

'''simple docstring'''






import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
 from transformers.trainer import DEFAULT_CALLBACKS

 from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel






class      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  def __init__( self  :	List[str]     ):
   __lowercase				    =					[]






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]   ,lowercase__  :	List[str]   ,lowercase__  :	Tuple   ,lowercase__  :	str   ,**lowercase__  :	Any     ):
   self.events.append('''on_init_end'''     )






  def    SCREAMING_SNAKE_CASE ( self  :	Any   ,lowercase__  :	List[str]   ,lowercase__  :	Optional[Any]   ,lowercase__  :	int   ,**lowercase__  :	Optional[int]     ):
   self.events.append('''on_train_begin'''     )






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple   ,lowercase__  :	Tuple   ,lowercase__  :	int   ,lowercase__  :	int   ,**lowercase__  :	List[str]     ):
   self.events.append('''on_train_end'''     )






  def    SCREAMING_SNAKE_CASE ( self  :	str   ,lowercase__  :	Any   ,lowercase__  :	Union[str, Any]   ,lowercase__  :	Any   ,**lowercase__  :	Optional[Any]     ):
   self.events.append('''on_epoch_begin'''     )






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple   ,lowercase__  :	Optional[Any]   ,lowercase__  :	int   ,lowercase__  :	Any   ,**lowercase__  :	Optional[int]     ):
   self.events.append('''on_epoch_end'''     )






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,lowercase__  :	List[str]   ,lowercase__  :	str   ,lowercase__  :	List[str]   ,**lowercase__  :	List[str]     ):
   self.events.append('''on_step_begin'''     )






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,lowercase__  :	Union[str, Any]   ,lowercase__  :	int   ,lowercase__  :	Optional[int]   ,**lowercase__  :	Dict     ):
   self.events.append('''on_step_end'''     )






  def    SCREAMING_SNAKE_CASE ( self  :	str   ,lowercase__  :	Any   ,lowercase__  :	Tuple   ,lowercase__  :	Union[str, Any]   ,**lowercase__  :	Any     ):
   self.events.append('''on_evaluate'''     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]   ,lowercase__  :	str   ,lowercase__  :	Union[str, Any]   ,lowercase__  :	int   ,**lowercase__  :	Optional[Any]     ):
   self.events.append('''on_predict'''     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]   ,lowercase__  :	List[str]   ,lowercase__  :	Union[str, Any]   ,lowercase__  :	Optional[Any]   ,**lowercase__  :	int     ):
   self.events.append('''on_save'''     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	List[str]   ,lowercase__  :	Tuple   ,lowercase__  :	List[str]   ,**lowercase__  :	List[str]     ):
   self.events.append('''on_log'''     )






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,lowercase__  :	str   ,lowercase__  :	int   ,lowercase__  :	Dict   ,**lowercase__  :	str     ):
   self.events.append('''on_prediction_step'''     )






@require_torch
class      lowercase_      (unittest.TestCase ):




  """simple docstring"""




  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   __lowercase				    =					tempfile.mkdtemp()






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   shutil.rmtree(self.output_dir     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]   ,lowercase__  :	Optional[Any]=0   ,lowercase__  :	Any=0   ,lowercase__  :	Tuple=6_4   ,lowercase__  :	Optional[int]=6_4   ,lowercase__  :	Optional[Any]=None   ,lowercase__  :	str=False   ,**lowercase__  :	Any     ):
   # disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
   # its set to False since the tests later on depend on its value.
   __lowercase				    =					RegressionDataset(length=lowercase__     )
   __lowercase				    =					RegressionDataset(length=lowercase__     )
   __lowercase				    =					RegressionModelConfig(a=lowercase__   ,b=lowercase__     )
   __lowercase				    =					RegressionPreTrainedModel(lowercase__     )

   __lowercase				    =					TrainingArguments(self.output_dir   ,disable_tqdm=lowercase__   ,report_to=[]   ,**lowercase__     )
   return Trainer(
       lowercase__   ,lowercase__   ,train_dataset=lowercase__   ,eval_dataset=lowercase__   ,callbacks=lowercase__   ,)






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]   ,lowercase__  :	Optional[int]   ,lowercase__  :	Any     ):
   self.assertEqual(len(lowercase__     )   ,len(lowercase__     )     )

