



"""simple docstring"""






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

_lowercase			:			int										=						[
    # (stable-diffusion, HF Diffusers)
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
]

_lowercase			:			Dict										=						[
    # (stable-diffusion, HF Diffusers)
    ("in_layers.0", "norm1"),
    ("in_layers.2", "conv1"),
    ("out_layers.0", "norm2"),
    ("out_layers.3", "conv2"),
    ("emb_layers.1", "time_emb_proj"),
    ("skip_connection", "conv_shortcut"),
]

_lowercase			:			List[str]										=						[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
		# loop over downblocks/upblocks

		for j in range(2):
				# loop over resnets/attentions for downblocks
				_lowercase			:			Optional[int]										=						f'down_blocks.{i}.resnets.{j}.'
				_lowercase			:			Any										=						f'input_blocks.{3*i + j + 1}.0.'
				unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

				if i < 3:
						# no attention layers in down_blocks.3
						_lowercase			:			Any										=						f'down_blocks.{i}.attentions.{j}.'
						_lowercase			:			int										=						f'input_blocks.{3*i + j + 1}.1.'
						unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

		for j in range(3):
				# loop over resnets/attentions for upblocks
				_lowercase			:			Optional[Any]										=						f'up_blocks.{i}.resnets.{j}.'
				_lowercase			:			Optional[int]										=						f'output_blocks.{3*i + j}.0.'
				unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

				if i > 0:
						# no attention layers in up_blocks.0
						_lowercase			:			Dict										=						f'up_blocks.{i}.attentions.{j}.'
						_lowercase			:			Any										=						f'output_blocks.{3*i + j}.1.'
						unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

		if i < 3:
				# no downsample in down_blocks.3
				_lowercase			:			str										=						f'down_blocks.{i}.downsamplers.0.conv.'
				_lowercase			:			str										=						f'input_blocks.{3*(i+1)}.0.op.'
				unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

				# no upsample in up_blocks.3
				_lowercase			:			Tuple										=						f'up_blocks.{i}.upsamplers.0.'
				_lowercase			:			Optional[int]										=						f'output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'
				unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

_lowercase			:			str										=						"mid_block.attentions.0."
_lowercase			:			int										=						"middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
		_lowercase			:			int										=						f'mid_block.resnets.{j}.'
		_lowercase			:			Optional[Any]										=						f'middle_block.{2*j}.'
		unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))






def 					snake_case__       (		__lowerCamelCase     ):
			"""simple docstring"""
			# buyer beware: this is a *brittle* function,
			# and correct output requires that all of these pieces interact in
			# the exact order in which I have arranged them.
			lowerCamelCase__		:		str		     ={k: k for k in unet_state_dict.keys()}
			for sd_name, hf_name in unet_conversion_map:
						lowerCamelCase__		:		Optional[int]		     =sd_name
			for k, v in mapping.items():
						if "resnets" in k:
									for sd_part, hf_part in unet_conversion_map_resnet:
												lowerCamelCase__		:		Optional[int]		     =v.replace(a__							,		a__     )
									lowerCamelCase__		:		Optional[int]		     =v
			for k, v in mapping.items():
						for sd_part, hf_part in unet_conversion_map_layer:
									lowerCamelCase__		:		Optional[int]		     =v.replace(a__							,		a__     )
						lowerCamelCase__		:		Optional[int]		     =v
			lowerCamelCase__		:		Optional[Any]		     ={v: unet_state_dict[k] for k, v in mapping.items()}
			return new_state_dict


# ================#
# VAE Conversion #
# ================#

_lowercase			:			Union[str, Any]										=						[
    # (stable-diffusion, HF Diffusers)
    ("nin_shortcut", "conv_shortcut"),
    ("norm_out", "conv_norm_out"),
    ("mid.attn_1.", "mid_block.attentions.0."),
]

for i in range(4):
		# down_blocks have two resnets
		for j in range(2):
				_lowercase			:			Any										=						f'encoder.down_blocks.{i}.resnets.{j}.'
				_lowercase			:			Tuple										=						f'encoder.down.{i}.block.{j}.'
				vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

		if i < 3:
				_lowercase			:			Optional[int]										=						f'down_blocks.{i}.downsamplers.0.'
				_lowercase			:			Dict										=						f'down.{i}.downsample.'
				vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

