






"""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


__a       :	Optional[Any]             = logging.get_logger(__name__)

__a       :	Dict             = '▁'

__a       :	Optional[int]             = {'vocab_file': 'sentencepiece.bpe.model'}

__a       :	Union[str, Any]             = {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

__a       :	Any             = {
    'facebook/xglm-564M': 2048,
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =VOCAB_FILES_NAMES
							_SCREAMING_SNAKE_CASE    =PRETRAINED_VOCAB_FILES_MAP
							_SCREAMING_SNAKE_CASE    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_SCREAMING_SNAKE_CASE    =['input_ids', 'attention_mask']




							def __init__(   self:     Union[str, Any]				,		__A:     str				,		__A:     Tuple="<s>"				,		__A:     List[str]="</s>"				,		__A:     List[Any]="</s>"				,		__A:     str="<s>"				,		__A:     str="<unk>"				,		__A:     int="<pad>"				,		__A:     Optional[Dict[str, Any]] = None				,		**__A:     Dict				,		):

												'''simple docstring'''

												a__          =  {} if sp_model_kwargs is None else sp_model_kwargs

												# Compatibility with the original tokenizer
												a__          =  7
												a__          =  [F'<madeupword{i}>' for i in range(self.num_madeup_words    )]

												a__          =  kwargs.get('''additional_special_tokens'''				,		[]    )
												kwargs["additional_special_tokens"] += [
												    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
												]

												super().__init__(
												    bos_token=__A				,		eos_token=__A				,		unk_token=__A				,		sep_token=__A				,		cls_token=__A				,		pad_token=__A				,		sp_model_kwargs=self.sp_model_kwargs				,		**__A				,		)

												a__          =  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
												self.sp_model.Load(str(__A    )    )
												a__          =  vocab_file

												# Original fairseq vocab and spm vocab must be "aligned":
												# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
												# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
												# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
												# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

												# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
												a__          =  1

												# Mimic fairseq token-to-id alignment for the first 4 token
												a__          =  {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

												a__          =  len(self.sp_model    )
												a__          =  {F'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words    )}
												self.fairseq_tokens_to_ids.update(__A    )

												a__          =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}




							def __getstate__(   self:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  self.__dict__.copy()
												a__          =  None
												a__          =  self.sp_model.serialized_model_proto()
												return state




							def __setstate__(   self:     Any				,		__A:     Any    ):

												'''simple docstring'''

												a__          =  d

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

												a__          =  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
												self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )




							def  lowercase		(   self:     str				,		__A:     List[int]				,		__A:     Optional[List[int]] = None    ):

												'''simple docstring'''

												if token_ids_a is None:
																	return [self.sep_token_id] + token_ids_a
												a__          =  [self.sep_token_id]
												return sep + token_ids_a + sep + sep + token_ids_a




							def  lowercase		(   self:     Optional[int]				,		__A:     List[int]				,		__A:     Optional[List[int]] = None				,		__A:     bool = False    ):

												'''simple docstring'''

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=__A				,		token_ids_a=__A				,		already_has_special_tokens=__A    )

												if token_ids_a is None:
																	return [1] + ([0] * len(__A    ))
												return [1] + ([0] * len(__A    )) + [1, 1] + ([0] * len(__A    ))




							def  lowercase		(   self:     int				,		__A:     List[int]				,		__A:     Optional[List[int]] = None    ):

												'''simple docstring'''

												a__          =  [self.sep_token_id]

												if token_ids_a is None:
																	return len(sep + token_ids_a    ) * [0]
												return len(sep + token_ids_a + sep + sep + token_ids_a    ) * [0]




							@property
							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												return len(self.sp_model    ) + self.fairseq_offset + self.num_madeup_words




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  {self.convert_ids_to_tokens(__A    ): i for i in range(self.vocab_size    )}
												vocab.update(self.added_tokens_encoder    )
												return vocab




							def  lowercase		(   self:     Optional[int]				,		__A:     str    ):

												'''simple docstring'''

												return self.sp_model.encode(__A				,		out_type=__A    )




							def  lowercase		(   self:     Dict				,		__A:     Optional[Any]    ):

												'''simple docstring'''

												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												a__          =  self.sp_model.PieceToId(__A    )

												# Need to return unknown token if the SP model returned 0
												return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




							def  lowercase		(   self:     Dict				,		__A:     int    ):

												'''simple docstring'''

												if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(index - self.fairseq_offset    )




							def  lowercase		(   self:     Optional[Any]				,		__A:     int    ):

												'''simple docstring'''

												a__          =  ''''''.join(__A    ).replace(__A				,		''' '''    ).strip()
												return out_string





							def  lowercase		(   self:     Dict				,		__A:     str				,		__A:     Optional[str] = None    ):

												'''simple docstring'''

												if not os.path.isdir(__A    ):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
																	return
												a__          =  os.path.join(
												    __A				,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

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

												return (out_vocab_file,)









