






'''simple docstring'''







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
							from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


UpperCamelCase    :      Union[str, Any]  			=		logging.get_logger(__name__)

UpperCamelCase    :      int  			=		{
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		_UpperCamelCase				  =					'bloom'
		_UpperCamelCase				  =					['past_key_values']
		_UpperCamelCase				  =					{
		    'num_hidden_layers': 'n_layer',
		    'num_attention_heads': 'n_head',
		}






		def __init__(      self   ,_lowerCAmelCase=25_08_80   ,_lowerCAmelCase=64   ,_lowerCAmelCase=2   ,_lowerCAmelCase=8   ,_lowerCAmelCase=1E-5   ,_lowerCAmelCase=0.02   ,_lowerCAmelCase=True   ,_lowerCAmelCase=1   ,_lowerCAmelCase=2   ,_lowerCAmelCase=False   ,_lowerCAmelCase=0.0   ,_lowerCAmelCase=0.0   ,_lowerCAmelCase=1   ,_lowerCAmelCase=False   ,**_lowerCAmelCase   ,):
								lowerCamelCase__         =  vocab_size
								# Backward compatibility with n_embed kwarg
								lowerCamelCase__         =  kwargs.pop("""n_embed"""   ,_lowerCAmelCase       )
								lowerCamelCase__         =  hidden_size if n_embed is None else n_embed
								lowerCamelCase__         =  n_layer
								lowerCamelCase__         =  n_head
								lowerCamelCase__         =  layer_norm_epsilon
								lowerCamelCase__         =  initializer_range
								lowerCamelCase__         =  use_cache
								lowerCamelCase__         =  pretraining_tp
								lowerCamelCase__         =  apply_residual_connection_post_layernorm
								lowerCamelCase__         =  hidden_dropout
								lowerCamelCase__         =  attention_dropout

								lowerCamelCase__         =  bos_token_id
								lowerCamelCase__         =  eos_token_id
								lowerCamelCase__         =  slow_but_exact

								super().__init__(bos_token_id=_lowerCAmelCase   ,eos_token_id=_lowerCAmelCase   ,**_lowerCAmelCase       )




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		_UpperCamelCase				  =					version.parse('1.12'			)






		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = "default"   ,_lowerCAmelCase = None   ,_lowerCAmelCase = False   ,):
								super().__init__(_lowerCAmelCase   ,task=_lowerCAmelCase   ,patching_specs=_lowerCAmelCase   ,use_past=_lowerCAmelCase       )
								if not getattr(self._config   ,"""pad_token_id"""   ,_lowerCAmelCase       ):
														# TODO: how to do that better?
														lowerCamelCase__         =  0






		@property
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}       )
								if self.use_past:
														# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
														self.fill_with_past_key_values_(_lowerCAmelCase   ,direction="""inputs"""   ,inverted_values_shape=_lowerCAmelCase       )
														lowerCamelCase__         =  {0: """batch""", 1: """past_sequence + sequence"""}
								else:
														lowerCamelCase__         =  {0: """batch""", 1: """sequence"""}

								return common_inputs






		@property
		def 			UpperCamelCase_					(      self       ):
								return self._config.n_layer






		@property
		def 			UpperCamelCase_					(      self       ):
								return self._config.n_head






		@property
		def 			UpperCamelCase_					(      self       ):
								return 1E-3






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = -1   ,_lowerCAmelCase = -1   ,_lowerCAmelCase = False   ,_lowerCAmelCase = None   ,):
								lowerCamelCase__         =  super(_lowerCAmelCase   ,self       ).generate_dummy_inputs(
								    _lowerCAmelCase   ,batch_size=_lowerCAmelCase   ,seq_length=_lowerCAmelCase   ,is_pair=_lowerCAmelCase   ,framework=_lowerCAmelCase       )

								# We need to order the input in the way they appears in the forward()
								lowerCamelCase__         =  OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}       )

								# Need to add the past_keys
								if self.use_past:
														if not is_torch_available():
																				raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""       )
														else:
																				import torch

																				lowerCamelCase__       ,			lowerCamelCase__         =  common_inputs["""input_ids"""].shape
																				# Not using the same length for past_key_values
																				lowerCamelCase__         =  seqlen + 2
																				lowerCamelCase__         =  self._config.hidden_size // self.num_attention_heads
																				lowerCamelCase__         =  (
																				    batch * self.num_attention_heads,
																				    head_dim,
																				    past_key_values_length,
																				)
																				lowerCamelCase__         =  (
																				    batch * self.num_attention_heads,
																				    past_key_values_length,
																				    head_dim,
																				)
																				lowerCamelCase__         =  [
																				    (torch.zeros(_lowerCAmelCase       ), torch.zeros(_lowerCAmelCase       )) for _ in range(self.num_layers       )
																				]

								lowerCamelCase__         =  common_inputs["""attention_mask"""]
								if self.use_past:
														lowerCamelCase__         =  ordered_inputs["""attention_mask"""].dtype
														lowerCamelCase__         =  torch.cat(
														    [ordered_inputs["""attention_mask"""], torch.ones(_lowerCAmelCase   ,_lowerCAmelCase   ,dtype=_lowerCAmelCase       )]   ,dim=1       )

								return ordered_inputs






		@property
		def 			UpperCamelCase_					(      self       ):
								return 13









'''simple docstring'''







import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			List[str]						):
						lowerCamelCase__         =  OmegaConf.load(__lowerCAmelCase						)
						lowerCamelCase__         =  torch.load(__lowerCAmelCase   ,    map_location="""cpu"""						)["""model"""]
						lowerCamelCase__         =  list(state_dict.keys()						)

						# extract state_dict for VQVAE
						lowerCamelCase__         =  {}
						lowerCamelCase__         =  """first_stage_model."""
						for key in keys:
												if key.startswith(__lowerCAmelCase						):
																		lowerCamelCase__         =  state_dict[key]

    # extract state_dict for UNetLDM
						lowerCamelCase__         =  {}
						lowerCamelCase__         =  """model.diffusion_model."""
						for key in keys:
												if key.startswith(__lowerCAmelCase						):
																		lowerCamelCase__         =  state_dict[key]

						lowerCamelCase__         =  config.model.params.first_stage_config.params
						lowerCamelCase__         =  config.model.params.unet_config.params

						lowerCamelCase__         =  VQModel(**__lowerCAmelCase						).eval()
						vqvae.load_state_dict(__lowerCAmelCase						)

						lowerCamelCase__         =  UNetLDMModel(**__lowerCAmelCase						).eval()
						unet.load_state_dict(__lowerCAmelCase						)

						lowerCamelCase__         =  DDIMScheduler(
						    timesteps=config.model.params.timesteps   ,    beta_schedule="""scaled_linear"""   ,    beta_start=config.model.params.linear_start   ,    beta_end=config.model.params.linear_end   ,    clip_sample=__lowerCAmelCase   ,    )

						lowerCamelCase__         =  LDMPipeline(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						)
						pipeline.save_pretrained(__lowerCAmelCase						)


if __name__ == "__main__":
							UpperCamelCase    :      Optional[int]  			=		argparse.ArgumentParser()
							parser.add_argument('--checkpoint_path', type=str, required=True)
							parser.add_argument('--config_path', type=str, required=True)
							parser.add_argument('--output_path', type=str, required=True)
							UpperCamelCase    :      List[Any]  			=		parser.parse_args()

							convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)

