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  (		):
 UpperCamelCase_      :		Tuple				=      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_      :		Dict				=      getattr(transformers.models.auto						,						lowerCamelCase						,						lowerCamelCase			)
  if module is None:
   continue
  # all mappings in a single auto modeling file
  UpperCamelCase_      :		str				=      [
      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:
   UpperCamelCase_      :		str				=      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
 UpperCamelCase_      :		Union[str, Any]				=      set(get_all_model_names()			)

 with open('tests/utils/tiny_model_summary.json'			) as fp:
  UpperCamelCase_      :		Tuple				=      json.load(lowerCamelCase			)
 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_      :		Optional[Any]				=      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      :  List[str]			):
 UpperCamelCase_      :		List[Any]				=      COMPOSITE_MODELS.values()

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

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

  UpperCamelCase_      :		Optional[Any]				=      sorted(content['tokenizer_classes']			)
  UpperCamelCase_      :		Any				=      sorted(content['processor_classes']			)
  UpperCamelCase_      :		Dict				=      sorted(content['model_classes']			)

  UpperCamelCase_      :		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__":
   a_					      = argparse.ArgumentParser()
   parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
   a_					      = parser.parse_args()

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

   a_					      = 'tiny_models'
   a_					      = True
   a_					      = None
   a_					      = get_tiny_model_names_from_repo()
   a_					      = True
   a_					      = True
   a_					      = '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 __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

a_					      = {
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}





def      __lowercase  (		lowerCamelCase      :  dict						,						lowerCamelCase      :  str			):
 UpperCamelCase_      :		Tuple				=      {start}
 UpperCamelCase_      :		Optional[Any]				=      [start]
 UpperCamelCase_      :		Queue				=      Queue()
 queue.put(lowerCamelCase			)
 while not queue.empty():
  UpperCamelCase_      :		Union[str, Any]				=      queue.get()
  for w in graph[v]:
   if w not in explored:
    explored.add(lowerCamelCase			)
    result.append(lowerCamelCase			)
    queue.put(lowerCamelCase			)
 return result





def      __lowercase  (		lowerCamelCase      :  dict						,						lowerCamelCase      :  str			):
 UpperCamelCase_      :		Dict				=      {start}
 UpperCamelCase_      :		Optional[int]				=      [start]
 UpperCamelCase_      :		Union[str, Any]				=      deque([start]			)
 while queue:
  UpperCamelCase_      :		Optional[int]				=      queue.popleft()
  for child in graph[v]:
   if child not in visited:
    visited.add(lowerCamelCase			)
    result.append(lowerCamelCase			)
    queue.append(lowerCamelCase			)
 return result





def      __lowercase  (		lowerCamelCase      :  str			):
 UpperCamelCase_      :		str				=      F"from __main__ import G, {name}"
 UpperCamelCase_      :		int				=      10000
 UpperCamelCase_      :		Optional[Any]				=      timeit(F"{name}(G, 'A')"						,						setup=lowerCamelCase						,						number=lowerCamelCase			)
 print(F"{name:<35} finished {number} runs in {res:.5f} seconds"			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   benchmark_function('breadth_first_search')
   benchmark_function('breadth_first_search_with_deque')
   # breadth_first_search                finished 10000 runs in 0.20999 seconds
   # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds



