
"""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 __magic_name__  (							):

	'''simple docstring'''




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

	return sorted(_UpperCamelCase )




def __magic_name__  (							):

	'''simple docstring'''




	lowerCAmelCase				       =     set(get_all_model_names() )

	with open('''tests/utils/tiny_model_summary.json''' ) as fp:
		lowerCAmelCase				       =     json.load(_UpperCamelCase )
	lowerCAmelCase				       =     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.
	lowerCAmelCase				       =     model_names.difference(_UpperCamelCase )
	for model_name in copy.copy(_UpperCamelCase ):
		if not model_name.startswith('''TF''' ) and F"""TF{model_name}""" in not_on_hub:
			tiny_models_names.remove(_UpperCamelCase )
		elif model_name.startswith('''TF''' ) and model_name[2:] in not_on_hub:
			tiny_models_names.remove(_UpperCamelCase )

	return sorted(_UpperCamelCase )




def __magic_name__  (							_lowerCamelCase:		Optional[int] ):

	'''simple docstring'''




	lowerCAmelCase				       =     COMPOSITE_MODELS.values()

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

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

		lowerCAmelCase				       =     sorted(content['''tokenizer_classes'''] )
		lowerCAmelCase				       =     sorted(content['''processor_classes'''] )
		lowerCAmelCase				       =     sorted(content['''model_classes'''] )

		lowerCAmelCase				       =     content
		with open(os.path.join(_UpperCamelCase,				'''hub_tiny_model_summary.json''' ),				'''w''' ) as fp:
			json.dump(_UpperCamelCase,				_UpperCamelCase,				ensure_ascii=_UpperCamelCase,				indent=4 )


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

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

					UpperCAmelCase          =							"tiny_models"
					UpperCAmelCase          =							True
					UpperCAmelCase          =							None
					UpperCAmelCase          =							get_tiny_model_names_from_repo()
					UpperCAmelCase          =							True
					UpperCAmelCase          =							True
					UpperCAmelCase          =							"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,
					)




"""simple docstring"""






from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCAmelCase          =							[
    """AutomaticSpeechRecognition""",
    """AudioClassification""",
    """ImageClassification""",
    """LanguageModeling""",
    """QuestionAnsweringExtractive""",
    """Summarization""",
    """TaskTemplate""",
    """TextClassification""",
]

UpperCAmelCase          =							get_logger(__name__)


UpperCAmelCase          =							{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}




def __magic_name__  (							_lowerCamelCase:		dict )				->	Optional[TaskTemplate]:

	'''simple docstring'''




	lowerCAmelCase				       =     task_template_dict.get('''task''' )
	if task_name is None:
		logger.warning(F"""Couldn't find template for task '{task_name}'. Available templates: {list(_lowerCamelCase )}""" )
		return None
	lowerCAmelCase				       =     NAMEaTEMPLATE.get(_lowerCamelCase )
	return template.from_dict(_lowerCamelCase )


