




import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


UpperCamelCase								=					'bert-base-cased'
UpperCamelCase								=					'google/pegasus-xsum'
UpperCamelCase								=					[' Sam ate lunch today.', 'Sams lunch ingredients.']
UpperCamelCase								=					['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
UpperCamelCase								=					'patrickvonplaten/t5-tiny-random'
UpperCamelCase								=					'sshleifer/bart-tiny-random'
UpperCamelCase								=					'sshleifer/tiny-mbart'
UpperCamelCase								=					'sshleifer/tiny-marian-en-de'



def 							_A       (						lowerCAmelCase_					: Path       ,    lowerCAmelCase_					: list      ):
     """simple docstring"""

     lowerCAmelCase__          =			"\n".join(lowerCAmelCase_      )
     Path(lowerCAmelCase_      ).open("w"      ).writelines(lowerCAmelCase_      )



def 							_A       (						lowerCAmelCase_					: str      ):
     """simple docstring"""

     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(lowerCAmelCase_       ,    F'{split}.source'      )       ,    lowerCAmelCase_      )
          _dump_articles(os.path.join(lowerCAmelCase_       ,    F'{split}.target'      )       ,    lowerCAmelCase_      )
     return tmp_dir






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]       ,     )
    @slow
    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					List[str]       )				->	Dict:
         lowerCAmelCase__          =			AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         lowerCAmelCase__          =			max(len(tokenizer.encode(SCREAMING_SNAKE_CASE__       )       ) for a in ARTICLES       )
         lowerCAmelCase__          =			max(len(tokenizer.encode(SCREAMING_SNAKE_CASE__       )       ) for a in SUMMARIES       )
         lowerCAmelCase__          =			4
         lowerCAmelCase__          =			8
         assert max_len_target > max_src_len  # Will be truncated
         assert max_len_source > max_src_len  # Will be truncated
         lowerCAmelCase__     ,		lowerCAmelCase__          =			"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
         lowerCAmelCase__          =			SeqaSeqDataset(
             SCREAMING_SNAKE_CASE__       ,     data_dir=SCREAMING_SNAKE_CASE__       ,     type_path="train"       ,     max_source_length=SCREAMING_SNAKE_CASE__       ,     max_target_length=SCREAMING_SNAKE_CASE__       ,     src_lang=SCREAMING_SNAKE_CASE__       ,     tgt_lang=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			DataLoader(SCREAMING_SNAKE_CASE__       ,     batch_size=2       ,     collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
              assert isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
              assert batch["attention_mask"].shape == batch["input_ids"].shape
              # show that articles were trimmed.
              assert batch["input_ids"].shape[1] == max_src_len
              # show that targets are the same len
              assert batch["labels"].shape[1] == max_tgt_len
              if tok_name != MBART_TINY:
                   continue
              # check language codes in correct place
              lowerCAmelCase__          =			shift_tokens_right(batch["labels"]       ,     tokenizer.pad_token_id       )
              assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
              assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
              assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
              assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

              break  # No need to test every batch


    @parameterized.expand([BART_TINY, BERT_BASE_CASED]       )
    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       )				->	int:
         lowerCAmelCase__          =			AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         lowerCAmelCase__          =			max(len(tokenizer.encode(SCREAMING_SNAKE_CASE__       )       ) for a in ARTICLES       )
         lowerCAmelCase__          =			max(len(tokenizer.encode(SCREAMING_SNAKE_CASE__       )       ) for a in SUMMARIES       )
         lowerCAmelCase__          =			4
         lowerCAmelCase__          =			LegacySeqaSeqDataset(
             SCREAMING_SNAKE_CASE__       ,     data_dir=SCREAMING_SNAKE_CASE__       ,     type_path="train"       ,     max_source_length=20       ,     max_target_length=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			DataLoader(SCREAMING_SNAKE_CASE__       ,     batch_size=2       ,     collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
              assert batch["attention_mask"].shape == batch["input_ids"].shape
              # show that articles were trimmed.
              assert batch["input_ids"].shape[1] == max_len_source
              assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
              # show that targets were truncated
              assert batch["labels"].shape[1] == trunc_target  # Truncated
              assert max_len_target > trunc_target  # Truncated
              break  # No need to test every batch


    def        a				(	self					:					Optional[int]       )				->	Optional[int]:
         lowerCAmelCase__          =			AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"       )

         lowerCAmelCase__          =			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         lowerCAmelCase__          =			tmp_dir.joinpath("train.source"       ).open().readlines()
         lowerCAmelCase__          =			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         pack_data_dir(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     128       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			{x.name for x in tmp_dir.iterdir()}
         lowerCAmelCase__          =			{x.name for x in save_dir.iterdir()}
         lowerCAmelCase__          =			save_dir.joinpath("train.source"       ).open().readlines()
         # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
         # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
         assert len(SCREAMING_SNAKE_CASE__       ) < len(SCREAMING_SNAKE_CASE__       )
         assert len(SCREAMING_SNAKE_CASE__       ) == 1
         assert len(packed_examples[0]       ) == sum(len(SCREAMING_SNAKE_CASE__       ) for x in orig_examples       )
         assert orig_paths == new_paths


