





from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__:  Union[str, Any]       					=					logging.get_logger(__name__)

A__:  Tuple       					=					{
    # 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    _a  (  UpperCamelCase_):

      """simple docstring"""


      UpperCamelCase__       						=   """graphormer"""
      UpperCamelCase__       						=   ["""past_key_values"""]






      def __init__(						self:	Any					,  __lowerCamelCase:	Optional[Any] = 1					,  __lowerCamelCase:	Tuple = 512 * 9					,  __lowerCamelCase:	Tuple = 512 * 3					,  __lowerCamelCase:	str = 512					,  __lowerCamelCase:	str = 512					,  __lowerCamelCase:	Optional[Any] = 512					,  __lowerCamelCase:	Union[str, Any] = 128					,  __lowerCamelCase:	int = 5					,  __lowerCamelCase:	Any = 1024					,  __lowerCamelCase:	Union[str, Any] = "multi_hop"					,  __lowerCamelCase:	List[Any] = 512					,  __lowerCamelCase:	List[Any] = False					,  __lowerCamelCase:	Union[str, Any] = 12					,  __lowerCamelCase:	Union[str, Any] = 768					,  __lowerCamelCase:	int = 768					,  __lowerCamelCase:	int = 32					,  __lowerCamelCase:	Any = 0.1					,  __lowerCamelCase:	Optional[Any] = 0.1					,  __lowerCamelCase:	List[Any] = 0.0					,  __lowerCamelCase:	List[str] = False					,  __lowerCamelCase:	Any = False					,  __lowerCamelCase:	Any = False					,  __lowerCamelCase:	str = "gelu"					,  __lowerCamelCase:	Optional[int] = None					,  __lowerCamelCase:	List[str] = False					,  __lowerCamelCase:	Dict = 0					,  __lowerCamelCase:	int = False					,  __lowerCamelCase:	int = 0.0					,  __lowerCamelCase:	Any = 8					,  __lowerCamelCase:	Optional[Any] = None					,  __lowerCamelCase:	List[str] = None					,  __lowerCamelCase:	Dict = True					,  __lowerCamelCase:	str = True					,  __lowerCamelCase:	Any=0					,  __lowerCamelCase:	Optional[int]=1					,  __lowerCamelCase:	Optional[Any]=2					,  **__lowerCamelCase:	int					,  ):
          '''simple docstring'''


          UpperCamelCase__:					Optional[Any]												=      num_classes
          UpperCamelCase__:					List[str]												=      num_atoms
          UpperCamelCase__:					int												=      num_in_degree
          UpperCamelCase__:					Union[str, Any]												=      num_out_degree
          UpperCamelCase__:					List[str]												=      num_edges
          UpperCamelCase__:					List[str]												=      num_spatial
          UpperCamelCase__:					Any												=      num_edge_dis
          UpperCamelCase__:					Optional[Any]												=      edge_type
          UpperCamelCase__:					List[str]												=      multi_hop_max_dist
          UpperCamelCase__:					str												=      spatial_pos_max
          UpperCamelCase__:					Any												=      max_nodes
          UpperCamelCase__:					Any												=      num_hidden_layers
          UpperCamelCase__:					Optional[int]												=      embedding_dim
          UpperCamelCase__:					List[str]												=      embedding_dim
          UpperCamelCase__:					str												=      ffn_embedding_dim
          UpperCamelCase__:					int												=      num_attention_heads
          UpperCamelCase__:					List[Any]												=      dropout
          UpperCamelCase__:					Any												=      attention_dropout
          UpperCamelCase__:					Optional[Any]												=      layerdrop
          UpperCamelCase__:					Union[str, Any]												=      encoder_normalize_before
          UpperCamelCase__:					Union[str, Any]												=      pre_layernorm
          UpperCamelCase__:					Optional[Any]												=      apply_graphormer_init
          UpperCamelCase__:					Union[str, Any]												=      activation_fn
          UpperCamelCase__:					Tuple												=      embed_scale
          UpperCamelCase__:					List[Any]												=      freeze_embeddings
          UpperCamelCase__:					List[Any]												=      num_trans_layers_to_freeze
          UpperCamelCase__:					Any												=      share_input_output_embed
          UpperCamelCase__:					List[Any]												=      traceable
          UpperCamelCase__:					List[str]												=      q_noise
          UpperCamelCase__:					List[Any]												=      qn_block_size

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

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








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

    return sorted(A_)



def 							lowerCAmelCase_				(    ):
    # All model names defined in auto mappings
    UpperCamelCase__:					Any												=      set(get_all_model_names())

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

    return sorted(A_)



def 							lowerCAmelCase_				(    A_):
    UpperCamelCase__:					int												=      COMPOSITE_MODELS.values()

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

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

        UpperCamelCase__:					Optional[int]												=      sorted(content["tokenizer_classes"])
        UpperCamelCase__:					List[str]												=      sorted(content["processor_classes"])
        UpperCamelCase__:					Optional[int]												=      sorted(content["model_classes"])

        UpperCamelCase__:					int												=      content
        with open(os.path.join(A_  ,"hub_tiny_model_summary.json")  ,"w") as fp:
            json.dump(A_  ,A_  ,ensure_ascii=A_  ,indent=4)


if __name__ == "__main__":
   A__:  Optional[Any]       					=					argparse.ArgumentParser()
   parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
   A__:  Dict       					=					parser.parse_args()

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

   A__:  List[Any]       					=					'''tiny_models'''
   A__:  Dict       					=					True
   A__:  Dict       					=					None
   A__:  Optional[Any]       					=					get_tiny_model_names_from_repo()
   A__:  int       					=					True
   A__:  int       					=					True
   A__:  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,
   )

