
'''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       _UpperCamelCase	(					)     ->       List[str]:


		'''simple docstring'''


		snake_case     :					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"]:
				snake_case     :					int     =	getattr(transformers.models.auto   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
				if module is None:
						continue
				# all mappings in a single auto modeling file
				snake_case     :					str     =	[
				    x
				    for x in dir(SCREAMING_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:
						snake_case     :					List[Any]     =	getattr(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
						if mapping is not None:
								for v in mapping.values():
										if isinstance(SCREAMING_SNAKE_CASE__   ,					(list, tuple) ):
												model_names.update(SCREAMING_SNAKE_CASE__ )
										elif isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ):
												model_names.add(SCREAMING_SNAKE_CASE__ )

		return sorted(SCREAMING_SNAKE_CASE__ )




def       _UpperCamelCase	(					)     ->       List[Any]:


		'''simple docstring'''


		snake_case     :					str     =	set(get_all_model_names() )

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

		return sorted(SCREAMING_SNAKE_CASE__ )




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Any:


		'''simple docstring'''


		snake_case     :					Union[str, Any]     =	COMPOSITE_MODELS.values()

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

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

				snake_case     :					str     =	sorted(content['''tokenizer_classes'''] )
				snake_case     :					Tuple     =	sorted(content['''processor_classes'''] )
				snake_case     :					Tuple     =	sorted(content['''model_classes'''] )

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


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

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

						lowercase__						=			"tiny_models"
						lowercase__						=			True
						lowercase__						=			None
						lowercase__						=			get_tiny_model_names_from_repo()
						lowercase__						=			True
						lowercase__						=			True
						lowercase__						=			"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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__						=			{
    "configuration_flava": [
        "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "FlavaConfig",
        "FlavaImageCodebookConfig",
        "FlavaImageConfig",
        "FlavaMultimodalConfig",
        "FlavaTextConfig",
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						=			["FlavaFeatureExtractor"]
						lowercase__						=			["FlavaImageProcessor"]
						lowercase__						=			["FlavaProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						=			[
						    "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "FlavaForPreTraining",
						    "FlavaImageCodebook",
						    "FlavaImageModel",
						    "FlavaModel",
						    "FlavaMultimodalModel",
						    "FlavaPreTrainedModel",
						    "FlavaTextModel",
						]

if TYPE_CHECKING:
						from .configuration_flava import (
						    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    FlavaConfig,
						    FlavaImageCodebookConfig,
						    FlavaImageConfig,
						    FlavaMultimodalConfig,
						    FlavaTextConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_flava import FlavaFeatureExtractor
												from .image_processing_flava import FlavaImageProcessor
												from .processing_flava import FlavaProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flava import (
												    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlavaForPreTraining,
												    FlavaImageCodebook,
												    FlavaImageModel,
												    FlavaModel,
												    FlavaMultimodalModel,
												    FlavaPreTrainedModel,
												    FlavaTextModel,
												)

else:
						import sys

						lowercase__						=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


