



'''simple docstring'''




import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

a     :    Tuple      						=					[
    # (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'''),
]

a     :    Tuple      						=					[
    # (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'''),
]

a     :    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
						a     :    Tuple      						=					F'''down_blocks.{i}.resnets.{j}.'''
						a     :    Optional[int]      						=					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
									a     :    Dict      						=					F'''down_blocks.{i}.attentions.{j}.'''
									a     :    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
						a     :    Dict      						=					F'''up_blocks.{i}.resnets.{j}.'''
						a     :    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
									a     :    Any      						=					F'''up_blocks.{i}.attentions.{j}.'''
									a     :    Optional[int]      						=					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
						a     :    Dict      						=					F'''down_blocks.{i}.downsamplers.0.conv.'''
						a     :    int      						=					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
						a     :    Tuple      						=					F'''up_blocks.{i}.upsamplers.0.'''
						a     :    Optional[Any]      						=					F'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
						unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

a     :    Optional[Any]      						=					'''mid_block.attentions.0.'''
a     :    Optional[int]      						=					'''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
			a     :    List[str]      						=					F'''mid_block.resnets.{j}.'''
			a     :    Optional[Any]      						=					F'''middle_block.{2*j}.'''
			unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))



def    __lowerCamelCase						(      _lowercase	)   ->    Any:
							UpperCAmelCase  :	Optional[Any]							  =	{k: k for k in unet_state_dict.keys()}
							for sd_name, hf_name in unet_conversion_map:
														UpperCAmelCase  :	str							  =	sd_name
							for k, v in mapping.items():
														if "resnets" in k:
																					for sd_part, hf_part in unet_conversion_map_resnet:
																												UpperCAmelCase  :	str							  =	v.replace(_lowercase			,       _lowercase	)
																					UpperCAmelCase  :	Tuple							  =	v
							for k, v in mapping.items():
														for sd_part, hf_part in unet_conversion_map_layer:
																					UpperCAmelCase  :	Any							  =	v.replace(_lowercase			,       _lowercase	)
														UpperCAmelCase  :	Any							  =	v
							UpperCAmelCase  :	Any							  =	{v: unet_state_dict[k] for k, v in mapping.items()}
							return new_state_dict


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

a     :    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):
						a     :    List[str]      						=					F'''encoder.down_blocks.{i}.resnets.{j}.'''
						a     :    Dict      						=					F'''encoder.down.{i}.block.{j}.'''
						vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

			if i < 3:
						a     :    int      						=					F'''down_blocks.{i}.downsamplers.0.'''
						a     :    List[Any]      						=					F'''down.{i}.downsample.'''
						vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

						a     :    Tuple      						=					F'''up_blocks.{i}.upsamplers.0.'''
						a     :    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):
						a     :    Union[str, Any]      						=					F'''decoder.up_blocks.{i}.resnets.{j}.'''
						a     :    Optional[Any]      						=					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):
			a     :    Dict      						=					F'''mid_block.resnets.{i}.'''
			a     :    Tuple      						=					F'''mid.block_{i+1}.'''
			vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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



def    __lowerCamelCase						(      _lowercase	)   ->    int:
							return w.reshape(*w.shape			,       1			,       1	)



def    __lowerCamelCase						(      _lowercase	)   ->    Optional[int]:
							UpperCAmelCase  :	Optional[int]							  =	{k: k for k in vae_state_dict.keys()}
							for k, v in mapping.items():
														for sd_part, hf_part in vae_conversion_map:
																					UpperCAmelCase  :	Tuple							  =	v.replace(_lowercase			,       _lowercase	)
														UpperCAmelCase  :	int							  =	v
							for k, v in mapping.items():
														if "attentions" in k:
																					for sd_part, hf_part in vae_conversion_map_attn:
																												UpperCAmelCase  :	int							  =	v.replace(_lowercase			,       _lowercase	)
																					UpperCAmelCase  :	int							  =	v
							UpperCAmelCase  :	Dict							  =	{v: vae_state_dict[k] for k, v in mapping.items()}
							UpperCAmelCase  :	int							  =	["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'''	)
																												UpperCAmelCase  :	Optional[int]							  =	reshape_weight_for_sd(_lowercase	)
							return new_state_dict


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


a     :    Optional[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'''),
]
a     :    Dict      						=					{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
a     :    str      						=					re.compile("""|""".join(protected.keys()))

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



def    __lowerCamelCase						(      _lowercase	)   ->    Union[str, Any]:
							UpperCAmelCase  :	int							  =	{}
							UpperCAmelCase  :	Optional[Any]							  =	{}
							UpperCAmelCase  :	Dict							  =	{}
							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"""	)
														):
																					UpperCAmelCase  :	Dict							  =	k[: -len(""".q_proj.weight"""	)]
																					UpperCAmelCase  :	str							  =	k[-len("""q_proj.weight"""	)]
																					if k_pre not in capture_qkv_weight:
																												UpperCAmelCase  :	List[Any]							  =	[None, None, None]
																					UpperCAmelCase  :	Union[str, Any]							  =	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"""	)
														):
																					UpperCAmelCase  :	str							  =	k[: -len(""".q_proj.bias"""	)]
																					UpperCAmelCase  :	Tuple							  =	k[-len("""q_proj.bias"""	)]
																					if k_pre not in capture_qkv_bias:
																												UpperCAmelCase  :	List[Any]							  =	[None, None, None]
																					UpperCAmelCase  :	str							  =	v
																					continue

														UpperCAmelCase  :	str							  =	textenc_pattern.sub(lambda _lowercase	: protected[re.escape(m.group(0	)	)]			,       _lowercase	)
														UpperCAmelCase  :	Any							  =	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"""	)
														UpperCAmelCase  :	str							  =	textenc_pattern.sub(lambda _lowercase	: protected[re.escape(m.group(0	)	)]			,       _lowercase	)
														UpperCAmelCase  :	Dict							  =	torch.cat(_lowercase	)

							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"""	)
														UpperCAmelCase  :	Optional[Any]							  =	textenc_pattern.sub(lambda _lowercase	: protected[re.escape(m.group(0	)	)]			,       _lowercase	)
														UpperCAmelCase  :	str							  =	torch.cat(_lowercase	)

							return new_state_dict



def    __lowerCamelCase						(      _lowercase	)   ->    Optional[int]:
							return text_enc_dict


if __name__ == "__main__":
			a     :    List[str]      						=					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."""
			)

			a     :    List[str]      						=					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
			a     :    Any      						=					osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
			a     :    Union[str, Any]      						=					osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
			a     :    Dict      						=					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):
						a     :    Dict      						=					load_file(unet_path, device="""cpu""")
			else:
						a     :    Optional[Any]      						=					osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
						a     :    int      						=					torch.load(unet_path, map_location="""cpu""")

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

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

			# Convert the UNet model
			a     :    Any      						=					convert_unet_state_dict(unet_state_dict)
			a     :    List[Any]      						=					{'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

			# Convert the VAE model
			a     :    Optional[int]      						=					convert_vae_state_dict(vae_state_dict)
			a     :    Tuple      						=					{'''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
			a     :    Optional[int]      						=					'''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
						a     :    int      						=					{'''transformer.''' + k: v for k, v in text_enc_dict.items()}
						a     :    List[Any]      						=					convert_text_enc_state_dict_vaa(text_enc_dict)
						a     :    Optional[int]      						=					{'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
			else:
						a     :    str      						=					convert_text_enc_state_dict(text_enc_dict)
						a     :    List[Any]      						=					{'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

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

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






'''simple docstring'''




from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


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

class     UpperCamelCase_				(							__magic_name__    ):





	def 				_lowercase(						self	,	A					)    ->    Optional[int]:
								if isinstance(A	,	A					):
															UpperCAmelCase  :	Union[str, Any]							  =	[label.strip() for label in labels.split(""","""					) if label.strip()]
								return labels






	def __call__(						self	,	A	,	A	,	A					)    ->    str:
								if len(A					) == 0 or len(A					) == 0:
															raise ValueError("""You must include at least one label and at least one sequence."""					)
								if hypothesis_template.format(labels[0]					) == hypothesis_template:
															raise ValueError(
															    (
															        """The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. """
															        """Make sure the passed template includes formatting syntax such as {{}} where the label should go."""
															    ).format(A					)					)

								if isinstance(A	,	A					):
															UpperCAmelCase  :	Tuple							  =	[sequences]

								UpperCAmelCase  :	Optional[Any]							  =	[]
								for sequence in sequences:
															sequence_pairs.extend([[sequence, hypothesis_template.format(A					)] for label in labels]					)

								return sequence_pairs, sequences




@add_end_docstrings(__magic_name__    )
class     UpperCamelCase_				(							__magic_name__    ):





	def __init__(						self	,	A=ZeroShotClassificationArgumentHandler()	,	*A	,	**A					)    ->    Optional[int]:
								UpperCAmelCase  :	Tuple							  =	args_parser
								super().__init__(*A	,	**A					)
								if self.entailment_id == -1:
															logger.warning(
															    """Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to """
															    """-1. Define a descriptive label2id mapping in the model config to ensure correct outputs."""					)





	@property
	def 				_lowercase(						self					)    ->    List[Any]:
								for label, ind in self.model.config.labelaid.items():
															if label.lower().startswith("""entail"""					):
																						return ind
								return -1





	def 				_lowercase(						self	,	A	,	A=True	,	A=True	,	A=TruncationStrategy.ONLY_FIRST	,	**A					)    ->    str:
								UpperCAmelCase  :	Tuple							  =	self.framework
								if self.tokenizer.pad_token is None:
															# Override for tokenizers not supporting padding
															logger.error(
															    """Tokenizer was not supporting padding necessary for zero-shot, attempting to use """
															    """ `pad_token=eos_token`"""					)
															UpperCAmelCase  :	Any							  =	self.tokenizer.eos_token
								try:
															UpperCAmelCase  :	Tuple							  =	self.tokenizer(
															    A	,	add_special_tokens=A	,	return_tensors=A	,	padding=A	,	truncation=A	,	)
								except Exception as e:
															if "too short" in str(A					):
																						# tokenizers might yell that we want to truncate
																						# to a value that is not even reached by the input.
																						# In that case we don't want to truncate.
																						# It seems there's not a really better way to catch that
																						# exception.