				_lowercase			:			Union[str, Any]										=						f'up_blocks.{i}.upsamplers.0.'
				_lowercase			:			List[str]										=						f'up.{3-i}.upsample.'
				vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

		# up_blocks have three resnets
		# also, up blocks in hf are numbered in reverse from sd
		for j in range(3):
				_lowercase			:			int										=						f'decoder.up_blocks.{i}.resnets.{j}.'
				_lowercase			:			Dict										=						f'decoder.up.{3-i}.block.{j}.'
				vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
		_lowercase			:			Any										=						f'mid_block.resnets.{i}.'
		_lowercase			:			int										=						f'mid.block_{i+1}.'
		vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


_lowercase			:			Optional[int]										=						[
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]






def 					snake_case__       (		__lowerCamelCase     ):
			"""simple docstring"""
			# convert HF linear weights to SD conv2d weights
			return w.reshape(*w.shape							,		1							,		1     )






def 					snake_case__       (		__lowerCamelCase     ):
			"""simple docstring"""
			lowerCamelCase__		:		str		     ={k: k for k in vae_state_dict.keys()}
			for k, v in mapping.items():
						for sd_part, hf_part in vae_conversion_map:
									lowerCamelCase__		:		Dict		     =v.replace(a__							,		a__     )
						lowerCamelCase__		:		List[str]		     =v
			for k, v in mapping.items():
						if "attentions" in k:
									for sd_part, hf_part in vae_conversion_map_attn:
												lowerCamelCase__		:		List[Any]		     =v.replace(a__							,		a__     )
									lowerCamelCase__		:		Union[str, Any]		     =v
			lowerCamelCase__		:		Dict		     ={v: vae_state_dict[k] for k, v in mapping.items()}
			lowerCamelCase__		:		Union[str, Any]		     =['''q''', '''k''', '''v''', '''proj_out''']
			for k, v in new_state_dict.items():
						for weight_name in weights_to_convert:
									if f'''mid.attn_1.{weight_name}.weight''' in k:
												print(f'''Reshaping {k} for SD format'''     )
												lowerCamelCase__		:		int		     =reshape_weight_for_sd(a__     )
			return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


_lowercase			:			Union[str, Any]										=						[
    # (stable-diffusion, HF Diffusers)
    ("resblocks.", "text_model.encoder.layers."),
    ("ln_1", "layer_norm1"),
    ("ln_2", "layer_norm2"),
    (".c_fc.", ".fc1."),
    (".c_proj.", ".fc2."),
    (".attn", ".self_attn"),
    ("ln_final.", "transformer.text_model.final_layer_norm."),
    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
_lowercase			:			List[Any]										=						{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_lowercase			:			List[Any]										=						re.compile("|".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
_lowercase			:			Optional[int]										=						{"q": 0, "k": 1, "v": 2}






def 					snake_case__       (		__lowerCamelCase     ):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]		     ={}
			lowerCamelCase__		:		Any		     ={}
			lowerCamelCase__		:		Tuple		     ={}
			for k, v in text_enc_dict.items():
						if (
						    k.endswith('''.self_attn.q_proj.weight'''     )
						    or k.endswith('''.self_attn.k_proj.weight'''     )
						    or k.endswith('''.self_attn.v_proj.weight'''     )
						):
									lowerCamelCase__		:		Dict		     =k[: -len('''.q_proj.weight'''     )]
									lowerCamelCase__		:		Union[str, Any]		     =k[-len('''q_proj.weight'''     )]
									if k_pre not in capture_qkv_weight:
												lowerCamelCase__		:		Tuple		     =[None, None, None]
									lowerCamelCase__		:		str		     =v
									continue

						if (
						    k.endswith('''.self_attn.q_proj.bias'''     )
						    or k.endswith('''.self_attn.k_proj.bias'''     )
						    or k.endswith('''.self_attn.v_proj.bias'''     )
						):
									lowerCamelCase__		:		Union[str, Any]		     =k[: -len('''.q_proj.bias'''     )]
									lowerCamelCase__		:		Optional[int]		     =k[-len('''q_proj.bias'''     )]
									if k_pre not in capture_qkv_bias:
												lowerCamelCase__		:		str		     =[None, None, None]
									lowerCamelCase__		:		Tuple		     =v
									continue