   # Order doesn't matter
   __lowercase				    =					sorted(lowercase__   ,key=lambda lowercase__     : cb.__name__ if isinstance(lowercase__   ,lowercase__     ) else cb.__class__.__name__     )
   __lowercase				    =					sorted(lowercase__   ,key=lambda lowercase__     : cb.__name__ if isinstance(lowercase__   ,lowercase__     ) else cb.__class__.__name__     )

   for cba, cba in zip(lowercase__   ,lowercase__     ):
    if isinstance(lowercase__   ,lowercase__     ) and isinstance(lowercase__   ,lowercase__     ):
     self.assertEqual(lowercase__   ,lowercase__     )
    elif isinstance(lowercase__   ,lowercase__     ) and not isinstance(lowercase__   ,lowercase__     ):
     self.assertEqual(lowercase__   ,cba.__class__     )
    elif not isinstance(lowercase__   ,lowercase__     ) and isinstance(lowercase__   ,lowercase__     ):
     self.assertEqual(cba.__class__   ,lowercase__     )
    else:
     self.assertEqual(lowercase__   ,lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,lowercase__  :	Union[str, Any]     ):
   __lowercase				    =					['''on_init_end''', '''on_train_begin''']
   __lowercase				    =					0
   __lowercase				    =					len(trainer.get_eval_dataloader()     )
   __lowercase				    =					['''on_prediction_step'''] * len(trainer.get_eval_dataloader()     ) + ['''on_log''', '''on_evaluate''']
   for _ in range(trainer.state.num_train_epochs     ):
    expected_events.append('''on_epoch_begin'''     )
    for _ in range(lowercase__     ):
     step += 1
     expected_events += ["on_step_begin", "on_step_end"]
     if step % trainer.args.logging_steps == 0:
      expected_events.append('''on_log'''     )
     if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
      expected_events += evaluation_events.copy()
     if step % trainer.args.save_steps == 0:
      expected_events.append('''on_save'''     )
    expected_events.append('''on_epoch_end'''     )
    if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
     expected_events += evaluation_events.copy()
   expected_events += ["on_log", "on_train_end"]
   return expected_events






  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   __lowercase				    =					self.get_trainer()
   __lowercase				    =					DEFAULT_CALLBACKS.copy() + [ProgressCallback]
   self.check_callbacks_equality(trainer.callback_handler.callbacks   ,lowercase__     )

   # Callbacks passed at init are added to the default callbacks
   __lowercase				    =					self.get_trainer(callbacks=[MyTestTrainerCallback]     )
   expected_callbacks.append(lowercase__     )
   self.check_callbacks_equality(trainer.callback_handler.callbacks   ,lowercase__     )

   # TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
   __lowercase				    =					self.get_trainer(disable_tqdm=lowercase__     )
   __lowercase				    =					DEFAULT_CALLBACKS.copy() + [PrinterCallback]
   self.check_callbacks_equality(trainer.callback_handler.callbacks   ,lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   __lowercase				    =					DEFAULT_CALLBACKS.copy() + [ProgressCallback]
   __lowercase				    =					self.get_trainer()

   # We can add, pop, or remove by class name
   trainer.remove_callback(lowercase__     )
   expected_callbacks.remove(lowercase__     )
   self.check_callbacks_equality(trainer.callback_handler.callbacks   ,lowercase__     )

   __lowercase				    =					self.get_trainer()
   __lowercase				    =					trainer.pop_callback(lowercase__     )
   self.assertEqual(cb.__class__   ,lowercase__     )
   self.check_callbacks_equality(trainer.callback_handler.callbacks   ,lowercase__     )

   trainer.add_callback(lowercase__     )
   expected_callbacks.insert(0   ,lowercase__     )
   self.check_callbacks_equality(trainer.callback_handler.callbacks   ,lowercase__     )

   # We can also add, pop, or remove by instance
   __lowercase				    =					self.get_trainer()
   __lowercase				    =					trainer.callback_handler.callbacks[0]
   trainer.remove_callback(lowercase__     )
   expected_callbacks.remove(lowercase__     )
   self.check_callbacks_equality(trainer.callback_handler.callbacks   ,lowercase__     )

   __lowercase				    =					self.get_trainer()
   __lowercase				    =					trainer.callback_handler.callbacks[0]
   __lowercase				    =					trainer.pop_callback(lowercase__     )
   self.assertEqual(lowercase__   ,lowercase__     )
   self.check_callbacks_equality(trainer.callback_handler.callbacks   ,lowercase__     )

   trainer.add_callback(lowercase__     )
   expected_callbacks.insert(0   ,lowercase__     )
   self.check_callbacks_equality(trainer.callback_handler.callbacks   ,lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   import warnings

   # XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
   warnings.simplefilter(action='''ignore'''   ,category=lowercase__     )

   __lowercase				    =					self.get_trainer(callbacks=[MyTestTrainerCallback]     )
   trainer.train()
   __lowercase				    =					trainer.callback_handler.callbacks[-2].events
   self.assertEqual(lowercase__   ,self.get_expected_events(lowercase__     )     )

   # Independent log/save/eval
   __lowercase				    =					self.get_trainer(callbacks=[MyTestTrainerCallback]   ,logging_steps=5     )
   trainer.train()
   __lowercase				    =					trainer.callback_handler.callbacks[-2].events
   self.assertEqual(lowercase__   ,self.get_expected_events(lowercase__     )     )

