






import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


snake_case_   :	Union[str, Any]							      ='''pcuenq/k-upscaler'''



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     *, lowerCAmelCase__					,     lowerCAmelCase__	):


		'''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      UpperCAmelCase						( lowerCAmelCase__					,     *, lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__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      UpperCAmelCase						( lowerCAmelCase__					,     *, lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__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      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''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(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      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''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(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      UpperCAmelCase						( lowerCAmelCase__	):


		'''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(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      UpperCAmelCase						( lowerCAmelCase__	):


		'''simple docstring'''

		__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__":
					snake_case_   :	Optional[Any]							      =argparse.ArgumentParser()

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

					main(args)









'''simple docstring'''

import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


A:   Dict						=					get_tests_dir("fixtures/spiece.model")






@require_sentencepiece
@require_tokenizers
class 				__magic_name__  (						UpperCAmelCase_,    unittest.TestCase					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   int   =	ErnieMTokenizer
	SCREAMING_SNAKE_CASE_       :   Optional[Any]   =	False
	SCREAMING_SNAKE_CASE_       :   Dict   =	True
	SCREAMING_SNAKE_CASE_       :   Union[str, Any]   =	False
	SCREAMING_SNAKE_CASE_       :   Dict   =	False
	def 							lowerCamelCase__		(				self							)    ->	Any:
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowercase_   : List[Any]   	=      ErnieMTokenizer(_lowercase	,   unk_token='<unk>'	,   pad_token='<pad>'							)
							tokenizer.save_pretrained(self.tmpdirname							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Optional[int]:
							lowercase_   : Optional[int]   	=      'this is a test'
							lowercase_   : Dict   	=      'this is a test'
							return input_text, output_text
	def 							lowerCamelCase__		(				self							)    ->	Any:
							lowercase_   : str   	=      '<pad>'
							lowercase_   : Optional[Any]   	=      0

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase							)	,   _lowercase							)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase							)	,   _lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							lowercase_   : List[str]   	=      list(self.get_tokenizer().get_vocab().keys()							)

							self.assertEqual(vocab_keys[0]	,   '<pad>'							)
							self.assertEqual(vocab_keys[1]	,   '<unk>'							)
							self.assertEqual(vocab_keys[-1]	,   '▁eloquent'							)
							self.assertEqual(len(_lowercase							)	,   3_0000							)
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							self.assertEqual(self.get_tokenizer().vocab_size	,   3_0000							)
	def 							lowerCamelCase__		(				self							)    ->	List[str]:
							if not self.test_rust_tokenizer:
													return

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

							lowercase_   : Tuple   	=      'I was born in 92000, and this is falsé.'

							lowercase_   : Union[str, Any]   	=      tokenizer.tokenize(_lowercase							)
							lowercase_   : List[Any]   	=      rust_tokenizer.tokenize(_lowercase							)
							self.assertListEqual(_lowercase	,   _lowercase							)

							lowercase_   : Any   	=      tokenizer.encode(_lowercase	,   add_special_tokens=_lowercase							)
							lowercase_   : List[Any]   	=      rust_tokenizer.encode(_lowercase	,   add_special_tokens=_lowercase							)
							self.assertListEqual(_lowercase	,   _lowercase							)

							lowercase_   : Union[str, Any]   	=      self.get_rust_tokenizer()
							lowercase_   : Optional[Any]   	=      tokenizer.encode(_lowercase							)
							lowercase_   : str   	=      rust_tokenizer.encode(_lowercase							)
							self.assertListEqual(_lowercase	,   _lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	Tuple:
							lowercase_   : Optional[int]   	=      ErnieMTokenizer(_lowercase	,   do_lower_case=_lowercase	,   unk_token='<unk>'	,   pad_token='<pad>'							)

							lowercase_   : Tuple   	=      tokenizer.tokenize('This is a test'							)
							self.assertListEqual(_lowercase	,   ['▁this', '▁is', '▁a', '▁test']							)

							self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase							)	,   [48, 25, 21, 1289]							)

							lowercase_   : Union[str, Any]   	=      tokenizer.tokenize('I was born in 92000, and this is falsé.'							)
							# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
							self.assertListEqual(
							    _lowercase	,   ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']							)
							lowercase_   : int   	=      tokenizer.convert_tokens_to_ids(_lowercase							)
							self.assertListEqual(_lowercase	,   [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]							)

							lowercase_   : Optional[int]   	=      tokenizer.convert_ids_to_tokens(_lowercase							)
							self.assertListEqual(
							    _lowercase	,   ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']	,   )
	def 							lowerCamelCase__		(				self							)    ->	Any:
							lowercase_   : Tuple   	=      ErnieMTokenizer(_lowercase	,   unk_token='<unk>'	,   pad_token='<pad>'							)

							lowercase_   : List[Any]   	=      tokenizer.encode('sequence builders'							)
							lowercase_   : Optional[int]   	=      tokenizer.encode('multi-sequence build'							)

							lowercase_   : Union[str, Any]   	=      tokenizer.build_inputs_with_special_tokens(_lowercase							)
							lowercase_   : Optional[int]   	=      tokenizer.build_inputs_with_special_tokens(_lowercase	,   _lowercase							)

							assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
							assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
							    tokenizer.sep_token_id
							] + text_a + [tokenizer.sep_token_id]







	@slow
	def 							lowerCamelCase__		(				self							)    ->	Tuple:
							# fmt: off
							lowercase_   : Dict   	=      {'input_ids': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 9, 304, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 5, 5, 5, 16, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 12_2009, 11_5774, 23, 3559, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=_lowercase	,   model_name='susnato/ernie-m-base_pytorch'	,   sequences=[
							        'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
							        'general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
							        'Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '
							        'models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.',
							        'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
							        'conditioning on both left and right context in all layers.',
							        'The quick brown fox jumps over the lazy dog.',
							    ]	,   )
