
'''simple docstring'''







import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


__SCREAMING_SNAKE_CASE		:  Any        =      'pcuenq/k-upscaler'

def 				_snake_case (		lowercase			,    lowercase			,    *, lowercase			,    lowercase						)       ->       Tuple:
				__a       :     Optional[Any]								=	{
				    # 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 				_snake_case (		lowercase			,    *, lowercase			,    lowercase						)       ->       Dict:
				__a							,	__a							,	__a       :     Any								=	checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3			,    dim=0						)
				__a							,	__a							,	__a       :     List[Any]								=	checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3			,    dim=0						)
				__a       :     List[Any]								=	{
				    # 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 				_snake_case (		lowercase			,    *, lowercase			,    lowercase			,    lowercase						)       ->       Optional[int]:
				__a							,	__a       :     Any								=	checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2			,    dim=0						)
				__a							,	__a       :     Any								=	checkpoint[F"""{attention_prefix}.kv_proj.bias"""].chunk(2			,    dim=0						)

				__a       :     Optional[int]								=	{
				    # 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 				_snake_case (		lowercase			,    lowercase			,    lowercase			,    lowercase						)       ->       Any:
				__a       :     Optional[int]								=	"""inner_model.u_net.u_blocks""" if block_type == """up""" else """inner_model.u_net.d_blocks"""
				__a       :     Union[str, Any]								=	F"""{block_prefix}.{block_idx}"""

				__a       :     Tuple								=	{}

				if not hasattr(lowercase			,    """attentions"""						):
								__a       :     Dict								=	1  # resnet only
				elif not block.attentions[0].add_self_attention:
								__a       :     Tuple								=	2  # resnet -> cross-attention
				else:
								__a       :     List[str]								=	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       :     Tuple								=	F"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
								__a       :     Optional[int]								=	n * resnet_idx if block_type == """up""" else n * resnet_idx + 1
								__a       :     Any								=	F"""{block_prefix}.{idx}""" if block_type == """up""" else F"""{block_prefix}.{idx}"""

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

				if hasattr(lowercase			,    """attentions"""						):
								for attention_idx, attention in enumerate(block.attentions						):
												__a       :     List[Any]								=	F"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
												__a       :     Any								=	n * attention_idx + 1 if block_type == """up""" else n * attention_idx + 2
												__a       :     Any								=	F"""{block_prefix}.{idx}"""
												__a       :     Dict								=	F"""{block_prefix}.{idx }"""
												__a       :     int								=	1 if not attention.add_self_attention else 2
												__a       :     Optional[Any]								=	(
												    n * attention_idx + cross_attention_index
												    if block_type == """up"""
												    else n * attention_idx + cross_attention_index + 1
												)
												__a       :     List[Any]								=	F"""{block_prefix}.{idx }"""

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

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

				return diffusers_checkpoint

def 				_snake_case (		lowercase			,    lowercase						)       ->       List[str]:
				__a       :     Union[str, Any]								=	{}

				# 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(lowercase			,    lowercase			,    lowercase			,    """down"""						)						)

				# up_blocks
				for up_block_idx, up_block in enumerate(model.up_blocks						):
								diffusers_checkpoint.update(block_to_diffusers_checkpoint(lowercase			,    lowercase			,    lowercase			,    """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 				_snake_case (		lowercase						)       ->       Tuple:
				__a       :     List[str]								=	original_config["""input_channels"""] + original_config["""unet_cond_dim"""]
				__a       :     List[str]								=	original_config["""input_channels"""] + (1 if original_config["""has_variance"""] else 0)

				__a       :     Any								=	original_config["""channels"""]

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

				__a       :     Any								=	original_config["""mapping_cond_dim"""]
				__a       :     Union[str, Any]								=	original_config["""cross_cond_dim"""]

				__a       :     str								=	[]
				__a       :     Any								=	[]
				for s, c in zip(original_config["""self_attn_depths"""]			,    original_config["""cross_attn_depths"""]						):
								if s:
												__a       :     Tuple								=	"""self"""
												__a       :     int								=	"""cross""" if c else None
								elif c:
												__a       :     Dict								=	"""cross"""
												__a       :     str								=	None
								else:
												__a       :     Any								=	None
												__a       :     Union[str, Any]								=	None
								attna_types.append(lowercase						)
								attna_types.append(lowercase						)

				__a       :     Tuple								=	UNetaDConditionModel(
				    in_channels=lowercase			,    out_channels=lowercase			,    down_block_types=("""KDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""")			,    mid_block_type=lowercase			,    up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")			,    block_out_channels=lowercase			,    layers_per_block=lowercase			,    act_fn="""gelu"""			,    norm_num_groups=lowercase			,    cross_attention_dim=lowercase			,    attention_head_dim=6_4			,    time_cond_proj_dim=lowercase			,    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 				_snake_case (		lowercase						)       ->       Tuple:
				__a       :     Union[str, Any]								=	torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""						)

