




'''simple docstring'''


import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)
def 		snake_case_   (UpperCamelCase	:  Optional[Any]			,						UpperCamelCase	:  str				):





							'''simple docstring'''


							_a     					=    XCLIPTextConfig()

							# derive patch size from model name
							_a     					=    model_name.find('''patch'''				)
							_a     					=    int(model_name[start_idx + len('''patch'''				) : start_idx + len('''patch'''				) + 2]				)
							_a     					=    XCLIPVisionConfig(patch_size=UpperCamelCase			,						num_frames=UpperCamelCase				)

							if "large" in model_name:
														_a     					=    768
														_a     					=    3072
														_a     					=    12

														_a     					=    1024
														_a     					=    4096
														_a     					=    16
														_a     					=    24
														_a     					=    768
														_a     					=    3072

							if model_name == "xclip-large-patch14-16-frames":
														_a     					=    336

							_a     					=    XCLIPConfig.from_text_vision_configs(UpperCamelCase			,						UpperCamelCase				)

							if "large" in model_name:
														_a     					=    768

							return config
def 		snake_case_   (UpperCamelCase	:  Dict				):





							'''simple docstring'''


							if name == "token_embedding.weight":
														_a     					=    name.replace('''token_embedding.weight'''			,						'''text_model.embeddings.token_embedding.weight'''				)
							if name == "positional_embedding":
														_a     					=    name.replace('''positional_embedding'''			,						'''text_model.embeddings.position_embedding.weight'''				)
							if "ln_1" in name:
														_a     					=    name.replace('''ln_1'''			,						'''layer_norm1'''				)
							if "ln_2" in name:
														_a     					=    name.replace('''ln_2'''			,						'''layer_norm2'''				)
							if "c_fc" in name:
														_a     					=    name.replace('''c_fc'''			,						'''fc1'''				)
							if "c_proj" in name:
														_a     					=    name.replace('''c_proj'''			,						'''fc2'''				)
							if name.startswith('''transformer.resblocks'''				):
														_a     					=    name.replace('''transformer.resblocks'''			,						'''text_model.encoder.layers'''				)
							if "attn.out_proj" in name and "message" not in name:
														_a     					=    name.replace('''attn.out_proj'''			,						'''self_attn.out_proj'''				)
							if "ln_final" in name:
														_a     					=    name.replace('''ln_final'''			,						'''text_model.final_layer_norm'''				)
							# visual encoder
							if name == "visual.class_embedding":
														_a     					=    name.replace('''visual.class_embedding'''			,						'''vision_model.embeddings.class_embedding'''				)
							if name == "visual.positional_embedding":
														_a     					=    name.replace('''visual.positional_embedding'''			,						'''vision_model.embeddings.position_embedding.weight'''				)
							if name.startswith('''visual.transformer.resblocks'''				):
														_a     					=    name.replace('''visual.transformer.resblocks'''			,						'''vision_model.encoder.layers'''				)
							if "visual.conv1" in name:
														_a     					=    name.replace('''visual.conv1'''			,						'''vision_model.embeddings.patch_embedding'''				)
							if "visual.ln_pre" in name:
														_a     					=    name.replace('''visual.ln_pre'''			,						'''vision_model.pre_layernorm'''				)
							if "visual.ln_post" in name:
														_a     					=    name.replace('''visual.ln_post'''			,						'''vision_model.post_layernorm'''				)
							if "visual.proj" in name:
														_a     					=    name.replace('''visual.proj'''			,						'''visual_projection.weight'''				)
							if "text_projection" in name:
														_a     					=    name.replace('''text_projection'''			,						'''text_projection.weight'''				)
							# things on top
							if "prompts_visual_proj" in name:
														_a     					=    name.replace('''prompts_visual_proj'''			,						'''prompts_visual_projection'''				)
							if "prompts_visual_ln" in name:
														_a     					=    name.replace('''prompts_visual_ln'''			,						'''prompts_visual_layernorm'''				)
							# mit
							if name == "mit.positional_embedding":
														_a     					=    name.replace('''positional'''			,						'''position'''				)
							if name.startswith('''mit.resblocks'''				):
														_a     					=    name.replace('''mit.resblocks'''			,						'''mit.encoder.layers'''				)
							# prompts generator
							if name.startswith('''prompts_generator.norm'''				):
														_a     					=    name.replace('''prompts_generator.norm'''			,						'''prompts_generator.layernorm'''				)

							return name
def 		snake_case_   (UpperCamelCase	:  Dict			,						UpperCamelCase	:  List[Any]				):





