





from __future__ import annotations



def 							lowerCAmelCase_				(    A_):
    UpperCamelCase__:					Tuple												=      {1}
    UpperCamelCase__:					Optional[Any]												=      [1]
    for _ in range(2  ,depth + 1):
        UpperCamelCase__:					Tuple												=      [*previous_coefficients, 0]
        UpperCamelCase__:					Union[str, Any]												=      [0, *previous_coefficients]
        UpperCamelCase__:					Union[str, Any]												=      []
        for x, y in zip(_a  ,_a):
            coefficients.add(x + y)
            previous_coefficients.append(x + y)
    return coefficients



def 							lowerCAmelCase_				(    A_):
    UpperCamelCase__:					Union[str, Any]												=      set()
    for number in unique_coefficients:
        UpperCamelCase__:					List[Any]												=      2
        UpperCamelCase__:					List[Any]												=      number
        while divisor**2 <= copy_number:
            UpperCamelCase__:					Optional[Any]												=      0
            while copy_number % divisor == 0:
                copy_number //= divisor
                multiplicity += 1
            if multiplicity >= 2:
                non_squarefrees.add(_a)
                break
            divisor += 1

    return unique_coefficients.difference(_a)



def 							lowerCAmelCase_				(    A_ = 51):
    UpperCamelCase__:					List[str]												=      get_pascal_triangle_unique_coefficients(_a)
    UpperCamelCase__:					List[Any]												=      get_squarefrees(_a)
    return sum(_a)


if __name__ == "__main__":
   print(f"{solution() = }")








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

    return sorted(A_)



def 							lowerCAmelCase_				(    ):
    # All model names defined in auto mappings
    UpperCamelCase__:					Any												=      set(get_all_model_names())

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

    return sorted(A_)



def 							lowerCAmelCase_				(    A_):
    UpperCamelCase__:					int												=      COMPOSITE_MODELS.values()

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

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

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


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

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

   A__:  List[Any]       					=					'''tiny_models'''
   A__:  Dict       					=					True
   A__:  Dict       					=					None
   A__:  Optional[Any]       					=					get_tiny_model_names_from_repo()
   A__:  int       					=					True
   A__:  int       					=					True
   A__:  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,
   )

