
"""simple docstring"""

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Union[str, Any]				  =     (PNDMScheduler,)
 UpperCAmelCase   :					List[Any]				  =     (('''num_inference_steps''', 50),)

 def       lowerCAmelCase_							(			self	:	Tuple   ,							**_UpperCAmelCase	:	str		):
       _A        =       {
           'num_train_timesteps': 1_000,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
       }

       config.update(**_UpperCAmelCase		)
       return config

 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	int=0   ,							**_UpperCAmelCase	:	str		):
       _A        =       dict(self.forward_default_kwargs		)
       _A        =       kwargs.pop('num_inference_steps'   ,							_UpperCAmelCase		)
       _A        =       self.dummy_sample
       _A        =       0.1 * sample
       _A        =       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
             _A        =       self.get_scheduler_config(**_UpperCAmelCase		)
             _A        =       scheduler_class(**_UpperCAmelCase		)
             scheduler.set_timesteps(_UpperCAmelCase		)
             # copy over dummy past residuals
             _A        =       dummy_past_residuals[:]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_UpperCAmelCase		)
                   _A        =       scheduler_class.from_pretrained(_UpperCAmelCase		)
                   new_scheduler.set_timesteps(_UpperCAmelCase		)
                   # copy over dummy past residuals
                   _A        =       dummy_past_residuals[:]

             _A        =       scheduler.step_prk(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step_prk(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

             _A        =       scheduler.step_plms(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step_plms(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

 def       lowerCAmelCase_							(			self	:	Dict		):
       pass

 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	Any=0   ,							**_UpperCAmelCase	:	Optional[int]		):
       _A        =       dict(self.forward_default_kwargs		)
       _A        =       kwargs.pop('num_inference_steps'   ,							_UpperCAmelCase		)
       _A        =       self.dummy_sample
       _A        =       0.1 * sample
       _A        =       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
             _A        =       self.get_scheduler_config()
             _A        =       scheduler_class(**_UpperCAmelCase		)
             scheduler.set_timesteps(_UpperCAmelCase		)

             # copy over dummy past residuals (must be after setting timesteps)
             _A        =       dummy_past_residuals[:]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_UpperCAmelCase		)
                   _A        =       scheduler_class.from_pretrained(_UpperCAmelCase		)
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(_UpperCAmelCase		)

                   # copy over dummy past residual (must be after setting timesteps)
                   _A        =       dummy_past_residuals[:]

             _A        =       scheduler.step_prk(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step_prk(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

             _A        =       scheduler.step_plms(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step_plms(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

 def       lowerCAmelCase_							(			self	:	Any   ,							**_UpperCAmelCase	:	str		):
       _A        =       self.scheduler_classes[0]
       _A        =       self.get_scheduler_config(**_UpperCAmelCase		)
       _A        =       scheduler_class(**_UpperCAmelCase		)

       _A        =       10
       _A        =       self.dummy_model()
       _A        =       self.dummy_sample_deter
       scheduler.set_timesteps(_UpperCAmelCase		)

       for i, t in enumerate(scheduler.prk_timesteps		):
             _A        =       model(_UpperCAmelCase   ,							_UpperCAmelCase		)
             _A        =       scheduler.step_prk(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		).prev_sample

       for i, t in enumerate(scheduler.plms_timesteps		):
             _A        =       model(_UpperCAmelCase   ,							_UpperCAmelCase		)
             _A        =       scheduler.step_plms(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		).prev_sample

       return sample

 def       lowerCAmelCase_							(			self	:	List[str]		):
       _A        =       dict(self.forward_default_kwargs		)

       _A        =       kwargs.pop('num_inference_steps'   ,							_UpperCAmelCase		)

       for scheduler_class in self.scheduler_classes:
             _A        =       self.get_scheduler_config()
             _A        =       scheduler_class(**_UpperCAmelCase		)

             _A        =       self.dummy_sample
             _A        =       0.1 * sample

             if num_inference_steps is not None and hasattr(_UpperCAmelCase   ,							'set_timesteps'		):
                   scheduler.set_timesteps(_UpperCAmelCase		)
             elif num_inference_steps is not None and not hasattr(_UpperCAmelCase   ,							'set_timesteps'		):
                   _A        =       num_inference_steps

             # copy over dummy past residuals (must be done after set_timesteps)
             _A        =       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
             _A        =       dummy_past_residuals[:]

             _A        =       scheduler.step_prk(_UpperCAmelCase   ,							0   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       scheduler.step_prk(_UpperCAmelCase   ,							1   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             self.assertEqual(output_a.shape   ,							sample.shape		)
             self.assertEqual(output_a.shape   ,							output_a.shape		)

             _A        =       scheduler.step_plms(_UpperCAmelCase   ,							0   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       scheduler.step_plms(_UpperCAmelCase   ,							1   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             self.assertEqual(output_a.shape   ,							sample.shape		)
             self.assertEqual(output_a.shape   ,							output_a.shape		)

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       for timesteps in [100, 1_000]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Tuple		):
       for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=_UpperCAmelCase		)

