






import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


A__     :      Dict             =			'pcuenq/k-upscaler'






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       *, lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      {
				    # 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   a      (						lowerCamelCase_		,       *, lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__			,						lowercase__			,						lowercase__       			=      checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3		,       dim=0    )
				lowercase__			,						lowercase__			,						lowercase__       			=      checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3		,       dim=0    )
				lowercase__       			=      {
				    # 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   a      (						lowerCamelCase_		,       *, lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__			,						lowercase__       			=      checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2		,       dim=0    )
				lowercase__			,						lowercase__       			=      checkpoint[F"""{attention_prefix}.kv_proj.bias"""].chunk(2		,       dim=0    )

				lowercase__       			=      {
				    # 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   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      '''inner_model.u_net.u_blocks''' if block_type == '''up''' else '''inner_model.u_net.d_blocks'''
				lowercase__       			=      F"""{block_prefix}.{block_idx}"""

				lowercase__       			=      {}

				if not hasattr(lowerCamelCase_		,       '''attentions'''    ):
								lowercase__       			=      1  # resnet only
				elif not block.attentions[0].add_self_attention:
								lowercase__       			=      2  # resnet -> cross-attention
				else:
								lowercase__       			=      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}"
								lowercase__       			=      F"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
								lowercase__       			=      n * resnet_idx if block_type == '''up''' else n * resnet_idx + 1
								lowercase__       			=      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    ):
												lowercase__       			=      F"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
												lowercase__       			=      n * attention_idx + 1 if block_type == '''up''' else n * attention_idx + 2
												lowercase__       			=      F"""{block_prefix}.{idx}"""
												lowercase__       			=      F"""{block_prefix}.{idx }"""
												lowercase__       			=      1 if not attention.add_self_attention else 2
												lowercase__       			=      (
												    n * attention_idx + cross_attention_index
												    if block_type == '''up'''
												    else n * attention_idx + cross_attention_index + 1
												)
												lowercase__       			=      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   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      {}

				# 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   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      original_config['''input_channels'''] + original_config['''unet_cond_dim''']
				lowercase__       			=      original_config['''input_channels'''] + (1 if original_config['''has_variance'''] else 0)

				lowercase__       			=      original_config['''channels''']

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

				lowercase__       			=      original_config['''mapping_cond_dim''']
				lowercase__       			=      original_config['''cross_cond_dim''']

				lowercase__       			=      []
				lowercase__       			=      []
				for s, c in zip(original_config['''self_attn_depths''']		,       original_config['''cross_attn_depths''']    ):
								if s:
												lowercase__       			=      '''self'''
												lowercase__       			=      '''cross''' if c else None
								elif c:
												lowercase__       			=      '''cross'''
												lowercase__       			=      None
								else:
												lowercase__       			=      None
												lowercase__       			=      None
								attna_types.append(lowerCamelCase_    )
								attna_types.append(lowerCamelCase_    )

				lowercase__       			=      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   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''    )

				lowercase__       			=      huggingface_hub.hf_hub_download(lowerCamelCase_		,       '''config_laion_text_cond_latent_upscaler_2.json'''    )
				lowercase__       			=      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'''    )
				lowercase__       			=      K.config.load_config(open(lowerCamelCase_    )    )['''model''']
				lowercase__       			=      unet_model_from_original_config(lowerCamelCase_    )

				lowercase__       			=      torch.load(lowerCamelCase_		,       map_location=lowerCamelCase_    )['''model_ema''']
				lowercase__       			=      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__     :      List[str]             =			argparse.ArgumentParser()

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

							main(args)












import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil






@pytest.fixture
def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      tmp_path / '''text.txt'''
				lowercase__       			=      textwrap.dedent(
				    '''\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        '''    )
				with open(lowerCamelCase_		,       '''w'''		,       encoding='''utf-8'''    ) as f:
								f.write(lowerCamelCase_    )
				return str(lowerCamelCase_    )






@pytest.fixture
def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      tmp_path / '''text_with_image.txt'''
				with open(lowerCamelCase_		,       '''w'''		,       encoding='''utf-8'''    ) as f:
								f.write(lowerCamelCase_    )
				return str(lowerCamelCase_    )






@pytest.mark.parametrize('''keep_linebreaks'''		,       [True, False]    )
def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				with open(lowerCamelCase_		,       encoding='''utf-8'''    ) as f:
								lowercase__       			=      f.read().splitlines(keepends=lowerCamelCase_    )
				lowercase__       			=      Text(keep_linebreaks=lowerCamelCase_		,       encoding='''utf-8'''    )
				lowercase__       			=      text._generate_tables([[text_file]]    )
				lowercase__       			=      pa.concat_tables([table for _, table in generator]    ).to_pydict()['''text''']
				assert generated_content == expected_content






@require_pil
def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				with open(lowerCamelCase_		,       encoding='''utf-8'''    ) as f:
								lowercase__       			=      f.read().splitlines()[0]
				lowercase__       			=      Text(encoding='''utf-8'''		,       features=Features({'''image''': Image()}    )    )
				lowercase__       			=      text._generate_tables([[text_file_with_image]]    )
				lowercase__       			=      pa.concat_tables([table for _, table in generator]    )
				assert pa_table.schema.field('''image'''    ).type == Image()()
				lowercase__       			=      pa_table.to_pydict()['''image''']
				assert generated_content == [{"path": image_file, "bytes": None}]






@pytest.mark.parametrize('''sample_by'''		,       ['''line''', '''paragraph''', '''document''']    )
def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				with open(lowerCamelCase_		,       encoding='''utf-8'''    ) as f:
								lowercase__       			=      f.read()
				if sample_by == "line":
								lowercase__       			=      expected_content.splitlines()
				elif sample_by == "paragraph":
								lowercase__       			=      expected_content.split('''\n\n'''    )
				elif sample_by == "document":
								lowercase__       			=      [expected_content]
				lowercase__       			=      Text(sample_by=lowerCamelCase_		,       encoding='''utf-8'''		,       chunksize=100    )
				lowercase__       			=      text._generate_tables([[text_file]]    )
				lowercase__       			=      pa.concat_tables([table for _, table in generator]    ).to_pydict()['''text''']
				assert generated_content == expected_content