																						UpperCAmelCase  :	List[str]							  =	self.tokenizer(
																						    A	,	add_special_tokens=A	,	return_tensors=A	,	padding=A	,	truncation=TruncationStrategy.DO_NOT_TRUNCATE	,	)
															else:
																						raise e

								return inputs





	def 				_lowercase(						self	,	**A					)    ->    Tuple:
								if kwargs.get("""multi_class"""	,	A					) is not None:
															UpperCAmelCase  :	Any							  =	kwargs["""multi_class"""]
															logger.warning(
															    """The `multi_class` argument has been deprecated and renamed to `multi_label`. """
															    """`multi_class` will be removed in a future version of Transformers."""					)
								UpperCAmelCase  :	int							  =	{}
								if "candidate_labels" in kwargs:
															UpperCAmelCase  :	Tuple							  =	self._args_parser._parse_labels(kwargs["""candidate_labels"""]					)
								if "hypothesis_template" in kwargs:
															UpperCAmelCase  :	List[Any]							  =	kwargs["""hypothesis_template"""]

								UpperCAmelCase  :	Dict							  =	{}
								if "multi_label" in kwargs:
															UpperCAmelCase  :	Union[str, Any]							  =	kwargs["""multi_label"""]
								return preprocess_params, {}, postprocess_params





	def __call__(						self	,	A	,	*A	,	**A	,	)    ->    Tuple:
								if len(A					) == 0:
															pass
								elif len(A					) == 1 and "candidate_labels" not in kwargs:
															UpperCAmelCase  :	Optional[Any]							  =	args[0]
								else:
															raise ValueError(f'''Unable to understand extra arguments {args}'''					)

								return super().__call__(A	,	**A					)





	def 				_lowercase(						self	,	A	,	A=None	,	A="This example is {}."					)    ->    List[Any]:
								UpperCAmelCase  ,  UpperCAmelCase  :	List[Any]							  =	self._args_parser(A	,	A	,	A					)

								for i, (candidate_label, sequence_pair) in enumerate(zip(A	,	A					)					):
															UpperCAmelCase  :	Any							  =	self._parse_and_tokenize([sequence_pair]					)

															yield {
															    "candidate_label": candidate_label,
															    "sequence": sequences[0],
															    "is_last": i == len(A					) - 1,
															    **model_input,
															}





	def 				_lowercase(						self	,	A					)    ->    Optional[int]:
								UpperCAmelCase  :	Optional[Any]							  =	inputs["""candidate_label"""]
								UpperCAmelCase  :	Tuple							  =	inputs["""sequence"""]
								UpperCAmelCase  :	List[Any]							  =	{k: inputs[k] for k in self.tokenizer.model_input_names}
								UpperCAmelCase  :	Tuple							  =	self.model(**A					)

								UpperCAmelCase  :	Optional[int]							  =	{
								    """candidate_label""": candidate_label,
								    """sequence""": sequence,
								    """is_last""": inputs["""is_last"""],
								    **outputs,
								}
								return model_outputs






	def 				_lowercase(						self	,	A	,	A=False					)    ->    List[str]:
								UpperCAmelCase  :	Dict							  =	[outputs["""candidate_label"""] for outputs in model_outputs]
								UpperCAmelCase  :	List[Any]							  =	[outputs["""sequence"""] for outputs in model_outputs]
								UpperCAmelCase  :	List[Any]							  =	np.concatenate([output["""logits"""].numpy() for output in model_outputs]					)
								UpperCAmelCase  :	Optional[Any]							  =	logits.shape[0]
								UpperCAmelCase  :	int							  =	len(A					)
								UpperCAmelCase  :	List[Any]							  =	N // n
								UpperCAmelCase  :	int							  =	logits.reshape((num_sequences, n, -1)					)

								if multi_label or len(A					) == 1:
															# softmax over the entailment vs. contradiction dim for each label independently
															UpperCAmelCase  :	str							  =	self.entailment_id
															UpperCAmelCase  :	str							  =	-1 if entailment_id == 0 else 0
															UpperCAmelCase  :	Optional[Any]							  =	reshaped_outputs[..., [contradiction_id, entailment_id]]
															UpperCAmelCase  :	int							  =	np.exp(A					) / np.exp(A					).sum(-1	,	keepdims=A					)
															UpperCAmelCase  :	int							  =	scores[..., 1]
								else:
															# softmax the "entailment" logits over all candidate labels
															UpperCAmelCase  :	Dict							  =	reshaped_outputs[..., self.entailment_id]
															UpperCAmelCase  :	Optional[int]							  =	np.exp(A					) / np.exp(A					).sum(-1	,	keepdims=A					)

								UpperCAmelCase  :	int							  =	list(reversed(scores[0].argsort()					)					)
								return {
								    "sequence": sequences[0],
								    "labels": [candidate_labels[i] for i in top_inds],
								    "scores": scores[0, top_inds].tolist(),
								}

