





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   {'''vocab_file''': '''vocab.txt'''}

SCREAMING_SNAKE_CASE_   :      List[str]			=   {
    '''vocab_file''': {
        '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''',
        '''YituTech/conv-bert-medium-small''': (
            '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'''
        ),
        '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''',
    }
}

SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   {
    '''YituTech/conv-bert-base''': 512,
    '''YituTech/conv-bert-medium-small''': 512,
    '''YituTech/conv-bert-small''': 512,
}


SCREAMING_SNAKE_CASE_   :      int			=   {
    '''YituTech/conv-bert-base''': {'''do_lower_case''': True},
    '''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True},
    '''YituTech/conv-bert-small''': {'''do_lower_case''': True},
}

class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''
	__UpperCamelCase   =		VOCAB_FILES_NAMES
	__UpperCamelCase   =		PRETRAINED_VOCAB_FILES_MAP
	__UpperCamelCase   =		PRETRAINED_INIT_CONFIGURATION
	__UpperCamelCase   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__UpperCamelCase   =		ConvBertTokenizer




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

			'''simple docstring'''




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

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

			__lowercase											=     do_lower_case




	def    UpperCAmelCase			(						self							:     Union[str, Any]					,      __lowerCamelCase							:     Optional[int]					,      __lowerCamelCase							:     Optional[int]=None   )       -> List[str]:

			'''simple docstring'''




			__lowercase											=     [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    UpperCAmelCase			(						self							:     Union[str, Any]					,      __lowerCamelCase							:     List[int]					,      __lowerCamelCase							:     Optional[List[int]] = None   )       -> List[int]:

			'''simple docstring'''




			__lowercase											=     [self.sep_token_id]
			__lowercase											=     [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    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     str					,      __lowerCamelCase							:     Optional[str] = None   )       -> Tuple[str]:

			'''simple docstring'''




			__lowercase											=     self._tokenizer.model.save(__lowerCamelCase					,      name=__lowerCamelCase   )
			return tuple(__lowerCamelCase   )











import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_   :      str			=   logging.get_logger(__name__)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		__lowercase											=     MobileNetVaConfig(layer_norm_eps=0.001							)

		if "_quant" in model_name:
				raise ValueError('Quantized models are not supported.'							)

		__lowercase											=     re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'						,   snake_case							)
		if matches:
				__lowercase											=     float(matches[1]							)
				__lowercase											=     int(matches[2]							)

		# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
		# the usual 1000. The first class (index 0) is "background".
		__lowercase											=     1_001
		__lowercase											=     'imagenet-1k-id2label.json'
		__lowercase											=     'huggingface/label-files'
		__lowercase											=     json.load(open(hf_hub_download(snake_case						,   snake_case						,   repo_type='dataset'							)						,   'r'							)							)
		__lowercase											=     {int(snake_case							) + 1: v for k, v in idalabel.items()}
		__lowercase											=     'background'
		__lowercase											=     idalabel
		__lowercase											=     {v: k for k, v in idalabel.items()}

		return config



def  SCREAMING_SNAKE_CASE				(      )		->    List[Any]:
		__lowercase											=     'http://images.cocodataset.org/val2017/000000039769.jpg'
		__lowercase											=     Image.open(requests.get(snake_case						,   stream=snake_case							).raw							)
		return im



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case=False							)		->    Union[str, Any]:
		__lowercase											=     get_mobilenet_va_config(snake_case							)

		# Load 🤗 model
		__lowercase											=     MobileNetVaForImageClassification(snake_case							).eval()

		# Load weights from TensorFlow checkpoint
		load_tf_weights_in_mobilenet_va(snake_case						,   snake_case						,   snake_case							)

		# Check outputs on an image, prepared by MobileNetV1ImageProcessor
		__lowercase											=     MobileNetVaImageProcessor(
		    crop_size={'width': config.image_size, 'height': config.image_size}						,   size={'shortest_edge': config.image_size + 32}						,   )
		__lowercase											=     image_processor(images=prepare_img()						,   return_tensors='pt'							)
		__lowercase											=     model(**snake_case							)
		__lowercase											=     outputs.logits

		assert logits.shape == (1, 1_001)

		if model_name == "mobilenet_v1_1.0_224":
				__lowercase											=     torch.tensor([-4.1739, -1.1233, 3.1205]							)
		elif model_name == "mobilenet_v1_0.75_192":
				__lowercase											=     torch.tensor([-3.9440, -2.3141, -0.3333]							)
		else:
				__lowercase											=     None

		if expected_logits is not None:
				assert torch.allclose(logits[0, :3]						,   snake_case						,   atol=1E-4							)

		Path(snake_case							).mkdir(exist_ok=snake_case							)
		print(F"Saving model {model_name} to {pytorch_dump_folder_path}"							)
		model.save_pretrained(snake_case							)
		print(F"Saving image processor to {pytorch_dump_folder_path}"							)
		image_processor.save_pretrained(snake_case							)

		if push_to_hub:
				print('Pushing to the hub...'							)
				__lowercase											=     'google/' + model_name
				image_processor.push_to_hub(snake_case							)
				model.push_to_hub(snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''mobilenet_v1_1.0_224''',
		    type=str,
		    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
		)
		parser.add_argument(
		    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt 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.'''
		)

		SCREAMING_SNAKE_CASE_   :      int			=   parser.parse_args()
		convert_movilevit_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)




