



'''simple docstring'''





import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 					snake_case			(UpperCamelCase ):
       lowerCAmelCase__	:Tuple     =       (PNDMScheduler,)
       lowerCAmelCase__	:Optional[int]     =       (("num_inference_steps", 50),)



       def 				_a   (					self  ,**UpperCAmelCase_   )	->				List[Any]:
           lowercase__									=    {
               "num_train_timesteps": 1_000,
               "beta_start": 0.00_01,
               "beta_end": 0.02,
               "beta_schedule": "linear",
           }

           config.update(**lowercase__   )
           return config



       def 				_a   (					self  ,UpperCAmelCase_=0  ,**UpperCAmelCase_   )	->				Optional[int]:
           lowercase__									=    dict(self.forward_default_kwargs   )
           lowercase__									=    kwargs.pop("num_inference_steps"  ,lowercase__   )
           lowercase__									=    self.dummy_sample
           lowercase__									=    0.1 * sample
           lowercase__									=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

               lowercase__									=    scheduler.step_prk(lowercase__  ,lowercase__  ,lowercase__  ,**lowercase__   ).prev_sample
               lowercase__									=    new_scheduler.step_prk(lowercase__  ,lowercase__  ,lowercase__  ,**lowercase__   ).prev_sample

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

               lowercase__									=    scheduler.step_plms(lowercase__  ,lowercase__  ,lowercase__  ,**lowercase__   ).prev_sample
               lowercase__									=    new_scheduler.step_plms(lowercase__  ,lowercase__  ,lowercase__  ,**lowercase__   ).prev_sample

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



       def 				_a   (					self   )	->				Tuple:
           pass



       def 				_a   (					self  ,UpperCAmelCase_=0  ,**UpperCAmelCase_   )	->				Optional[int]:
           lowercase__									=    dict(self.forward_default_kwargs   )
           lowercase__									=    kwargs.pop("num_inference_steps"  ,lowercase__   )
           lowercase__									=    self.dummy_sample
           lowercase__									=    0.1 * sample
           lowercase__									=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

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

               lowercase__									=    scheduler.step_prk(lowercase__  ,lowercase__  ,lowercase__  ,**lowercase__   ).prev_sample
               lowercase__									=    new_scheduler.step_prk(lowercase__  ,lowercase__  ,lowercase__  ,**lowercase__   ).prev_sample

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

               lowercase__									=    scheduler.step_plms(lowercase__  ,lowercase__  ,lowercase__  ,**lowercase__   ).prev_sample
               lowercase__									=    new_scheduler.step_plms(lowercase__  ,lowercase__  ,lowercase__  ,**lowercase__   ).prev_sample

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



       def 				_a   (					self  ,**UpperCAmelCase_   )	->				Union[str, Any]:
           lowercase__									=    self.scheduler_classes[0]
           lowercase__									=    self.get_scheduler_config(**lowercase__   )
           lowercase__									=    scheduler_class(**lowercase__   )

           lowercase__									=    10
           lowercase__									=    self.dummy_model()
           lowercase__									=    self.dummy_sample_deter
           scheduler.set_timesteps(lowercase__   )

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

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

           return sample



       def 				_a   (					self   )	->				Tuple:
           lowercase__									=    dict(self.forward_default_kwargs   )

           lowercase__									=    kwargs.pop("num_inference_steps"  ,lowercase__   )

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

               lowercase__									=    self.dummy_sample
               lowercase__									=    0.1 * sample

               if num_inference_steps is not None and hasattr(lowercase__  ,"set_timesteps"   ):
                   scheduler.set_timesteps(lowercase__   )
               elif num_inference_steps is not None and not hasattr(lowercase__  ,"set_timesteps"   ):
                   lowercase__									=    num_inference_steps

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

               lowercase__									=    scheduler.step_prk(lowercase__  ,0  ,lowercase__  ,**lowercase__   ).prev_sample
               lowercase__									=    scheduler.step_prk(lowercase__  ,1  ,lowercase__  ,**lowercase__   ).prev_sample

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

               lowercase__									=    scheduler.step_plms(lowercase__  ,0  ,lowercase__  ,**lowercase__   ).prev_sample
               lowercase__									=    scheduler.step_plms(lowercase__  ,1  ,lowercase__  ,**lowercase__   ).prev_sample

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



       def 				_a   (					self   )	->				str:



           for timesteps in [100, 1_000]:
               self.check_over_configs(num_train_timesteps=lowercase__   )



       def 				_a   (					self   )	->				List[str]:



           for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=lowercase__   )

           lowercase__									=    self.scheduler_classes[0]
           lowercase__									=    self.get_scheduler_config(steps_offset=1   )
           lowercase__									=    scheduler_class(**lowercase__   )
           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 				_a   (					self   )	->				Dict:



           for beta_start, beta_end in zip([0.00_01, 0.0_01]  ,[0.0_02, 0.02]   ):
               self.check_over_configs(beta_start=lowercase__  ,beta_end=lowercase__   )



       def 				_a   (					self   )	->				str:



           for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=lowercase__   )



       def 				_a   (					self   )	->				Union[str, Any]:



