




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	str	     =      (PNDMScheduler,)
    lowerCamelCase_      :	Tuple	     =      (("""num_inference_steps""", 5_0),)







    def     _lowercase     (    self     ,  **UpperCamelCase__     )     ->      Dict:
           lowerCamelCase   :      Optional[Any]			 =					{
               "num_train_timesteps": 1000,
               "beta_start": 0.0001,
               "beta_end": 0.02,
               "beta_schedule": "linear",
           }

           config.update(**UpperCamelCase__     )
           return config







    def     _lowercase     (    self     ,  UpperCamelCase__=0     ,  **UpperCamelCase__     )     ->      List[Any]:
           lowerCamelCase   :      List[Any]			 =					dict(self.forward_default_kwargs     )
           lowerCamelCase   :      Optional[Any]			 =					kwargs.pop("num_inference_steps"     ,  UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					self.dummy_sample
           lowerCamelCase   :      List[str]			 =					0.1 * sample
           lowerCamelCase   :      Any			 =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                  lowerCamelCase   :      str			 =					self.get_scheduler_config(**UpperCamelCase__     )
                  lowerCamelCase   :      List[Any]			 =					scheduler_class(**UpperCamelCase__     )
                  scheduler.set_timesteps(UpperCamelCase__     )
                  # copy over dummy past residuals
                  lowerCamelCase   :      Optional[int]			 =					dummy_past_residuals[:]

                  with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(UpperCamelCase__     )
                         lowerCamelCase   :      Union[str, Any]			 =					scheduler_class.from_pretrained(UpperCamelCase__     )
                         new_scheduler.set_timesteps(UpperCamelCase__     )
                         # copy over dummy past residuals
                         lowerCamelCase   :      List[str]			 =					dummy_past_residuals[:]

                  lowerCamelCase   :      Optional[Any]			 =					scheduler.step_prk(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__     ).prev_sample
                  lowerCamelCase   :      Dict			 =					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"

                  lowerCamelCase   :      str			 =					scheduler.step_plms(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__     ).prev_sample
                  lowerCamelCase   :      Optional[int]			 =					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     _lowercase     (    self     )     ->      Optional[int]:
           pass







    def     _lowercase     (    self     ,  UpperCamelCase__=0     ,  **UpperCamelCase__     )     ->      Optional[Any]:
           lowerCamelCase   :      str			 =					dict(self.forward_default_kwargs     )
           lowerCamelCase   :      Dict			 =					kwargs.pop("num_inference_steps"     ,  UpperCamelCase__     )
           lowerCamelCase   :      Optional[Any]			 =					self.dummy_sample
           lowerCamelCase   :      Optional[Any]			 =					0.1 * sample
           lowerCamelCase   :      Tuple			 =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                  lowerCamelCase   :      Tuple			 =					self.get_scheduler_config()
                  lowerCamelCase   :      List[str]			 =					scheduler_class(**UpperCamelCase__     )
                  scheduler.set_timesteps(UpperCamelCase__     )

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

                  with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(UpperCamelCase__     )
                         lowerCamelCase   :      Optional[Any]			 =					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)
                         lowerCamelCase   :      int			 =					dummy_past_residuals[:]

                  lowerCamelCase   :      List[str]			 =					scheduler.step_prk(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__     ).prev_sample
                  lowerCamelCase   :      Optional[Any]			 =					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"

                  lowerCamelCase   :      Tuple			 =					scheduler.step_plms(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__     ).prev_sample
                  lowerCamelCase   :      Tuple			 =					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     _lowercase     (    self     ,  **UpperCamelCase__     )     ->      List[str]:
           lowerCamelCase   :      Any			 =					self.scheduler_classes[0]
           lowerCamelCase   :      Tuple			 =					self.get_scheduler_config(**UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					scheduler_class(**UpperCamelCase__     )

           lowerCamelCase   :      int			 =					10
           lowerCamelCase   :      str			 =					self.dummy_model()
           lowerCamelCase   :      Union[str, Any]			 =					self.dummy_sample_deter
           scheduler.set_timesteps(UpperCamelCase__     )

