






"""simple docstring"""



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


__A		:	Tuple      =		logging.get_logger(__name__)

__A		:	List[str]      =		{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__A		:	Optional[int]      =		{
    '''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''',
    },
}

__A		:	Dict      =		{
    '''microsoft/mpnet-base''': 512,
}

__A		:	List[str]      =		{
    '''microsoft/mpnet-base''': {'''do_lower_case''': True},
}





class  _UpperCAmelCase	(			_A							):
					SCREAMING_SNAKE_CASE_		:    List[str]							   =       VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE_		:    Optional[Any]							   =       PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE_		:    Optional[int]							   =       PRETRAINED_INIT_CONFIGURATION
					SCREAMING_SNAKE_CASE_		:    int							   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE_		:    Dict							   =       MPNetTokenizer
					SCREAMING_SNAKE_CASE_		:    Dict							   =       ["input_ids", "attention_mask"]




					def __init__(    self					:						Optional[Any]   ,						A					:						Optional[Any]=None   ,						A					:						str=None   ,						A					:						Optional[Any]=True   ,						A					:						List[str]="<s>"   ,						A					:						Tuple="</s>"   ,						A					:						Any="</s>"   ,						A					:						Optional[Any]="<s>"   ,						A					:						Tuple="[UNK]"   ,						A					:						Dict="<pad>"   ,						A					:						List[str]="<mask>"   ,						A					:						Optional[int]=True   ,						A					:						Optional[Any]=None   ,						**A					:						List[Any]   ,						)  ->  Any:
							super().__init__(
							    A   ,						tokenizer_file=A   ,						do_lower_case=A   ,						bos_token=A   ,						eos_token=A   ,						sep_token=A   ,						cls_token=A   ,						unk_token=A   ,						pad_token=A   ,						mask_token=A   ,						tokenize_chinese_chars=A   ,						strip_accents=A   ,						**A   ,						)

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

							lowercase_			:			Optional[Any]								=       do_lower_case




					@property
					def   A  (    self					:						Union[str, Any]       )  ->  str:
							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   A  (    self					:						str   ,						A					:						Union[str, Any]       )  ->  Dict:
							lowercase_			:			Optional[Any]								=       AddedToken(A   ,						lstrip=A   ,						rstrip=A       ) if isinstance(A   ,						A       ) else value
							lowercase_			:			List[Any]								=       value




					def   A  (    self					:						Any   ,						A					:						Any   ,						A					:						Union[str, Any]=None       )  ->  int:
							lowercase_			:			str								=       [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   A  (    self					:						List[str]   ,						A					:						List[int]   ,						A					:						Optional[List[int]] = None       )  ->  List[int]:
							lowercase_			:			int								=       [self.sep_token_id]
							lowercase_			:			List[Any]								=       [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   A  (    self					:						int   ,						A					:						str   ,						A					:						Optional[str] = None       )  ->  Tuple[str]:
							lowercase_			:			Union[str, Any]								=       self._tokenizer.model.save(A   ,						name=A       )
							return tuple(A       )











"""simple docstring"""



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 						lowercase   (    ):
		lowercase_			:			Tuple								=       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"]:
				lowercase_			:			str								=       getattr(transformers.models.auto		, __snake_case		, __snake_case     )
				if module is None:
						continue
				# all mappings in a single auto modeling file
				lowercase_			:			Tuple								=       [
				    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:
						lowercase_			:			Tuple								=       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 						lowercase   (    ):
		# All model names defined in auto mappings
		lowercase_			:			Tuple								=       set(get_all_model_names()     )

		with open('''tests/utils/tiny_model_summary.json'''     ) as fp:
				lowercase_			:			Tuple								=       json.load(__snake_case     )
		lowercase_			:			Union[str, Any]								=       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.
		lowercase_			:			Union[str, 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 						lowercase   (    __snake_case    :			List[Any]     ):
		lowercase_			:			List[str]								=       COMPOSITE_MODELS.values()

		# All tiny model base names on Hub
		lowercase_			:			List[Any]								=       get_all_model_names()
		lowercase_			:			Tuple								=       hf_api.list_models(
		    filter=ModelFilter(
		        author='''hf-internal-testing'''		, )     )
		lowercase_			:			Tuple								=       set()
		for x in models:
				lowercase_			:			Tuple								=       x.modelId
				lowercase_	, lowercase_			:			Tuple								=       model.split('''/'''     )
				if not model.startswith('''tiny-random-'''     ):
						continue
				lowercase_			:			List[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     )

		lowercase_			:			Tuple								=       sorted(_models     )
		# All tiny model names on Hub
		lowercase_			:			int								=       {}
		for model in models:
				lowercase_			:			str								=       F'''hf-internal-testing/tiny-random-{model}'''
				lowercase_			:			str								=       model.split('''-'''     )[0]
				try:
						lowercase_			:			Any								=       hf_api.repo_info(__snake_case     )
						lowercase_			:			str								=       {
						    '''tokenizer_classes''': set(),
						    '''processor_classes''': set(),
						    '''model_classes''': set(),
						    '''sha''': repo_info.sha,
						}
				except Exception:
						continue
				try:
						time.sleep(1     )
						lowercase_			:			Any								=       AutoTokenizer.from_pretrained(__snake_case     )
						content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__     )
				except Exception:
						pass
				try:
						time.sleep(1     )
						lowercase_			:			List[str]								=       AutoTokenizer.from_pretrained(__snake_case		, use_fast=__snake_case     )
						content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__     )
				except Exception:
						pass
				try:
						time.sleep(1     )
						lowercase_			:			Union[str, Any]								=       AutoImageProcessor.from_pretrained(__snake_case     )
						content["processor_classes"].add(img_p.__class__.__name__     )
				except Exception:
						pass
				try:
						time.sleep(1     )
						lowercase_			:			Optional[Any]								=       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     )
						lowercase_			:			Union[str, Any]								=       getattr(__snake_case		, __snake_case     )
						lowercase_			:			Dict								=       model_class.from_pretrained(__snake_case     )
						content["model_classes"].add(m.__class__.__name__     )
				except Exception:
						pass
				try:
						time.sleep(1     )
						lowercase_			:			List[str]								=       getattr(__snake_case		, F'''TF{model}'''     )
						lowercase_			:			List[str]								=       model_class.from_pretrained(__snake_case     )
						content["model_classes"].add(m.__class__.__name__     )
				except Exception:
						pass

				lowercase_			:			Dict								=       sorted(content['''tokenizer_classes''']     )
				lowercase_			:			Any								=       sorted(content['''processor_classes''']     )
				lowercase_			:			Union[str, Any]								=       sorted(content['''model_classes''']     )

				lowercase_			:			str								=       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__":
						__A		:	Optional[int]      =		argparse.ArgumentParser()
						parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
						__A		:	Optional[int]      =		parser.parse_args()

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

						__A		:	List[str]      =		'''tiny_models'''
						__A		:	List[str]      =		True
						__A		:	int      =		None
						__A		:	Dict      =		get_tiny_model_names_from_repo()
						__A		:	Union[str, Any]      =		True
						__A		:	str      =		True
						__A		:	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,
						)