							'''simple docstring'''


							for key in orig_state_dict.copy().keys():
														_a     					=    orig_state_dict.pop(UpperCamelCase				)

														if "attn.in_proj" in key:
																					_a     					=    key.split('''.'''				)
																					if key.startswith('''visual'''				):
																												_a     					=    key_split[3]
																												_a     					=    config.vision_config.hidden_size
																												if "message_attn" in key:
																																			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:
																																			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:]
																					elif key.startswith('''mit'''				):
																												_a     					=    key_split[2]
																												_a     					=    config.vision_config.mit_hidden_size
																												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     					=    key_split[2]
																												_a     					=    config.text_config.hidden_size
																												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     					=    rename_key(UpperCamelCase				)
																					if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																												_a     					=    val.T
																					_a     					=    val

							return orig_state_dict
def 		snake_case_   (UpperCamelCase	:  Union[str, Any]				):





							'''simple docstring'''


							if num_frames == 8:
														_a     					=    '''eating_spaghetti_8_frames.npy'''
							elif num_frames == 16:
														_a     					=    '''eating_spaghetti.npy'''
							elif num_frames == 32:
														_a     					=    '''eating_spaghetti_32_frames.npy'''
							_a     					=    hf_hub_download(
							    repo_id='''hf-internal-testing/spaghetti-video'''			,						filename=UpperCamelCase			,						repo_type='''dataset'''			,						)
							_a     					=    np.load(UpperCamelCase				)
							return list(UpperCamelCase				)
def 		snake_case_   (UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  int=None			,						UpperCamelCase	:  Optional[int]=False				):





							'''simple docstring'''


							_a     					=    {
							    # fully supervised kinetics-400 checkpoints
							    '''xclip-base-patch32''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth''',
							    '''xclip-base-patch32-16-frames''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'''
							    ),
							    '''xclip-base-patch16''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth''',
							    '''xclip-base-patch16-16-frames''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'''
							    ),
							    '''xclip-large-patch14''': '''https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb''',
							    '''xclip-large-patch14-16-frames''': '''https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f''',
							    # fully supervised kinetics-600 checkpoints
							    '''xclip-base-patch16-kinetics-600''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'''
							    ),
							    '''xclip-base-patch16-kinetics-600-16-frames''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'''
							    ),
							    '''xclip-large-patch14-kinetics-600''': '''https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be''',
							    # few shot
							    '''xclip-base-patch16-hmdb-2-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'''
							    ),
							    '''xclip-base-patch16-hmdb-4-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'''
							    ),
							    '''xclip-base-patch16-hmdb-8-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'''
							    ),
							    '''xclip-base-patch16-hmdb-16-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'''
							    ),
							    '''xclip-base-patch16-ucf-2-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'''
							    ),
							    '''xclip-base-patch16-ucf-4-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'''
							    ),
							    '''xclip-base-patch16-ucf-8-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'''
							    ),
							    '''xclip-base-patch16-ucf-16-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'''
							    ),
							    # zero shot
							    '''xclip-base-patch16-zero-shot''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth''',
							}

							_a     					=    model_to_url[model_name]
							_a     					=    8
							if "16-frames" in model_name:
														_a     					=    16
							elif "shot" in model_name:
														_a     					=    32

							_a     					=    get_xclip_config(UpperCamelCase			,						UpperCamelCase				)
							_a     					=    XCLIPModel(UpperCamelCase				)
							model.eval()

							if "drive" in checkpoint_url:
														_a     					=    '''pytorch_model.bin'''
														gdown.cached_download(UpperCamelCase			,						UpperCamelCase			,						quiet=UpperCamelCase				)
														_a     					=    torch.load(UpperCamelCase			,						map_location='''cpu'''				)['''model''']
							else:
														_a     					=    torch.hub.load_state_dict_from_url(UpperCamelCase				)['''model''']

