






"""simple docstring"""







import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


__a							:						Optional[Any]   							=			'pcuenq/k-upscaler'





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  *, lowerCamelCase_			,  lowerCamelCase_):
					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 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  *, lowerCamelCase_			,  lowerCamelCase_):
					a__							,a__							,a__ 							=	checkpoint[f'{attention_prefix}.qkv_proj.weight'].chunk(3			,  dim=0)
					a__							,a__							,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 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  *, lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):
					a__							,a__ 							=	checkpoint[f'{attention_prefix}.kv_proj.weight'].chunk(2			,  dim=0)
					a__							,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 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):
					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(lowerCamelCase_			,  '''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(
										        lowerCamelCase_			,  lowerCamelCase_			,  diffusers_resnet_prefix=lowerCamelCase_			,  resnet_prefix=lowerCamelCase_))

					if hasattr(lowerCamelCase_			,  '''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(
															        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 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_):
					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(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 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_):
					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(lowerCamelCase_)
										attna_types.append(lowerCamelCase_)

					a__ 							=	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 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_):
					a__ 							=	torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''')

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

					a__ 							=	torch.load(lowerCamelCase_			,  map_location=lowerCamelCase_)['''model_ema''']
					a__ 							=	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[Any]   							=			argparse.ArgumentParser()

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

	main(args)










"""simple docstring"""







import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


__a							:						Optional[int]   							=			{0: 'null', 1: 'short', 2: 'long', 3: 'yes', 4: 'no'}
__a							:						Dict   							=			set(''.join(['‘', '’', '´', '`', '.', ',', '-', '"']))





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_=0			,  lowerCamelCase_=None):
					return [" ".join(x.split(''' ''')[begin:end]) for x in answers if len(x.split(''' ''')) > 1]





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_=False):
					if make_sub_answers:
										# if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
										# *e.g.* if the correct answer contains a prefix such as "the", or "a"
										a__ 							=	(
										    given_answers + get_sub_answers(lowerCamelCase_			,  begin=1) + get_sub_answers(lowerCamelCase_			,  end=-1)
										)
					a__ 							=	[]
					for answer in given_answers:
										a__ 							=	answer.replace('''_'''			,  ''' ''').lower()
										a__ 							=	''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias)
										answers.append(''' '''.join(alias.split()).strip())
					return set(lowerCamelCase_)





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_=1			,  lowerCamelCase_=100):
					a__							,a__ 							=	jax.lax.top_k(lowerCamelCase_			,  lowerCamelCase_)
					a__							,a__ 							=	jax.lax.top_k(lowerCamelCase_			,  lowerCamelCase_)

					a__ 							=	best_end_idx[:, None] - best_start_idx[None, :]
					a__ 							=	jnp.logical_or(widths < 0			,  widths > max_size)
					a__ 							=	(best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
					a__ 							=	jnp.argmax(lowerCamelCase_).item()

					return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_):
					a__ 							=	sample['''question''']['''text''']
					a__ 							=	sample['''document''']['''tokens''']['''token''']
					a__ 							=	sample['''document''']['''tokens''']['''is_html''']
					a__ 							=	sample['''annotations''']['''long_answer''']
					a__ 							=	sample['''annotations''']['''short_answers''']

					a__ 							=	''' '''.join([context[i] for i in range(len(lowerCamelCase_)) if not is_html[i]])

					# 0 - No ; 1 - Yes
					for answer in sample["annotations"]["yes_no_answer"]:
										if answer == 0 or answer == 1:
															return {
															    "question": question,
															    "context": context_string,
															    "short": [],
															    "long": [],
															    "category": "no" if answer == 0 else "yes",
															}

					a__ 							=	[]
					for s in short_answers:
										short_targets.extend(s['''text'''])
					a__ 							=	list(set(lowerCamelCase_))

