


'''simple docstring'''


import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english





@require_tokenizers
class 					lowercase__						(		_snake_case  ,  unittest.TestCase      ):





							'''simple docstring'''


							A_							:   List[str]   			=						RealmTokenizer
							A_							:   List[Any]   			=						RealmTokenizerFast
							A_							:   str   			=						True
							A_							:   str   			=						True
							A_							:   Dict   			=						filter_non_english






							def UpperCAmelCase_			(					self						):
											super().setUp()

											_SCREAMING_SNAKE_CASE				: Tuple  	=   [
											    """[UNK]""",
											    """[CLS]""",
											    """[SEP]""",
											    """[PAD]""",
											    """[MASK]""",
											    """want""",
											    """##want""",
											    """##ed""",
											    """wa""",
											    """un""",
											    """runn""",
											    """##ing""",
											    """,""",
											    """low""",
											    """lowest""",
											]
											_SCREAMING_SNAKE_CASE				: str  	=   os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES["""vocab_file"""]						)
											with open(self.vocab_file					,      """w"""					,      encoding="""utf-8"""						) as vocab_writer:
															vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)






							def UpperCAmelCase_			(					self					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: List[str]  	=   """UNwant\u00E9d,running"""
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   """unwanted, running"""
											return input_text, output_text






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   self.tokenizer_class(self.vocab_file						)

											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   tokenizer.tokenize("""UNwant\u00E9d,running"""						)
											self.assertListEqual(__snake_case					,      ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]						)
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case						)					,      [9, 6, 7, 12, 10, 11]						)






							def UpperCAmelCase_			(					self						):
											if not self.test_rust_tokenizer:
															return

											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   self.get_tokenizer()
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   self.get_rust_tokenizer()

											_SCREAMING_SNAKE_CASE				: Dict  	=   """UNwant\u00E9d,running"""

											_SCREAMING_SNAKE_CASE				: List[Any]  	=   tokenizer.tokenize(__snake_case						)
											_SCREAMING_SNAKE_CASE				: str  	=   rust_tokenizer.tokenize(__snake_case						)
											self.assertListEqual(__snake_case					,      __snake_case						)

											_SCREAMING_SNAKE_CASE				: Any  	=   tokenizer.encode(__snake_case					,      add_special_tokens=__snake_case						)
											_SCREAMING_SNAKE_CASE				: List[str]  	=   rust_tokenizer.encode(__snake_case					,      add_special_tokens=__snake_case						)
											self.assertListEqual(__snake_case					,      __snake_case						)

											_SCREAMING_SNAKE_CASE				: Dict  	=   self.get_rust_tokenizer()
											_SCREAMING_SNAKE_CASE				: int  	=   tokenizer.encode(__snake_case						)
											_SCREAMING_SNAKE_CASE				: Dict  	=   rust_tokenizer.encode(__snake_case						)
											self.assertListEqual(__snake_case					,      __snake_case						)

											# With lower casing
											_SCREAMING_SNAKE_CASE				: Tuple  	=   self.get_tokenizer(do_lower_case=__snake_case						)
											_SCREAMING_SNAKE_CASE				: Any  	=   self.get_rust_tokenizer(do_lower_case=__snake_case						)

											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   """UNwant\u00E9d,running"""

											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   tokenizer.tokenize(__snake_case						)
											_SCREAMING_SNAKE_CASE				: Any  	=   rust_tokenizer.tokenize(__snake_case						)
											self.assertListEqual(__snake_case					,      __snake_case						)

											_SCREAMING_SNAKE_CASE				: Any  	=   tokenizer.encode(__snake_case					,      add_special_tokens=__snake_case						)
											_SCREAMING_SNAKE_CASE				: int  	=   rust_tokenizer.encode(__snake_case					,      add_special_tokens=__snake_case						)
											self.assertListEqual(__snake_case					,      __snake_case						)

