"""simple docstring"""


import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
	    RwkvForCausalLM,
	    RwkvModel,
	)
	from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
	a							=False






class    __UpperCAmelCase						:

				def __init__(      self	, _lowerCamelCase	, _lowerCamelCase=14	, _lowerCamelCase=7	, _lowerCamelCase=True	, _lowerCamelCase=False	, _lowerCamelCase=True	, _lowerCamelCase=True	, _lowerCamelCase=True	, _lowerCamelCase=99	, _lowerCamelCase=32	, _lowerCamelCase=5	, _lowerCamelCase=37	, _lowerCamelCase="gelu"	, _lowerCamelCase=0.1	, _lowerCamelCase=0.1	, _lowerCamelCase=512	, _lowerCamelCase=16	, _lowerCamelCase=2	, _lowerCamelCase=3	, _lowerCamelCase=4	, _lowerCamelCase=None	, ):
										lowerCamelCase__					=parent
										lowerCamelCase__					=batch_size
										lowerCamelCase__					=seq_length
										lowerCamelCase__					=is_training
										lowerCamelCase__					=use_token_type_ids
										lowerCamelCase__					=use_input_mask
										lowerCamelCase__					=use_labels
										lowerCamelCase__					=use_mc_token_ids
										lowerCamelCase__					=vocab_size
										lowerCamelCase__					=hidden_size
										lowerCamelCase__					=num_hidden_layers
										lowerCamelCase__					=intermediate_size
										lowerCamelCase__					=hidden_act
										lowerCamelCase__					=hidden_dropout_prob
										lowerCamelCase__					=attention_probs_dropout_prob
										lowerCamelCase__					=max_position_embeddings
										lowerCamelCase__					=type_vocab_size
										lowerCamelCase__					=type_sequence_label_size
										lowerCamelCase__					=num_labels
										lowerCamelCase__					=num_choices
										lowerCamelCase__					=scope
										lowerCamelCase__					=vocab_size - 1
										lowerCamelCase__					=vocab_size - 1
										lowerCamelCase__					=vocab_size - 1

				def 		_a	(      self      ):
										return RwkvConfig.from_pretrained("sgugger/rwkv-4-pile-7b"      )

				def 		_a	(      self	, _lowerCamelCase=False	, _lowerCamelCase=False	, _lowerCamelCase=False      ):
										lowerCamelCase__					=ids_tensor([self.batch_size, self.seq_length]	, self.vocab_size      )

										lowerCamelCase__					=None
										if self.use_input_mask:
																lowerCamelCase__					=random_attention_mask([self.batch_size, self.seq_length]      )

										lowerCamelCase__					=None
										if self.use_token_type_ids:
																lowerCamelCase__					=ids_tensor([self.batch_size, self.seq_length]	, self.type_vocab_size      )

										lowerCamelCase__					=None
										if self.use_mc_token_ids:
																lowerCamelCase__					=ids_tensor([self.batch_size, self.num_choices]	, self.seq_length      )

										lowerCamelCase__					=None
										lowerCamelCase__					=None
										lowerCamelCase__					=None
										if self.use_labels:
																lowerCamelCase__					=ids_tensor([self.batch_size]	, self.type_sequence_label_size      )
																lowerCamelCase__					=ids_tensor([self.batch_size, self.seq_length]	, self.num_labels      )
																lowerCamelCase__					=ids_tensor([self.batch_size]	, self.num_choices      )

										lowerCamelCase__					=self.get_config(
										    gradient_checkpointing=_lowerCamelCase	, scale_attn_by_inverse_layer_idx=_lowerCamelCase	, reorder_and_upcast_attn=_lowerCamelCase	, )

										return (
										    config,
										    input_ids,
										    input_mask,
										    None,
										    token_type_ids,
										    mc_token_ids,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										)

				def 		_a	(      self	, _lowerCamelCase=False	, _lowerCamelCase=False	, _lowerCamelCase=False      ):
										return RwkvConfig(
										    vocab_size=self.vocab_size	, hidden_size=self.hidden_size	, num_hidden_layers=self.num_hidden_layers	, intermediate_size=self.intermediate_size	, activation_function=self.hidden_act	, resid_pdrop=self.hidden_dropout_prob	, attn_pdrop=self.attention_probs_dropout_prob	, n_positions=self.max_position_embeddings	, type_vocab_size=self.type_vocab_size	, use_cache=_lowerCamelCase	, bos_token_id=self.bos_token_id	, eos_token_id=self.eos_token_id	, pad_token_id=self.pad_token_id	, gradient_checkpointing=_lowerCamelCase	, scale_attn_by_inverse_layer_idx=_lowerCamelCase	, reorder_and_upcast_attn=_lowerCamelCase	, )

