





'''simple docstring'''






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class  _A (					__lowercase		):
		lowercase__:   Any				     =   (EulerDiscreteScheduler,)
		lowercase__:   Optional[Any]				     =   10

		def 	lowercase__     (   self					:   str  ,      **__magic_name__					:   str			)					->    Any:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     {
					    """num_train_timesteps""": 11_00,
					    """beta_start""": 0.0001,
					    """beta_end""": 0.02,
					    """beta_schedule""": """linear""",
					}

					config.update(**__magic_name__			)
					return config

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


					"""simple docstring"""

					for timesteps in [10, 50, 1_00, 10_00]:
								self.check_over_configs(num_train_timesteps=__magic_name__			)

		def 	lowercase__     (   self					:   List[str]			)					->    int:


					"""simple docstring"""

					for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]  ,      [0.0002, 0.002, 0.02]			):
								self.check_over_configs(beta_start=__magic_name__  ,      beta_end=__magic_name__			)

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


					"""simple docstring"""

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

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


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   List[str]			)					->    Any:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     self.scheduler_classes[0]
					__snake_case					:      List[str]							      =     self.get_scheduler_config()
					__snake_case					:      int							      =     scheduler_class(**__magic_name__			)

					scheduler.set_timesteps(self.num_inference_steps			)

					__snake_case					:      List[Any]							      =     torch.manual_seed(0			)

					__snake_case					:      Union[str, Any]							      =     self.dummy_model()
					__snake_case					:      List[str]							      =     self.dummy_sample_deter * scheduler.init_noise_sigma
					__snake_case					:      Dict							      =     sample.to(__magic_name__			)

					for i, t in enumerate(scheduler.timesteps			):
								__snake_case					:      Union[str, Any]							      =     scheduler.scale_model_input(__magic_name__  ,      __magic_name__			)

								__snake_case					:      Optional[Any]							      =     model(__magic_name__  ,      __magic_name__			)

								__snake_case					:      List[Any]							      =     scheduler.step(__magic_name__  ,      __magic_name__  ,      __magic_name__  ,      generator=__magic_name__			)
								__snake_case					:      Union[str, Any]							      =     output.prev_sample

					__snake_case					:      Union[str, Any]							      =     torch.sum(torch.abs(__magic_name__			)			)
					__snake_case					:      str							      =     torch.mean(torch.abs(__magic_name__			)			)

					assert abs(result_sum.item() - 10.0807			) < 1E-2
					assert abs(result_mean.item() - 0.0131			) < 1E-3

		def 	lowercase__     (   self					:   Optional[int]			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      Any							      =     self.scheduler_classes[0]
					__snake_case					:      Dict							      =     self.get_scheduler_config(prediction_type="""v_prediction"""			)
					__snake_case					:      Optional[int]							      =     scheduler_class(**__magic_name__			)

					scheduler.set_timesteps(self.num_inference_steps			)

					__snake_case					:      List[str]							      =     torch.manual_seed(0			)

					__snake_case					:      List[str]							      =     self.dummy_model()
					__snake_case					:      int							      =     self.dummy_sample_deter * scheduler.init_noise_sigma
					__snake_case					:      Union[str, Any]							      =     sample.to(__magic_name__			)

					for i, t in enumerate(scheduler.timesteps			):
								__snake_case					:      Union[str, Any]							      =     scheduler.scale_model_input(__magic_name__  ,      __magic_name__			)

								__snake_case					:      str							      =     model(__magic_name__  ,      __magic_name__			)

								__snake_case					:      Any							      =     scheduler.step(__magic_name__  ,      __magic_name__  ,      __magic_name__  ,      generator=__magic_name__			)
								__snake_case					:      Optional[int]							      =     output.prev_sample

					__snake_case					:      List[Any]							      =     torch.sum(torch.abs(__magic_name__			)			)
					__snake_case					:      int							      =     torch.mean(torch.abs(__magic_name__			)			)

					assert abs(result_sum.item() - 0.0002			) < 1E-2
					assert abs(result_mean.item() - 2.2_676E-06			) < 1E-3

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


					"""simple docstring"""

					__snake_case					:      int							      =     self.scheduler_classes[0]
					__snake_case					:      Union[str, Any]							      =     self.get_scheduler_config()
					__snake_case					:      int							      =     scheduler_class(**__magic_name__			)

					scheduler.set_timesteps(self.num_inference_steps  ,      device=__magic_name__			)