						lowerCamelCase__		:		str		     =textenc_pattern.sub(lambda __lowerCamelCase     : protected[re.escape(m.group(0     )     )]							,		a__     )
						lowerCamelCase__		:		int		     =v

			for k_pre, tensors in capture_qkv_weight.items():
						if None in tensors:
									raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''     )
						lowerCamelCase__		:		Any		     =textenc_pattern.sub(lambda __lowerCamelCase     : protected[re.escape(m.group(0     )     )]							,		a__     )
						lowerCamelCase__		:		Union[str, Any]		     =torch.cat(a__     )

			for k_pre, tensors in capture_qkv_bias.items():
						if None in tensors:
									raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''     )
						lowerCamelCase__		:		List[Any]		     =textenc_pattern.sub(lambda __lowerCamelCase     : protected[re.escape(m.group(0     )     )]							,		a__     )
						lowerCamelCase__		:		List[Any]		     =torch.cat(a__     )

			return new_state_dict






def 					snake_case__       (		__lowerCamelCase     ):
			"""simple docstring"""
			return text_enc_dict


if __name__ == "__main__":
		_lowercase			:			Optional[Any]										=						argparse.ArgumentParser()

		parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
		parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
		parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
		parser.add_argument(
		    "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
		)

		_lowercase			:			Dict										=						parser.parse_args()

		assert args.model_path is not None, "Must provide a model path!"

		assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

		# Path for safetensors
		_lowercase			:			str										=						osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
		_lowercase			:			Any										=						osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
		_lowercase			:			Union[str, Any]										=						osp.join(args.model_path, "text_encoder", "model.safetensors")

		# Load models from safetensors if it exists, if it doesn't pytorch
		if osp.exists(unet_path):
				_lowercase			:			str										=						load_file(unet_path, device="cpu")
		else:
				_lowercase			:			Union[str, Any]										=						osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
				_lowercase			:			Tuple										=						torch.load(unet_path, map_location="cpu")

		if osp.exists(vae_path):
				_lowercase			:			str										=						load_file(vae_path, device="cpu")
		else:
				_lowercase			:			Any										=						osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
				_lowercase			:			List[str]										=						torch.load(vae_path, map_location="cpu")

		if osp.exists(text_enc_path):
				_lowercase			:			Optional[int]										=						load_file(text_enc_path, device="cpu")
		else:
				_lowercase			:			Any										=						osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
				_lowercase			:			List[str]										=						torch.load(text_enc_path, map_location="cpu")

		# Convert the UNet model
		_lowercase			:			int										=						convert_unet_state_dict(unet_state_dict)
		_lowercase			:			Union[str, Any]										=						{"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

		# Convert the VAE model
		_lowercase			:			int										=						convert_vae_state_dict(vae_state_dict)
		_lowercase			:			List[Any]										=						{"first_stage_model." + k: v for k, v in vae_state_dict.items()}

		# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
		_lowercase			:			Union[str, Any]										=						"text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict

		if is_vaa_model:
				# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
				_lowercase			:			Union[str, Any]										=						{"transformer." + k: v for k, v in text_enc_dict.items()}
				_lowercase			:			Any										=						convert_text_enc_state_dict_vaa(text_enc_dict)
				_lowercase			:			Dict										=						{"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
		else:
				_lowercase			:			Any										=						convert_text_enc_state_dict(text_enc_dict)
				_lowercase			:			Any										=						{"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

		# Put together new checkpoint
		_lowercase			:			Optional[Any]										=						{**unet_state_dict, **vae_state_dict, **text_enc_dict}
		if args.half:
				_lowercase			:			Dict										=						{k: v.half() for k, v in state_dict.items()}

		if args.use_safetensors:
				save_file(state_dict, args.checkpoint_path)
		else:
				_lowercase			:			str										=						{"state_dict": state_dict}
				torch.save(state_dict, args.checkpoint_path)









"""simple docstring"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


_lowercase			:			Tuple										=						r"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"

