



"""simple docstring"""
__a           =							[0.33, 0.44, 0.55, 0.44, 0.33]
__a           =							3
__a           =							5

# Buffer Space (with Parameters Space)
__a           =							[]
__a           =							[]

# Machine Time
__a           =							0
def   A_    (     _lowercase      ):







						'''simple docstring'''


						global buffer_space, params_space, machine_time, K, m, t

						# Choosing Dynamical Systems (All)
						for key, value in enumerate(_lowercase      ):
												# Evolution Parameter
												snake_case_					:Optional[int]		=     float(seed / value      )

												# Control Theory: Orbit Change
												snake_case_					:Optional[int]		=     (buffer_space[(key + 1) % m] + e) % 1

												# Control Theory: Trajectory Change
												snake_case_					:Tuple		=     (params_space[key] + e) % 1 + 3

												# Modification (Transition Function) - Jumps
												snake_case_					:Any		=     round(float(r * value * (1 - value)      ),   10      )
												snake_case_					:str		=     r  # Saving to Parameters Space

						# Logistic Map
						assert max(_lowercase      ) < 1
						assert max(_lowercase      ) < 4

						# Machine Time
						machine_time += 1
def   A_    (     ):







						'''simple docstring'''


						global buffer_space, params_space, machine_time, K, m, t

						# PRNG (Xorshift by George Marsaglia)
						def xorshift(_lowercase,   _lowercase      ):
												x ^= y >> 13
												y ^= x << 17
												x ^= y >> 5
												return x

						# Choosing Dynamical Systems (Increment)
						snake_case_					:int		=     machine_time % m

						# Evolution (Time Length)
						for _ in range(0,   _lowercase      ):
												# Variables (Position + Parameters)
												snake_case_					:List[Any]		=     params_space[key]
												snake_case_					:str		=     buffer_space[key]

												# Modification (Transition Function) - Flow
												snake_case_					:Dict		=     round(float(r * value * (1 - value)      ),   10      )
												snake_case_					:str		=     (machine_time * 0.01 + r * 1.01) % 1 + 3

						# Choosing Chaotic Data
						snake_case_					:Tuple		=     int(buffer_space[(key + 2) % m] * (10**10)      )
						snake_case_					:int		=     int(buffer_space[(key - 2) % m] * (10**10)      )

						# Machine Time
						machine_time += 1

						return xorshift(_lowercase,   _lowercase      ) % 0Xffff_ffff
def   A_    (     ):







						'''simple docstring'''


						global buffer_space, params_space, machine_time, K, m, t

						snake_case_					:int		=     K
						snake_case_					:Union[str, Any]		=     [0] * m
						snake_case_					:Dict		=     0


if __name__ == "__main__":
						# Initialization
						reset()

						# Pushing Data (Input)
						import random

						__a           =							random.sample(range(0XFF_FFF_FFF), 1_00)
						for chunk in message:
												push(chunk)

						# for controlling
						__a           =							""

						# Pulling Data (Output)
						while inp in ("e", "E"):
												print(F"""{format(pull(), '#04x')}""")
												print(buffer_space)
												print(params_space)
												__a           =							input("(e)exit? ").strip()





"""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   A_    (     ):







						'''simple docstring'''


						snake_case_					: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"]:
												snake_case_					:Union[str, Any]		=     getattr(transformers.models.auto,   _lowercase,   _lowercase      )
												if module is None:
																		continue
												# all mappings in a single auto modeling file
												snake_case_					:int		=     [
												    x
												    for x in dir(_lowercase      )
												    if x.endswith("""_MAPPING_NAMES"""      )
												    and (x.startswith("""MODEL_"""      ) or x.startswith("""TF_MODEL_"""      ) or x.startswith("""FLAX_MODEL_"""      ))
												]
												for name in mapping_names:
																		snake_case_					:Tuple		=     getattr(_lowercase,   _lowercase      )
																		if mapping is not None:
																								for v in mapping.values():
																														if isinstance(_lowercase,   (list, tuple)      ):
																																				model_names.update(_lowercase      )
																														elif isinstance(_lowercase,   _lowercase      ):
																																				model_names.add(_lowercase      )

						return sorted(_lowercase      )
def   A_    (     ):







						'''simple docstring'''


						snake_case_					:List[str]		=     set(get_all_model_names()      )

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

						return sorted(_lowercase      )
def   A_    (     _lowercase      ):







						'''simple docstring'''


						snake_case_					:Tuple		=     COMPOSITE_MODELS.values()

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

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

												snake_case_					:Union[str, Any]		=     sorted(content["""tokenizer_classes"""]      )
												snake_case_					:Dict		=     sorted(content["""processor_classes"""]      )
												snake_case_					:List[str]		=     sorted(content["""model_classes"""]      )

												snake_case_					:Tuple		=     content
												with open(os.path.join(_lowercase,   """hub_tiny_model_summary.json"""      ),   """w"""      ) as fp:
																		json.dump(_lowercase,   _lowercase,   ensure_ascii=_lowercase,   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,
						)