"""simple docstring"""







import argparse
import json
from pathlib import Path

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

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__a       :	Optional[Any]             = logging.get_logger(__name__)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					a__          =  MobileViTConfig()

					# size of the architecture
					if "mobilevit_s" in mobilevit_name:
										a__          =  [144, 192, 240]
										a__          =  [16, 32, 64, 96, 128, 160, 640]
					elif "mobilevit_xs" in mobilevit_name:
										a__          =  [96, 120, 144]
										a__          =  [16, 32, 48, 64, 80, 96, 384]
					elif "mobilevit_xxs" in mobilevit_name:
										a__          =  [64, 80, 96]
										a__          =  [16, 16, 24, 48, 64, 80, 320]
										a__          =  0.05
										a__          =  2.0

					if mobilevit_name.startswith('''deeplabv3_'''):
										a__          =  512
										a__          =  16
										a__          =  21
										a__          =  '''pascal-voc-id2label.json'''
					else:
										a__          =  1000
										a__          =  '''imagenet-1k-id2label.json'''

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

					return config





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_=False):
					for i in range(1 ,     6):
										if f'layer_{i}.' in name:
															a__          =  name.replace(f'layer_{i}.' ,     f'encoder.layer.{i - 1}.')

					if "conv_1." in name:
										a__          =  name.replace('''conv_1.''' ,     '''conv_stem.''')
					if ".block." in name:
										a__          =  name.replace('''.block.''' ,     '''.''')

					if "exp_1x1" in name:
										a__          =  name.replace('''exp_1x1''' ,     '''expand_1x1''')
					if "red_1x1" in name:
										a__          =  name.replace('''red_1x1''' ,     '''reduce_1x1''')
					if ".local_rep.conv_3x3." in name:
										a__          =  name.replace('''.local_rep.conv_3x3.''' ,     '''.conv_kxk.''')
					if ".local_rep.conv_1x1." in name:
										a__          =  name.replace('''.local_rep.conv_1x1.''' ,     '''.conv_1x1.''')
					if ".norm." in name:
										a__          =  name.replace('''.norm.''' ,     '''.normalization.''')
					if ".conv." in name:
										a__          =  name.replace('''.conv.''' ,     '''.convolution.''')
					if ".conv_proj." in name:
										a__          =  name.replace('''.conv_proj.''' ,     '''.conv_projection.''')

					for i in range(0 ,     2):
										for j in range(0 ,     4):
															if f'.{i}.{j}.' in name:
																				a__          =  name.replace(f'.{i}.{j}.' ,     f'.{i}.layer.{j}.')

					for i in range(2 ,     6):
										for j in range(0 ,     4):
															if f'.{i}.{j}.' in name:
																				a__          =  name.replace(f'.{i}.{j}.' ,     f'.{i}.')
																				if "expand_1x1" in name:
																									a__          =  name.replace('''expand_1x1''' ,     '''downsampling_layer.expand_1x1''')
																				if "conv_3x3" in name:
																									a__          =  name.replace('''conv_3x3''' ,     '''downsampling_layer.conv_3x3''')
																				if "reduce_1x1" in name:
																									a__          =  name.replace('''reduce_1x1''' ,     '''downsampling_layer.reduce_1x1''')

					for i in range(2 ,     5):
										if f'.global_rep.{i}.weight' in name:
															a__          =  name.replace(f'.global_rep.{i}.weight' ,     '''.layernorm.weight''')
										if f'.global_rep.{i}.bias' in name:
															a__          =  name.replace(f'.global_rep.{i}.bias' ,     '''.layernorm.bias''')

					if ".global_rep." in name:
										a__          =  name.replace('''.global_rep.''' ,     '''.transformer.''')
					if ".pre_norm_mha.0." in name:
										a__          =  name.replace('''.pre_norm_mha.0.''' ,     '''.layernorm_before.''')
					if ".pre_norm_mha.1.out_proj." in name:
										a__          =  name.replace('''.pre_norm_mha.1.out_proj.''' ,     '''.attention.output.dense.''')
					if ".pre_norm_ffn.0." in name:
										a__          =  name.replace('''.pre_norm_ffn.0.''' ,     '''.layernorm_after.''')
					if ".pre_norm_ffn.1." in name:
										a__          =  name.replace('''.pre_norm_ffn.1.''' ,     '''.intermediate.dense.''')
					if ".pre_norm_ffn.4." in name:
										a__          =  name.replace('''.pre_norm_ffn.4.''' ,     '''.output.dense.''')
					if ".transformer." in name:
										a__          =  name.replace('''.transformer.''' ,     '''.transformer.layer.''')

					if ".aspp_layer." in name:
										a__          =  name.replace('''.aspp_layer.''' ,     '''.''')
					if ".aspp_pool." in name:
										a__          =  name.replace('''.aspp_pool.''' ,     '''.''')
					if "seg_head." in name:
										a__          =  name.replace('''seg_head.''' ,     '''segmentation_head.''')
					if "segmentation_head.classifier.classifier." in name:
										a__          =  name.replace('''segmentation_head.classifier.classifier.''' ,     '''segmentation_head.classifier.''')

