

from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


_SCREAMING_SNAKE_CASE          = logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE          = {
    '''layoutlmv2-base-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json''',
    '''layoutlmv2-large-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json''',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
      import detectrona


class     lowerCamelCase_   ( _lowerCAmelCase):
 _lowerCAmelCase       		=  '''layoutlmv2'''




 def __init__( self     ,     _UpperCAmelCase=30_522     ,     _UpperCAmelCase=768     ,     _UpperCAmelCase=12     ,     _UpperCAmelCase=12     ,     _UpperCAmelCase=3_072     ,     _UpperCAmelCase="gelu"     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=512     ,     _UpperCAmelCase=2     ,     _UpperCAmelCase=0.0_2     ,     _UpperCAmelCase=1E-12     ,     _UpperCAmelCase=0     ,     _UpperCAmelCase=1_024     ,     _UpperCAmelCase=128     ,     _UpperCAmelCase=32     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=256     ,     _UpperCAmelCase=64     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=[7, 7, 256]     ,     _UpperCAmelCase=128     ,     _UpperCAmelCase=128     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=False     ,     _UpperCAmelCase=None     ,     **_UpperCAmelCase     ,     ):
   super().__init__(
       vocab_size=_UpperCAmelCase     ,     hidden_size=_UpperCAmelCase     ,     num_hidden_layers=_UpperCAmelCase     ,     num_attention_heads=_UpperCAmelCase     ,     intermediate_size=_UpperCAmelCase     ,     hidden_act=_UpperCAmelCase     ,     hidden_dropout_prob=_UpperCAmelCase     ,     attention_probs_dropout_prob=_UpperCAmelCase     ,     max_position_embeddings=_UpperCAmelCase     ,     type_vocab_size=_UpperCAmelCase     ,     initializer_range=_UpperCAmelCase     ,     layer_norm_eps=_UpperCAmelCase     ,     pad_token_id=_UpperCAmelCase     ,     **_UpperCAmelCase     ,     )
   __UpperCAmelCase  :     Union[str, Any]		=    max_ad_position_embeddings
   __UpperCAmelCase  :     Any		=    max_rel_pos
   __UpperCAmelCase  :     int		=    rel_pos_bins
   __UpperCAmelCase  :     int		=    fast_qkv
   __UpperCAmelCase  :     Union[str, Any]		=    max_rel_ad_pos
   __UpperCAmelCase  :     Any		=    rel_ad_pos_bins
   __UpperCAmelCase  :     int		=    convert_sync_batchnorm
   __UpperCAmelCase  :     Dict		=    image_feature_pool_shape
   __UpperCAmelCase  :     Optional[Any]		=    coordinate_size
   __UpperCAmelCase  :     Union[str, Any]		=    shape_size
   __UpperCAmelCase  :     List[Any]		=    has_relative_attention_bias
   __UpperCAmelCase  :     Any		=    has_spatial_attention_bias
   __UpperCAmelCase  :     List[Any]		=    has_visual_segment_embedding
   __UpperCAmelCase  :     str		=    (
       detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
   )




 @classmethod
 def 						a					( self       ):
   return {
       "MODEL.MASK_ON": True,
       "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
       "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
       "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
       "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
       "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
       "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_000,
       "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_000,
       "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_000,
       "MODEL.POST_NMS_TOPK_TEST": 1_000,
       "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
       "MODEL.ROI_HEADS.NUM_CLASSES": 5,
       "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
       "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
       "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
       "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
       "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
       "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
       "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
       "MODEL.RESNETS.DEPTH": 101,
       "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
       "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
       "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
       "MODEL.RESNETS.NUM_GROUPS": 32,
       "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
       "MODEL.RESNETS.STRIDE_IN_1X1": False,
   }




 def 						a					( self       ):
   __UpperCAmelCase  :     Tuple		=    detectrona.config.get_cfg()
   for k, v in self.detectrona_config_args.items():
     __UpperCAmelCase  :     List[Any]		=    k.split('.'       )
     __UpperCAmelCase  :     int		=    detectrona_config
     for attribute in attributes[:-1]:
       __UpperCAmelCase  :     List[Any]		=    getattr(_UpperCAmelCase     ,     _UpperCAmelCase       )
     setattr(_UpperCAmelCase     ,     attributes[-1]     ,     _UpperCAmelCase       )

   return detectrona_config








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		()       ->		Optional[int]:
  __UpperCAmelCase  :     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  :     str		=    getattr(transformers.models.auto						,					snake_case_						,					snake_case_     )
    if module is None:
      continue
    # all mappings in a single auto modeling file
    __UpperCAmelCase  :     Dict		=    [
        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:
      __UpperCAmelCase  :     Optional[int]		=    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    _snake_case		()       ->		Any:
  # All model names defined in auto mappings
  __UpperCAmelCase  :     int		=    set(get_all_model_names()     )

  with open('tests/utils/tiny_model_summary.json'     ) as fp:
    __UpperCAmelCase  :     Tuple		=    json.load(snake_case_     )
  __UpperCAmelCase  :     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.
  __UpperCAmelCase  :     Tuple		=    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    _snake_case		(snake_case_				:    List[str]     )       ->		Tuple:
  __UpperCAmelCase  :     Dict		=    COMPOSITE_MODELS.values()

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

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

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

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

      _SCREAMING_SNAKE_CASE          = '''tiny_models'''
      _SCREAMING_SNAKE_CASE          = True
      _SCREAMING_SNAKE_CASE          = None
      _SCREAMING_SNAKE_CASE          = get_tiny_model_names_from_repo()
      _SCREAMING_SNAKE_CASE          = True
      _SCREAMING_SNAKE_CASE          = True
      _SCREAMING_SNAKE_CASE          = '''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,
      )