					__snake_case					:      Dict							      =     torch.manual_seed(0			)

					__snake_case					:      Union[str, Any]							      =     self.dummy_model()
					__snake_case					:      Optional[int]							      =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
					__snake_case					:      Any							      =     sample.to(__magic_name__			)

					for t in scheduler.timesteps:
								__snake_case					:      Union[str, Any]							      =     scheduler.scale_model_input(__magic_name__  ,      __magic_name__			)

								__snake_case					:      Optional[int]							      =     model(__magic_name__  ,      __magic_name__			)

								__snake_case					:      Optional[int]							      =     scheduler.step(__magic_name__  ,      __magic_name__  ,      __magic_name__  ,      generator=__magic_name__			)
								__snake_case					:      Optional[int]							      =     output.prev_sample

					__snake_case					:      List[Any]							      =     torch.sum(torch.abs(__magic_name__			)			)
					__snake_case					:      List[Any]							      =     torch.mean(torch.abs(__magic_name__			)			)

					assert abs(result_sum.item() - 10.0807			) < 1E-2
					assert abs(result_mean.item() - 0.0131			) < 1E-3

		def 	lowercase__     (   self					:   Optional[int]			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     self.scheduler_classes[0]
					__snake_case					:      Optional[int]							      =     self.get_scheduler_config()
					__snake_case					:      int							      =     scheduler_class(**__magic_name__  ,      use_karras_sigmas=__magic_name__			)

					scheduler.set_timesteps(self.num_inference_steps  ,      device=__magic_name__			)

					__snake_case					:      Dict							      =     torch.manual_seed(0			)

					__snake_case					:      int							      =     self.dummy_model()
					__snake_case					:      Optional[int]							      =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
					__snake_case					:      int							      =     sample.to(__magic_name__			)

					for t in scheduler.timesteps:
								__snake_case					:      Any							      =     scheduler.scale_model_input(__magic_name__  ,      __magic_name__			)

								__snake_case					:      Optional[Any]							      =     model(__magic_name__  ,      __magic_name__			)

								__snake_case					:      int							      =     scheduler.step(__magic_name__  ,      __magic_name__  ,      __magic_name__  ,      generator=__magic_name__			)
								__snake_case					:      Tuple							      =     output.prev_sample

					__snake_case					:      str							      =     torch.sum(torch.abs(__magic_name__			)			)
					__snake_case					:      Tuple							      =     torch.mean(torch.abs(__magic_name__			)			)

					assert abs(result_sum.item() - 124.52299499511719			) < 1E-2
					assert abs(result_mean.item() - 0.16213932633399963			) < 1E-3






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
a			:			List[str]												=					logging.get_logger(__name__)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = torch.load(_UpperCamelCase							,      map_location='''cpu'''	)
			if "model" in sd.keys():
						__lowercase			       = torch.load(_UpperCamelCase							,      map_location='''cpu'''	)['''model''']

			# pop unnecessary weights
			__lowercase			       = [
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(_UpperCamelCase	)

			__lowercase			       = {
			    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
			    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
			    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									__lowercase			       = sd.pop(_UpperCamelCase	)

			__lowercase			       = list(sd.keys()	)
			for key in keys:
						if ".qkv_proj." in key:
									__lowercase			       = sd[key]
									# We split QKV in separate Q,K,V

									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.q_proj.'''	)
									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.k_proj.'''	)
									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.v_proj.'''	)

									__lowercase			       = value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									__lowercase       ,			__lowercase       ,			__lowercase			       = torch.split(_UpperCamelCase							,      depth // 3							,      dim=0	)

									__lowercase			       = q
									__lowercase			       = k
									__lowercase			       = v
									del sd[key]

			return sd


@torch.no_grad()
def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = load_checkpoint(_UpperCamelCase	)

			if config is not None:
						__lowercase			       = OPTConfig.from_pretrained(_UpperCamelCase	)
			else:
						__lowercase			       = OPTConfig()

			__lowercase			       = OPTModel(_UpperCamelCase	).half().eval()
			model.load_state_dict(_UpperCamelCase	)

			# Check results
			Path(_UpperCamelCase	).mkdir(exist_ok=_UpperCamelCase	)
			model.save_pretrained(_UpperCamelCase	)


if __name__ == "__main__":
	a			:			List[Any]												=					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--fairseq_path''',
	    type=str,
	    help=(
	        '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
	        ''' https://huggingface.co/models?other=opt_metasq'''
	    ),
	)
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
	a			:			Any												=					parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