           for i, t in enumerate(scheduler.prk_timesteps     ):
                  lowerCamelCase   :      List[str]			 =					model(UpperCamelCase__     ,  UpperCamelCase__     )
                  lowerCamelCase   :      Optional[Any]			 =					scheduler.step_prk(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ).prev_sample

           for i, t in enumerate(scheduler.plms_timesteps     ):
                  lowerCamelCase   :      List[str]			 =					model(UpperCamelCase__     ,  UpperCamelCase__     )
                  lowerCamelCase   :      Tuple			 =					scheduler.step_plms(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ).prev_sample

           return sample







    def     _lowercase     (    self     )     ->      str:
           lowerCamelCase   :      Optional[Any]			 =					dict(self.forward_default_kwargs     )

           lowerCamelCase   :      List[Any]			 =					kwargs.pop("num_inference_steps"     ,  UpperCamelCase__     )

           for scheduler_class in self.scheduler_classes:
                  lowerCamelCase   :      List[Any]			 =					self.get_scheduler_config()
                  lowerCamelCase   :      Dict			 =					scheduler_class(**UpperCamelCase__     )

                  lowerCamelCase   :      str			 =					self.dummy_sample
                  lowerCamelCase   :      Union[str, Any]			 =					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"     ):
                         lowerCamelCase   :      Union[str, Any]			 =					num_inference_steps

                  # copy over dummy past residuals (must be done after set_timesteps)
                  lowerCamelCase   :      Optional[int]			 =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                  lowerCamelCase   :      Union[str, Any]			 =					dummy_past_residuals[:]

                  lowerCamelCase   :      Any			 =					scheduler.step_prk(UpperCamelCase__     ,  0     ,  UpperCamelCase__     ,  **UpperCamelCase__     ).prev_sample
                  lowerCamelCase   :      Dict			 =					scheduler.step_prk(UpperCamelCase__     ,  1     ,  UpperCamelCase__     ,  **UpperCamelCase__     ).prev_sample

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

                  lowerCamelCase   :      Tuple			 =					scheduler.step_plms(UpperCamelCase__     ,  0     ,  UpperCamelCase__     ,  **UpperCamelCase__     ).prev_sample
                  lowerCamelCase   :      Any			 =					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     _lowercase     (    self     )     ->      int:
           for timesteps in [100, 1000]:
                  self.check_over_configs(num_train_timesteps=UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Tuple:
           for steps_offset in [0, 1]:
                  self.check_over_configs(steps_offset=UpperCamelCase__     )

           lowerCamelCase   :      int			 =					self.scheduler_classes[0]
           lowerCamelCase   :      Union[str, Any]			 =					self.get_scheduler_config(steps_offset=1     )
           lowerCamelCase   :      List[str]			 =					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     _lowercase     (    self     )     ->      Tuple:
           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     _lowercase     (    self     )     ->      List[Any]:
           for schedule in ["linear", "squaredcos_cap_v2"]:
                  self.check_over_configs(beta_schedule=UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      str:
           for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Any:
           for t in [1, 5, 10]:
                  self.check_over_forward(time_step=UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Any:
           for t, num_inference_steps in zip([1, 5, 10]     ,  [10, 50, 100]     ):
                  self.check_over_forward(num_inference_steps=UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Tuple:
           # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
           lowerCamelCase   :      Dict			 =					27

           for scheduler_class in self.scheduler_classes:
                  lowerCamelCase   :      Optional[int]			 =					self.dummy_sample
                  lowerCamelCase   :      Optional[Any]			 =					0.1 * sample

                  lowerCamelCase   :      Optional[Any]			 =					self.get_scheduler_config()
                  lowerCamelCase   :      List[str]			 =					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]     ):
                         lowerCamelCase   :      Dict			 =					scheduler.step_prk(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ).prev_sample







    def     _lowercase     (    self     )     ->      str:
           with self.assertRaises(UpperCamelCase__     ):
                  lowerCamelCase   :      Optional[Any]			 =					self.scheduler_classes[0]
                  lowerCamelCase   :      int			 =					self.get_scheduler_config()
                  lowerCamelCase   :      int			 =					scheduler_class(**UpperCamelCase__     )