    @pytest.mark.skipif(not FAIRSEQ_AVAILABLE       ,     reason="This test requires fairseq"       )
    def        a				(	self					:					Optional[Any]       )				->	Any:
         if not FAIRSEQ_AVAILABLE:
              return
         lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			self._get_dataset(max_len=64       )
         lowerCAmelCase__          =			64
         lowerCAmelCase__          =			ds.make_dynamic_sampler(SCREAMING_SNAKE_CASE__       ,     required_batch_size_multiple=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[len(SCREAMING_SNAKE_CASE__       ) for x in batch_sampler]
         assert len(set(SCREAMING_SNAKE_CASE__       )       ) > 1  # it's not dynamic batch size if every batch is the same length
         assert sum(SCREAMING_SNAKE_CASE__       ) == len(SCREAMING_SNAKE_CASE__       )  # no dropped or added examples
         lowerCAmelCase__          =			DataLoader(SCREAMING_SNAKE_CASE__       ,     batch_sampler=SCREAMING_SNAKE_CASE__       ,     collate_fn=ds.collate_fn       ,     num_workers=2       )
         lowerCAmelCase__          =			[]
         lowerCAmelCase__          =			[]
         for batch in data_loader:
              lowerCAmelCase__          =			batch["input_ids"].shape
              lowerCAmelCase__          =			src_shape[0]
              assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
              lowerCAmelCase__          =			np.product(batch["input_ids"].shape       )
              num_src_per_batch.append(SCREAMING_SNAKE_CASE__       )
              if num_src_tokens > (max_tokens * 1.1):
                   failures.append(SCREAMING_SNAKE_CASE__       )
         assert num_src_per_batch[0] == max(SCREAMING_SNAKE_CASE__       )
         if failures:
              raise AssertionError(f'too many tokens in {len(SCREAMING_SNAKE_CASE__       )} batches'       )


    def        a				(	self					:					Tuple       )				->	Optional[int]:
         lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			self._get_dataset(max_len=512       )
         lowerCAmelCase__          =			2
         lowerCAmelCase__          =			ds.make_sortish_sampler(SCREAMING_SNAKE_CASE__       ,     shuffle=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			DataLoader(SCREAMING_SNAKE_CASE__       ,     batch_size=SCREAMING_SNAKE_CASE__       ,     collate_fn=ds.collate_fn       ,     num_workers=2       )
         lowerCAmelCase__          =			DataLoader(SCREAMING_SNAKE_CASE__       ,     batch_size=SCREAMING_SNAKE_CASE__       ,     collate_fn=ds.collate_fn       ,     num_workers=2       ,     sampler=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			tokenizer.pad_token_id

         def count_pad_tokens(SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]="input_ids"       ):
              return [batch[k].eq(SCREAMING_SNAKE_CASE__       ).sum().item() for batch in data_loader]

         assert sum(count_pad_tokens(SCREAMING_SNAKE_CASE__       ,     k="labels"       )       ) < sum(count_pad_tokens(SCREAMING_SNAKE_CASE__       ,     k="labels"       )       )
         assert sum(count_pad_tokens(SCREAMING_SNAKE_CASE__       )       ) < sum(count_pad_tokens(SCREAMING_SNAKE_CASE__       )       )
         assert len(SCREAMING_SNAKE_CASE__       ) == len(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=1_000       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=128       )				->	Union[str, Any]:
         if os.getenv("USE_REAL_DATA"       ,     SCREAMING_SNAKE_CASE__       ):
              lowerCAmelCase__          =			"examples/seq2seq/wmt_en_ro"
              lowerCAmelCase__          =			max_len * 2 * 64
              if not Path(SCREAMING_SNAKE_CASE__       ).joinpath("train.len"       ).exists():
                   save_len_file(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         else:
              lowerCAmelCase__          =			"examples/seq2seq/test_data/wmt_en_ro"
              lowerCAmelCase__          =			max_len * 4
              save_len_file(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			SeqaSeqDataset(
             SCREAMING_SNAKE_CASE__       ,     data_dir=SCREAMING_SNAKE_CASE__       ,     type_path="train"       ,     max_source_length=SCREAMING_SNAKE_CASE__       ,     max_target_length=SCREAMING_SNAKE_CASE__       ,     n_obs=SCREAMING_SNAKE_CASE__       ,     )
         return ds, max_tokens, tokenizer