											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   self.get_rust_tokenizer()
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   tokenizer.encode(__snake_case						)
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   rust_tokenizer.encode(__snake_case						)
											self.assertListEqual(__snake_case					,      __snake_case						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   BasicTokenizer()

											self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""						)					,      ["""ah""", """\u535A""", """\u63A8""", """zz"""]						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: int  	=   BasicTokenizer(do_lower_case=__snake_case						)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """						)					,      ["""hello""", """!""", """how""", """are""", """you""", """?"""]						)
											self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""						)					,      ["""hello"""]						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Tuple  	=   BasicTokenizer(do_lower_case=__snake_case					,      strip_accents=__snake_case						)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """						)					,      ["""hällo""", """!""", """how""", """are""", """you""", """?"""]						)
											self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""						)					,      ["""h\u00E9llo"""]						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   BasicTokenizer(do_lower_case=__snake_case					,      strip_accents=__snake_case						)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """						)					,      ["""hallo""", """!""", """how""", """are""", """you""", """?"""]						)
											self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""						)					,      ["""hello"""]						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Tuple  	=   BasicTokenizer(do_lower_case=__snake_case						)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """						)					,      ["""hallo""", """!""", """how""", """are""", """you""", """?"""]						)
											self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""						)					,      ["""hello"""]						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Dict  	=   BasicTokenizer(do_lower_case=__snake_case						)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """						)					,      ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: str  	=   BasicTokenizer(do_lower_case=__snake_case					,      strip_accents=__snake_case						)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """						)					,      ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Any  	=   BasicTokenizer(do_lower_case=__snake_case					,      strip_accents=__snake_case						)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """						)					,      ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   BasicTokenizer(do_lower_case=__snake_case					,      never_split=["""[UNK]"""]						)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""						)					,      ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Any  	=   ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   {}
											for i, token in enumerate(__snake_case						):
															_SCREAMING_SNAKE_CASE				: int  	=   i
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   WordpieceTokenizer(vocab=__snake_case					,      unk_token="""[UNK]"""						)

											self.assertListEqual(tokenizer.tokenize(""""""						)					,      []						)

											self.assertListEqual(tokenizer.tokenize("""unwanted running"""						)					,      ["""un""", """##want""", """##ed""", """runn""", """##ing"""]						)

											self.assertListEqual(tokenizer.tokenize("""unwantedX running"""						)					,      ["""[UNK]""", """runn""", """##ing"""]						)






							def UpperCAmelCase_			(					self						):
											self.assertTrue(_is_whitespace(""" """						)						)
											self.assertTrue(_is_whitespace("""\t"""						)						)
											self.assertTrue(_is_whitespace("""\r"""						)						)
											self.assertTrue(_is_whitespace("""\n"""						)						)
											self.assertTrue(_is_whitespace("""\u00A0"""						)						)

											self.assertFalse(_is_whitespace("""A"""						)						)
											self.assertFalse(_is_whitespace("""-"""						)						)






							def UpperCAmelCase_			(					self						):
											self.assertTrue(_is_control("""\u0005"""						)						)

											self.assertFalse(_is_control("""A"""						)						)
											self.assertFalse(_is_control(""" """						)						)
											self.assertFalse(_is_control("""\t"""						)						)
											self.assertFalse(_is_control("""\r"""						)						)






							def UpperCAmelCase_			(					self						):
											self.assertTrue(_is_punctuation("""-"""						)						)
											self.assertTrue(_is_punctuation("""$"""						)						)
											self.assertTrue(_is_punctuation("""`"""						)						)
											self.assertTrue(_is_punctuation("""."""						)						)

											self.assertFalse(_is_punctuation("""A"""						)						)
											self.assertFalse(_is_punctuation(""" """						)						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Tuple  	=   self.get_tokenizer()

											# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
											self.assertListEqual([tokenizer.tokenize(__snake_case						) for t in ["""Test""", """\xad""", """test"""]]					,      [["""[UNK]"""], [], ["""[UNK]"""]]						)

											if self.test_rust_tokenizer:
															_SCREAMING_SNAKE_CASE				: Any  	=   self.get_rust_tokenizer()
															self.assertListEqual(
															    [rust_tokenizer.tokenize(__snake_case						) for t in ["""Test""", """\xad""", """test"""]]					,      [["""[UNK]"""], [], ["""[UNK]"""]]						)






							@slow
							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   self.tokenizer_class.from_pretrained("""bert-base-uncased"""						)

											_SCREAMING_SNAKE_CASE				: List[Any]  	=   tokenizer.encode("""sequence builders"""					,      add_special_tokens=__snake_case						)
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   tokenizer.encode("""multi-sequence build"""					,      add_special_tokens=__snake_case						)

											_SCREAMING_SNAKE_CASE				: str  	=   tokenizer.build_inputs_with_special_tokens(__snake_case						)
											_SCREAMING_SNAKE_CASE				: List[str]  	=   tokenizer.build_inputs_with_special_tokens(__snake_case					,      __snake_case						)

											assert encoded_sentence == [101] + text + [102]
											assert encoded_pair == [101] + text + [102] + text_a + [102]






							def UpperCAmelCase_			(					self						):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
																			_SCREAMING_SNAKE_CASE				: Any  	=   self.rust_tokenizer_class.from_pretrained(__snake_case					,      **__snake_case						)

																			_SCREAMING_SNAKE_CASE				: List[str]  	=   f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
																			_SCREAMING_SNAKE_CASE				: str  	=   tokenizer_r.encode_plus(
																			    __snake_case					,      return_attention_mask=__snake_case					,      return_token_type_ids=__snake_case					,      return_offsets_mapping=__snake_case					,      add_special_tokens=__snake_case					,      )

																			_SCREAMING_SNAKE_CASE				: Dict  	=   tokenizer_r.do_lower_case if hasattr(__snake_case					,      """do_lower_case"""						) else False
																			_SCREAMING_SNAKE_CASE				: Tuple  	=   (
																			    [
																			        ((0, 0), tokenizer_r.cls_token),
																			        ((0, 1), """A"""),
																			        ((1, 2), ""","""),
																			        ((3, 5), """na"""),
																			        ((5, 6), """##ï"""),
																			        ((6, 8), """##ve"""),
																			        ((9, 15), tokenizer_r.mask_token),
																			        ((16, 21), """Allen"""),
																			        ((21, 23), """##NL"""),
																			        ((23, 24), """##P"""),
																			        ((25, 33), """sentence"""),
																			        ((33, 34), """."""),
																			        ((0, 0), tokenizer_r.sep_token),
																			    ]
																			    if not do_lower_case
																			    else [
																			        ((0, 0), tokenizer_r.cls_token),
																			        ((0, 1), """a"""),
																			        ((1, 2), ""","""),
																			        ((3, 8), """naive"""),
																			        ((9, 15), tokenizer_r.mask_token),
																			        ((16, 21), """allen"""),
																			        ((21, 23), """##nl"""),
																			        ((23, 24), """##p"""),
																			        ((25, 33), """sentence"""),
																			        ((33, 34), """."""),
																			        ((0, 0), tokenizer_r.sep_token),
																			    ]
																			)

																			self.assertEqual(
																			    [e[1] for e in expected_results]					,      tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]						)						)
																			self.assertEqual([e[0] for e in expected_results]					,      tokens["""offset_mapping"""]						)






							@slow
							def UpperCAmelCase_			(					self						):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
																			_SCREAMING_SNAKE_CASE				: int  	=   self.rust_tokenizer_class.from_pretrained(__snake_case					,      **__snake_case						)
																			_SCREAMING_SNAKE_CASE				: List[str]  	=   self.tokenizer_class.from_pretrained(__snake_case					,      **__snake_case						)
																			_SCREAMING_SNAKE_CASE				: List[Any]  	=   [["""Hello world!""", """Nice to meet you!"""], ["""The cute cat.""", """The adorable dog."""]]

