


'''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       snake_case_       (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	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"]:
								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				getattr(transformers.models.auto							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)
								if module is None:
												continue
								# all mappings in a single auto modeling file
								_SCREAMING_SNAKE_CASE			:	List[Any]							=				[
								    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:
												_SCREAMING_SNAKE_CASE			:	Dict							=				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       snake_case_       (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				set(get_all_model_names()					)

				with open("""tests/utils/tiny_model_summary.json"""					) as fp:
								_SCREAMING_SNAKE_CASE			:	Any							=				json.load(SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	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.
				_SCREAMING_SNAKE_CASE			:	Tuple							=				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       snake_case_       (  SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Any							=				COMPOSITE_MODELS.values()

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

				_SCREAMING_SNAKE_CASE			:	Dict							=				sorted(_models					)
				# All tiny model names on Hub
				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				{}
				for model in models:
								_SCREAMING_SNAKE_CASE			:	List[Any]							=				f"""hf-internal-testing/tiny-random-{model}"""
								_SCREAMING_SNAKE_CASE			:	List[str]							=				model.split("""-"""					)[0]
								try:
												_SCREAMING_SNAKE_CASE			:	str							=				hf_api.repo_info(SCREAMING_SNAKE_CASE__					)
												_SCREAMING_SNAKE_CASE			:	Optional[int]							=				{
												    """tokenizer_classes""": set(),
												    """processor_classes""": set(),
												    """model_classes""": set(),
												    """sha""": repo_info.sha,
												}
								except Exception:
												continue
								try:
												time.sleep(1					)
												_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__					)
												content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__					)
								except Exception:
												pass
								try:
												time.sleep(1					)
												_SCREAMING_SNAKE_CASE			:	Any							=				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					)
												_SCREAMING_SNAKE_CASE			:	Tuple							=				AutoImageProcessor.from_pretrained(SCREAMING_SNAKE_CASE__					)
												content["processor_classes"].add(img_p.__class__.__name__					)
								except Exception:
												pass
								try:
												time.sleep(1					)
												_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				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					)
												_SCREAMING_SNAKE_CASE			:	Tuple							=				getattr(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)
												_SCREAMING_SNAKE_CASE			:	int							=				model_class.from_pretrained(SCREAMING_SNAKE_CASE__					)
												content["model_classes"].add(m.__class__.__name__					)
								except Exception:
												pass
								try:
												time.sleep(1					)
												_SCREAMING_SNAKE_CASE			:	List[Any]							=				getattr(SCREAMING_SNAKE_CASE__							,				f"""TF{model}"""					)
												_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				model_class.from_pretrained(SCREAMING_SNAKE_CASE__					)
												content["model_classes"].add(m.__class__.__name__					)
								except Exception:
												pass

								_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				sorted(content["""tokenizer_classes"""]					)
								_SCREAMING_SNAKE_CASE			:	Dict							=				sorted(content["""processor_classes"""]					)
								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				sorted(content["""model_classes"""]					)

								_SCREAMING_SNAKE_CASE			:	Tuple							=				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__":
				UpperCAmelCase_		:       str							       =		argparse.ArgumentParser()
				parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
				UpperCAmelCase_		:       int							       =		parser.parse_args()

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

				UpperCAmelCase_		:       List[str]							       =		'tiny_models'
				UpperCAmelCase_		:       str							       =		True
				UpperCAmelCase_		:       List[Any]							       =		None
				UpperCAmelCase_		:       List[Any]							       =		get_tiny_model_names_from_repo()
				UpperCAmelCase_		:       Optional[Any]							       =		True
				UpperCAmelCase_		:       Optional[Any]							       =		True
				UpperCAmelCase_		:       Optional[int]							       =		'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


UpperCAmelCase_		:       Tuple							       =		logging.get_logger(__name__)

UpperCAmelCase_		:       List[Any]							       =		{
    # pcqm4mv1 now deprecated
    'graphormer-base': 'https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}





class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							A_							:				Union[str, Any]				     =  """graphormer"""
							A_							:				Tuple				     =  ["""past_key_values"""]






							def __init__(   self   , __snake_case = 1   , __snake_case = 512 * 9   , __snake_case = 512 * 3   , __snake_case = 512   , __snake_case = 512   , __snake_case = 512   , __snake_case = 128   , __snake_case = 5   , __snake_case = 1024   , __snake_case = "multi_hop"   , __snake_case = 512   , __snake_case = False   , __snake_case = 12   , __snake_case = 768   , __snake_case = 768   , __snake_case = 32   , __snake_case = 0.1   , __snake_case = 0.1   , __snake_case = 0.0   , __snake_case = False   , __snake_case = False   , __snake_case = False   , __snake_case = "gelu"   , __snake_case = None   , __snake_case = False   , __snake_case = 0   , __snake_case = False   , __snake_case = 0.0   , __snake_case = 8   , __snake_case = None   , __snake_case = None   , __snake_case = True   , __snake_case = True   , __snake_case=0   , __snake_case=1   , __snake_case=2   , **__snake_case   , ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_classes
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_atoms
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_in_degree
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				num_out_degree
											_SCREAMING_SNAKE_CASE			:	Any							=				num_edges
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_spatial
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_edge_dis
											_SCREAMING_SNAKE_CASE			:	int							=				edge_type
											_SCREAMING_SNAKE_CASE			:	Tuple							=				multi_hop_max_dist
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				spatial_pos_max
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				max_nodes
											_SCREAMING_SNAKE_CASE			:	str							=				num_hidden_layers
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				embedding_dim
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				embedding_dim
											_SCREAMING_SNAKE_CASE			:	int							=				ffn_embedding_dim
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_attention_heads
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				dropout
											_SCREAMING_SNAKE_CASE			:	List[str]							=				attention_dropout
											_SCREAMING_SNAKE_CASE			:	int							=				layerdrop
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				encoder_normalize_before
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				pre_layernorm
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				apply_graphormer_init
											_SCREAMING_SNAKE_CASE			:	List[str]							=				activation_fn
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				embed_scale
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				freeze_embeddings
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_trans_layers_to_freeze
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				share_input_output_embed
											_SCREAMING_SNAKE_CASE			:	Tuple							=				traceable
											_SCREAMING_SNAKE_CASE			:	List[str]							=				q_noise
											_SCREAMING_SNAKE_CASE			:	str							=				qn_block_size

											# These parameters are here for future extensions
											# atm, the model only supports self attention
											_SCREAMING_SNAKE_CASE			:	Any							=				kdim
											_SCREAMING_SNAKE_CASE			:	int							=				vdim
											_SCREAMING_SNAKE_CASE			:	Any							=				self_attention
											_SCREAMING_SNAKE_CASE			:	Tuple							=				bias

											super().__init__(
											    pad_token_id=__snake_case   , bos_token_id=__snake_case   , eos_token_id=__snake_case   , **__snake_case   , )




