
"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


_snake_case          =						logging.get_logger(__name__)

_snake_case          =						{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_snake_case          =						{
    '''vocab_file''': {'''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt'''},
    '''tokenizer_file''': {
        '''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json'''
    },
}

_snake_case          =						{'''mobilebert-uncased''': 5_1_2}


_snake_case          =						{}
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							List[str]							=					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_:							Tuple							=					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_:							Tuple							=					PRETRAINED_INIT_CONFIGURATION
				SCREAMING_SNAKE_CASE_:							Union[str, Any]							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					MobileBertTokenizer


				def __init__(					self	:			int				,     UpperCAmelCase_	:			Dict=None				,     UpperCAmelCase_	:			Any=None				,     UpperCAmelCase_	:			Union[str, Any]=True				,     UpperCAmelCase_	:			Tuple="[UNK]"				,     UpperCAmelCase_	:			Dict="[SEP]"				,     UpperCAmelCase_	:			Optional[Any]="[PAD]"				,     UpperCAmelCase_	:			Optional[Any]="[CLS]"				,     UpperCAmelCase_	:			Optional[int]="[MASK]"				,     UpperCAmelCase_	:			List[Any]=True				,     UpperCAmelCase_	:			Any=None				,     **UpperCAmelCase_	:			Tuple				,     )		->  Union[str, Any]:







											"""simple docstring"""




											super().__init__(
											    UpperCAmelCase_				,     tokenizer_file=UpperCAmelCase_				,     do_lower_case=UpperCAmelCase_				,     unk_token=UpperCAmelCase_				,     sep_token=UpperCAmelCase_				,     pad_token=UpperCAmelCase_				,     cls_token=UpperCAmelCase_				,     mask_token=UpperCAmelCase_				,     tokenize_chinese_chars=UpperCAmelCase_				,     strip_accents=UpperCAmelCase_				,     **UpperCAmelCase_				,     )

											_lowerCAmelCase  	=			json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
											if (
											    normalizer_state.get('lowercase'				,     UpperCAmelCase_  ) != do_lower_case
											    or normalizer_state.get('strip_accents'				,     UpperCAmelCase_  ) != strip_accents
											    or normalizer_state.get('handle_chinese_chars'				,     UpperCAmelCase_  ) != tokenize_chinese_chars
											):
																		_lowerCAmelCase  	=			getattr(UpperCAmelCase_				,     normalizer_state.pop('type'  )  )
																		_lowerCAmelCase  	=			do_lower_case
																		_lowerCAmelCase  	=			strip_accents
																		_lowerCAmelCase  	=			tokenize_chinese_chars
																		_lowerCAmelCase  	=			normalizer_class(**UpperCAmelCase_  )

											_lowerCAmelCase  	=			do_lower_case


				def 				__lowerCamelCase	(					self	:			Dict				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			Union[str, Any]=None  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

											if token_ids_a:
																		output += token_ids_a + [self.sep_token_id]

											return output


				def 				__lowerCamelCase	(					self	:			Optional[int]				,     UpperCAmelCase_	:			List[int]				,     UpperCAmelCase_	:			Optional[List[int]] = None  )		->  List[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			[self.sep_token_id]
											_lowerCAmelCase  	=			[self.cls_token_id]
											if token_ids_a is None:
																		return len(cls + token_ids_a + sep  ) * [0]
											return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]







				def 				__lowerCamelCase	(					self	:			str				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Optional[str] = None  )		->  Tuple[str]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self._tokenizer.model.save(UpperCAmelCase_				,     name=UpperCAmelCase_  )
											return tuple(UpperCAmelCase_  )








"""simple docstring"""



import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							embed.append(
							    (
							        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
							        f"""stage{idx}.patch_embed.proj.weight""",
							    )						)
							embed.append(
							    (
							        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
							        f"""stage{idx}.patch_embed.proj.bias""",
							    )						)
							embed.append(
							    (
							        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
							        f"""stage{idx}.patch_embed.norm.weight""",
							    )						)
							embed.append(
							    (
							        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
							        f"""stage{idx}.patch_embed.norm.bias""",
							    )						)
							return embed






