






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     lowerCamelCase__				(   ):
		__UpperCAmelCase							:     Optional[int]									=  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"]:
				__UpperCAmelCase							:     Optional[Any]									=  getattr(transformers.models.auto  ,		__lowerCamelCase  ,		__lowerCamelCase		)
				if module is None:
						continue
				# all mappings in a single auto modeling file
				__UpperCAmelCase							:     Dict									=  [
				    x
				    for x in dir(__lowerCamelCase		)
				    if x.endswith("""_MAPPING_NAMES"""		)
				    and (x.startswith("""MODEL_"""		) or x.startswith("""TF_MODEL_"""		) or x.startswith("""FLAX_MODEL_"""		))
				]
				for name in mapping_names:
						__UpperCAmelCase							:     str									=  getattr(__lowerCamelCase  ,		__lowerCamelCase		)
						if mapping is not None:
								for v in mapping.values():
										if isinstance(__lowerCamelCase  ,		(list, tuple)		):
												model_names.update(__lowerCamelCase		)
										elif isinstance(__lowerCamelCase  ,		__lowerCamelCase		):
												model_names.add(__lowerCamelCase		)

		return sorted(__lowerCamelCase		)






def     lowerCamelCase__				(   ):
		# All model names defined in auto mappings
		__UpperCAmelCase							:     List[Any]									=  set(get_all_model_names()		)

		with open("""tests/utils/tiny_model_summary.json"""		) as fp:
				__UpperCAmelCase							:     Optional[Any]									=  json.load(__lowerCamelCase		)
		__UpperCAmelCase							:     List[str]									=  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.
		__UpperCAmelCase							:     int									=  model_names.difference(__lowerCamelCase		)
		for model_name in copy.copy(__lowerCamelCase		):
				if not model_name.startswith("""TF"""		) and f"""TF{model_name}""" in not_on_hub:
						tiny_models_names.remove(__lowerCamelCase		)
				elif model_name.startswith("""TF"""		) and model_name[2:] in not_on_hub:
						tiny_models_names.remove(__lowerCamelCase		)

		return sorted(__lowerCamelCase		)






def     lowerCamelCase__				(   __lowerCamelCase      :					Dict		):
		__UpperCAmelCase							:     Tuple									=  COMPOSITE_MODELS.values()

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

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

				__UpperCAmelCase							:     Tuple									=  sorted(content["""tokenizer_classes"""]		)
				__UpperCAmelCase							:     Optional[Any]									=  sorted(content["""processor_classes"""]		)
				__UpperCAmelCase							:     Any									=  sorted(content["""model_classes"""]		)

				__UpperCAmelCase							:     int									=  content
				with open(os.path.join(__lowerCamelCase  ,		"""hub_tiny_model_summary.json"""		)  ,		"""w"""		) as fp:
						json.dump(__lowerCamelCase  ,		__lowerCamelCase  ,		ensure_ascii=__lowerCamelCase  ,		indent=4		)


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

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

							a		:	List[Any]      					=			"tiny_models"
							a		:	str      					=			True
							a		:	List[str]      					=			None
							a		:	int      					=			get_tiny_model_names_from_repo()
							a		:	Optional[int]      					=			True
							a		:	int      					=			True
							a		:	Union[str, 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,
							)










import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


a		:	Any      					=			logging.get_logger(__name__)

a		:	Dict      					=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a		:	Optional[int]      					=			{
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json",
    },
}
class    a   (			lowercase__       ):



      """simple docstring"""


      def __init__(				self :      List[Any]		,				__lowercase :      Union[str, Any]=None		,				__lowercase :      Tuple=None		,				__lowercase :      Optional[int]=False		,				__lowercase :      Any=True		,				__lowercase :      List[str]=False		,				__lowercase :      Union[str, Any]="<s>"		,				__lowercase :      List[str]="</s>"		,				__lowercase :      Dict="<unk>"		,				__lowercase :      List[str]="<sep>"		,				__lowercase :      int="<pad>"		,				__lowercase :      Any="<cls>"		,				__lowercase :      int="<mask>"		,				__lowercase :      Tuple=["<eop>", "<eod>"]		,				**__lowercase :      Union[str, Any]		,				)							->				Optional[Any]:
        __UpperCAmelCase							:     Any									=  AddedToken(__lowercase		,				lstrip=__lowercase		,				rstrip=__lowercase ) if isinstance(__lowercase		,				__lowercase ) else mask_token

