


import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


__lowerCamelCase       :       Optional[Any]				   =			TypeVar("T", bound="TaskTemplate")



@dataclass(frozen=UpperCamelCase_   )
class 			a      (      abc.ABC   ):
		# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
		__lowercase													=		42
		__lowercase													=		42
		__lowercase													=		42






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			T:
						'''simple docstring'''


						return copy.deepcopy(self							)






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


						return Features(**self.input_schema	,     **self.label_schema							)






		@property
		@abc.abstractmethod
		def       lowerCAmelCase_  (  self							)->			Dict[str, str]:
						'''simple docstring'''


						raise NotImplementedError






		@classmethod
		def       lowerCAmelCase_  (  cls	,     __UpperCamelCase							)->			T:
						'''simple docstring'''


						A__     :							int   				={f.name for f in dataclasses.fields(cls							)}
						return cls(**{k: v for k, v in template_dict.items() if k in field_names}							)







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,
						)