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Tuple						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
							        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
							        f"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
							        f"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
							        f"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
							        f"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
							    )						)
							attention_weights.append(
							    (
							        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
							        f"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
							    )						)
							attention_weights.append(
							    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""")						)
							attention_weights.append(
							    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""")						)
							attention_weights.append(
							    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""")						)
							attention_weights.append(
							    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""")						)
							attention_weights.append(
							    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", f"""stage{idx}.blocks.{cnt}.norm1.weight""")						)
							attention_weights.append(
							    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", f"""stage{idx}.blocks.{cnt}.norm1.bias""")						)
							attention_weights.append(
							    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", f"""stage{idx}.blocks.{cnt}.norm2.weight""")						)
							attention_weights.append(
							    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", f"""stage{idx}.blocks.{cnt}.norm2.bias""")						)
							return attention_weights






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							token.append((f"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')						)
							return token






def 	__snake_case  (    ):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							head.append(('layernorm.weight', 'norm.weight')						)
							head.append(('layernorm.bias', 'norm.bias')						)
							head.append(('classifier.weight', 'head.weight')						)
							head.append(('classifier.bias', 'head.bias')						)
							return head






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			'imagenet-1k-id2label.json'
							_lowerCAmelCase  	=			1000

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

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

							_lowerCAmelCase  	=			_lowerCAmelCase  	=			CvtConfig(num_labels=SCREAMING_SNAKE_CASE     ,  idalabel=SCREAMING_SNAKE_CASE     ,  labelaid=SCREAMING_SNAKE_CASE						)

							# For depth size 13 (13 = 1+2+10)
							if cvt_model.rsplit('/'     ,  1						)[-1][4:6] == "13":
														_lowerCAmelCase  	=			[1, 2, 10]

							# For depth size 21 (21 = 1+4+16)
							elif cvt_model.rsplit('/'     ,  1						)[-1][4:6] == "21":
														_lowerCAmelCase  	=			[1, 4, 16]

							# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
							else:
														_lowerCAmelCase  	=			[2, 2, 20]
														_lowerCAmelCase  	=			[3, 12, 16]
														_lowerCAmelCase  	=			[192, 768, 1024]

							_lowerCAmelCase  	=			CvtForImageClassification(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'						)
							_lowerCAmelCase  	=			image_size
							_lowerCAmelCase  	=			torch.load(SCREAMING_SNAKE_CASE     ,  map_location=torch.device('cpu'						)						)

							_lowerCAmelCase  	=			OrderedDict()
							_lowerCAmelCase  	=			[]

							for idx in range(len(config.depth						)						):
														if config.cls_token[idx]:
																					_lowerCAmelCase  	=			list_of_state_dict + cls_token(SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			list_of_state_dict + embeddings(SCREAMING_SNAKE_CASE						)
														for cnt in range(config.depth[idx]						):
																					_lowerCAmelCase  	=			list_of_state_dict + attention(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

							_lowerCAmelCase  	=			list_of_state_dict + final()
							for gg in list_of_state_dict:
														print(SCREAMING_SNAKE_CASE						)
							for i in range(len(SCREAMING_SNAKE_CASE						)						):
														_lowerCAmelCase  	=			original_weights[list_of_state_dict[i][1]]

							model.load_state_dict(SCREAMING_SNAKE_CASE						)
							model.save_pretrained(SCREAMING_SNAKE_CASE						)
							image_processor.save_pretrained(SCREAMING_SNAKE_CASE						)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
	_snake_case          =						argparse.ArgumentParser()
	parser.add_argument(
	    '''--cvt_model''',
	    default='''cvt-w24''',
	    type=str,
	    help='''Name of the cvt model you\'d like to convert.''',
	)
	parser.add_argument(
	    '''--image_size''',
	    default=3_8_4,
	    type=int,
	    help='''Input Image Size''',
	)
	parser.add_argument(
	    '''--cvt_file_name''',
	    default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
	    type=str,
	    help='''Input Image Size''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
	)

	_snake_case          =						parser.parse_args()
	convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)






