






import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


A										=      'pcuenq/k-upscaler'



def        _lowerCamelCase(      lowerCAmelCase__					:		Any     ,     lowerCAmelCase__					:		Dict     ,     *, lowerCAmelCase__					:		str     ,     lowerCAmelCase__					:		Dict   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						List[Any]    				= {
				    # norm1
				    F'''{diffusers_resnet_prefix}.norm1.linear.weight''': checkpoint[F'''{resnet_prefix}.main.0.mapper.weight'''],
				    F'''{diffusers_resnet_prefix}.norm1.linear.bias''': checkpoint[F'''{resnet_prefix}.main.0.mapper.bias'''],
				    # conv1
				    F'''{diffusers_resnet_prefix}.conv1.weight''': checkpoint[F'''{resnet_prefix}.main.2.weight'''],
				    F'''{diffusers_resnet_prefix}.conv1.bias''': checkpoint[F'''{resnet_prefix}.main.2.bias'''],
				    # norm2
				    F'''{diffusers_resnet_prefix}.norm2.linear.weight''': checkpoint[F'''{resnet_prefix}.main.4.mapper.weight'''],
				    F'''{diffusers_resnet_prefix}.norm2.linear.bias''': checkpoint[F'''{resnet_prefix}.main.4.mapper.bias'''],
				    # conv2
				    F'''{diffusers_resnet_prefix}.conv2.weight''': checkpoint[F'''{resnet_prefix}.main.6.weight'''],
				    F'''{diffusers_resnet_prefix}.conv2.bias''': checkpoint[F'''{resnet_prefix}.main.6.bias'''],
				}

				if resnet.conv_shortcut is not None:
								rv.update(
								    {
								        F'''{diffusers_resnet_prefix}.conv_shortcut.weight''': checkpoint[F'''{resnet_prefix}.skip.weight'''],
								    }   )

				return rv



def        _lowerCamelCase(      lowerCAmelCase__					:		str     ,     *, lowerCAmelCase__					:		List[Any]     ,     lowerCAmelCase__					:		Dict   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						Optional[int]    				= checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3     ,     dim=0   )
				SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						List[Any]    				= checkpoint[F'''{attention_prefix}.qkv_proj.bias'''].chunk(3     ,     dim=0   )
				SCREAMING_SNAKE_CASE_ :						List[str]    				= {
				    # norm
				    F'''{diffusers_attention_prefix}.norm1.linear.weight''': checkpoint[F'''{attention_prefix}.norm_in.mapper.weight'''],
				    F'''{diffusers_attention_prefix}.norm1.linear.bias''': checkpoint[F'''{attention_prefix}.norm_in.mapper.bias'''],
				    # to_q
				    F'''{diffusers_attention_prefix}.attn1.to_q.weight''': weight_q.squeeze(-1   ).squeeze(-1   ),
				    F'''{diffusers_attention_prefix}.attn1.to_q.bias''': bias_q,
				    # to_k
				    F'''{diffusers_attention_prefix}.attn1.to_k.weight''': weight_k.squeeze(-1   ).squeeze(-1   ),
				    F'''{diffusers_attention_prefix}.attn1.to_k.bias''': bias_k,
				    # to_v
				    F'''{diffusers_attention_prefix}.attn1.to_v.weight''': weight_v.squeeze(-1   ).squeeze(-1   ),
				    F'''{diffusers_attention_prefix}.attn1.to_v.bias''': bias_v,
				    # to_out
				    F'''{diffusers_attention_prefix}.attn1.to_out.0.weight''': checkpoint[F'''{attention_prefix}.out_proj.weight''']
				    .squeeze(-1   )
				    .squeeze(-1   ),
				    F'''{diffusers_attention_prefix}.attn1.to_out.0.bias''': checkpoint[F'''{attention_prefix}.out_proj.bias'''],
				}

				return rv



def        _lowerCamelCase(      lowerCAmelCase__					:		List[Any]     ,     *, lowerCAmelCase__					:		int     ,     lowerCAmelCase__					:		Union[str, Any]     ,     lowerCAmelCase__					:		List[Any]   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= checkpoint[F'''{attention_prefix}.kv_proj.weight'''].chunk(2     ,     dim=0   )
				SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						List[Any]    				= checkpoint[F'''{attention_prefix}.kv_proj.bias'''].chunk(2     ,     dim=0   )

