
"""simple docstring"""




import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

snake_case					  =						logging.getLogger()
snake_case					  =						torch.cuda.is_available()
snake_case					  =						{
    'max_tokens_per_batch': None,
    'supervise_forward': True,
    'normalize_hidden': True,
    'label_smoothing': 0.2,
    'eval_max_gen_length': None,
    'eval_beams': 1,
    'val_metric': 'loss',
    'save_top_k': 1,
    'adafactor': True,
    'early_stopping_patience': 2,
    'logger_name': 'default',
    'length_penalty': 0.5,
    'cache_dir': '',
    'task': 'summarization',
    'num_workers': 2,
    'alpha_hid': 0,
    'freeze_embeds': True,
    'enc_only': False,
    'tgt_suffix': '',
    'resume_from_checkpoint': None,
    'sortish_sampler': True,
    'student_decoder_layers': 1,
    'val_check_interval': 1.0,
    'output_dir': '',
    'fp16': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    'no_teacher': False,
    'fp16_opt_level': 'O1',
    'gpus': 1 if CUDA_AVAILABLE else 0,
    'n_tpu_cores': 0,
    'max_grad_norm': 1.0,
    'do_train': True,
    'do_predict': True,
    'accumulate_grad_batches': 1,
    'server_ip': '',
    'server_port': '',
    'seed': 4_2,
    'model_name_or_path': 'sshleifer/bart-tiny-random',
    'config_name': '',
    'tokenizer_name': 'facebook/bart-large',
    'do_lower_case': False,
    'learning_rate': 0.3,
    'lr_scheduler': 'linear',
    'weight_decay': 0.0,
    'adam_epsilon': 1E-08,
    'warmup_steps': 0,
    'max_epochs': 1,
    'train_batch_size': 2,
    'eval_batch_size': 2,
    'max_source_length': 1_2,
    'max_target_length': 1_2,
    'val_max_target_length': 1_2,
    'test_max_target_length': 1_2,
    'fast_dev_run': False,
    'no_cache': False,
    'n_train': -1,
    'n_val': -1,
    'n_test': -1,
    'student_encoder_layers': 1,
    'freeze_encoder': False,
    'auto_scale_batch_size': False,
    'overwrite_output_dir': False,
    'student': None,
}

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      '\n'.join(snake_case_     )
					Path(snake_case_     ).open('w'     ).writelines(snake_case_     )


snake_case					  =						[' Sam ate lunch today.', 'Sams lunch ingredients.']
snake_case					  =						['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
snake_case					  =						'patrickvonplaten/t5-tiny-random'
snake_case					  =						'sshleifer/t5-tinier-random'
snake_case					  =						'sshleifer/bart-tiny-random'
snake_case					  =						'sshleifer/tiny-mbart'
snake_case					  =						'sshleifer/tiny-marian-en-de'
snake_case					  =						'stas/tiny-wmt19-en-de'


snake_case					  =						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(snake_case_,	f'''{split}.source'''     ),	snake_case_     )
										_dump_articles(os.path.join(snake_case_,	f'''{split}.target'''     ),	snake_case_     )
					return tmp_dir




class 	UpperCamelCase      (			_snake_case ):

				"""simple docstring"""



				@classmethod
				def 				A   (							cls     )       ->       str:
									"""simple docstring"""







									logging.disable(logging.CRITICAL     )  # remove noisy download output from tracebacks
									return cls


				@slow
				@require_torch_gpu
				def 				A   (							self     )       ->       Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      list_models()
									SCREAMING_SNAKE_CASE           =      'sshleifer'
									SCREAMING_SNAKE_CASE           =      [x.modelId for x in model_list if x.modelId.startswith(lowercase__     )]
									SCREAMING_SNAKE_CASE           =      ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
									SCREAMING_SNAKE_CASE           =      []
									for m in model_ids:
														if m in allowed_to_be_broken:
																			continue
														try:
																			AutoConfig.from_pretrained(lowercase__     )
														except Exception:
																			failures.append(lowercase__     )
									assert not failures, f'''The following models could not be loaded through AutoConfig: {failures}'''


				def 				A   (							self     )       ->       List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
									self._test_distiller_cli(lowercase__     )


				def 				A   (							self     )       ->       Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      {
									    'student_encoder_layers': 2,
									    'student_decoder_layers': 1,
									    'max_epochs': 4,
									    'val_check_interval': 0.25,
									    'alpha_hid': 2.0,
									    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
									}
									SCREAMING_SNAKE_CASE           =      self._test_distiller_cli(lowercase__						,      check_contents=lowercase__     )

									SCREAMING_SNAKE_CASE           =      list(Path(model.output_dir     ).glob('*.ckpt'     )     )
									self.assertEqual(1						,      len(lowercase__     )     )
									SCREAMING_SNAKE_CASE           =      list(Path(model.output_dir     ).glob('**/*.bin'     )     )
									self.assertEqual(len(lowercase__     )						,      2     )
									SCREAMING_SNAKE_CASE           =      lmap(str.strip						,      Path(model.hparams.data_dir     ).joinpath('test.source'     ).open().readlines()     )
									SCREAMING_SNAKE_CASE           =      tempfile.mktemp()  # XXX: not being cleaned up
									generate_summaries_or_translations(lowercase__						,      lowercase__						,      str(model.output_dir / 'best_tfmr'     )     )
									self.assertTrue(Path(lowercase__     ).exists()     )

									SCREAMING_SNAKE_CASE           =      self.get_auto_remove_tmp_dir()
									convert_pl_to_hf(ckpts[0]						,      transformer_ckpts[0].parent						,      lowercase__     )
									assert os.path.exists(os.path.join(lowercase__						,      'pytorch_model.bin'     )     )


				def 				A   (							self     )       ->       Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      AutoModelForSeqaSeqLM.from_pretrained(lowercase__     )
									SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
									SCREAMING_SNAKE_CASE           =      torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]						,      dtype=torch.long						,      device=model.device     )
									SCREAMING_SNAKE_CASE           =      target_ids[:, :-1].contiguous()  # Why this line?
									SCREAMING_SNAKE_CASE           =      target_ids[:, 1:].clone()  # why clone?
									SCREAMING_SNAKE_CASE           =      model(
									    lowercase__						,      attention_mask=lowercase__						,      decoder_input_ids=lowercase__						,      labels=lowercase__						,      use_cache=lowercase__     ).loss

									SCREAMING_SNAKE_CASE           =      model(lowercase__						,      attention_mask=lowercase__						,      decoder_input_ids=lowercase__						,      use_cache=lowercase__     ).logits

									SCREAMING_SNAKE_CASE           =      nn.functional.log_softmax(lowercase__						,      dim=-1     )
									SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      label_smoothed_nll_loss(
									    lowercase__						,      lowercase__						,      0.1						,      ignore_index=model.config.pad_token_id     )
									with self.assertRaises(lowercase__     ):
														# TODO: understand why this breaks
														self.assertEqual(lowercase__						,      lowercase__     )


				def 				A   (							self     )       ->       Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      {
									    'student_encoder_layers': 2,
									    'student_decoder_layers': 1,
									    'num_train_epochs': 4,
									    'val_check_interval': 0.25,
									    'alpha_hid': 2.0,
									    'task': 'translation',
									    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
									    'tokenizer_name': MBART_TINY,
									    'teacher': MBART_TINY,
									    'src_lang': 'en_XX',
									    'tgt_lang': 'ro_RO',
									}
									SCREAMING_SNAKE_CASE           =      self._test_distiller_cli(lowercase__						,      check_contents=lowercase__     )
									assert model.model.config.model_type == "mbart"

									SCREAMING_SNAKE_CASE           =      list(Path(model.output_dir     ).glob('*.ckpt'     )     )
									self.assertEqual(1						,      len(lowercase__     )     )
									SCREAMING_SNAKE_CASE           =      list(Path(model.output_dir     ).glob('**/*.bin'     )     )
									SCREAMING_SNAKE_CASE           =      list(Path(model.output_dir     ).glob('best_tfmr/*'     )     )
									assert len(lowercase__     ) > 2
									self.assertEqual(len(lowercase__     )						,      2     )


				def 				A   (							self     )       ->       int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      {
									    'student_encoder_layers': 1,
									    'student_decoder_layers': 1,
									    'alpha_hid': 2.0,
									    'teacher': T5_TINY,
									    'model_name_or_path': T5_TINY,
									    'tokenizer_name': T5_TINY,
									}
									self._test_distiller_cli(lowercase__     )


				def 				A   (							self     )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      {
									    'teacher': T5_TINY,
									    'student': T5_TINIER,
									    'model_name_or_path': T5_TINIER,
									    'tokenizer_name': T5_TINIER,
									}
									self._test_distiller_cli(lowercase__     )




				def 				A   (							self						,      lowercase__						,      lowercase__=True     )       ->       Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      {
									    'label_smoothing': 0.0,
									    'early_stopping_patience': -1,
									    'train_batch_size': 1,
									    'eval_batch_size': 2,
									    'max_epochs': 2,
									    'alpha_mlm': 0.2,
									    'alpha_ce': 0.8,
									    'do_predict': True,
									    'model_name_or_path': 'sshleifer/tinier_bart',
									    'teacher': CHEAP_ARGS['model_name_or_path'],
									    'val_check_interval': 0.5,
									}
									default_updates.update(lowercase__     )
									SCREAMING_SNAKE_CASE           =      CHEAP_ARGS.copy()
									SCREAMING_SNAKE_CASE           =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
									SCREAMING_SNAKE_CASE           =      self.get_auto_remove_tmp_dir()