       _A        =       self.scheduler_classes[0]
       _A        =       self.get_scheduler_config(steps_offset=1		)
       _A        =       scheduler_class(**_UpperCAmelCase		)
       scheduler.set_timesteps(10		)
       assert torch.equal(
           scheduler.timesteps   ,							torch.LongTensor(
               [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]		)   ,							)

 def       lowerCAmelCase_							(			self	:	str		):
       for beta_start, beta_end in zip([0.0001, 0.001]   ,							[0.002, 0.02]		):
             self.check_over_configs(beta_start=_UpperCAmelCase   ,							beta_end=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	str		):
       for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	int		):
       for t in [1, 5, 10]:
             self.check_over_forward(time_step=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Dict		):
       for t, num_inference_steps in zip([1, 5, 10]   ,							[10, 50, 100]		):
             self.check_over_forward(num_inference_steps=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Tuple		):
       # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
       _A        =       27

       for scheduler_class in self.scheduler_classes:
             _A        =       self.dummy_sample
             _A        =       0.1 * sample

             _A        =       self.get_scheduler_config()
             _A        =       scheduler_class(**_UpperCAmelCase		)

             scheduler.set_timesteps(_UpperCAmelCase		)

             # before power of 3 fix, would error on first step, so we only need to do two
             for i, t in enumerate(scheduler.prk_timesteps[:2]		):
                   _A        =       scheduler.step_prk(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		).prev_sample

 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       with self.assertRaises(_UpperCAmelCase		):
             _A        =       self.scheduler_classes[0]
             _A        =       self.get_scheduler_config()
             _A        =       scheduler_class(**_UpperCAmelCase		)

             scheduler.step_plms(self.dummy_sample   ,							1   ,							self.dummy_sample		).prev_sample

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       self.full_loop()
       _A        =       torch.sum(torch.abs(_UpperCAmelCase		)		)
       _A        =       torch.mean(torch.abs(_UpperCAmelCase		)		)

       assert abs(result_sum.item() - 198.1318		) < 1E-2
       assert abs(result_mean.item() - 0.2580		) < 1E-3

 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       self.full_loop(prediction_type='v_prediction'		)
       _A        =       torch.sum(torch.abs(_UpperCAmelCase		)		)
       _A        =       torch.mean(torch.abs(_UpperCAmelCase		)		)

       assert abs(result_sum.item() - 67.3986		) < 1E-2
       assert abs(result_mean.item() - 0.0878		) < 1E-3

 def       lowerCAmelCase_							(			self	:	str		):
       # We specify different beta, so that the first alpha is 0.99
       _A        =       self.full_loop(set_alpha_to_one=_UpperCAmelCase   ,							beta_start=0.01		)
       _A        =       torch.sum(torch.abs(_UpperCAmelCase		)		)
       _A        =       torch.mean(torch.abs(_UpperCAmelCase		)		)

       assert abs(result_sum.item() - 230.0399		) < 1E-2
       assert abs(result_mean.item() - 0.2995		) < 1E-3



 def       lowerCAmelCase_							(			self	:	int		):
       # We specify different beta, so that the first alpha is 0.99
       _A        =       self.full_loop(set_alpha_to_one=_UpperCAmelCase   ,							beta_start=0.01		)
       _A        =       torch.sum(torch.abs(_UpperCAmelCase		)		)
       _A        =       torch.mean(torch.abs(_UpperCAmelCase		)		)

       assert abs(result_sum.item() - 186.9482		) < 1E-2
       assert abs(result_mean.item() - 0.2434		) < 1E-3







"""simple docstring"""

import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


a         =      {
    '''cola''': 2,
    '''mnli''': 3,
    '''mrpc''': 2,
    '''sst-2''': 2,
    '''sts-b''': 1,
    '''qqp''': 2,
    '''qnli''': 2,
    '''rte''': 2,
    '''wnli''': 2,
}


logging.set_verbosity_info()






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: Optional[int]    ,  _snake_case							: List[Any]    ,  _snake_case							: Tuple=None							)   -> List[str]:






      '''simple docstring'''


      _A        =       XLNetConfig.from_json_file(_snake_case							)

      _A        =       finetuning_task.lower() if finetuning_task is not None else ''
      if finetuning_task in GLUE_TASKS_NUM_LABELS:
            print(F'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}'''							)
            _A        =       finetuning_task
            _A        =       GLUE_TASKS_NUM_LABELS[finetuning_task]
            _A        =       XLNetForSequenceClassification(_snake_case							)
      elif "squad" in finetuning_task:
            _A        =       finetuning_task
            _A        =       XLNetForQuestionAnswering(_snake_case							)
      else:
            _A        =       XLNetLMHeadModel(_snake_case							)

      # Load weights from tf checkpoint
      load_tf_weights_in_xlnet(_snake_case    ,  _snake_case    ,  _snake_case							)

      # Save pytorch-model
      _A        =       os.path.join(_snake_case    ,  _snake_case							)
      _A        =       os.path.join(_snake_case    ,  _snake_case							)
      print(F'''Save PyTorch model to {os.path.abspath(_snake_case							)}'''							)
      torch.save(model.state_dict()    ,  _snake_case							)
      print(F'''Save configuration file to {os.path.abspath(_snake_case							)}'''							)
      with open(_snake_case    ,  'w'    ,  encoding='utf-8'							) as f:
            f.write(config.to_json_string()							)


if __name__ == "__main__":
       a         =      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
       )
       parser.add_argument(
           '''--xlnet_config_file''',
           default=None,
           type=str,
           required=True,
           help=(
               '''The config json file corresponding to the pre-trained XLNet model. \n'''
               '''This specifies the model architecture.'''
           ),
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''',
           default=None,
           type=str,
           required=True,
           help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
       )
       parser.add_argument(
           '''--finetuning_task''',
           default=None,
           type=str,
           help='''Name of a task on which the XLNet TensorFlow model was fine-tuned''',
       )
       a         =      parser.parse_args()
       print(args)

       convert_xlnet_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
       )

