
'''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_(			):
							UpperCamelCase					       =			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					       =			getattr(transformers.models.auto						,			A						,			A)
														if module is None:
																					continue
														# all mappings in a single auto modeling file
														UpperCamelCase					       =			[
														    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					       =			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 						A_(			):
							# All model names defined in auto mappings
							UpperCamelCase					       =			set(get_all_model_names())

							with open('tests/utils/tiny_model_summary.json') as fp:
														UpperCamelCase					       =			json.load(A)
							UpperCamelCase					       =			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					       =			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 						A_(			A		:						Optional[Any]):
							UpperCamelCase					       =			COMPOSITE_MODELS.values()

							# All tiny model base names on Hub
							UpperCamelCase					       =			get_all_model_names()
							UpperCamelCase					       =			hf_api.list_models(
							    filter=ModelFilter(
							        author='hf-internal-testing'						,			))
							UpperCamelCase					       =			set()
							for x in models:
														UpperCamelCase					       =			x.modelId
														UpperCamelCase       ,     UpperCamelCase					       =			model.split('/')
														if not model.startswith('tiny-random-'):
																					continue
														UpperCamelCase					       =			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					       =			sorted(_models)
							# All tiny model names on Hub
							UpperCamelCase					       =			{}
							for model in models:
														UpperCamelCase					       =			f'''hf-internal-testing/tiny-random-{model}'''
														UpperCamelCase					       =			model.split('-')[0]
														try:
																					UpperCamelCase					       =			hf_api.repo_info(A)
																					UpperCamelCase					       =			{
																					    'tokenizer_classes': set(),
																					    'processor_classes': set(),
																					    'model_classes': set(),
																					    'sha': repo_info.sha,
																					}
														except Exception:
																					continue
														try:
																					time.sleep(1)
																					UpperCamelCase					       =			AutoTokenizer.from_pretrained(A)
																					content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__)
														except Exception:
																					pass
														try:
																					time.sleep(1)
																					UpperCamelCase					       =			AutoTokenizer.from_pretrained(A						,			use_fast=A)
																					content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__)
														except Exception:
																					pass
														try:
																					time.sleep(1)
																					UpperCamelCase					       =			AutoImageProcessor.from_pretrained(A)
																					content["processor_classes"].add(img_p.__class__.__name__)
														except Exception:
																					pass
														try:
																					time.sleep(1)
																					UpperCamelCase					       =			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					       =			getattr(A						,			A)
																					UpperCamelCase					       =			model_class.from_pretrained(A)
																					content["model_classes"].add(m.__class__.__name__)
														except Exception:
																					pass
														try:
																					time.sleep(1)
																					UpperCamelCase					       =			getattr(A						,			f'''TF{model}''')
																					UpperCamelCase					       =			model_class.from_pretrained(A)
																					content["model_classes"].add(m.__class__.__name__)
														except Exception:
																					pass

														UpperCamelCase					       =			sorted(content['tokenizer_classes'])
														UpperCamelCase					       =			sorted(content['processor_classes'])
														UpperCamelCase					       =			sorted(content['model_classes'])

														UpperCamelCase					       =			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__":
						lowerCAmelCase :  int      =      argparse.ArgumentParser()
						parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
						lowerCAmelCase :  Optional[Any]      =      parser.parse_args()

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

						lowerCAmelCase :  List[Any]      =      'tiny_models'
						lowerCAmelCase :  Optional[Any]      =      True
						lowerCAmelCase :  int      =      None
						lowerCAmelCase :  str      =      get_tiny_model_names_from_repo()
						lowerCAmelCase :  Any      =      True
						lowerCAmelCase :  Tuple      =      True
						lowerCAmelCase :  Any      =      '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'''
from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
lowerCAmelCase :  Union[str, Any]      =      [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]







def 						A_(			A		:						int):
							return [int(A) for c in f'''{ruleset:08}'''[:8]]







def 						A_(			A		:						list[list[int]]						,			A		:						list[int]						,			A		:						int):
							UpperCamelCase					       =			len(cells[0])  # 31
							UpperCamelCase					       =			[]
							for i in range(A):
														# Get the neighbors of each cell
														# Handle neighbours outside bounds by using 0 as their value
														UpperCamelCase					       =			0 if i == 0 else cells[time][i - 1]
														UpperCamelCase					       =			0 if i == population - 1 else cells[time][i + 1]
														# Define a new cell and add it to the new generation
														UpperCamelCase					       =			7 - int(f'''{left_neighbor}{cells[time][i]}{right_neighbor}'''						,			2)
														next_generation.append(rule[situation])
							return next_generation







def 						A_(			A		:						list[list[int]]):
							UpperCamelCase					       =			Image.new('RGB'						,			(len(cells[0]), len(A)))
							UpperCamelCase					       =			img.load()
							# Generates image
							for w in range(img.width):
														for h in range(img.height):
																					UpperCamelCase					       =			255 - int(255 * cells[h][w])
																					UpperCamelCase					       =			(color, color, color)
							return img


if __name__ == "__main__":
						lowerCAmelCase :  Optional[int]      =      bin(int(input('Rule:\n').strip()))[2:]
						lowerCAmelCase :  Tuple      =      format_ruleset(int(rule_num))
						for time in range(16):
												CELLS.append(new_generation(CELLS, rule, time))
						lowerCAmelCase :  List[str]      =      generate_image(CELLS)
						# Uncomment to save the image
						# img.save(f"rule_{rule_num}.png")
						img.show()