									args_d.update(data_dir=lowercase__						,      output_dir=lowercase__						,      **lowercase__     )
									SCREAMING_SNAKE_CASE           =      distill_main(argparse.Namespace(**lowercase__     )     )
									if not check_contents:
														return model
									SCREAMING_SNAKE_CASE           =      os.listdir(lowercase__     )
									SCREAMING_SNAKE_CASE           =      {os.path.basename(lowercase__     ) for p in contents}
									SCREAMING_SNAKE_CASE           =      [p for p in contents if p.endswith('ckpt'     )]
									assert len(lowercase__     ) > 0

									self.assertIn('test_generations.txt'						,      lowercase__     )
									self.assertIn('test_results.txt'						,      lowercase__     )

									SCREAMING_SNAKE_CASE           =      load_json(model.metrics_save_path     )
									SCREAMING_SNAKE_CASE           =      metrics['val'][-1]
									self.assertGreaterEqual(last_step_stats['val_avg_gen_time']						,      0.01     )
									self.assertGreaterEqual(1.0						,      last_step_stats['val_avg_gen_time']     )
									self.assertIsInstance(last_step_stats[f'''val_avg_{model.val_metric}''']						,      lowercase__     )
									SCREAMING_SNAKE_CASE           =      int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1     )
									self.assertEqual(len(metrics['val']     )						,      lowercase__     )
									self.assertEqual(len(metrics['test']     )						,      1     )
									return model






class 	UpperCamelCase      (			_snake_case ):

				"""simple docstring"""



				@parameterized.expand(
				    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]						,      )
				def 				A   (							self						,      lowercase__     )       ->       Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      CHEAP_ARGS.copy()
									SCREAMING_SNAKE_CASE           =      'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
									SCREAMING_SNAKE_CASE           =      0.1 if task == 'translation' else 0

									SCREAMING_SNAKE_CASE           =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
									SCREAMING_SNAKE_CASE           =      self.get_auto_remove_tmp_dir()
									args_d.update(
									    data_dir=lowercase__						,      model_name_or_path=lowercase__						,      tokenizer_name=lowercase__						,      train_batch_size=2						,      eval_batch_size=2						,      output_dir=lowercase__						,      do_predict=lowercase__						,      task=lowercase__						,      src_lang='en_XX'						,      tgt_lang='ro_RO'						,      freeze_encoder=lowercase__						,      freeze_embeds=lowercase__						,      )
									assert "n_train" in args_d
									SCREAMING_SNAKE_CASE           =      argparse.Namespace(**lowercase__     )
									SCREAMING_SNAKE_CASE           =      main(lowercase__     )

									SCREAMING_SNAKE_CASE           =      module.model.get_input_embeddings()
									assert not input_embeds.weight.requires_grad
									if model == T5_TINY:
														SCREAMING_SNAKE_CASE           =      module.model.lm_head
														assert not lm_head.weight.requires_grad
														assert (lm_head.weight == input_embeds.weight).all().item()
									elif model == FSMT_TINY:
														SCREAMING_SNAKE_CASE           =      module.model.model
														SCREAMING_SNAKE_CASE           =      fsmt.decoder.embed_positions
														assert not embed_pos.weight.requires_grad
														assert not fsmt.decoder.embed_tokens.weight.requires_grad
														# check that embeds are not the same
														assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
									else:
														SCREAMING_SNAKE_CASE           =      module.model.model
														SCREAMING_SNAKE_CASE           =      bart.decoder.embed_positions
														assert not embed_pos.weight.requires_grad
														assert not bart.shared.weight.requires_grad
														# check that embeds are the same
														assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
														assert bart.decoder.embed_tokens == bart.shared

									SCREAMING_SNAKE_CASE           =      load_json(module.output_dir / 'text_batch.json'     )
									assert isinstance(lowercase__						,      lowercase__     )
									assert len(lowercase__     ) >= 4


				def 				A   (							self     )       ->       Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      CHEAP_ARGS.copy()

									SCREAMING_SNAKE_CASE           =      'summarization'
									SCREAMING_SNAKE_CASE           =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )

									args_d.update(
									    data_dir=lowercase__						,      tokenizer_name=lowercase__						,      train_batch_size=2						,      eval_batch_size=2						,      do_predict=lowercase__						,      task=lowercase__						,      src_lang='en_XX'						,      tgt_lang='ro_RO'						,      freeze_encoder=lowercase__						,      freeze_embeds=lowercase__						,      )