							_a     					=    convert_state_dict(UpperCamelCase			,						UpperCamelCase				)

							_a     					=    XCLIPModel(UpperCamelCase				)
							_a					,							_a     					=    model.load_state_dict(UpperCamelCase			,						strict=UpperCamelCase				)
							assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
							model.eval()

							_a     					=    336 if model_name == '''xclip-large-patch14-16-frames''' else 224
							_a     					=    VideoMAEImageProcessor(size=UpperCamelCase				)
							_a     					=    CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''				)
							_a     					=    CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32'''				)
							_a     					=    XCLIPProcessor(image_processor=UpperCamelCase			,						tokenizer=UpperCamelCase				)

							_a     					=    prepare_video(UpperCamelCase				)
							_a     					=    processor(
							    text=['''playing sports''', '''eating spaghetti''', '''go shopping''']			,						videos=UpperCamelCase			,						return_tensors='''pt'''			,						padding=UpperCamelCase				)

							print('''Shape of pixel values:'''			,						inputs.pixel_values.shape				)

							with torch.no_grad():
														_a     					=    model(**UpperCamelCase				)

							# Verify outputs
							_a     					=    outputs.logits_per_video
							_a     					=    logits_per_video.softmax(dim=1				)
							print('''Probs:'''			,						UpperCamelCase				)
							# kinetics-400
							if model_name == "xclip-base-patch32":
														_a     					=    torch.tensor([[0.0019, 0.9951, 0.0030]]				)
							elif model_name == "xclip-base-patch32-16-frames":
														_a     					=    torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]				)
							elif model_name == "xclip-base-patch16":
														_a     					=    torch.tensor([[0.0083, 0.9681, 0.0236]]				)
							elif model_name == "xclip-base-patch16-16-frames":
														_a     					=    torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]				)
							elif model_name == "xclip-large-patch14":
														_a     					=    torch.tensor([[0.0062, 0.9864, 0.0075]]				)
							elif model_name == "xclip-large-patch14-16-frames":
														_a     					=    torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]				)
							# kinetics-600
							elif model_name == "xclip-base-patch16-kinetics-600":
														_a     					=    torch.tensor([[0.0555, 0.8914, 0.0531]]				)
							elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
														_a     					=    torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]				)
							elif model_name == "xclip-large-patch14-kinetics-600":
														_a     					=    torch.tensor([[0.0036, 0.9920, 0.0045]]				)
							# few shot
							elif model_name == "xclip-base-patch16-hmdb-2-shot":
														_a     					=    torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]				)
							elif model_name == "xclip-base-patch16-hmdb-4-shot":
														_a     					=    torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]				)
							elif model_name == "xclip-base-patch16-hmdb-8-shot":
														_a     					=    torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]				)
							elif model_name == "xclip-base-patch16-hmdb-16-shot":
														_a     					=    torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]				)
							elif model_name == "xclip-base-patch16-ucf-2-shot":
														_a     					=    torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]				)
							elif model_name == "xclip-base-patch16-ucf-4-shot":
														_a     					=    torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]				)
							elif model_name == "xclip-base-patch16-ucf-8-shot":
														_a     					=    torch.tensor([[0.0027, 0.9904, 0.0070]]				)
							elif model_name == "xclip-base-patch16-ucf-16-shot":
														_a     					=    torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]				)
							# zero shot
							elif model_name == "xclip-base-patch16-zero-shot":
														_a     					=    torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]]				)
							else:
														raise ValueError(f'Model name {model_name} not supported'				)
							assert torch.allclose(UpperCamelCase			,						UpperCamelCase			,						atol=1e-3				)
							print('''Looks ok!'''				)

							if pytorch_dump_folder_path is not None:
														print(f'Saving model {model_name} to {pytorch_dump_folder_path}'				)
														model.save_pretrained(UpperCamelCase				)

