






'''simple docstring'''







import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       Union[str, Any]:
							lowerCamelCase__					:	Optional[Any]         =       checkpoint

							lowerCamelCase__					:	Optional[Any]         =       {}

							lowerCamelCase__					:	Dict         =       vae_state_dict["""encoder.conv_in.weight"""]
							lowerCamelCase__					:	List[str]         =       vae_state_dict["""encoder.conv_in.bias"""]
							lowerCamelCase__					:	str         =       vae_state_dict["""encoder.conv_out.weight"""]
							lowerCamelCase__					:	Union[str, Any]         =       vae_state_dict["""encoder.conv_out.bias"""]
							lowerCamelCase__					:	Dict         =       vae_state_dict["""encoder.norm_out.weight"""]
							lowerCamelCase__					:	Dict         =       vae_state_dict["""encoder.norm_out.bias"""]

							lowerCamelCase__					:	Dict         =       vae_state_dict["""decoder.conv_in.weight"""]
							lowerCamelCase__					:	Dict         =       vae_state_dict["""decoder.conv_in.bias"""]
							lowerCamelCase__					:	int         =       vae_state_dict["""decoder.conv_out.weight"""]
							lowerCamelCase__					:	int         =       vae_state_dict["""decoder.conv_out.bias"""]
							lowerCamelCase__					:	Optional[int]         =       vae_state_dict["""decoder.norm_out.weight"""]
							lowerCamelCase__					:	Tuple         =       vae_state_dict["""decoder.norm_out.bias"""]

							lowerCamelCase__					:	Optional[int]         =       vae_state_dict["""quant_conv.weight"""]
							lowerCamelCase__					:	Optional[Any]         =       vae_state_dict["""quant_conv.bias"""]
							lowerCamelCase__					:	str         =       vae_state_dict["""post_quant_conv.weight"""]
							lowerCamelCase__					:	List[str]         =       vae_state_dict["""post_quant_conv.bias"""]

							# Retrieves the keys for the encoder down blocks only
							lowerCamelCase__					:	int         =       len({""".""".join(layer.split("""."""      )[:3]      ) for layer in vae_state_dict if """encoder.down""" in layer}      )
							lowerCamelCase__					:	Any         =       {
							    layer_id: [key for key in vae_state_dict if f'''down.{layer_id}''' in key] for layer_id in range(UpperCamelCase      )
							}

							# Retrieves the keys for the decoder up blocks only
							lowerCamelCase__					:	Tuple         =       len({""".""".join(layer.split("""."""      )[:3]      ) for layer in vae_state_dict if """decoder.up""" in layer}      )
							lowerCamelCase__					:	List[str]         =       {
							    layer_id: [key for key in vae_state_dict if f'''up.{layer_id}''' in key] for layer_id in range(UpperCamelCase      )
							}

							for i in range(UpperCamelCase      ):
														lowerCamelCase__					:	Tuple         =       [key for key in down_blocks[i] if f'''down.{i}''' in key and f'''down.{i}.downsample''' not in key]

														if f'''encoder.down.{i}.downsample.conv.weight''' in vae_state_dict:
																					lowerCamelCase__					:	List[str]         =       vae_state_dict.pop(
																					    f'''encoder.down.{i}.downsample.conv.weight'''      )
																					lowerCamelCase__					:	Dict         =       vae_state_dict.pop(
																					    f'''encoder.down.{i}.downsample.conv.bias'''      )

														lowerCamelCase__					:	List[Any]         =       renew_vae_resnet_paths(UpperCamelCase      )
														lowerCamelCase__					:	Optional[int]         =       {"""old""": f'''down.{i}.block''', """new""": f'''down_blocks.{i}.resnets'''}
														assign_to_checkpoint(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			additional_replacements=[meta_path]				,			config=UpperCamelCase      )

							lowerCamelCase__					:	Tuple         =       [key for key in vae_state_dict if """encoder.mid.block""" in key]
							lowerCamelCase__					:	Optional[int]         =       2
							for i in range(1				,			num_mid_res_blocks + 1      ):
														lowerCamelCase__					:	List[Any]         =       [key for key in mid_resnets if f'''encoder.mid.block_{i}''' in key]

														lowerCamelCase__					:	Union[str, Any]         =       renew_vae_resnet_paths(UpperCamelCase      )
														lowerCamelCase__					:	int         =       {"""old""": f'''mid.block_{i}''', """new""": f'''mid_block.resnets.{i - 1}'''}
														assign_to_checkpoint(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			additional_replacements=[meta_path]				,			config=UpperCamelCase      )

							lowerCamelCase__					:	Tuple         =       [key for key in vae_state_dict if """encoder.mid.attn""" in key]
							lowerCamelCase__					:	List[str]         =       renew_vae_attention_paths(UpperCamelCase      )
							lowerCamelCase__					:	Any         =       {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
							assign_to_checkpoint(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			additional_replacements=[meta_path]				,			config=UpperCamelCase      )
							conv_attn_to_linear(UpperCamelCase      )