				__a       :     List[str]								=	huggingface_hub.hf_hub_download(lowercase			,    """config_laion_text_cond_latent_upscaler_2.json"""						)
				__a       :     str								=	huggingface_hub.hf_hub_download(
				    lowercase			,    """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       :     List[Any]								=	K.config.load_config(open(lowercase						)						)["""model"""]
				__a       :     int								=	unet_model_from_original_config(lowercase						)

				__a       :     List[str]								=	torch.load(lowercase			,    map_location=lowercase						)["""model_ema"""]
				__a       :     Union[str, Any]								=	unet_to_diffusers_checkpoint(lowercase			,    lowercase						)

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


if __name__ == "__main__":
					__SCREAMING_SNAKE_CASE		:  List[str]        =      argparse.ArgumentParser()

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

					main(args)

'''simple docstring'''







import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class  SCREAMING_SNAKE_CASE__       (  __UpperCamelCase			,					unittest.TestCase   ):
	lowercase__	    =							GPTNeoXJapaneseTokenizer
	lowercase__	    =							False
	lowercase__	    =							{"do_clean_text": False, "add_prefix_space": False}
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					super().setUp()

					__a       :     Optional[int]								=	[
					    """こん""",
					    """こんに""",
					    """にちは""",
					    """ばんは""",
					    """世界,㔺界""",
					    """、""",
					    """。""",
					    """<BR>""",
					    """<SP>""",
					    """<TAB>""",
					    """<URL>""",
					    """<EMAIL>""",
					    """<TEL>""",
					    """<DATE>""",
					    """<PRICE>""",
					    """<BLOCK>""",
					    """<KIGOU>""",
					    """<U2000U2BFF>""",
					    """<|emoji1|>""",
					    """<unk>""",
					    """<|startoftext|>""",
					    """<|endoftext|>""",
					]
					__a       :     Any								=	{"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
					__a       :     List[str]								=	{"""unk_token""": """<unk>"""}

					__a       :     Any								=	os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES["""vocab_file"""]			)
					__a       :     Tuple								=	os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES["""emoji_file"""]			)
					with open(self.vocab_file  ,    """w"""  ,    encoding="""utf-8"""			) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]			)			)
					with open(self.emoji_file  ,    """w"""			) as emoji_writer:
									emoji_writer.write(json.dumps(__UpperCamelCase			)			)
	def __lowerCamelCase    (				self  ,    **__UpperCamelCase			):



					'''simple docstring'''






					kwargs.update(self.special_tokens_map			)
					return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname  ,    **__UpperCamelCase			)
	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Any								=	"""こんにちは、世界。 \nこんばんは、㔺界。😀"""
					__a       :     Optional[int]								=	"""こんにちは、世界。 \nこんばんは、世界。😀"""
					return input_text, output_text
	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a							,	__a       :     List[str]								=	self.get_input_output_texts(__UpperCamelCase			)
					__a       :     List[str]								=	tokenizer.encode(__UpperCamelCase  ,    add_special_tokens=__UpperCamelCase			)
					__a       :     str								=	tokenizer.decode(__UpperCamelCase  ,    clean_up_tokenization_spaces=__UpperCamelCase			)
					return text, ids
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass  # TODO add if relevant
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass  # TODO add if relevant
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass  # TODO add if relevant
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     List[Any]								=	self.get_tokenizer()

					# Testing tokenization
					__a       :     Dict								=	"""こんにちは、世界。　こんばんは、㔺界。"""
					__a       :     Optional[int]								=	["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
					__a       :     str								=	tokenizer.tokenize(__UpperCamelCase			)
					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)

					# Testing conversion to ids without special tokens
					__a       :     Optional[Any]								=	[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
					__a       :     Optional[int]								=	tokenizer.convert_tokens_to_ids(__UpperCamelCase			)
					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)

					# Testing conversion to ids with special tokens
					__a       :     List[str]								=	tokens + [tokenizer.unk_token]
					__a       :     Dict								=	[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
					__a       :     Any								=	tokenizer.convert_tokens_to_ids(__UpperCamelCase			)
					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)
	@slow
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     int								=	self.tokenizer_class.from_pretrained("""abeja/gpt-neox-japanese-2.7b"""			)

					__a       :     Union[str, Any]								=	tokenizer.encode("""ありがとう。"""  ,    add_special_tokens=__UpperCamelCase			)
					__a       :     Dict								=	tokenizer.encode("""どういたしまして。"""  ,    add_special_tokens=__UpperCamelCase			)

					__a       :     Optional[Any]								=	tokenizer.build_inputs_with_special_tokens(__UpperCamelCase			)
					__a       :     int								=	tokenizer.build_inputs_with_special_tokens(__UpperCamelCase  ,    __UpperCamelCase			)

					assert encoded_sentence == ids_a
					assert encoded_pair == ids_a + ids_a
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass

	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass