

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_SCREAMING_SNAKE_CASE          = {}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _SCREAMING_SNAKE_CASE          = ['''BarthezTokenizer''']

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _SCREAMING_SNAKE_CASE          = ['''BarthezTokenizerFast''']


if TYPE_CHECKING:
      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_barthez import BarthezTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_barthez_fast import BarthezTokenizerFast

else:
      import sys

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








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,
      )