   __lowercase				    =					self.get_trainer(callbacks=[MyTestTrainerCallback]   ,save_steps=5     )
   trainer.train()
   __lowercase				    =					trainer.callback_handler.callbacks[-2].events
   self.assertEqual(lowercase__   ,self.get_expected_events(lowercase__     )     )

   __lowercase				    =					self.get_trainer(callbacks=[MyTestTrainerCallback]   ,eval_steps=5   ,evaluation_strategy='''steps'''     )
   trainer.train()
   __lowercase				    =					trainer.callback_handler.callbacks[-2].events
   self.assertEqual(lowercase__   ,self.get_expected_events(lowercase__     )     )

   __lowercase				    =					self.get_trainer(callbacks=[MyTestTrainerCallback]   ,evaluation_strategy='''epoch'''     )
   trainer.train()
   __lowercase				    =					trainer.callback_handler.callbacks[-2].events
   self.assertEqual(lowercase__   ,self.get_expected_events(lowercase__     )     )

   # A bit of everything
   __lowercase				    =					self.get_trainer(
       callbacks=[MyTestTrainerCallback]   ,logging_steps=3   ,save_steps=1_0   ,eval_steps=5   ,evaluation_strategy='''steps'''   ,)
   trainer.train()
   __lowercase				    =					trainer.callback_handler.callbacks[-2].events
   self.assertEqual(lowercase__   ,self.get_expected_events(lowercase__     )     )

   # warning should be emitted for duplicated callbacks
   with patch('''transformers.trainer_callback.logger.warning'''     ) as warn_mock:
    __lowercase				    =					self.get_trainer(
        callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]   ,)
    assert str(lowercase__     ) in warn_mock.call_args[0][0]






'''simple docstring'''
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def A__       (	UpperCAmelCase_				):
						if "model" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('model.'    ,    ''				)
						if "norm1" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('norm1'    ,    'attention.output.LayerNorm'				)
						if "norm2" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.replace('norm2'    ,    'output.LayerNorm'				)
						if "norm" in orig_key:
												_UpperCamelCase   :						str												=  orig_key.replace('norm'    ,    'LayerNorm'				)
						if "transformer" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.split('.'				)[0].split('_'				)[-1]
												_UpperCamelCase   :						Tuple												=  orig_key.replace(f'transformer_{layer_num}'    ,    f'encoder.layer.{layer_num}'				)
						if "mha.attn" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('mha.attn'    ,    'attention.self'				)
						if "mha" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('mha'    ,    'attention'				)
						if "W_q" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('W_q'    ,    'self.query'				)
						if "W_k" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('W_k'    ,    'self.key'				)
						if "W_v" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('W_v'    ,    'self.value'				)
						if "ff1" in orig_key:
												_UpperCamelCase   :						Optional[Any]												=  orig_key.replace('ff1'    ,    'intermediate.dense'				)
						if "ff2" in orig_key:
												_UpperCamelCase   :						List[str]												=  orig_key.replace('ff2'    ,    'output.dense'				)
						if "ff" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('ff'    ,    'output.dense'				)
						if "mlm_class" in orig_key:
												_UpperCamelCase   :						Tuple												=  orig_key.replace('mlm.mlm_class'    ,    'cls.predictions.decoder'				)
						if "mlm" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.replace('mlm'    ,    'cls.predictions.transform'				)
						if "cls" not in orig_key:
												_UpperCamelCase   :						Optional[int]												=  'yoso.' + orig_key

						return orig_key






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						for key in orig_state_dict.copy().keys():
												_UpperCamelCase   :						Any												=  orig_state_dict.pop(UpperCAmelCase_				)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		_UpperCamelCase   :						Union[str, Any]												=  val

						_UpperCamelCase   :						Any												=  orig_state_dict['cls.predictions.decoder.bias']
						_UpperCamelCase   :						Union[str, Any]												=  torch.arange(UpperCAmelCase_				).expand((1, -1)				) + 2

						return orig_state_dict






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						List[Any]												=  torch.load(UpperCAmelCase_    ,    map_location='cpu'				)['model_state_dict']
						_UpperCamelCase   :						List[str]												=  YosoConfig.from_json_file(UpperCAmelCase_				)
						_UpperCamelCase   :						Union[str, Any]												=  YosoForMaskedLM(UpperCAmelCase_				)

						_UpperCamelCase   :						Tuple												=  convert_checkpoint_helper(config.max_position_embeddings    ,    UpperCAmelCase_				)

						print(model.load_state_dict(UpperCAmelCase_				)				)
						model.eval()
						model.save_pretrained(UpperCAmelCase_				)

						print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'				)


if __name__ == "__main__":
					snake_case_					:      int    			=    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					snake_case_					:      List[Any]    			=    parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