        super().__init__(
            vocab_file=__lowercase		,				tokenizer_file=__lowercase		,				do_lower_case=__lowercase		,				remove_space=__lowercase		,				keep_accents=__lowercase		,				bos_token=__lowercase		,				eos_token=__lowercase		,				unk_token=__lowercase		,				sep_token=__lowercase		,				pad_token=__lowercase		,				cls_token=__lowercase		,				mask_token=__lowercase		,				additional_special_tokens=__lowercase		,				**__lowercase		,				)

        __UpperCAmelCase							:     Optional[int]									=  3
        __UpperCAmelCase							:     Dict									=  do_lower_case
        __UpperCAmelCase							:     Union[str, Any]									=  remove_space
        __UpperCAmelCase							:     Tuple									=  keep_accents
        __UpperCAmelCase							:     int									=  vocab_file
        __UpperCAmelCase							:     Dict									=  False if not self.vocab_file else True

        try:
          import jieba
        except ModuleNotFoundError as error:
          raise error.__class__(
              """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
              """See https://pypi.org/project/jieba/ for installation.""" )
        __UpperCAmelCase							:     Dict									=  jieba
        __UpperCAmelCase							:     str									=  str.maketrans(""" \n"""		,				"""\u2582\u2583""" )
      def      UpperCAmelCase				(				self :      Tuple		,				__lowercase :      List[int]		,				__lowercase :      Optional[List[int]] = None )							->				List[int]:
        __UpperCAmelCase							:     List[str]									=  [self.sep_token_id]
        __UpperCAmelCase							:     List[str]									=  [self.cls_token_id]
        if token_ids_a is None:
          return token_ids_a + sep + cls
        return token_ids_a + sep + token_ids_a + sep + cls
      def      UpperCAmelCase				(				self :      Union[str, Any]		,				__lowercase :      List[int]		,				__lowercase :      Optional[List[int]] = None )							->				List[int]:
        __UpperCAmelCase							:     Dict									=  [self.sep_token_id]
        __UpperCAmelCase							:     List[str]									=  [2]

        if token_ids_a is None:
          return len(token_ids_a + sep ) * [0] + cls_segment_id
        return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
      def      UpperCAmelCase				(				self :      Union[str, Any]		,				__lowercase :      str		,				__lowercase :      Optional[str] = None )							->				Tuple[str]:
        if not self.can_save_slow_tokenizer:
          raise ValueError(
              """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
              """tokenizer.""" )

        if not os.path.isdir(__lowercase ):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
          return
        __UpperCAmelCase							:     List[str]									=  os.path.join(
            __lowercase		,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

        if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowercase ):
          copyfile(self.vocab_file		,				__lowercase )

        return (out_vocab_file,)
      def      UpperCAmelCase				(				self :      Optional[Any]		,				__lowercase :      Any		,				*__lowercase :      Dict		,				**__lowercase :      int )							->				Optional[Any]:
        __UpperCAmelCase							:     List[str]									=  [
            """ """.join([x.translate(self.translator ) for x in self.jieba.cut(__lowercase		,				cut_all=__lowercase )] )
            for text in batch_text_or_text_pairs
        ]
        return super()._batch_encode_plus(__lowercase		,				*__lowercase		,				**__lowercase )




      def      UpperCAmelCase				(				self :      int		,				*__lowercase :      List[Any]		,				**__lowercase :      Tuple )							->				List[str]:
        __UpperCAmelCase							:     List[str]									=  super()._decode(*__lowercase		,				**__lowercase )
        __UpperCAmelCase							:     List[Any]									=  text.replace(""" """		,				"""""" ).replace("""\u2582"""		,				""" """ ).replace("""\u2583"""		,				"""\n""" )
        return text


