


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


__lowerCamelCase       :       int				   =			logging.get_logger(__name__)

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

__lowerCamelCase       :       Dict				   =			{
    'vocab_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json',
    },
}

__lowerCamelCase       :       List[str]				   =			{
    'microsoft/mpnet-base': 512,
}

__lowerCamelCase       :       Tuple				   =			{
    'microsoft/mpnet-base': {'do_lower_case': True},
}



class 			a      (      UpperCAmelCase_   ):
		__lowercase													=		VOCAB_FILES_NAMES
		__lowercase													=		PRETRAINED_VOCAB_FILES_MAP
		__lowercase													=		PRETRAINED_INIT_CONFIGURATION
		__lowercase													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase													=		MPNetTokenizer
		__lowercase													=		["""input_ids""", """attention_mask"""]






		def __init__(  self	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     __UpperCamelCase=True	,     __UpperCamelCase="<s>"	,     __UpperCamelCase="</s>"	,     __UpperCamelCase="</s>"	,     __UpperCamelCase="<s>"	,     __UpperCamelCase="[UNK]"	,     __UpperCamelCase="<pad>"	,     __UpperCamelCase="<mask>"	,     __UpperCamelCase=True	,     __UpperCamelCase=None	,     **__UpperCamelCase	,     )->			Union[str, Any]:
						'''simple docstring'''


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

						A__     :							str   				=json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
						if (
						    pre_tok_state.get('''lowercase'''	,     __UpperCamelCase							) != do_lower_case
						    or pre_tok_state.get('''strip_accents'''	,     __UpperCamelCase							) != strip_accents
						):
										A__     :							Any   				=getattr(__UpperCamelCase	,     pre_tok_state.pop('''type'''							)							)
										A__     :							Union[str, Any]   				=do_lower_case
										A__     :							Optional[int]   				=strip_accents
										A__     :							Optional[int]   				=pre_tok_class(**__UpperCamelCase							)

						A__     :							Optional[int]   				=do_lower_case






		@property
		def       lowerCAmelCase_  (  self							)->			int:
						'''simple docstring'''


						if self._mask_token is None:
										if self.verbose:
														logger.error('''Using mask_token, but it is not set yet.'''							)
										return None
						return str(self._mask_token							)






		@mask_token.setter
		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			Dict:
						'''simple docstring'''


						A__     :							Dict   				=AddedToken(__UpperCamelCase	,     lstrip=__UpperCamelCase	,     rstrip=__UpperCamelCase							) if isinstance(__UpperCamelCase	,     __UpperCamelCase							) else value
						A__     :							Optional[Any]   				=value






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase=None							)->			Union[str, Any]:
						'''simple docstring'''


						A__     :							Tuple   				=[self.bos_token_id] + token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
										return output

						return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase = None							)->			Tuple:
						'''simple docstring'''


						A__     :							str   				=[self.sep_token_id]
						A__     :							Optional[int]   				=[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 + sep + token_ids_a + sep							) * [0]






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase = None							)->			Union[str, Any]:
						'''simple docstring'''


						A__     :							Tuple   				=self._tokenizer.model.save(__UpperCamelCase	,     name=__UpperCamelCase							)
						return tuple(__UpperCamelCase							)







import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor



def   SCREAMING_SNAKE_CASE__						(							)			->							Tuple:
				A__     :							List[Any]   				=set()
				# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
				for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
								A__     :							Optional[int]   				=getattr(transformers.models.auto,	snake_case_,	snake_case_	)
								if module is None:
												continue
								# all mappings in a single auto modeling file
								A__     :							Optional[int]   				=[
								    x
								    for x in dir(snake_case_	)
								    if x.endswith('''_MAPPING_NAMES'''	)
								    and (x.startswith('''MODEL_'''	) or x.startswith('''TF_MODEL_'''	) or x.startswith('''FLAX_MODEL_'''	))
								]
								for name in mapping_names:
												A__     :							Any   				=getattr(snake_case_,	snake_case_	)
												if mapping is not None:
																for v in mapping.values():
																				if isinstance(snake_case_,	(list, tuple)	):
																								model_names.update(snake_case_	)
																				elif isinstance(snake_case_,	snake_case_	):
																								model_names.add(snake_case_	)

				return sorted(snake_case_	)



def   SCREAMING_SNAKE_CASE__						(							)			->							Optional[Any]:
				# All model names defined in auto mappings
				A__     :							int   				=set(get_all_model_names()	)

				with open('''tests/utils/tiny_model_summary.json'''	) as fp:
								A__     :							Union[str, Any]   				=json.load(snake_case_	)
				A__     :							int   				=set()
				for model_base_name in tiny_model_info:
								tiny_models_names.update(tiny_model_info[model_base_name]['''model_classes''']	)

				# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
				A__     :							Optional[Any]   				=model_names.difference(snake_case_	)
				for model_name in copy.copy(snake_case_	):
								if not model_name.startswith('''TF'''	) and f'TF{model_name}' in not_on_hub:
												tiny_models_names.remove(snake_case_	)
								elif model_name.startswith('''TF'''	) and model_name[2:] in not_on_hub:
												tiny_models_names.remove(snake_case_	)

				return sorted(snake_case_	)



def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Tuple:
				A__     :							Dict   				=COMPOSITE_MODELS.values()

				# All tiny model base names on Hub
				A__     :							Union[str, Any]   				=get_all_model_names()
				A__     :							Optional[Any]   				=hf_api.list_models(
				    filter=ModelFilter(
				        author='''hf-internal-testing''',	)	)
				A__     :							str   				=set()
				for x in models:
								A__     :							List[Any]   				=x.modelId
								A__				,  A__     :							List[str]   				=model.split('''/'''	)
								if not model.startswith('''tiny-random-'''	):
												continue
								A__     :							str   				=model.replace('''tiny-random-''',	''''''	)
								if not model[0].isupper():
												continue
								if model not in model_names and model not in special_models:
												continue
								_models.add(snake_case_	)

				A__     :							Dict   				=sorted(_models	)
				# All tiny model names on Hub
				A__     :							Optional[int]   				={}
				for model in models:
								A__     :							Optional[int]   				=f'hf-internal-testing/tiny-random-{model}'
								A__     :							str   				=model.split('''-'''	)[0]
								try:
												A__     :							int   				=hf_api.repo_info(snake_case_	)
												A__     :							Optional[int]   				={
												    '''tokenizer_classes''': set(),
												    '''processor_classes''': set(),
												    '''model_classes''': set(),
												    '''sha''': repo_info.sha,
												}
								except Exception:
												continue
								try:
												time.sleep(1	)
												A__     :							List[str]   				=AutoTokenizer.from_pretrained(snake_case_	)
												content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__	)
								except Exception:
												pass
								try:
												time.sleep(1	)
												A__     :							int   				=AutoTokenizer.from_pretrained(snake_case_,	use_fast=snake_case_	)
												content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__	)
								except Exception:
												pass
								try:
												time.sleep(1	)
												A__     :							int   				=AutoImageProcessor.from_pretrained(snake_case_	)
												content["processor_classes"].add(img_p.__class__.__name__	)
								except Exception:
												pass
								try:
												time.sleep(1	)
												A__     :							Optional[int]   				=AutoFeatureExtractor.from_pretrained(snake_case_	)
												if not isinstance(snake_case_,	snake_case_	):
																content["processor_classes"].add(feat_p.__class__.__name__	)
								except Exception:
												pass
								try:
												time.sleep(1	)
												A__     :							Tuple   				=getattr(snake_case_,	snake_case_	)
												A__     :							Tuple   				=model_class.from_pretrained(snake_case_	)
												content["model_classes"].add(m.__class__.__name__	)
								except Exception:
												pass
								try:
												time.sleep(1	)
												A__     :							int   				=getattr(snake_case_,	f'TF{model}'	)
												A__     :							Union[str, Any]   				=model_class.from_pretrained(snake_case_	)
												content["model_classes"].add(m.__class__.__name__	)
								except Exception:
												pass

								A__     :							List[str]   				=sorted(content['''tokenizer_classes''']	)
								A__     :							Tuple   				=sorted(content['''processor_classes''']	)
								A__     :							Optional[Any]   				=sorted(content['''model_classes''']	)

								A__     :							List[Any]   				=content
								with open(os.path.join(snake_case_,	'''hub_tiny_model_summary.json'''	),	'''w'''	) as fp:
												json.dump(snake_case_,	snake_case_,	ensure_ascii=snake_case_,	indent=4	)


if __name__ == "__main__":
						__lowerCamelCase       :       Any				   =			argparse.ArgumentParser()
						parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
						__lowerCamelCase       :       int				   =			parser.parse_args()

						# This has to be `spawn` to avoid hanging forever!
						multiprocessing.set_start_method("spawn")

						__lowerCamelCase       :       Any				   =			"tiny_models"
						__lowerCamelCase       :       Optional[int]				   =			True
						__lowerCamelCase       :       int				   =			None
						__lowerCamelCase       :       str				   =			get_tiny_model_names_from_repo()
						__lowerCamelCase       :       List[Any]				   =			True
						__lowerCamelCase       :       List[Any]				   =			True
						__lowerCamelCase       :       Optional[Any]				   =			"hf-internal-testing"

						create_tiny_models(
						    output_path,
						    all,
						    model_types,
						    models_to_skip,
						    no_check,
						    upload,
						    organization,
						    token=os.environ.get("TOKEN", None),
						    num_workers=args.num_workers,
						)



