



'''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  (   )    ->   Dict:
		_a  :   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"]:
				_a  :   str				 =							getattr(transformers.models.auto		,    lowerCAmelCase_		,    lowerCAmelCase_						)
				if module is None:
						continue
				# all mappings in a single auto modeling file
				_a  :   Union[str, Any]				 =							[
				    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:
						_a  :   List[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   __lowerCamelCase  (   )    ->   List[Any]:
		# All model names defined in auto mappings
		_a  :   List[Any]				 =							set(get_all_model_names()						)

		with open('tests/utils/tiny_model_summary.json'						) as fp:
				_a  :   Optional[int]				 =							json.load(lowerCAmelCase_						)
		_a  :   Dict				 =							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  :   Dict				 =							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   __lowerCamelCase  (   lowerCAmelCase_						)    ->   Tuple:
		_a  :   Tuple				 =							COMPOSITE_MODELS.values()

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

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

				_a  :   List[Any]				 =							sorted(content['tokenizer_classes']						)
				_a  :   List[str]				 =							sorted(content['processor_classes']						)
				_a  :   str				 =							sorted(content['model_classes']						)

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

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

							__lowerCAmelCase		    =     '''tiny_models'''
							__lowerCAmelCase		    =     True
							__lowerCAmelCase		    =     None
							__lowerCAmelCase		    =     get_tiny_model_names_from_repo()
							__lowerCAmelCase		    =     True
							__lowerCAmelCase		    =     True
							__lowerCAmelCase		    =     '''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 transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					int            =      SqueezeBertTokenizer
							lowerCAmelCase :					int            =      SqueezeBertTokenizerFast
							lowerCAmelCase :					List[Any]            =      True







							def 							__lowercase    (							self      : List[str]			,**_UpperCAmelCase      : Tuple	):
									return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname			,**_UpperCAmelCase	)







							@slow
							def 							__lowercase    (							self      : Tuple	):
									_a  :   Optional[int]				 =							SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'	)

									_a  :   int				 =							tokenizer.encode('sequence builders'			,add_special_tokens=_UpperCAmelCase	)
									_a  :   List[Any]				 =							tokenizer.encode('multi-sequence build'			,add_special_tokens=_UpperCAmelCase	)

									_a  :   Union[str, Any]				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase	)
									_a  :   Dict				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,_UpperCAmelCase	)

									assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
									assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
									    tokenizer.sep_token_id
									]