																			_SCREAMING_SNAKE_CASE				: int  	=   tokenizer_r.batch_encode_candidates(__snake_case					,      max_length=10					,      return_tensors="""np"""						)
																			_SCREAMING_SNAKE_CASE				: str  	=   tokenizer_p.batch_encode_candidates(__snake_case					,      max_length=10					,      return_tensors="""np"""						)

																			_SCREAMING_SNAKE_CASE				: Tuple  	=   (2, 2, 10)

																			self.assertEqual(encoded_sentence_r["""input_ids"""].shape					,      __snake_case						)
																			self.assertEqual(encoded_sentence_r["""attention_mask"""].shape					,      __snake_case						)
																			self.assertEqual(encoded_sentence_r["""token_type_ids"""].shape					,      __snake_case						)

																			self.assertEqual(encoded_sentence_p["""input_ids"""].shape					,      __snake_case						)
																			self.assertEqual(encoded_sentence_p["""attention_mask"""].shape					,      __snake_case						)
																			self.assertEqual(encoded_sentence_p["""token_type_ids"""].shape					,      __snake_case						)








'''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     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: str  	=   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"]:
								_SCREAMING_SNAKE_CASE				: Any  	=   getattr(transformers.models.auto							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
								if module is None:
												continue
								# all mappings in a single auto modeling file
								_SCREAMING_SNAKE_CASE				: str  	=   [
								    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:
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   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     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   set(get_all_model_names()  )

				with open("""tests/utils/tiny_model_summary.json"""  ) as fp:
								_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   json.load(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: str  	=   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.
				_SCREAMING_SNAKE_CASE				: str  	=   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     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   COMPOSITE_MODELS.values()

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

				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   sorted(_models  )
				# All tiny model names on Hub
				_SCREAMING_SNAKE_CASE				: Dict  	=   {}
				for model in models:
								_SCREAMING_SNAKE_CASE				: str  	=   f"""hf-internal-testing/tiny-random-{model}"""
								_SCREAMING_SNAKE_CASE				: List[Any]  	=   model.split("""-"""  )[0]
								try:
												_SCREAMING_SNAKE_CASE				: Tuple  	=   hf_api.repo_info(SCREAMING_SNAKE_CASE__  )
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   {
												    """tokenizer_classes""": set(),
												    """processor_classes""": set(),
												    """model_classes""": set(),
												    """sha""": repo_info.sha,
												}
								except Exception:
												continue
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: int  	=   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  )
												_SCREAMING_SNAKE_CASE				: str  	=   AutoImageProcessor.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["processor_classes"].add(img_p.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: Any  	=   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  )
												_SCREAMING_SNAKE_CASE				: int  	=   getattr(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
												_SCREAMING_SNAKE_CASE				: Tuple  	=   model_class.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["model_classes"].add(m.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: List[Any]  	=   getattr(SCREAMING_SNAKE_CASE__							,   f"""TF{model}"""  )
												_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   model_class.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["model_classes"].add(m.__class__.__name__  )
								except Exception:
												pass

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

								_SCREAMING_SNAKE_CASE				: List[Any]  	=   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_	:     Any							     =	argparse.ArgumentParser()
				parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
				UpperCAmelCase_	:     Optional[int]							     =	parser.parse_args()

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

				UpperCAmelCase_	:     Any							     =	'tiny_models'
				UpperCAmelCase_	:     Tuple							     =	True
				UpperCAmelCase_	:     Tuple							     =	None
				UpperCAmelCase_	:     Optional[int]							     =	get_tiny_model_names_from_repo()
				UpperCAmelCase_	:     Dict							     =	True
				UpperCAmelCase_	:     str							     =	True
				UpperCAmelCase_	:     Optional[int]							     =	'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,
				)




