






"""simple docstring"""




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 						_SCREAMING_SNAKE_CASE							( ):
  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 						_SCREAMING_SNAKE_CASE							( ):
  # All model names defined in auto mappings
  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 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	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__			=   argparse.ArgumentParser()
      parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
      lowerCamelCase__			=   parser.parse_args()

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

      lowerCamelCase__			=   "tiny_models"
      lowerCamelCase__			=   True
      lowerCamelCase__			=   None
      lowerCamelCase__			=   get_tiny_model_names_from_repo()
      lowerCamelCase__			=   True
      lowerCamelCase__			=   True
      lowerCamelCase__			=   "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"""




def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	int		,				UpperCamelCase     :	int = 1000    ):
  if (not isinstance(UpperCamelCase		,				UpperCamelCase    )) or (digit_position <= 0):
    raise ValueError("""Digit position must be a positive integer"""    )
  elif (not isinstance(UpperCamelCase		,				UpperCamelCase    )) or (precision < 0):
    raise ValueError("""Precision must be a nonnegative integer"""    )

  # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
  # accurate
  A__											=							(
      4 * _subsum(UpperCamelCase		,				1		,				UpperCamelCase    )
      - 2 * _subsum(UpperCamelCase		,				4		,				UpperCamelCase    )
      - _subsum(UpperCamelCase		,				5		,				UpperCamelCase    )
      - _subsum(UpperCamelCase		,				6		,				UpperCamelCase    )
  )

  # return the first hex digit of the fractional part of the result
  return hex(int((sum_result % 1) * 16    )    )[2:]






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	int		,				UpperCamelCase     :	int		,				UpperCamelCase     :	int    ):
  A__											=							0.0
  for sum_index in range(digit_pos_to_extract + precision    ):
    A__											=							8 * sum_index + denominator_addend
    if sum_index < digit_pos_to_extract:
      # if the exponential term is an integer and we mod it by the denominator
      # before dividing, only the integer part of the sum will change;
      # the fractional part will not
      A__											=							pow(
          16		,				digit_pos_to_extract - 1 - sum_index		,				UpperCamelCase    )
    else:
      A__											=							pow(16		,				digit_pos_to_extract - 1 - sum_index    )
    total += exponential_term / denominator
  return total


if __name__ == "__main__":
      import doctest

      doctest.testmod()