					a__ 							=	[]
					for s in long_answers:
										if s["start_token"] == -1:
															continue
										a__ 							=	context[s['''start_token'''] : s['''end_token''']]
										a__ 							=	is_html[s['''start_token'''] : s['''end_token''']]
										a__ 							=	''' '''.join([answer[i] for i in range(len(lowerCamelCase_)) if not html[i]])
										if new_answer not in long_targets:
															long_targets.append(lowerCamelCase_)

					a__ 							=	'''long_short''' if len(short_targets + long_targets) > 0 else '''null'''

					return {
					    "question": question,
					    "context": context_string,
					    "short": short_targets,
					    "long": long_targets,
					    "category": category,
					}





def 							SCREAMING_SNAKE_CASE							(   ):
					a__ 							=	load_from_disk('''natural-questions-validation''')
					a__ 							=	dataset.map(lowerCamelCase_).remove_columns(['''annotations''', '''document''', '''id'''])
					print(lowerCamelCase_)

					a__ 							=	dataset.filter(lambda lowerCamelCase_: (len(x['''question''']) + len(x['''context'''])) < 4 * 4096)
					a__ 							=	short_validation_dataset.filter(lambda lowerCamelCase_: x["category"] != "null")
					short_validation_dataset

					a__ 							=	'''vasudevgupta/flax-bigbird-natural-questions'''
					a__ 							=	FlaxBigBirdForNaturalQuestions.from_pretrained(lowerCamelCase_)
					a__ 							=	BigBirdTokenizerFast.from_pretrained(lowerCamelCase_)

					@jax.jit
					def forward(*lowerCamelCase_			,  **lowerCamelCase_):
										a__							,a__							,a__ 							=	model(*lowerCamelCase_			,  **lowerCamelCase_)
										return start_logits, end_logits, jnp.argmax(lowerCamelCase_			,  axis=-1)

					def evaluate(lowerCamelCase_):
										# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
										a__ 							=	tokenizer(
										    example['''question''']			,  example['''context''']			,  return_tensors='''np'''			,  max_length=4096			,  padding='''max_length'''			,  truncation=lowerCamelCase_			,  )

										a__							,a__							,a__ 							=	forward(**lowerCamelCase_)

										a__ 							=	CATEGORY_MAPPING[category.item()]

										a__ 							=	example['''long'''] + example['''short''']
										if example["category"] in ["yes", "no", "null"]:
															a__ 							=	[example['''category''']]
										a__ 							=	example['''category'''] != '''null'''
										# Now target can be: "yes", "no", "null", "list of long & short answers"

										if predicted_category in ["yes", "no", "null"]:
															a__ 							=	[predicted_category]
															a__ 							=	example['''output'''] == example['''targets''']
															a__ 							=	predicted_category != '''null'''
															return example

										a__ 							=	38 if predicted_category == '''short''' else 1024
										a__							,a__ 							=	get_best_valid_start_end_idx(
										    start_scores[0]			,  end_scores[0]			,  top_k=8			,  max_size=lowerCamelCase_)

										a__ 							=	inputs['''input_ids'''][0].tolist()
										a__ 							=	[tokenizer.decode(input_ids[start_score : end_score + 1])]

										a__ 							=	expand_to_aliases(example['''targets''']			,  make_sub_answers=lowerCamelCase_)
										a__ 							=	expand_to_aliases(example['''output'''])

										# some preprocessing to both prediction and answer
										a__ 							=	{''''''.join(a.split()) for a in answers}
										a__ 							=	{''''''.join(p.split()) for p in predictions}
										a__ 							=	{s for s in predictions if s not in ['''``''', '''\'\'''', '''`''', '''\'''']}

										# if there is a common element, it's a exact match
										a__ 							=	len(list(answers & predictions)) > 0
										a__ 							=	predicted_category != '''null''' and len(lowerCamelCase_) > 0

										return example

					a__ 							=	short_validation_dataset.map(lowerCamelCase_)

					a__ 							=	len(lowerCamelCase_)
					a__ 							=	len(short_validation_dataset.filter(lambda lowerCamelCase_: x["match"] == 1))
					print('''EM score:'''			,  (matched / total) * 100			,  '''%''')


if __name__ == "__main__":
	main()