           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=lowercase__   )



       def 				_a   (					self   )	->				Tuple:



           for t in [1, 5, 10]:
               self.check_over_forward(time_step=lowercase__   )



       def 				_a   (					self   )	->				Optional[int]:



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



       def 				_a   (					self   )	->				Optional[Any]:
           lowercase__									=    27

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

               lowercase__									=    self.get_scheduler_config()
               lowercase__									=    scheduler_class(**lowercase__   )

               scheduler.set_timesteps(lowercase__   )

               # 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]   ):
                   lowercase__									=    scheduler.step_prk(lowercase__  ,lowercase__  ,lowercase__   ).prev_sample



       def 				_a   (					self   )	->				List[Any]:



           with self.assertRaises(lowercase__   ):
               lowercase__									=    self.scheduler_classes[0]
               lowercase__									=    self.get_scheduler_config()
               lowercase__									=    scheduler_class(**lowercase__   )

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



       def 				_a   (					self   )	->				int:
           lowercase__									=    self.full_loop()
           lowercase__									=    torch.sum(torch.abs(lowercase__   )   )
           lowercase__									=    torch.mean(torch.abs(lowercase__   )   )

           assert abs(result_sum.item() - 1_98.13_18   ) < 1E-2
           assert abs(result_mean.item() - 0.25_80   ) < 1E-3



       def 				_a   (					self   )	->				Any:
           lowercase__									=    self.full_loop(prediction_type="v_prediction"   )
           lowercase__									=    torch.sum(torch.abs(lowercase__   )   )
           lowercase__									=    torch.mean(torch.abs(lowercase__   )   )

           assert abs(result_sum.item() - 67.39_86   ) < 1E-2
           assert abs(result_mean.item() - 0.08_78   ) < 1E-3



       def 				_a   (					self   )	->				Union[str, Any]:
           lowercase__									=    self.full_loop(set_alpha_to_one=lowercase__  ,beta_start=0.01   )
           lowercase__									=    torch.sum(torch.abs(lowercase__   )   )
           lowercase__									=    torch.mean(torch.abs(lowercase__   )   )

           assert abs(result_sum.item() - 2_30.03_99   ) < 1E-2
           assert abs(result_mean.item() - 0.29_95   ) < 1E-3







       def 				_a   (					self   )	->				Any:
           lowercase__									=    self.full_loop(set_alpha_to_one=lowercase__  ,beta_start=0.01   )
           lowercase__									=    torch.sum(torch.abs(lowercase__   )   )
           lowercase__									=    torch.mean(torch.abs(lowercase__   )   )

           assert abs(result_sum.item() - 1_86.94_82   ) < 1E-2
           assert abs(result_mean.item() - 0.24_34   ) < 1E-3







import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()





def      A__					(			__A  :						Any     ,				__A  :						Dict     ,				__A  :						Optional[int]=None			)					->Tuple:
      # set parameter of one layer
      assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
      __A    =nn.Parameter(__A			)
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
            __A    =nn.Parameter(__A			)





def      A__					(			__A  :						List[Any]     ,				__A  :						Tuple     ,				__A  :						List[Any]			)					->Dict:
      # set torch weights for 1-to-1 comparison
      __A    =np.asarray(weights[0]			)
      __A    =np.asarray(weights[1]			)
      __A    =np.asarray(weights[2]			)

      set_param(
          torch_layer.self_attention.query_key     ,				torch.tensor(__A			).transpose(1     ,				2			).contiguous().view(-1     ,				__A			)     ,				)
      set_param(
          torch_layer.self_attention.value     ,				torch.tensor(__A			).transpose(1     ,				2			).contiguous().view(-1     ,				__A			)     ,				)
      set_param(
          torch_layer.output.dense     ,				torch.tensor(__A			).view(-1     ,				__A			).contiguous().transpose(0     ,				1			)     ,				)





def      A__					(			__A  :						Optional[Any]     ,				__A  :						Tuple     ,				__A  :						Optional[Any]			)					->int:
      # set torch weights for 1-to-1 comparison
      __A    =np.asarray(weights[0]			)
      __A    =np.asarray(weights[1]			)
      __A    =np.asarray(weights[2]			)
      __A    =np.asarray(weights[3]			)

