



'''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 			_lowerCAmelCase(      ) ->							Any:
							lowerCAmelCase__  =   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"]:
														lowerCAmelCase__  =   getattr(transformers.models.auto					, _SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE						)
														if module is None:
																					continue
														# all mappings in a single auto modeling file
														lowerCAmelCase__  =   [
														    x
														    for x in dir(_SCREAMING_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:
																					lowerCAmelCase__  =   getattr(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE						)
																					if mapping is not None:
																												for v in mapping.values():
																																			if isinstance(_SCREAMING_SNAKE_CASE					, (list, tuple)						):
																																										model_names.update(_SCREAMING_SNAKE_CASE						)
																																			elif isinstance(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE						):
																																										model_names.add(_SCREAMING_SNAKE_CASE						)

							return sorted(_SCREAMING_SNAKE_CASE						)
def 			_lowerCAmelCase(      ) ->							Tuple:
							lowerCAmelCase__  =   set(get_all_model_names()						)

							with open("""tests/utils/tiny_model_summary.json"""						) as fp:
														lowerCAmelCase__  =   json.load(_SCREAMING_SNAKE_CASE						)
							lowerCAmelCase__  =   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.
							lowerCAmelCase__  =   model_names.difference(_SCREAMING_SNAKE_CASE						)
							for model_name in copy.copy(_SCREAMING_SNAKE_CASE						):
														if not model_name.startswith("""TF"""						) and F'''TF{model_name}''' in not_on_hub:
																					tiny_models_names.remove(_SCREAMING_SNAKE_CASE						)
														elif model_name.startswith("""TF"""						) and model_name[2:] in not_on_hub:
																					tiny_models_names.remove(_SCREAMING_SNAKE_CASE						)

							return sorted(_SCREAMING_SNAKE_CASE						)
def 			_lowerCAmelCase(      UpperCAmelCase_      :	str						) ->							List[str]:
							lowerCAmelCase__  =   COMPOSITE_MODELS.values()

							# All tiny model base names on Hub
							lowerCAmelCase__  =   get_all_model_names()
							lowerCAmelCase__  =   hf_api.list_models(
							    filter=ModelFilter(
							        author="""hf-internal-testing"""					, )						)
							lowerCAmelCase__  =   set()
							for x in models:
														lowerCAmelCase__  =   x.modelId
														lowerCAmelCase__  ,lowerCAmelCase__  =   model.split("""/"""						)
														if not model.startswith("""tiny-random-"""						):
																					continue
														lowerCAmelCase__  =   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(_SCREAMING_SNAKE_CASE						)

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

														lowerCAmelCase__  =   sorted(content["""tokenizer_classes"""]						)
														lowerCAmelCase__  =   sorted(content["""processor_classes"""]						)
														lowerCAmelCase__  =   sorted(content["""model_classes"""]						)

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


if __name__ == "__main__":
		_UpperCamelCase  						=			argparse.ArgumentParser()
		parser.add_argument("""--num_workers""", default=1, type=int, help="""The number of workers to run.""")
		_UpperCamelCase  						=			parser.parse_args()

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

		_UpperCamelCase  						=			"""tiny_models"""
		_UpperCamelCase  						=			True
		_UpperCamelCase  						=			None
		_UpperCamelCase  						=			get_tiny_model_names_from_repo()
		_UpperCamelCase  						=			True
		_UpperCamelCase  						=			True
		_UpperCamelCase  						=			"""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    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						sorted(_SCREAMING_SNAKE_CASE	,						key=lambda _SCREAMING_SNAKE_CASE     : value[2]	,						reverse=_SCREAMING_SNAKE_CASE     )

		# Create a list of size equal to the maximum deadline
		# and initialize it with -1
		UpperCamelCase =						max(_SCREAMING_SNAKE_CASE	,						key=lambda _SCREAMING_SNAKE_CASE     : value[1]     )[1]
		UpperCamelCase =						[-1] * max_deadline

		# Finding the maximum profit and the count of jobs
		UpperCamelCase =						0
		UpperCamelCase =						0
		for job in jobs:
				# Find a free time slot for this job
				# (Note that we start from the last possible slot)
				for i in range(job[1] - 1	,						-1	,						-1     ):
						if time_slots[i] == -1:
								UpperCamelCase =						job[0]
								count += 1
								max_profit += job[2]
								break
		return [count, max_profit]


if __name__ == "__main__":
						import doctest

						doctest.testmod()