				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= {
				    # norm2 (ada groupnorm)
				    F'''{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight''': checkpoint[
				        F'''{attention_prefix}.norm_dec.mapper.weight'''
				    ],
				    F'''{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias''': checkpoint[
				        F'''{attention_prefix}.norm_dec.mapper.bias'''
				    ],
				    # layernorm on encoder_hidden_state
				    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight''': checkpoint[
				        F'''{attention_prefix}.norm_enc.weight'''
				    ],
				    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias''': checkpoint[
				        F'''{attention_prefix}.norm_enc.bias'''
				    ],
				    # to_q
				    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight''': checkpoint[
				        F'''{attention_prefix}.q_proj.weight'''
				    ]
				    .squeeze(-1   )
				    .squeeze(-1   ),
				    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias''': checkpoint[
				        F'''{attention_prefix}.q_proj.bias'''
				    ],
				    # to_k
				    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight''': weight_k.squeeze(-1   ).squeeze(-1   ),
				    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias''': bias_k,
				    # to_v
				    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight''': weight_v.squeeze(-1   ).squeeze(-1   ),
				    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias''': bias_v,
				    # to_out
				    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight''': checkpoint[
				        F'''{attention_prefix}.out_proj.weight'''
				    ]
				    .squeeze(-1   )
				    .squeeze(-1   ),
				    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias''': checkpoint[
				        F'''{attention_prefix}.out_proj.bias'''
				    ],
				}

				return rv



def        _lowerCamelCase(      lowerCAmelCase__					:		int     ,     lowerCAmelCase__					:		int     ,     lowerCAmelCase__					:		str     ,     lowerCAmelCase__					:		str   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						int    				= 'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= F'''{block_prefix}.{block_idx}'''

				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= {}

				if not hasattr(lowerCAmelCase__     ,     'attentions'   ):
								SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= 1  # resnet only
				elif not block.attentions[0].add_self_attention:
								SCREAMING_SNAKE_CASE_ :						Any    				= 2  # resnet -> cross-attention
				else:
								SCREAMING_SNAKE_CASE_ :						Dict    				= 3  # resnet -> self-attention -> cross-attention)

				for resnet_idx, resnet in enumerate(block.resnets   ):
								# diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
								SCREAMING_SNAKE_CASE_ :						int    				= F'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
								SCREAMING_SNAKE_CASE_ :						List[str]    				= n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
								SCREAMING_SNAKE_CASE_ :						Any    				= F'''{block_prefix}.{idx}''' if block_type == 'up' else F'''{block_prefix}.{idx}'''

								diffusers_checkpoint.update(
								    resnet_to_diffusers_checkpoint(
								        lowerCAmelCase__     ,     lowerCAmelCase__     ,     diffusers_resnet_prefix=lowerCAmelCase__     ,     resnet_prefix=lowerCAmelCase__   )   )

				if hasattr(lowerCAmelCase__     ,     'attentions'   ):
								for attention_idx, attention in enumerate(block.attentions   ):
												SCREAMING_SNAKE_CASE_ :						Tuple    				= F'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
												SCREAMING_SNAKE_CASE_ :						int    				= n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
												SCREAMING_SNAKE_CASE_ :						List[str]    				= F'''{block_prefix}.{idx}'''
												SCREAMING_SNAKE_CASE_ :						List[str]    				= F'''{block_prefix}.{idx }'''
												SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= 1 if not attention.add_self_attention else 2
												SCREAMING_SNAKE_CASE_ :						str    				= (
												    n * attention_idx + cross_attention_index
												    if block_type == 'up'
												    else n * attention_idx + cross_attention_index + 1
												)
												SCREAMING_SNAKE_CASE_ :						str    				= F'''{block_prefix}.{idx }'''

												diffusers_checkpoint.update(
												    cross_attn_to_diffusers_checkpoint(
												        lowerCAmelCase__     ,     diffusers_attention_prefix=lowerCAmelCase__     ,     diffusers_attention_index=2     ,     attention_prefix=lowerCAmelCase__     ,     )   )

												if attention.add_self_attention is True:
																diffusers_checkpoint.update(
																    self_attn_to_diffusers_checkpoint(
																        lowerCAmelCase__     ,     diffusers_attention_prefix=lowerCAmelCase__     ,     attention_prefix=lowerCAmelCase__     ,     )   )

				return diffusers_checkpoint



def        _lowerCamelCase(      lowerCAmelCase__					:		Union[str, Any]     ,     lowerCAmelCase__					:		Tuple   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= {}

				# pre-processing
				diffusers_checkpoint.update(
				    {
				        'conv_in.weight': checkpoint['inner_model.proj_in.weight'],
				        'conv_in.bias': checkpoint['inner_model.proj_in.bias'],
				    }   )

				# timestep and class embedding
				diffusers_checkpoint.update(
				    {
				        'time_proj.weight': checkpoint['inner_model.timestep_embed.weight'].squeeze(-1   ),
				        'time_embedding.linear_1.weight': checkpoint['inner_model.mapping.0.weight'],
				        'time_embedding.linear_1.bias': checkpoint['inner_model.mapping.0.bias'],
				        'time_embedding.linear_2.weight': checkpoint['inner_model.mapping.2.weight'],
				        'time_embedding.linear_2.bias': checkpoint['inner_model.mapping.2.bias'],
				        'time_embedding.cond_proj.weight': checkpoint['inner_model.mapping_cond.weight'],
				    }   )

				# down_blocks
				for down_block_idx, down_block in enumerate(model.down_blocks   ):
								diffusers_checkpoint.update(block_to_diffusers_checkpoint(lowerCAmelCase__     ,     lowerCAmelCase__     ,     lowerCAmelCase__     ,     'down'   )   )

				# up_blocks
				for up_block_idx, up_block in enumerate(model.up_blocks   ):
								diffusers_checkpoint.update(block_to_diffusers_checkpoint(lowerCAmelCase__     ,     lowerCAmelCase__     ,     lowerCAmelCase__     ,     'up'   )   )

				# post-processing
				diffusers_checkpoint.update(
				    {
				        'conv_out.weight': checkpoint['inner_model.proj_out.weight'],
				        'conv_out.bias': checkpoint['inner_model.proj_out.bias'],
				    }   )

				return diffusers_checkpoint



def        _lowerCamelCase(      lowerCAmelCase__					:		List[Any]   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						List[str]    				= original_config['input_channels'] + original_config['unet_cond_dim']
				SCREAMING_SNAKE_CASE_ :						Tuple    				= original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

				SCREAMING_SNAKE_CASE_ :						Tuple    				= original_config['channels']

				assert (
				    len(set(original_config['depths']   )   ) == 1
				), "UNet2DConditionModel currently do not support blocks with different number of layers"
				SCREAMING_SNAKE_CASE_ :						int    				= original_config['depths'][0]

				SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= original_config['mapping_cond_dim']
				SCREAMING_SNAKE_CASE_ :						str    				= original_config['cross_cond_dim']

				SCREAMING_SNAKE_CASE_ :						List[Any]    				= []
				SCREAMING_SNAKE_CASE_ :						Dict    				= []
				for s, c in zip(original_config['self_attn_depths']     ,     original_config['cross_attn_depths']   ):
								if s:
												SCREAMING_SNAKE_CASE_ :						List[str]    				= 'self'
												SCREAMING_SNAKE_CASE_ :						Dict    				= 'cross' if c else None
								elif c:
												SCREAMING_SNAKE_CASE_ :						List[str]    				= 'cross'
												SCREAMING_SNAKE_CASE_ :						List[str]    				= None
								else:
												SCREAMING_SNAKE_CASE_ :						Any    				= None
												SCREAMING_SNAKE_CASE_ :						Dict    				= None
								attna_types.append(lowerCAmelCase__   )
								attna_types.append(lowerCAmelCase__   )

				SCREAMING_SNAKE_CASE_ :						List[str]    				= UNetaDConditionModel(
				    in_channels=lowerCAmelCase__     ,     out_channels=lowerCAmelCase__     ,     down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D')     ,     mid_block_type=lowerCAmelCase__     ,     up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')     ,     block_out_channels=lowerCAmelCase__     ,     layers_per_block=lowerCAmelCase__     ,     act_fn='gelu'     ,     norm_num_groups=lowerCAmelCase__     ,     cross_attention_dim=lowerCAmelCase__     ,     attention_head_dim=64     ,     time_cond_proj_dim=lowerCAmelCase__     ,     resnet_time_scale_shift='scale_shift'     ,     time_embedding_type='fourier'     ,     timestep_post_act='gelu'     ,     conv_in_kernel=1     ,     conv_out_kernel=1     ,     )

				return unet



def        _lowerCamelCase(      lowerCAmelCase__					:		Optional[Any]   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Tuple    				= torch.device('cuda' if torch.cuda.is_available() else 'cpu'   )

				SCREAMING_SNAKE_CASE_ :						str    				= huggingface_hub.hf_hub_download(lowerCAmelCase__     ,     'config_laion_text_cond_latent_upscaler_2.json'   )
				SCREAMING_SNAKE_CASE_ :						int    				= huggingface_hub.hf_hub_download(
				    lowerCAmelCase__     ,     'laion_text_cond_latent_upscaler_2_1_00470000_slim.pth'   )
				print(F'''loading original model configuration from {orig_config_path}'''   )
				print(F'''loading original model checkpoint from {orig_weights_path}'''   )

				print('converting to diffusers unet'   )
				SCREAMING_SNAKE_CASE_ :						List[str]    				= K.config.load_config(open(lowerCAmelCase__   )   )['model']
				SCREAMING_SNAKE_CASE_ :						Any    				= unet_model_from_original_config(lowerCAmelCase__   )

				SCREAMING_SNAKE_CASE_ :						List[str]    				= torch.load(lowerCAmelCase__     ,     map_location=lowerCAmelCase__   )['model_ema']
				SCREAMING_SNAKE_CASE_ :						List[Any]    				= unet_to_diffusers_checkpoint(lowerCAmelCase__     ,     lowerCAmelCase__   )

				model.load_state_dict(lowerCAmelCase__     ,     strict=lowerCAmelCase__   )
				model.save_pretrained(args.dump_path   )
				print(F'''saving converted unet model in {args.dump_path}'''   )


if __name__ == "__main__":
				A										=      argparse.ArgumentParser()

				parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
				A										=      parser.parse_args()

				main(args)







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 				__a				(	__A			,						unittest.TestCase    ):





		'''simple docstring'''



		UpperCAmelCase__   :					Optional[Any]					     =    HerbertTokenizer
		UpperCAmelCase__   :					List[Any]					     =    HerbertTokenizerFast
		UpperCAmelCase__   :					Optional[Any]					     =    True


		def 	__snake_case      (    self							):
						super().setUp()

						# Use a simpler test file without japanese/chinese characters
						with open(F'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''			,     encoding='utf-8'							) as f_data:
										SCREAMING_SNAKE_CASE_ :						Dict    				= f_data.read().replace('\n\n'			,     '\n'							).strip()

						SCREAMING_SNAKE_CASE_ :						str    				= [
						    '<s>',
						    '</s>',
						    'l',
						    'o',
						    'w',
						    'e',
						    'r',
						    's',
						    't',
						    'i',
						    'd',
						    'n',
						    'w</w>',
						    'r</w>',
						    't</w>',
						    'lo',
						    'low',
						    'er</w>',
						    'low</w>',
						    'lowest</w>',
						    'newer</w>',
						    'wider</w>',
						    ',</w>',
						    '<unk>',
						]
						SCREAMING_SNAKE_CASE_ :						str    				= dict(zip(UpperCamelCase__			,     range(len(UpperCamelCase__							)							)							)							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

						SCREAMING_SNAKE_CASE_ :						int    				= os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['vocab_file']							)
						SCREAMING_SNAKE_CASE_ :						int    				= os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['merges_file']							)
						with open(self.vocab_file			,     'w'							) as fp:
										fp.write(json.dumps(UpperCamelCase__							)							)
						with open(self.merges_file			,     'w'							) as fp:
										fp.write('\n'.join(UpperCamelCase__							)							)


		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= 'lower newer'
						SCREAMING_SNAKE_CASE_ :						int    				= 'lower newer'
						return input_text, output_text


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= self.tokenizer_class(vocab_file=self.vocab_file			,     merges_file=self.merges_file							)

						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= 'lower'
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= ['low', 'er</w>']
						SCREAMING_SNAKE_CASE_ :						int    				= tokenizer.tokenize(UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= tokens + ['<unk>']
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= [16, 17, 23]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__							)			,     UpperCamelCase__							)


		def 	__snake_case      (    self							):
						if not self.test_rust_tokenizer:
										return

						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.get_tokenizer()
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.get_rust_tokenizer()

						SCREAMING_SNAKE_CASE_ :						List[str]    				= 'lower,newer'

						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= tokenizer.tokenize(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Tuple    				= rust_tokenizer.tokenize(UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= tokenizer.encode(UpperCamelCase__			,     add_special_tokens=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Any    				= rust_tokenizer.encode(UpperCamelCase__			,     add_special_tokens=UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE_ :						Dict    				= tokenizer.encode(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= rust_tokenizer.encode(UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)






		@slow
		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= self.tokenizer_class.from_pretrained('allegro/herbert-base-cased'							)

						SCREAMING_SNAKE_CASE_ :						List[str]    				= tokenizer.encode('konstruowanie sekwencji'			,     add_special_tokens=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= tokenizer.encode('konstruowanie wielu sekwencji'			,     add_special_tokens=UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Any    				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Dict    				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase__			,     UpperCamelCase__							)

						assert encoded_sentence == [0] + text + [2]
						assert encoded_pair == [0] + text + [2] + text_a + [2]