




import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = (CMStochasticIterativeScheduler,)
    lowerCAmelCase         = 10





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						List[str]:
         UpperCAmelCase_     :				Union[str, Any]  =     {
             '''num_train_timesteps''': 201,
             '''sigma_min''': 0.0_02,
             '''sigma_max''': 80.0,
         }

         config.update(**_SCREAMING_SNAKE_CASE    )
         return config





    def 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				str  =     10

         UpperCAmelCase_     :				str  =     self.get_scheduler_config()
         UpperCAmelCase_     :				List[str]  =     self.scheduler_classes[0](**_SCREAMING_SNAKE_CASE    )

         scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Tuple  =     scheduler.timesteps[0]
         UpperCAmelCase_     :				Optional[int]  =     scheduler.timesteps[1]

         UpperCAmelCase_     :				int  =     self.dummy_sample
         UpperCAmelCase_     :				List[Any]  =     0.1 * sample

         UpperCAmelCase_     :				Dict  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ).prev_sample
         UpperCAmelCase_     :				Any  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ).prev_sample

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





    def 				a__ ( self    )					->						Any:
         for timesteps in [10, 50, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Any:
         for clip_denoised in [True, False]:
              self.check_over_configs(clip_denoised=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				Tuple  =     self.scheduler_classes[0]
         UpperCAmelCase_     :				str  =     self.get_scheduler_config()
         UpperCAmelCase_     :				int  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				List[Any]  =     1
         scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[int]  =     scheduler.timesteps

         UpperCAmelCase_     :				Dict  =     torch.manual_seed(0    )

         UpperCAmelCase_     :				List[str]  =     self.dummy_model()
         UpperCAmelCase_     :				Dict  =     self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(_SCREAMING_SNAKE_CASE    ):
              # 1. scale model input
              UpperCAmelCase_     :				Union[str, Any]  =     scheduler.scale_model_input(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

              # 2. predict noise residual
              UpperCAmelCase_     :				List[Any]  =     model(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

              # 3. predict previous sample x_t-1
              UpperCAmelCase_     :				Dict  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,generator=_SCREAMING_SNAKE_CASE    ).prev_sample

              UpperCAmelCase_     :				List[Any]  =     pred_prev_sample

         UpperCAmelCase_     :				Dict  =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE    )    )
         UpperCAmelCase_     :				Tuple  =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

         assert abs(result_sum.item() - 1_92.76_14    ) < 1e-2
         assert abs(result_mean.item() - 0.25_10    ) < 1e-3





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

         UpperCAmelCase_     :				Optional[int]  =     [106, 0]
         scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				int  =     scheduler.timesteps

         UpperCAmelCase_     :				Union[str, Any]  =     torch.manual_seed(0    )

         UpperCAmelCase_     :				Union[str, Any]  =     self.dummy_model()
         UpperCAmelCase_     :				Optional[int]  =     self.dummy_sample_deter * scheduler.init_noise_sigma

         for t in timesteps:
              # 1. scale model input
              UpperCAmelCase_     :				Dict  =     scheduler.scale_model_input(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

              # 2. predict noise residual
              UpperCAmelCase_     :				Union[str, Any]  =     model(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

              # 3. predict previous sample x_t-1
              UpperCAmelCase_     :				Dict  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,generator=_SCREAMING_SNAKE_CASE    ).prev_sample

              UpperCAmelCase_     :				Optional[int]  =     pred_prev_sample

         UpperCAmelCase_     :				Optional[int]  =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE    )    )
         UpperCAmelCase_     :				Tuple  =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

         assert abs(result_sum.item() - 3_47.63_57    ) < 1e-2
         assert abs(result_mean.item() - 0.45_27    ) < 1e-3





    def 				a__ ( self    )					->						Optional[Any]:
         UpperCAmelCase_     :				str  =     self.scheduler_classes[0]
         UpperCAmelCase_     :				Optional[Any]  =     self.get_scheduler_config()
         UpperCAmelCase_     :				Any  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				int  =     [39, 30, 12, 15, 0]

         with self.assertRaises(_SCREAMING_SNAKE_CASE  ,msg='''`timesteps` must be in descending order.'''    ):
              scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				Optional[Any]  =     self.scheduler_classes[0]
         UpperCAmelCase_     :				Union[str, Any]  =     self.get_scheduler_config()
         UpperCAmelCase_     :				Dict  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Optional[Any]  =     [39, 30, 12, 1, 0]
         UpperCAmelCase_     :				List[Any]  =     len(_SCREAMING_SNAKE_CASE    )

         with self.assertRaises(_SCREAMING_SNAKE_CASE  ,msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''    ):
              scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE  ,timesteps=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				Any  =     self.scheduler_classes[0]
         UpperCAmelCase_     :				List[Any]  =     self.get_scheduler_config()
         UpperCAmelCase_     :				Any  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Dict  =     [scheduler.config.num_train_timesteps]

         with self.assertRaises(
             _SCREAMING_SNAKE_CASE  ,msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''  ,):
              scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE    )






"""simple docstring"""






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      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]							,			lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			List[Any]=None			)    ->			str:



			'''simple docstring'''
			# set parameter of one layer
			assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
			a__  :	Optional[Any]		 =			nn.Parameter(lowerCAmelCase__			)
			if bias is not None:
						assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
						a__  :	str		 =			nn.Parameter(lowerCAmelCase__			)






def      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]							,			lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			str:



			'''simple docstring'''
			# set torch weights for 1-to-1 comparison
			a__  :	Tuple		 =			np.asarray(weights[0]			)
			a__  :	Any		 =			np.asarray(weights[1]			)
			a__  :	Optional[Any]		 =			np.asarray(weights[2]			)

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






def      lowercase__ (					lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			str:



			'''simple docstring'''
			# set torch weights for 1-to-1 comparison
			a__  :	int		 =			np.asarray(weights[0]			)
			a__  :	List[str]		 =			np.asarray(weights[1]			)
			a__  :	Optional[int]		 =			np.asarray(weights[2]			)
			a__  :	int		 =			np.asarray(weights[3]			)

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






def      lowercase__ (					lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			Any:



			'''simple docstring'''
			# layernorm 1
			a__  :	int		 =			weights[0][0][0]
			a__  :	Optional[Any]		 =			np.asarray(layer_norm_a[0]			)
			a__  :	Optional[int]		 =			np.asarray(layer_norm_a[1]			)
			set_param(
			    torch_block.attention.layer_norm							,			torch.tensor(lowerCAmelCase__			)							,			torch.tensor(lowerCAmelCase__			)							,			)

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

			# intermediate weighs
			a__  :	Optional[int]		 =			weights[2][0][1][2]

			# Chunked Feed Forward
			if len(lowerCAmelCase__			) == 4:
						a__  :	List[str]		 =			intermediate_weights[2]

			# layernorm 2
			a__  :	List[Any]		 =			np.asarray(intermediate_weights[0][0]			)
			a__  :	Optional[int]		 =			np.asarray(intermediate_weights[0][1]			)
			set_param(
			    torch_block.feed_forward.layer_norm							,			torch.tensor(lowerCAmelCase__			)							,			torch.tensor(lowerCAmelCase__			)							,			)

			# intermediate dense
			a__  :	Tuple		 =			np.asarray(intermediate_weights[1][0]			)
			a__  :	Union[str, Any]		 =			np.asarray(intermediate_weights[1][1]			)
			set_param(
			    torch_block.feed_forward.dense.dense							,			torch.tensor(lowerCAmelCase__			).transpose(0							,			1			).contiguous()							,			torch.tensor(lowerCAmelCase__			)							,			)

			# intermediate out
			a__  :	Optional[Any]		 =			np.asarray(intermediate_weights[4][0]			)
			a__  :	Dict		 =			np.asarray(intermediate_weights[4][1]			)
			set_param(
			    torch_block.feed_forward.output.dense							,			torch.tensor(lowerCAmelCase__			).transpose(0							,			1			).contiguous()							,			torch.tensor(lowerCAmelCase__			)							,			)






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			Any:



			'''simple docstring'''
			# reformer model
			a__  :	int		 =			torch_model.reformer

			# word embeds
			a__  :	List[Any]		 =			np.asarray(weights[1]			)
			set_param(
			    torch_model_reformer.embeddings.word_embeddings							,			torch.tensor(lowerCAmelCase__			)							,			)

			if isinstance(weights[3]							,			lowerCAmelCase__			):
						a__  :	Dict		 =			torch_model_reformer.embeddings.position_embeddings
						for emb_idx in range(len(position_embeddings.weights			)			):
									a__  :	str		 =			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__  :	List[str]		 =			nn.Parameter(torch.tensor(lowerCAmelCase__			)			)

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

			# output layer norm
			a__  :	str		 =			np.asarray(weights[7][0]			)
			a__  :	List[Any]		 =			np.asarray(weights[7][1]			)
			set_param(
			    torch_model_reformer.encoder.layer_norm							,			torch.tensor(lowerCAmelCase__			)							,			torch.tensor(lowerCAmelCase__			)							,			)

			# output embeddings
			a__  :	str		 =			np.asarray(weights[9][0]			)
			a__  :	Optional[int]		 =			np.asarray(weights[9][1]			)
			set_param(
			    torch_model.lm_head.decoder							,			torch.tensor(lowerCAmelCase__			).transpose(0							,			1			).contiguous()							,			torch.tensor(lowerCAmelCase__			)							,			)






def      lowercase__ (					lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			List[str]:



			'''simple docstring'''
			# Initialise PyTorch model
			a__  :	List[Any]		 =			ReformerConfig.from_json_file(lowerCAmelCase__			)
			print(F"Building PyTorch model from configuration: {config}"			)
			a__  :	Optional[Any]		 =			ReformerModelWithLMHead(lowerCAmelCase__			)

			with open(lowerCAmelCase__							,			"rb"			) as f:
						a__  :	List[str]		 =			pickle.load(lowerCAmelCase__			)["weights"]

			set_model_weights_in_torch(lowerCAmelCase__							,			lowerCAmelCase__							,			config.hidden_size			)

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


if __name__ == "__main__":
		__UpperCAmelCase						=			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.'''
		)
		__UpperCAmelCase						=			parser.parse_args()
		convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)