				def 		_a	(      self      ):
										lowerCamelCase__					=self.get_config()
										lowerCamelCase__					=300
										return config

				def 		_a	(      self      ):
										(
										    (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      
										)					=self.prepare_config_and_inputs()

										lowerCamelCase__					=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
										lowerCamelCase__					=ids_tensor([self.batch_size, self.seq_length]	, vocab_size=2      )

										return (
										    config,
										    input_ids,
										    input_mask,
										    head_mask,
										    token_type_ids,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, *_lowerCamelCase      ):
										lowerCamelCase__					=True
										lowerCamelCase__					=RwkvModel(config=_lowerCamelCase      )
										model.to(_lowerCamelCase      )
										model.eval()

										lowerCamelCase__					=model(_lowerCamelCase      )

										self.parent.assertEqual(result.last_hidden_state.shape	, (self.batch_size, self.seq_length, self.hidden_size)      )
										self.parent.assertEqual(len(result.hidden_states      )	, config.num_hidden_layers + 1      )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, *_lowerCamelCase      ):
										lowerCamelCase__					=RwkvForCausalLM(_lowerCamelCase      )
										model.to(_lowerCamelCase      )
										model.eval()

										lowerCamelCase__					=model(_lowerCamelCase	, labels=_lowerCamelCase      )
										self.parent.assertEqual(result.loss.shape	, ()      )
										self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.vocab_size)      )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, *_lowerCamelCase      ):
										lowerCamelCase__					=RwkvModel(config=_lowerCamelCase      )
										model.to(_lowerCamelCase      )
										model.eval()

										lowerCamelCase__					=model(_lowerCamelCase      )
										lowerCamelCase__					=outputs.last_hidden_state

										lowerCamelCase__					=model(input_ids[:, :2]      )
										lowerCamelCase__					=outputs.last_hidden_state

										# Using the state computed on the first inputs, we will get the same output
										lowerCamelCase__					=model(input_ids[:, 2:]	, state=outputs.state      )
										lowerCamelCase__					=outputs.last_hidden_state

										self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]	, dim=1      )	, _lowerCamelCase	, atol=1E-5      )      )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, *_lowerCamelCase	, _lowerCamelCase=False      ):
										lowerCamelCase__					=RwkvForCausalLM(_lowerCamelCase      )
										model.to(_lowerCamelCase      )
										if gradient_checkpointing:
																model.gradient_checkpointing_enable()

										lowerCamelCase__					=model(_lowerCamelCase	, labels=_lowerCamelCase      )
										self.parent.assertEqual(result.loss.shape	, ()      )
										self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.vocab_size)      )
										result.loss.backward()






				def 		_a	(      self      ):
										lowerCamelCase__					=self.prepare_config_and_inputs()

										(
										    (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      
										)					=config_and_inputs

										lowerCamelCase__					={"input_ids": input_ids}

										return config, inputs_dict






@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0      ,      reason='''See https://github.com/huggingface/transformers/pull/24204'''	)
@require_torch
class    __UpperCAmelCase						(	__lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase      ,      unittest.TestCase	):
				A__      :   Tuple  							=       (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
				A__      :   int  							=       (
				    {'''feature-extraction''': RwkvModel, '''text-generation''': RwkvForCausalLM} if is_torch_available() else {}
				)
				# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
				A__      :   Union[str, Any]  							=       False
				A__      :   List[Any]  							=       False
				A__      :   int  							=       False
				A__      :   Any  							=       False
				A__      :   Optional[Any]  							=       False  # Rwkv does not support head masking

				def 		_a	(      self      ):
										lowerCamelCase__					=RwkvModelTester(self      )
										lowerCamelCase__					=ConfigTester(
										    self	, config_class=_lowerCamelCase	, n_embd=37	, common_properties=["hidden_size", "num_hidden_layers"]      )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase=None      ):
										if isinstance(_lowerCamelCase	, torch.Tensor      ):
																lowerCamelCase__					,      lowerCamelCase__					=member.max().item(), member.min().item()
										elif isinstance(_lowerCamelCase	, _lowerCamelCase      ) or isinstance(_lowerCamelCase	, _lowerCamelCase      ):
																lowerCamelCase__					,      lowerCamelCase__					=max(_lowerCamelCase      ), min(_lowerCamelCase      )