							if push_to_hub:
														print('''Pushing model, processor and slow tokenizer files to the hub...'''				)
														model.push_to_hub(UpperCamelCase			,						organization='''nielsr'''				)
														processor.push_to_hub(UpperCamelCase			,						organization='''nielsr'''				)
														slow_tokenizer.push_to_hub(UpperCamelCase			,						organization='''nielsr'''				)


if __name__ == "__main__":
				_snake_case				:				Optional[int]       							=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='xclip-base-patch32',
				    type=str,
				    help='Name of the model.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, 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.'
				)

				_snake_case				:				Dict       							=		parser.parse_args()
				convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''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_bert import BertTokenizer


_snake_case				:				Any       							=		logging.get_logger(__name__)

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

_snake_case				:				List[str]       							=		{
    'vocab_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
        ),
        'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
        ),
        'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
        ),
        'bert-base-multilingual-cased': (
            'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-cased': (
            'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
        ),
    },
}

_snake_case				:				int       							=		{
    'bert-base-uncased': 512,
    'bert-large-uncased': 512,
    'bert-base-cased': 512,
    'bert-large-cased': 512,
    'bert-base-multilingual-uncased': 512,
    'bert-base-multilingual-cased': 512,
    'bert-base-chinese': 512,
    'bert-base-german-cased': 512,
    'bert-large-uncased-whole-word-masking': 512,
    'bert-large-cased-whole-word-masking': 512,
    'bert-large-uncased-whole-word-masking-finetuned-squad': 512,
    'bert-large-cased-whole-word-masking-finetuned-squad': 512,
    'bert-base-cased-finetuned-mrpc': 512,
    'bert-base-german-dbmdz-cased': 512,
    'bert-base-german-dbmdz-uncased': 512,
    'TurkuNLP/bert-base-finnish-cased-v1': 512,
    'TurkuNLP/bert-base-finnish-uncased-v1': 512,
    'wietsedv/bert-base-dutch-cased': 512,
}

_snake_case				:				int       							=		{
    'bert-base-uncased': {'do_lower_case': True},
    'bert-large-uncased': {'do_lower_case': True},
    'bert-base-cased': {'do_lower_case': False},
    'bert-large-cased': {'do_lower_case': False},
    'bert-base-multilingual-uncased': {'do_lower_case': True},
    'bert-base-multilingual-cased': {'do_lower_case': False},
    'bert-base-chinese': {'do_lower_case': False},
    'bert-base-german-cased': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
    'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
    'bert-base-german-dbmdz-cased': {'do_lower_case': False},
    'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
    'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
    'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
    'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}

class       A   (		_a   ):
							lowercase_          =    VOCAB_FILES_NAMES
							lowercase_          =    PRETRAINED_VOCAB_FILES_MAP
							lowercase_          =    PRETRAINED_INIT_CONFIGURATION
							lowercase_          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_          =    BertTokenizer







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


														"""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_							,      )

														_a     					=    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
														):
																					_a     					=    getattr(lowerCAmelCase_							,      normalizer_state.pop('''type''' ) )
																					_a     					=    do_lower_case
																					_a     					=    strip_accents
																					_a     					=    tokenize_chinese_chars
																					_a     					=    normalizer_class(**lowerCAmelCase_ )

														_a     					=    do_lower_case







							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  Dict							,      lowerCAmelCase_	:  int=None )      ->							List[str]:


														"""simple docstring"""


														_a     					=    [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	:  Any							,      lowerCAmelCase_	:  List[int]							,      lowerCAmelCase_	:  Optional[List[int]] = None )      ->							List[int]:


														"""simple docstring"""


														_a     					=    [self.sep_token_id]
														_a     					=    [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	:  List[Any]							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  Optional[str] = None )      ->							Tuple[str]:


														"""simple docstring"""


														_a     					=    self._tokenizer.model.save(lowerCAmelCase_							,      name=lowerCAmelCase_ )
														return tuple(lowerCAmelCase_ )

