






'''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 						_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							,			snake_case__							,			snake_case__							)
            if module is None:
                  continue
            # all mappings in a single auto modeling file
            __A		   =      [
                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:
                  __A		   =      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 						_UpperCamelCase							(     ):
      # 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(snake_case__							)
      __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(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 						_UpperCamelCase							(     snake_case__							):
      __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(snake_case__							)

      __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(snake_case__							)
                  __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(snake_case__							)
                  content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__							)
            except Exception:
                  pass
            try:
                  time.sleep(1							)
                  __A		   =      AutoTokenizer.from_pretrained(snake_case__							,			use_fast=snake_case__							)
                  content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__							)
            except Exception:
                  pass
            try:
                  time.sleep(1							)
                  __A		   =      AutoImageProcessor.from_pretrained(snake_case__							)
                  content["processor_classes"].add(img_p.__class__.__name__							)
            except Exception:
                  pass
            try:
                  time.sleep(1							)
                  __A		   =      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							)
                  __A		   =      getattr(snake_case__							,			snake_case__							)
                  __A		   =      model_class.from_pretrained(snake_case__							)
                  content["model_classes"].add(m.__class__.__name__							)
            except Exception:
                  pass
            try:
                  time.sleep(1							)
                  __A		   =      getattr(snake_case__							,			f"TF{model}"							)
                  __A		   =      model_class.from_pretrained(snake_case__							)
                  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(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__":
       __A      :       List[Any]      =	argparse.ArgumentParser()
       parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
       __A      :       List[Any]      =	parser.parse_args()

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

       __A      :       int      =	'tiny_models'
       __A      :       int      =	True
       __A      :       Tuple      =	None
       __A      :       Dict      =	get_tiny_model_names_from_repo()
       __A      :       Optional[int]      =	True
       __A      :       Any      =	True
       __A      :       str      =	'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 						_UpperCamelCase							(     snake_case__							,			snake_case__							):
      if n < 0:
            return 0
      # table[i] represents the number of ways to get to amount i
      __A		   =      [0] * (n + 1)

      # There is exactly 1 way to get to zero(You pick no coins).
      __A		   =      1

      # Pick all coins one by one and update table[] values
      # after the index greater than or equal to the value of the
      # picked coin
      for coin_val in s:
            for j in range(snake_case__							,			n + 1							):
                  table[j] += table[j - coin_val]

      return table[n]


if __name__ == "__main__":
       import doctest

       doctest.testmod()


