"""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 SCREAMING_SNAKE_CASE__    (     )						->						Tuple:
			lowercase__:     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"]:
						lowercase__:     Optional[int]      =   getattr(transformers.models.auto ,					snake_case_ ,					snake_case_				)
						if module is None:
									continue
						# all mappings in a single auto modeling file
						lowercase__:     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:
									lowercase__:     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
			lowercase__:     int      =   set(get_all_model_names()				)

			with open('''tests/utils/tiny_model_summary.json'''				) as fp:
						lowercase__:     Union[str, Any]      =   json.load(snake_case_				)
			lowercase__:     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.
			lowercase__:     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__    (     __UpperCAmelCase				)						->						Tuple:
			lowercase__:     Dict      =   COMPOSITE_MODELS.values()

			# All tiny model base names on Hub
			lowercase__:     Union[str, Any]      =   get_all_model_names()
			lowercase__:     Optional[Any]      =   hf_api.list_models(
			    filter=ModelFilter(
			        author='''hf-internal-testing''' ,					)				)
			lowercase__:     str      =   set()
			for x in models:
						lowercase__:     List[Any]      =   x.modelId
						lowercase__:     List[str]      =   model.split('''/'''				)
						if not model.startswith('''tiny-random-'''				):
									continue
						lowercase__:     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__:     Dict      =   sorted(_models				)
			# All tiny model names on Hub
			lowercase__:     Optional[int]      =   {}
			for model in models:
						lowercase__:     Optional[int]      =   F"""hf-internal-testing/tiny-random-{model}"""
						lowercase__:     str      =   model.split('''-'''				)[0]
						try:
									lowercase__:     int      =   hf_api.repo_info(snake_case_				)
									lowercase__:     Optional[int]      =   {
									    '''tokenizer_classes''': set(),
									    '''processor_classes''': set(),
									    '''model_classes''': set(),
									    '''sha''': repo_info.sha,
									}
						except Exception:
									continue
						try:
									time.sleep(1				)
									lowercase__:     List[str]      =   AutoTokenizer.from_pretrained(snake_case_				)
									content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__				)
						except Exception:
									pass
						try:
									time.sleep(1				)
									lowercase__:     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				)
									lowercase__:     int      =   AutoImageProcessor.from_pretrained(snake_case_				)
									content["processor_classes"].add(img_p.__class__.__name__				)
						except Exception:
									pass
						try:
									time.sleep(1				)
									lowercase__:     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				)
									lowercase__:     Tuple      =   getattr(snake_case_ ,					snake_case_				)
									lowercase__:     Tuple      =   model_class.from_pretrained(snake_case_				)
									content["model_classes"].add(m.__class__.__name__				)
						except Exception:
									pass
						try:
									time.sleep(1				)
									lowercase__:     int      =   getattr(snake_case_ ,					F"""TF{model}"""				)
									lowercase__:     Union[str, Any]      =   model_class.from_pretrained(snake_case_				)
									content["model_classes"].add(m.__class__.__name__				)
						except Exception:
									pass

						lowercase__:     List[str]      =   sorted(content['''tokenizer_classes''']				)
						lowercase__:     Tuple      =   sorted(content['''processor_classes''']				)
						lowercase__:     Optional[Any]      =   sorted(content['''model_classes''']				)

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

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

							__A         =				"tiny_models"
							__A         =				True
							__A         =				None
							__A         =				get_tiny_model_names_from_repo()
							__A         =				True
							__A         =				True
							__A         =				"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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__A         =				logging.get_logger(__name__)

__A         =				{
    "uw-madison/yoso-4096": "https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}


class 					UpperCAmelCase		(_UpperCAmelCase						):



    """simple docstring"""

    _UpperCAmelCase			:Tuple							=				"yoso"






    def __init__(     self					,    _UpperCAmelCase=50265					,    _UpperCAmelCase=768					,    _UpperCAmelCase=12					,    _UpperCAmelCase=12					,    _UpperCAmelCase=3072					,    _UpperCAmelCase="gelu"					,    _UpperCAmelCase=0.1					,    _UpperCAmelCase=0.1					,    _UpperCAmelCase=4096					,    _UpperCAmelCase=1					,    _UpperCAmelCase=0.02					,    _UpperCAmelCase=1e-1_2					,    _UpperCAmelCase="absolute"					,    _UpperCAmelCase=True					,    _UpperCAmelCase=9					,    _UpperCAmelCase=64					,    _UpperCAmelCase=None					,    _UpperCAmelCase=True					,    _UpperCAmelCase=True					,    _UpperCAmelCase=1					,    _UpperCAmelCase=0					,    _UpperCAmelCase=2					,    **_UpperCAmelCase					,    ):
       super().__init__(pad_token_id=_UpperCAmelCase					,    bos_token_id=_UpperCAmelCase					,    eos_token_id=_UpperCAmelCase					,    **_UpperCAmelCase							)

       lowercase__:     int      =   vocab_size
       lowercase__:     List[str]      =   max_position_embeddings
       lowercase__:     Tuple      =   hidden_size
       lowercase__:     Optional[Any]      =   num_hidden_layers
       lowercase__:     Optional[int]      =   num_attention_heads
       lowercase__:     List[Any]      =   intermediate_size
       lowercase__:     Union[str, Any]      =   hidden_act
       lowercase__:     int      =   hidden_dropout_prob
       lowercase__:     Optional[Any]      =   attention_probs_dropout_prob
       lowercase__:     List[Any]      =   initializer_range
       lowercase__:     Optional[Any]      =   type_vocab_size
       lowercase__:     int      =   layer_norm_eps
       lowercase__:     Optional[Any]      =   position_embedding_type
       lowercase__:     int      =   use_expectation
       lowercase__:     List[str]      =   hash_code_len
       lowercase__:     Union[str, Any]      =   num_hash
       lowercase__:     Any      =   conv_window
       lowercase__:     Dict      =   use_fast_hash
       lowercase__:     Dict      =   lsh_backward