@add_start_docstrings(lowerCAmelCase_       )
class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							'rag'
					_a             =							True



					def __init__(	self							:  Optional[int],  lowerCamelCase							:  Union[str, Any]=None,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  Dict=None,  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  Any=None,  lowerCamelCase							:  List[Any]=None,  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  Any=" / ",  lowerCamelCase							:  Union[str, Any]=" // ",  lowerCamelCase							:  List[Any]=5,  lowerCamelCase							:  int=300,  lowerCamelCase							:  Optional[Any]=768,  lowerCamelCase							:  Optional[Any]=8,  lowerCamelCase							:  Tuple="wiki_dpr",  lowerCamelCase							:  Tuple="train",  lowerCamelCase							:  Union[str, Any]="compressed",  lowerCamelCase							:  List[str]=None,  lowerCamelCase							:  Any=None,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  Dict=False,  lowerCamelCase							:  Optional[int]=0.0,  lowerCamelCase							:  Any=True,  lowerCamelCase							:  Dict=False,  lowerCamelCase							:  Tuple=False,  lowerCamelCase							:  List[str]=False,  lowerCamelCase							:  List[Any]=True,  lowerCamelCase							:  Optional[Any]=None,  **lowerCamelCase							:  str,  )-> List[Any]:
								super().__init__(
								    bos_token_id=lowerCamelCase,  pad_token_id=lowerCamelCase,  eos_token_id=lowerCamelCase,  decoder_start_token_id=lowerCamelCase,  forced_eos_token_id=lowerCamelCase,  is_encoder_decoder=lowerCamelCase,  prefix=lowerCamelCase,  vocab_size=lowerCamelCase,  **lowerCamelCase,  )
								assert (
								    "question_encoder" in kwargs and "generator" in kwargs
								), "Config has to be initialized with question_encoder and generator config"
								lowerCamelCase__		:		Tuple		     =kwargs.pop('''question_encoder'''					)
								lowerCamelCase__		:		int		     =question_encoder_config.pop('''model_type'''					)
								lowerCamelCase__		:		Dict		     =kwargs.pop('''generator'''					)
								lowerCamelCase__		:		Tuple		     =decoder_config.pop('''model_type'''					)

								from ..auto.configuration_auto import AutoConfig

								lowerCamelCase__		:		Tuple		     =AutoConfig.for_model(lowerCamelCase,  **lowerCamelCase					)
								lowerCamelCase__		:		Dict		     =AutoConfig.for_model(lowerCamelCase,  **lowerCamelCase					)

								lowerCamelCase__		:		Any		     =reduce_loss
								lowerCamelCase__		:		Tuple		     =label_smoothing
								lowerCamelCase__		:		List[str]		     =exclude_bos_score
								lowerCamelCase__		:		Dict		     =do_marginalize

								lowerCamelCase__		:		Union[str, Any]		     =title_sep
								lowerCamelCase__		:		Dict		     =doc_sep
								lowerCamelCase__		:		List[Any]		     =n_docs
								lowerCamelCase__		:		List[str]		     =max_combined_length

								lowerCamelCase__		:		List[Any]		     =dataset
								lowerCamelCase__		:		int		     =dataset_split
								lowerCamelCase__		:		List[Any]		     =index_name

								lowerCamelCase__		:		int		     =retrieval_vector_size
								lowerCamelCase__		:		Dict		     =retrieval_batch_size
								lowerCamelCase__		:		str		     =passages_path
								lowerCamelCase__		:		Any		     =index_path
								lowerCamelCase__		:		List[Any]		     =use_dummy_dataset

								lowerCamelCase__		:		Optional[int]		     =output_retrieved

								lowerCamelCase__		:		List[str]		     =do_deduplication

								lowerCamelCase__		:		Tuple		     =use_cache

								if self.forced_eos_token_id is None:
											lowerCamelCase__		:		int		     =getattr(self.generator,  '''forced_eos_token_id''',  lowerCamelCase					)



					@classmethod
					def 		snake_case	(	cls							:  List[str],  lowerCamelCase							:  PretrainedConfig,  lowerCamelCase							:  PretrainedConfig,  **lowerCamelCase							:  int					)-> PretrainedConfig:
								return cls(question_encoder=question_encoder_config.to_dict(),  generator=generator_config.to_dict(),  **lowerCamelCase					)




					def 		snake_case	(	self							:  Optional[int]					)-> int:
								lowerCamelCase__		:		Union[str, Any]		     =copy.deepcopy(self.__dict__					)
								lowerCamelCase__		:		Optional[int]		     =self.question_encoder.to_dict()
								lowerCamelCase__		:		Tuple		     =self.generator.to_dict()
								lowerCamelCase__		:		Optional[Any]		     =self.__class__.model_type
								return output