      set_param(
          torch_layer.self_attention.query     ,				torch.tensor(__A			).transpose(1     ,				2			).contiguous().view(-1     ,				__A			)     ,				)
      set_param(
          torch_layer.self_attention.key     ,				torch.tensor(__A			).transpose(1     ,				2			).contiguous().view(-1     ,				__A			)     ,				)
      set_param(
          torch_layer.self_attention.value     ,				torch.tensor(__A			).transpose(1     ,				2			).contiguous().view(-1     ,				__A			)     ,				)
      set_param(
          torch_layer.output.dense     ,				torch.tensor(__A			).view(-1     ,				__A			).contiguous().transpose(0     ,				1			)     ,				)





def      A__					(			__A  :						int     ,				__A  :						List[str]     ,				__A  :						Optional[Any]			)					->Any:
      # layernorm 1
      __A    =weights[0][0][0]
      __A    =np.asarray(layer_norm_a[0]			)
      __A    =np.asarray(layer_norm_a[1]			)
      set_param(
          torch_block.attention.layer_norm     ,				torch.tensor(__A			)     ,				torch.tensor(__A			)     ,				)

      # lsh weights + output
      __A    =weights[0][1]
      if len(__A			) < 4:
            set_layer_weights_in_torch_lsh(__A     ,				torch_block.attention     ,				__A			)
      else:
            set_layer_weights_in_torch_local(__A     ,				torch_block.attention     ,				__A			)

      # intermediate weighs
      __A    =weights[2][0][1][2]

      # Chunked Feed Forward
      if len(__A			) == 4:
            __A    =intermediate_weights[2]

      # layernorm 2
      __A    =np.asarray(intermediate_weights[0][0]			)
      __A    =np.asarray(intermediate_weights[0][1]			)
      set_param(
          torch_block.feed_forward.layer_norm     ,				torch.tensor(__A			)     ,				torch.tensor(__A			)     ,				)

      # intermediate dense
      __A    =np.asarray(intermediate_weights[1][0]			)
      __A    =np.asarray(intermediate_weights[1][1]			)
      set_param(
          torch_block.feed_forward.dense.dense     ,				torch.tensor(__A			).transpose(0     ,				1			).contiguous()     ,				torch.tensor(__A			)     ,				)

      # intermediate out
      __A    =np.asarray(intermediate_weights[4][0]			)
      __A    =np.asarray(intermediate_weights[4][1]			)
      set_param(
          torch_block.feed_forward.output.dense     ,				torch.tensor(__A			).transpose(0     ,				1			).contiguous()     ,				torch.tensor(__A			)     ,				)





def      A__					(			__A  :						Tuple     ,				__A  :						List[Any]     ,				__A  :						Optional[int]			)					->List[Any]:
      # reformer model
      __A    =torch_model.reformer

      # word embeds
      __A    =np.asarray(weights[1]			)
      set_param(
          torch_model_reformer.embeddings.word_embeddings     ,				torch.tensor(__A			)     ,				)

      if isinstance(weights[3]     ,				__A			):
            __A    =torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights			)			):
                  __A    =np.asarray(weights[3][emb_idx][0]			)
                  assert (
                      position_embeddings.weights[emb_idx].shape == emb_weights.shape
                  ), F'''{position_embeddings[emb_idx]} emb does not match'''
                  __A    =nn.Parameter(torch.tensor(__A			)			)

      __A    =weights[5]
      assert len(torch_model_reformer.encoder.layers			) * 4 == len(
          __A			), "HF and trax model do not have the same number of layers"
      for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers			):
            __A    =trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(__A     ,				__A     ,				__A			)

      # output layer norm
      __A    =np.asarray(weights[7][0]			)
      __A    =np.asarray(weights[7][1]			)
      set_param(
          torch_model_reformer.encoder.layer_norm     ,				torch.tensor(__A			)     ,				torch.tensor(__A			)     ,				)

      # output embeddings
      __A    =np.asarray(weights[9][0]			)
      __A    =np.asarray(weights[9][1]			)
      set_param(
          torch_model.lm_head.decoder     ,				torch.tensor(__A			).transpose(0     ,				1			).contiguous()     ,				torch.tensor(__A			)     ,				)





def      A__					(			__A  :						int     ,				__A  :						Any     ,				__A  :						Tuple			)					->Union[str, Any]:
      # Initialise PyTorch model
      __A    =ReformerConfig.from_json_file(__A			)
      print(F'''Building PyTorch model from configuration: {config}'''			)
      __A    =ReformerModelWithLMHead(__A			)

      with open(__A     ,				'''rb'''			) as f:
            __A    =pickle.load(__A			)['''weights''']

      set_model_weights_in_torch(__A     ,				__A     ,				config.hidden_size			)

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''			)
      torch.save(model.state_dict()     ,				__A			)


if __name__ == "__main__":
   _lowerCamelCase      :					Union[str, Any]					    =      argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained Reformer model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   _lowerCamelCase      :					Tuple					    =      parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)