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







    def     _lowercase     (    self     )     ->      Union[str, Any]:
           lowerCamelCase   :      Dict			 =					self.full_loop()
           lowerCamelCase   :      Optional[Any]			 =					torch.sum(torch.abs(UpperCamelCase__     )     )
           lowerCamelCase   :      Union[str, Any]			 =					torch.mean(torch.abs(UpperCamelCase__     )     )

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







    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      str			 =					self.full_loop(prediction_type="v_prediction"     )
           lowerCamelCase   :      Optional[int]			 =					torch.sum(torch.abs(UpperCamelCase__     )     )
           lowerCamelCase   :      int			 =					torch.mean(torch.abs(UpperCamelCase__     )     )

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







    def     _lowercase     (    self     )     ->      Optional[int]:
           # We specify different beta, so that the first alpha is 0.99
           lowerCamelCase   :      Optional[Any]			 =					self.full_loop(set_alpha_to_one=UpperCamelCase__     ,  beta_start=0.01     )
           lowerCamelCase   :      Dict			 =					torch.sum(torch.abs(UpperCamelCase__     )     )
           lowerCamelCase   :      List[str]			 =					torch.mean(torch.abs(UpperCamelCase__     )     )

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







    def     _lowercase     (    self     )     ->      List[Any]:
           # We specify different beta, so that the first alpha is 0.99
           lowerCamelCase   :      List[str]			 =					self.full_loop(set_alpha_to_one=UpperCamelCase__     ,  beta_start=0.01     )
           lowerCamelCase   :      Tuple			 =					torch.sum(torch.abs(UpperCamelCase__     )     )
           lowerCamelCase   :      Optional[int]			 =					torch.mean(torch.abs(UpperCamelCase__     )     )

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












import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,)	-> List[str]:
       if config_name_or_path is None:
              lowerCamelCase   :      Any			 =					"facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

       if generator_tokenizer_name_or_path is None:
              lowerCamelCase   :      Dict			 =					generator_name_or_path

       if question_encoder_tokenizer_name_or_path is None:
              lowerCamelCase   :      Any			 =					question_encoder_name_or_path

       lowerCamelCase   :      str			 =					RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

       # Save model.
       lowerCamelCase   :      List[Any]			 =					RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[int]			 =					AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Optional[Any]			 =					gen_config
       lowerCamelCase   :      Optional[Any]			 =					question_encoder_config

       lowerCamelCase   :      List[Any]			 =					model_class.from_pretrained_question_encoder_generator(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,config=_SCREAMING_SNAKE_CASE    )
       rag_model.save_pretrained(_SCREAMING_SNAKE_CASE    )

       # Sanity check.
       model_class.from_pretrained(_SCREAMING_SNAKE_CASE    )

       # Save tokenizers.
       lowerCamelCase   :      List[str]			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"    )
       lowerCamelCase   :      int			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Any  =   argparse.ArgumentParser()
       parser.add_argument(
           '--model_type',
           choices=['rag_sequence', 'rag_token'],
           required=True,
           type=str,
           help='RAG model type: rag_sequence, rag_token',
       )
       parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
       parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
       parser.add_argument(
           '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
       )

       parser.add_argument(
           '--generator_tokenizer_name_or_path',
           type=str,
           help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
       )
       parser.add_argument(
           '--question_encoder_tokenizer_name_or_path',
           type=str,
           help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
       )
       parser.add_argument(
           '--config_name_or_path',
           type=str,
           help=(
               'Identifier of the model config to use, if not provided, resolves to a base config for a given'
               ' ``model_type``'
           ),
       )

       SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   parser.parse_args()

       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   Path(args.dest)
       dest_dir.mkdir(exist_ok=True)

       consolidate(
           args.model_type,
           args.generator_name_or_path,
           args.question_encoder_name_or_path,
           dest_dir,
           args.config_name_or_path,
           args.generator_tokenizer_name_or_path,
           args.question_encoder_tokenizer_name_or_path,
       )