									# test models whose config includes the extra_model_args
									SCREAMING_SNAKE_CASE           =      BART_TINY
									SCREAMING_SNAKE_CASE           =      self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE           =      args_d.copy()
									args_da.update(
									    model_name_or_path=lowercase__						,      output_dir=lowercase__						,      )
									SCREAMING_SNAKE_CASE           =      ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
									for p in extra_model_params:
														SCREAMING_SNAKE_CASE           =      0.5
									SCREAMING_SNAKE_CASE           =      argparse.Namespace(**lowercase__     )
									SCREAMING_SNAKE_CASE           =      main(lowercase__     )
									for p in extra_model_params:
														assert getattr(model.config						,      lowercase__     ) == 0.5, f'''failed to override the model config for param {p}'''

									# test models whose config doesn't include the extra_model_args
									SCREAMING_SNAKE_CASE           =      T5_TINY
									SCREAMING_SNAKE_CASE           =      self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE           =      args_d.copy()
									args_da.update(
									    model_name_or_path=lowercase__						,      output_dir=lowercase__						,      )
									SCREAMING_SNAKE_CASE           =      'encoder_layerdrop'
									SCREAMING_SNAKE_CASE           =      0.5
									SCREAMING_SNAKE_CASE           =      argparse.Namespace(**lowercase__     )
									with pytest.raises(lowercase__     ) as excinfo:
														SCREAMING_SNAKE_CASE           =      main(lowercase__     )
									assert str(excinfo.value     ) == f'''model config doesn\'t have a `{unsupported_param}` attribute'''




				def 				A   (							self     )       ->       Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      CHEAP_ARGS.copy()

									SCREAMING_SNAKE_CASE           =      'summarization'
									SCREAMING_SNAKE_CASE           =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )

									SCREAMING_SNAKE_CASE           =      BART_TINY
									SCREAMING_SNAKE_CASE           =      self.get_auto_remove_tmp_dir()

									args_d.update(
									    data_dir=lowercase__						,      model_name_or_path=lowercase__						,      output_dir=lowercase__						,      tokenizer_name=lowercase__						,      train_batch_size=2						,      eval_batch_size=2						,      do_predict=lowercase__						,      task=lowercase__						,      src_lang='en_XX'						,      tgt_lang='ro_RO'						,      freeze_encoder=lowercase__						,      freeze_embeds=lowercase__						,      )

									# emulate finetune.py
									SCREAMING_SNAKE_CASE           =      argparse.ArgumentParser()
									SCREAMING_SNAKE_CASE           =      pl.Trainer.add_argparse_args(lowercase__     )
									SCREAMING_SNAKE_CASE           =      SummarizationModule.add_model_specific_args(lowercase__						,      os.getcwd()     )
									SCREAMING_SNAKE_CASE           =      {'--help': True}

									# --help test
									with pytest.raises(lowercase__     ) as excinfo:
														with CaptureStdout() as cs:
																			SCREAMING_SNAKE_CASE           =      parser.parse_args(lowercase__     )
														assert False, "--help is expected to sys.exit"
									assert excinfo.type == SystemExit
									SCREAMING_SNAKE_CASE           =      lightning_base.arg_to_scheduler_metavar
									assert expected in cs.out, "--help is expected to list the supported schedulers"

									# --lr_scheduler=non_existing_scheduler test
									SCREAMING_SNAKE_CASE           =      'non_existing_scheduler'
									SCREAMING_SNAKE_CASE           =      {f'''--lr_scheduler={unsupported_param}'''}
									with pytest.raises(lowercase__     ) as excinfo:
														with CaptureStderr() as cs:
																			SCREAMING_SNAKE_CASE           =      parser.parse_args(lowercase__     )
														assert False, "invalid argument is expected to sys.exit"
									assert excinfo.type == SystemExit
									SCREAMING_SNAKE_CASE           =      f'''invalid choice: \'{unsupported_param}\''''
									assert expected in cs.err, f'''should have bailed on invalid choice of scheduler {unsupported_param}'''

									# --lr_scheduler=existing_scheduler test
									SCREAMING_SNAKE_CASE           =      'cosine'
									SCREAMING_SNAKE_CASE           =      args_d.copy()
									SCREAMING_SNAKE_CASE           =      supported_param
									SCREAMING_SNAKE_CASE           =      argparse.Namespace(**lowercase__     )
									SCREAMING_SNAKE_CASE           =      main(lowercase__     )
									assert (
									    getattr(model.hparams						,      'lr_scheduler'     ) == supported_param
									), f'''lr_scheduler={supported_param} shouldn\'t fail'''



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

	return sorted(snake_case_	)






def 					_SCREAMING_SNAKE_CASE		(						):
	# All model names defined in auto mappings
	_lowercase							=     set(get_all_model_names()	)

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

	return sorted(snake_case_	)






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	_lowercase							=     COMPOSITE_MODELS.values()

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

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

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

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