										if not isinstance(_lowerCamelCase	, _lowerCamelCase      ):
																raise TypeError("container should be a list or tuple"      )
										elif len(_lowerCamelCase      ) != 2:
																raise ValueError("container should have 2 elements"      )

										lowerCamelCase__					,      lowerCamelCase__					=container

										lowerCamelCase__					=(min_value >= expected_min) and (max_value <= expected_max)

										if not is_inside_interval:
																lowerCamelCase__					="%s not found in %s" % (safe_repr(_lowerCamelCase      ), safe_repr(_lowerCamelCase      ))
																self.fail(self._formatMessage(_lowerCamelCase	, _lowerCamelCase      )      )

				def 		_a	(      self      ):
										self.config_tester.run_common_tests()

				def 		_a	(      self      ):
										lowerCamelCase__					=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_rwkv_model(*_lowerCamelCase      )

				def 		_a	(      self      ):
										lowerCamelCase__					=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_causl_lm(*_lowerCamelCase      )

				def 		_a	(      self      ):
										lowerCamelCase__					=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_state_equivalency(*_lowerCamelCase      )

				def 		_a	(      self      ):
										lowerCamelCase__					,      lowerCamelCase__					=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																lowerCamelCase__					=model_class(config=_lowerCamelCase      )
																for name, param in model.named_parameters():
																						if "time_decay" in name:
																												if param.requires_grad:
																																		self.assertTrue(param.data.max().item() == 3.0      )
																																		self.assertTrue(param.data.min().item() == -5.0      )
																						elif "time_first" in name:
																												if param.requires_grad:
																																		# check if it's a ones like
																																		self.assertTrue(torch.allclose(param.data	, torch.ones_like(param.data      )	, atol=1E-5	, rtol=1E-5      )      )
																						elif any(x in name for x in ["time_mix_key", "time_mix_receptance"]      ):
																												if param.requires_grad:
																																		self.assertInterval(
																																		    param.data	, [0.0, 1.0]	, msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''	, )
																						elif "time_mix_value" in name:
																												if param.requires_grad:
																																		self.assertInterval(
																																		    param.data	, [0.0, 1.3]	, msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''	, )

				def 		_a	(      self      ):
										lowerCamelCase__					,      lowerCamelCase__					=self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase__					=True

										lowerCamelCase__					=getattr(self.model_tester	, "seq_length"	, _lowerCamelCase      )

										for model_class in self.all_model_classes:
																lowerCamelCase__					=True
																lowerCamelCase__					=False
																lowerCamelCase__					=True
																lowerCamelCase__					=model_class(_lowerCamelCase      )
																model.to(_lowerCamelCase      )
																model.eval()

																lowerCamelCase__					=self._prepare_for_class(_lowerCamelCase	, _lowerCamelCase      )
																lowerCamelCase__					=inputs["input_ids"].shape[0]
																with torch.no_grad():
																						lowerCamelCase__					=model(**_lowerCamelCase      )
																lowerCamelCase__					=outputs.attentions
																self.assertEqual(len(_lowerCamelCase      )	, self.model_tester.num_hidden_layers      )

																# check that output_attentions also work using config
																del inputs_dict["output_attentions"]
																lowerCamelCase__					=True
																lowerCamelCase__					=model_class(_lowerCamelCase      )
																model.to(_lowerCamelCase      )
																model.eval()

																lowerCamelCase__					=self._prepare_for_class(_lowerCamelCase	, _lowerCamelCase      )
																lowerCamelCase__					=inputs["input_ids"].shape[0]
																with torch.no_grad():
																						lowerCamelCase__					=model(**_lowerCamelCase      )
																lowerCamelCase__					=outputs.attentions
																self.assertEqual(len(_lowerCamelCase      )	, self.model_tester.num_hidden_layers      )

																self.assertListEqual(
																    list(attentions[0].shape[-3:]      )	, [batch_size, seq_len, config.hidden_size]	, )
																lowerCamelCase__					=len(_lowerCamelCase      )

