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     lowercase_       (    ):
    lowercase__				:     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[Any]					      =       getattr(transformers.models.auto	,			_lowerCamelCase	,			_lowerCamelCase)
        if module is None:
            continue
        # all mappings in a single auto modeling file
        lowercase__				:     Tuple					      =       [
            x
            for x in dir(_lowerCamelCase)
            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__				:     Optional[int]					      =       getattr(_lowerCamelCase	,			_lowerCamelCase)
            if mapping is not None:
                for v in mapping.values():
                    if isinstance(_lowerCamelCase	,			(list, tuple)):
                        model_names.update(_lowerCamelCase)
                    elif isinstance(_lowerCamelCase	,			_lowerCamelCase):
                        model_names.add(_lowerCamelCase)

    return sorted(_lowerCamelCase)

def     lowercase_       (    ):
    # All model names defined in auto mappings
    lowercase__				:     List[Any]					      =       set(get_all_model_names())

    with open("tests/utils/tiny_model_summary.json") as fp:
        lowercase__				:     Dict					      =       json.load(_lowerCamelCase)
    lowercase__				:     str					      =       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__				:     str					      =       model_names.difference(_lowerCamelCase)
    for model_name in copy.copy(_lowerCamelCase):
        if not model_name.startswith("TF") and f'''TF{model_name}''' in not_on_hub:
            tiny_models_names.remove(_lowerCamelCase)
        elif model_name.startswith("TF") and model_name[2:] in not_on_hub:
            tiny_models_names.remove(_lowerCamelCase)

    return sorted(_lowerCamelCase)

def     lowercase_       (    _lowerCamelCase							: Optional[int]):
    lowercase__				:     int					      =       COMPOSITE_MODELS.values()

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

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

        lowercase__				:     Any					      =       sorted(content["tokenizer_classes"])
        lowercase__				:     Optional[int]					      =       sorted(content["processor_classes"])
        lowercase__				:     Optional[Any]					      =       sorted(content["model_classes"])

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


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

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

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase													=		{
    '''configuration_mobilenet_v1''': [
        '''MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV1Config''',
        '''MobileNetV1OnnxConfig''',
    ],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		['''MobileNetV1FeatureExtractor''']
      UpperCamelCase													=		['''MobileNetV1ImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		[
          '''MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''MobileNetV1ForImageClassification''',
          '''MobileNetV1Model''',
          '''MobileNetV1PreTrainedModel''',
          '''load_tf_weights_in_mobilenet_v1''',
      ]


if TYPE_CHECKING:
      from .configuration_mobilenet_va import (
          MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MobileNetVaConfig,
          MobileNetVaOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
            from .image_processing_mobilenet_va import MobileNetVaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilenet_va import (
                MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileNetVaForImageClassification,
                MobileNetVaModel,
                MobileNetVaPreTrainedModel,
                load_tf_weights_in_mobilenet_va,
            )


else:
      import sys

      UpperCamelCase													=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