					if "classifier.fc." in name:
										a__          =  name.replace('''classifier.fc.''' ,     '''classifier.''')
					elif (not base_model) and ("segmentation_head." not in name):
										a__          =  '''mobilevit.''' + name

					return name





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False):
					if base_model:
										a__          =  ''''''
					else:
										a__          =  '''mobilevit.'''

					for key in orig_state_dict.copy().keys():
										a__          =  orig_state_dict.pop(lowerCamelCase_)

										if key[:8] == "encoder.":
															a__          =  key[8:]

										if "qkv" in key:
															a__          =  key.split('''.''')
															a__          =  int(key_split[0][6:]) - 1
															a__          =  int(key_split[3])
															a__          =  model.get_submodule(f'{model_prefix}encoder.layer.{layer_num}')
															a__          =  layer.transformer.layer[transformer_num].attention.attention.all_head_size
															a__          =  (
															    f'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
															)
															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        SCREAMING_SNAKE_CASE       (      ):
					a__          =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					a__          =  Image.open(requests.get(lowerCamelCase_ ,     stream=lowerCamelCase_).raw)
					return im





@torch.no_grad()
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False):
					a__          =  get_mobilevit_config(lowerCamelCase_)

					# load original state_dict
					a__          =  torch.load(lowerCamelCase_ ,     map_location='''cpu''')

					# load 🤗 model
					if mobilevit_name.startswith('''deeplabv3_'''):
										a__          =  MobileViTForSemanticSegmentation(lowerCamelCase_).eval()
					else:
										a__          =  MobileViTForImageClassification(lowerCamelCase_).eval()

					a__          =  convert_state_dict(lowerCamelCase_ ,     lowerCamelCase_)
					model.load_state_dict(lowerCamelCase_)

					# Check outputs on an image, prepared by MobileViTImageProcessor
					a__          =  MobileViTImageProcessor(crop_size=config.image_size ,     size=config.image_size + 32)
					a__          =  image_processor(images=prepare_img() ,     return_tensors='''pt''')
					a__          =  model(**lowerCamelCase_)
					a__          =  outputs.logits

					if mobilevit_name.startswith('''deeplabv3_'''):
										assert logits.shape == (1, 21, 32, 32)

										if mobilevit_name == "deeplabv3_mobilevit_s":
															a__          =  torch.tensor(
															    [
															        [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
															        [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
															        [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
															    ])
										elif mobilevit_name == "deeplabv3_mobilevit_xs":
															a__          =  torch.tensor(
															    [
															        [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
															        [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
															        [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
															    ])
										elif mobilevit_name == "deeplabv3_mobilevit_xxs":
															a__          =  torch.tensor(
															    [
															        [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
															        [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
															        [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
															    ])
										else:
															raise ValueError(f'Unknown mobilevit_name: {mobilevit_name}')

										assert torch.allclose(logits[0, :3, :3, :3] ,     lowerCamelCase_ ,     atol=1E-4)
					else:
										assert logits.shape == (1, 1000)

										if mobilevit_name == "mobilevit_s":
															a__          =  torch.tensor([-0.9866, 0.2392, -1.1241])
										elif mobilevit_name == "mobilevit_xs":
															a__          =  torch.tensor([-2.4761, -0.9399, -1.9587])
										elif mobilevit_name == "mobilevit_xxs":
															a__          =  torch.tensor([-1.9364, -1.2327, -0.4653])
										else:
															raise ValueError(f'Unknown mobilevit_name: {mobilevit_name}')

										assert torch.allclose(logits[0, :3] ,     lowerCamelCase_ ,     atol=1E-4)

					Path(lowerCamelCase_).mkdir(exist_ok=lowerCamelCase_)
					print(f'Saving model {mobilevit_name} to {pytorch_dump_folder_path}')
					model.save_pretrained(lowerCamelCase_)
					print(f'Saving image processor to {pytorch_dump_folder_path}')
					image_processor.save_pretrained(lowerCamelCase_)

					if push_to_hub:
										a__          =  {
										    '''mobilevit_s''': '''mobilevit-small''',
										    '''mobilevit_xs''': '''mobilevit-x-small''',
										    '''mobilevit_xxs''': '''mobilevit-xx-small''',
										    '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
										    '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
										    '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
										}

										print('''Pushing to the hub...''')
										a__          =  model_mapping[mobilevit_name]
										image_processor.push_to_hub(lowerCamelCase_ ,     organization='''apple''')
										model.push_to_hub(lowerCamelCase_ ,     organization='''apple''')


if __name__ == "__main__":
	__a       :	Optional[int]             = argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--mobilevit_name',
	    default='mobilevit_s',
	    type=str,
	    help=(
	        'Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','
	        ' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'
	    ),
	)
	parser.add_argument(
	    '--checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt 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.'
	)

	__a       :	List[str]             = parser.parse_args()
	convert_movilevit_checkpoint(
	    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)