																# Check attention is always last and order is fine
																lowerCamelCase__					=True
																lowerCamelCase__					=True
																lowerCamelCase__					=model_class(_lowerCamelCase      )
																model.to(_lowerCamelCase      )
																model.eval()

																lowerCamelCase__					=self._prepare_for_class(_lowerCamelCase	, _lowerCamelCase      )
																lowerCamelCase__					=inputs["input_ids"].shape[0]
																with torch.no_grad():
																						lowerCamelCase__					=model(**_lowerCamelCase      )

																lowerCamelCase__					=1
																self.assertEqual(out_len + added_hidden_states	, len(_lowerCamelCase      )      )

																lowerCamelCase__					=outputs.attentions

																self.assertEqual(len(_lowerCamelCase      )	, self.model_tester.num_hidden_layers      )
																self.assertListEqual(
																    list(self_attentions[0].shape[-3:]      )	, [batch_size, seq_len, config.hidden_size]	, )






				@slow
				def 		_a	(      self      ):
										for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																lowerCamelCase__					=RwkvModel.from_pretrained(_lowerCamelCase      )
																self.assertIsNotNone(_lowerCamelCase      )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0      ,      reason='''See https://github.com/huggingface/transformers/pull/24204'''	)
@slow
class    __UpperCAmelCase						(	unittest.TestCase	):

				def 		_a	(      self      ):
										lowerCamelCase__					="RWKV/rwkv-4-169m-pile"
										lowerCamelCase__					=AutoTokenizer.from_pretrained(self.model_id      )

				def 		_a	(      self      ):
										lowerCamelCase__					="Hello my name is Jasmine and I am a newbie to the"
										lowerCamelCase__					=RwkvForCausalLM.from_pretrained(self.model_id      ).to(_lowerCamelCase      )

										lowerCamelCase__					=self.tokenizer("Hello my name is"	, return_tensors="pt"      ).input_ids.to(_lowerCamelCase      )
										lowerCamelCase__					=model.generate(_lowerCamelCase	, max_new_tokens=10      )
										lowerCamelCase__					=self.tokenizer.decode(output[0].tolist()      )

										self.assertEqual(_lowerCamelCase	, _lowerCamelCase      )






				def 		_a	(      self      ):
										lowerCamelCase__					="Hello my name is Jasmine and I am a newbie to the"

										lowerCamelCase__					=self.tokenizer("Hello my name is"	, return_tensors="pt"      ).input_ids.to(_lowerCamelCase      )
										lowerCamelCase__					=RwkvForCausalLM.from_pretrained(self.model_id	, torch_dtype=torch.bfloataa      ).to(_lowerCamelCase      )

										lowerCamelCase__					=model.generate(_lowerCamelCase	, max_new_tokens=10      )
										lowerCamelCase__					=self.tokenizer.decode(output[0].tolist()      )

										self.assertEqual(_lowerCamelCase	, _lowerCamelCase      )



"""simple docstring"""


import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


a							='pcuenq/k-upscaler'





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Dict:




						'''simple docstring'''



						lowerCamelCase__					={
						    # 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   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase				)							->					int:




						'''simple docstring'''



						lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3   ,    dim=0				)
						lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.qkv_proj.bias'''].chunk(3   ,    dim=0				)
						lowerCamelCase__					={
						    # 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   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					str:




						'''simple docstring'''



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

						lowerCamelCase__					={
						    # 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   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[int]:




						'''simple docstring'''



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

						lowerCamelCase__					={}

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




						'''simple docstring'''



						lowerCamelCase__					={}

						# 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				)							->					Optional[int]:




						'''simple docstring'''



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

						lowerCamelCase__					=original_config["channels"]

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

						lowerCamelCase__					=original_config["mapping_cond_dim"]
						lowerCamelCase__					=original_config["cross_cond_dim"]

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

						lowerCamelCase__					=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				)							->					int:




						'''simple docstring'''



						lowerCamelCase__					=torch.device("cuda" if torch.cuda.is_available() else "cpu"				)

						lowerCamelCase__					=huggingface_hub.hf_hub_download(__lowerCAmelCase   ,    "config_laion_text_cond_latent_upscaler_2.json"				)
						lowerCamelCase__					=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"				)
						lowerCamelCase__					=K.config.load_config(open(__lowerCAmelCase				)				)["model"]
						lowerCamelCase__					=unet_model_from_original_config(__lowerCAmelCase				)

						lowerCamelCase__					=torch.load(__lowerCAmelCase   ,    map_location=__lowerCAmelCase				)["model_ema"]
						lowerCamelCase__					=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)