							for i in range(UpperCamelCase      ):
														lowerCamelCase__					:	Union[str, Any]         =       num_up_blocks - 1 - i
														lowerCamelCase__					:	List[str]         =       [
														    key for key in up_blocks[block_id] if f'''up.{block_id}''' in key and f'''up.{block_id}.upsample''' not in key
														]

														if f'''decoder.up.{block_id}.upsample.conv.weight''' in vae_state_dict:
																					lowerCamelCase__					:	Dict         =       vae_state_dict[
																					    f'''decoder.up.{block_id}.upsample.conv.weight'''
																					]
																					lowerCamelCase__					:	Any         =       vae_state_dict[
																					    f'''decoder.up.{block_id}.upsample.conv.bias'''
																					]

														lowerCamelCase__					:	Optional[Any]         =       renew_vae_resnet_paths(UpperCamelCase      )
														lowerCamelCase__					:	Dict         =       {"""old""": f'''up.{block_id}.block''', """new""": f'''up_blocks.{i}.resnets'''}
														assign_to_checkpoint(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			additional_replacements=[meta_path]				,			config=UpperCamelCase      )

							lowerCamelCase__					:	Optional[int]         =       [key for key in vae_state_dict if """decoder.mid.block""" in key]
							lowerCamelCase__					:	Optional[Any]         =       2
							for i in range(1				,			num_mid_res_blocks + 1      ):
														lowerCamelCase__					:	Tuple         =       [key for key in mid_resnets if f'''decoder.mid.block_{i}''' in key]

														lowerCamelCase__					:	Tuple         =       renew_vae_resnet_paths(UpperCamelCase      )
														lowerCamelCase__					:	str         =       {"""old""": f'''mid.block_{i}''', """new""": f'''mid_block.resnets.{i - 1}'''}
														assign_to_checkpoint(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			additional_replacements=[meta_path]				,			config=UpperCamelCase      )

							lowerCamelCase__					:	List[Any]         =       [key for key in vae_state_dict if """decoder.mid.attn""" in key]
							lowerCamelCase__					:	Optional[int]         =       renew_vae_attention_paths(UpperCamelCase      )
							lowerCamelCase__					:	List[str]         =       {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
							assign_to_checkpoint(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			additional_replacements=[meta_path]				,			config=UpperCamelCase      )
							conv_attn_to_linear(UpperCamelCase      )
							return new_checkpoint





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			)      ->       Tuple:
							# Only support V1
							lowerCamelCase__					:	Any         =       requests.get(
							    """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"""      )
							lowerCamelCase__					:	Tuple         =       io.BytesIO(r.content      )

							lowerCamelCase__					:	Union[str, Any]         =       OmegaConf.load(UpperCamelCase      )
							lowerCamelCase__					:	List[Any]         =       512
							lowerCamelCase__					:	List[str]         =       """cuda""" if torch.cuda.is_available() else """cpu"""
							if checkpoint_path.endswith("""safetensors"""      ):
														from safetensors import safe_open

														lowerCamelCase__					:	Optional[int]         =       {}
														with safe_open(UpperCamelCase				,			framework="""pt"""				,			device="""cpu"""      ) as f:
																					for key in f.keys():
																												lowerCamelCase__					:	Any         =       f.get_tensor(UpperCamelCase      )
							else:
														lowerCamelCase__					:	Any         =       torch.load(UpperCamelCase				,			map_location=UpperCamelCase      )["""state_dict"""]

							# Convert the VAE model.
							lowerCamelCase__					:	List[Any]         =       create_vae_diffusers_config(UpperCamelCase				,			image_size=UpperCamelCase      )
							lowerCamelCase__					:	Union[str, Any]         =       custom_convert_ldm_vae_checkpoint(UpperCamelCase				,			UpperCamelCase      )

							lowerCamelCase__					:	Tuple         =       AutoencoderKL(**UpperCamelCase      )
							vae.load_state_dict(UpperCamelCase      )
							vae.save_pretrained(UpperCamelCase      )


if __name__ == "__main__":
			_A	:			Tuple         =argparse.ArgumentParser()

			parser.add_argument('''--vae_pt_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
			parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')

			_A	:			Union[str, Any]         =parser.parse_args()

			vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)









'''simple docstring'''







# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_A	:			Dict         ='''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
_A	:			List[str]         =[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
_A	:			str         =dict(zip(vocab, range(len(vocab))))
_A	:			List[str]         =['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
			_A	:			Union[str, Any]         =Path(tmpdirname)
			_A	:			str         =build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
			_A	:			int         =build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
			_A	:			List[Any]         =build_dir / VOCAB_FILES_NAMES['''merges_file''']
			with open(src_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(tgt_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(merges_file, '''w''') as fp:
						fp.write('''\n'''.join(merges))

			_A	:			int         =FSMTTokenizer(
			    langs=['''en''', '''ru'''],
			    src_vocab_size=len(vocab),
			    tgt_vocab_size=len(vocab),
			    src_vocab_file=src_vocab_file,
			    tgt_vocab_file=tgt_vocab_file,
			    merges_file=merges_file,
			)

_A	:			List[str]         =FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_A	:			Union[str, Any]         =FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')

# Test
_A	:			List[str]         =tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A	:			Tuple         =tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-ru

