






'''simple docstring'''
import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


__lowerCAmelCase							="""pcuenq/k-upscaler"""

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  *, _UpperCAmelCase					,  _UpperCAmelCase ):






					"""simple docstring"""







					a_									=   {
					    # 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	(   _UpperCAmelCase					,  *, _UpperCAmelCase					,  _UpperCAmelCase ):






					"""simple docstring"""







					a_									=   checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3					,  dim=0 )
					a_									=   checkpoint[F'''{attention_prefix}.qkv_proj.bias'''].chunk(3					,  dim=0 )
					a_									=   {
					    # 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	(   _UpperCAmelCase					,  *, _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase ):






					"""simple docstring"""







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

					a_									=   {
					    # 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	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase ):






					"""simple docstring"""







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

					a_									=   {}

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

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

					if hasattr(_UpperCAmelCase					,  'attentions' ):
										for attention_idx, attention in enumerate(block.attentions ):
															a_									=   F'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
															a_									=   n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
															a_									=   F'''{block_prefix}.{idx}'''
															a_									=   F'''{block_prefix}.{idx }'''
															a_									=   1 if not attention.add_self_attention else 2
															a_									=   (
															    n * attention_idx + cross_attention_index
															    if block_type == "up"
															    else n * attention_idx + cross_attention_index + 1
															)
															a_									=   F'''{block_prefix}.{idx }'''

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

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

					return diffusers_checkpoint

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase ):






					"""simple docstring"""







					a_									=   {}

					# 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(_UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  'down' ) )

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






					"""simple docstring"""







					a_									=   original_config["input_channels"] + original_config["unet_cond_dim"]
					a_									=   original_config["input_channels"] + (1 if original_config["has_variance"] else 0)

					a_									=   original_config["channels"]

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

					a_									=   original_config["mapping_cond_dim"]
					a_									=   original_config["cross_cond_dim"]

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

					a_									=   UNetaDConditionModel(
					    in_channels=_UpperCAmelCase					,  out_channels=_UpperCAmelCase					,  down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D')					,  mid_block_type=_UpperCAmelCase					,  up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')					,  block_out_channels=_UpperCAmelCase					,  layers_per_block=_UpperCAmelCase					,  act_fn='gelu'					,  norm_num_groups=_UpperCAmelCase					,  cross_attention_dim=_UpperCAmelCase					,  attention_head_dim=6_4					,  time_cond_proj_dim=_UpperCAmelCase					,  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	(   _UpperCAmelCase ):






					"""simple docstring"""







					a_									=   torch.device('cuda' if torch.cuda.is_available() else 'cpu' )

					a_									=   huggingface_hub.hf_hub_download(_UpperCAmelCase					,  'config_laion_text_cond_latent_upscaler_2.json' )
					a_									=   huggingface_hub.hf_hub_download(
					    _UpperCAmelCase					,  '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' )
					a_									=   K.config.load_config(open(_UpperCAmelCase ) )["model"]
					a_									=   unet_model_from_original_config(_UpperCAmelCase )

					a_									=   torch.load(_UpperCAmelCase					,  map_location=_UpperCAmelCase )["model_ema"]
					a_									=   unet_to_diffusers_checkpoint(_UpperCAmelCase					,  _UpperCAmelCase )

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


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

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

						main(args)









'''simple docstring'''
import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch


class    _snake_case   (				unittest.TestCase					):

					"""simple docstring"""





					@patch('sys.argv'   , ['fakeprogrampath', 'env']     )
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										# test transformers-cli env
										import transformers.commands.transformers_cli

										with CaptureStd() as cs:
															transformers.commands.transformers_cli.main()
										self.assertIn('Python version'   , cs.out     )
										self.assertIn('Platform'   , cs.out     )
										self.assertIn('Using distributed or parallel set-up in script?'   , cs.out     )




					@is_pt_tf_cross_test
					@patch(
					    'sys.argv'   , ['fakeprogrampath', 'pt-to-tf', '--model-name', 'hf-internal-testing/tiny-random-gptj', '--no-pr']     )
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[str]:
										import transformers.commands.transformers_cli

										shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'   , ignore_errors=UpperCAmelCase__     )  # cleans potential past runs
										transformers.commands.transformers_cli.main()

										# The original repo has no TF weights -- if they exist, they were created by the CLI
										self.assertTrue(os.path.exists('/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5'     )     )




					@require_torch
					@patch('sys.argv'   , ['fakeprogrampath', 'download', 'hf-internal-testing/tiny-random-gptj', '--cache-dir', '/tmp']     )
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[Any]:
										import transformers.commands.transformers_cli

										# # remove any previously downloaded model to start clean
										shutil.rmtree('/tmp/models--hf-internal-testing--tiny-random-gptj'   , ignore_errors=UpperCAmelCase__     )

										# run the command
										transformers.commands.transformers_cli.main()

										# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/blobs'     )     )
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/refs'     )     )
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots'     )     )




					@require_torch
					@patch(
					    'sys.argv'   , [
					        'fakeprogrampath',
					        'download',
					        'hf-internal-testing/test_dynamic_model_with_tokenizer',
					        '--trust-remote-code',
					        '--cache-dir',
					        '/tmp',
					    ]   , )
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										import transformers.commands.transformers_cli

										# # remove any previously downloaded model to start clean
										shutil.rmtree('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer'   , ignore_errors=UpperCAmelCase__     )

										# run the command
										transformers.commands.transformers_cli.main()

										# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs'     )     )
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs'     )     )
										self.assertTrue(
										    os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots'     )     )

