





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

    return sorted(_UpperCamelCase )





def 		UpperCamelCase       (							):
    A__       =    set(get_all_model_names() )

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

    return sorted(_UpperCamelCase )





def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int] ):
    A__       =    COMPOSITE_MODELS.values()

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

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

        A__       =    sorted(content["tokenizer_classes"] )
        A__       =    sorted(content["processor_classes"] )
        A__       =    sorted(content["model_classes"] )

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


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

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

  __lowerCAmelCase							:			List[str]				="tiny_models"
  __lowerCAmelCase							:			Union[str, Any]				=True
  __lowerCAmelCase							:			str				=None
  __lowerCAmelCase							:			Union[str, Any]				=get_tiny_model_names_from_repo()
  __lowerCAmelCase							:			Any				=True
  __lowerCAmelCase							:			Tuple				=True
  __lowerCAmelCase							:			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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase							:			int				={
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase							:			Any				=["LayoutLMTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase							:			Dict				=[
      "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "LayoutLMForMaskedLM",
      "LayoutLMForSequenceClassification",
      "LayoutLMForTokenClassification",
      "LayoutLMForQuestionAnswering",
      "LayoutLMModel",
      "LayoutLMPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase							:			str				=[
      "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFLayoutLMForMaskedLM",
      "TFLayoutLMForSequenceClassification",
      "TFLayoutLMForTokenClassification",
      "TFLayoutLMForQuestionAnswering",
      "TFLayoutLMMainLayer",
      "TFLayoutLMModel",
      "TFLayoutLMPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
  from .tokenization_layoutlm import LayoutLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )

else:
  import sys

  __lowerCAmelCase							:			int				=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



