_token=_lowercase,     pad_token=_lowercase,     sp_model_kwargs=self.sp_model_kwargs,     **_lowercase,     )

											SCREAMING_SNAKE_CASE_				=				vocab_file

											SCREAMING_SNAKE_CASE_				=				spm.SentencePieceProcessor(**self.sp_model_kwargs       )
											self.sp_model.Load(_lowercase       )



				@property
				def   a__					(    self       )     ->							List[Any]:
											return self.sp_model.get_piece_size()



				def   a__					(    self       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				=				{self.convert_ids_to_tokens(_lowercase       ): i for i in range(self.vocab_size       )}
											vocab.update(self.added_tokens_encoder       )
											return vocab



				def __getstate__(    self       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				=				self.__dict__.copy()
											SCREAMING_SNAKE_CASE_				=				None
											return state



				def __setstate__(    self,     _lowercase       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				d

											# for backward compatibility
											if not hasattr(self,     'sp_model_kwargs'       ):
																		SCREAMING_SNAKE_CASE_				=				{}

											SCREAMING_SNAKE_CASE_				=				spm.SentencePieceProcessor(**self.sp_model_kwargs       )
											self.sp_model.Load(self.vocab_file       )



				def   a__					(    self,     _lowercase       )     ->							List[str]:
											return self.sp_model.encode(_lowercase,     out_type=_lowercase       )



				def   a__					(    self,     _lowercase       )     ->							List[str]:
											return self.sp_model.piece_to_id(_lowercase       )



				def   a__					(    self,     _lowercase       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				=				self.sp_model.IdToPiece(_lowercase       )
											return token



				def   a__					(    self,     _lowercase       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				[]
											SCREAMING_SNAKE_CASE_				=				''
											for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																									out_string += self.sp_model.decode(_lowercase       ) + token
																									SCREAMING_SNAKE_CASE_				=				[]
																		else:
																									current_sub_tokens.append(_lowercase       )
											out_string += self.sp_model.decode(_lowercase       )
											return out_string.strip()



				def   a__					(    self,     _lowercase,     _lowercase=None       )     ->							List[int]:
											if token_ids_a is None:
																		return token_ids_a + [self.eos_token_id]
											# We don't expect to process pairs, but leave the pair logic for API consistency
											return token_ids_a + token_ids_a + [self.eos_token_id]



				def   a__					(    self,     _lowercase,     _lowercase = None,     _lowercase = False       )     ->							List[int]:
											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=_lowercase,     token_ids_a=_lowercase,     already_has_special_tokens=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				[1]
											if token_ids_a is None:
																		return ([0] * len(_lowercase       )) + suffix_ones
											return ([0] * len(_lowercase       )) + ([0] * len(_lowercase       )) + suffix_ones





				def   a__					(    self,     _lowercase,     _lowercase = None       )     ->							Tuple[str]:
											if not os.path.isdir(_lowercase       ):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""       )
																		return
											SCREAMING_SNAKE_CASE_				=				os.path.join(
											    _lowercase,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

											if os.path.abspath(self.vocab_file       ) != os.path.abspath(_lowercase       ) and os.path.isfile(self.vocab_file       ):
																		copyfile(self.vocab_file,     _lowercase       )
											elif not os.path.isfile(self.vocab_file       ):
																		with open(_lowercase,     'wb'       ) as fi:
																									SCREAMING_SNAKE_CASE_				=				self.sp_model.serialized_model_proto()
																									fi.write(_lowercase       )

											return (out_vocab_file,)








'''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()
SCREAMING_SNAKE_CASE							:				Dict     	=			logging.get_logger()




def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					str		,lowerCAmelCase__:					LevitConfig		,lowerCAmelCase__:					Path		,lowerCAmelCase__:					bool = True   )      -> Optional[int]:
							print(F"""Converting {name}..."""   )

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

														from_model.eval()
														SCREAMING_SNAKE_CASE_				=				LevitForImageClassificationWithTeacher(lowerCAmelCase__   ).eval()
														SCREAMING_SNAKE_CASE_				=				OrderedDict()

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

														SCREAMING_SNAKE_CASE_				=				torch.randn((2, 3, 224, 224)   )
														SCREAMING_SNAKE_CASE_				=				from_model(lowerCAmelCase__   )
														SCREAMING_SNAKE_CASE_				=				our_model(lowerCAmelCase__   ).logits

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

							SCREAMING_SNAKE_CASE_				=				name
							print(lowerCAmelCase__   )

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

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




def 							_UpperCamelCase				(   lowerCAmelCase__:					Path		,lowerCAmelCase__:					str = None		,lowerCAmelCase__:					bool = True   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				'imagenet-1k-id2label.json'
							SCREAMING_SNAKE_CASE_				=				1000
							SCREAMING_SNAKE_CASE_				=				(1, num_labels)

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

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

							SCREAMING_SNAKE_CASE_				=				partial(lowerCAmelCase__		,num_labels=lowerCAmelCase__		,idalabel=lowerCAmelCase__		,labelaid=lowerCAmelCase__   )

							SCREAMING_SNAKE_CASE_				=				{
							    'levit-128S': 128,
							    'levit-128': 128,
							    'levit-192': 192,
							    'levit-256': 256,
							    'levit-384': 384,
							}

							SCREAMING_SNAKE_CASE_				=				{
							    '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]		,lowerCAmelCase__		,names_to_config[model_name]		,lowerCAmelCase__		,lowerCAmelCase__   )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(names_to_hidden_sizes[model_name]		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__   )
							return config, expected_shape


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE							:				str     	=			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",
							)

							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			parser.parse_args()
							SCREAMING_SNAKE_CASE							:				Path     	=			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)