    def        a				(	self					:					Optional[Any]       )				->	List[Any]:
         lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			self._get_dataset()
         lowerCAmelCase__          =			set(DistributedSortishSampler(SCREAMING_SNAKE_CASE__       ,     256       ,     num_replicas=2       ,     rank=0       ,     add_extra_examples=SCREAMING_SNAKE_CASE__       )       )
         lowerCAmelCase__          =			set(DistributedSortishSampler(SCREAMING_SNAKE_CASE__       ,     256       ,     num_replicas=2       ,     rank=1       ,     add_extra_examples=SCREAMING_SNAKE_CASE__       )       )
         assert idsa.intersection(SCREAMING_SNAKE_CASE__       ) == set()


    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]       ,     )
    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       )				->	str:
         lowerCAmelCase__          =			AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__       ,     use_fast=SCREAMING_SNAKE_CASE__       )
         if tok_name == MBART_TINY:
              lowerCAmelCase__          =			SeqaSeqDataset(
                  SCREAMING_SNAKE_CASE__       ,     data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       ,     type_path="train"       ,     max_source_length=4       ,     max_target_length=8       ,     src_lang="EN"       ,     tgt_lang="FR"       ,     )
              lowerCAmelCase__          =			train_dataset.dataset_kwargs
              assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
              lowerCAmelCase__          =			SeqaSeqDataset(
                  SCREAMING_SNAKE_CASE__       ,     data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       ,     type_path="train"       ,     max_source_length=4       ,     max_target_length=8       ,     )
              lowerCAmelCase__          =			train_dataset.dataset_kwargs
              assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
              assert len(SCREAMING_SNAKE_CASE__       ) == 1 if tok_name == BART_TINY else len(SCREAMING_SNAKE_CASE__       ) == 0






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

UpperCamelCase								=					[
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

UpperCamelCase								=					[
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

UpperCamelCase								=					[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
     # loop over downblocks/upblocks

     for j in range(2):
          # loop over resnets/attentions for downblocks
          UpperCamelCase								=					F"""down_blocks.{i}.resnets.{j}."""
          UpperCamelCase								=					F"""input_blocks.{3*i + j + 1}.0."""
          unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

          if i < 3:
               # no attention layers in down_blocks.3
               UpperCamelCase								=					F"""down_blocks.{i}.attentions.{j}."""
               UpperCamelCase								=					F"""input_blocks.{3*i + j + 1}.1."""
               unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

     for j in range(3):
          # loop over resnets/attentions for upblocks
          UpperCamelCase								=					F"""up_blocks.{i}.resnets.{j}."""
          UpperCamelCase								=					F"""output_blocks.{3*i + j}.0."""
          unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

          if i > 0:
               # no attention layers in up_blocks.0
               UpperCamelCase								=					F"""up_blocks.{i}.attentions.{j}."""
               UpperCamelCase								=					F"""output_blocks.{3*i + j}.1."""
               unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

     if i < 3:
          # no downsample in down_blocks.3
          UpperCamelCase								=					F"""down_blocks.{i}.downsamplers.0.conv."""
          UpperCamelCase								=					F"""input_blocks.{3*(i+1)}.0.op."""
          unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

          # no upsample in up_blocks.3
          UpperCamelCase								=					F"""up_blocks.{i}.upsamplers.0."""
          UpperCamelCase								=					F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
          unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

UpperCamelCase								=					'mid_block.attentions.0.'
UpperCamelCase								=					'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
     UpperCamelCase								=					F"""mid_block.resnets.{j}."""
     UpperCamelCase								=					F"""middle_block.{2*j}."""
     unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))



def 							_A       (						lowerCAmelCase_					: Any      ):
     """simple docstring"""

     lowerCAmelCase__          =			{k: k for k in unet_state_dict.keys()}
     for sd_name, hf_name in unet_conversion_map:
          lowerCAmelCase__          =			sd_name
     for k, v in mapping.items():
          if "resnets" in k:
               for sd_part, hf_part in unet_conversion_map_resnet:
                    lowerCAmelCase__          =			v.replace(lowerCAmelCase_       ,    lowerCAmelCase_      )
               lowerCAmelCase__          =			v
     for k, v in mapping.items():
          for sd_part, hf_part in unet_conversion_map_layer:
               lowerCAmelCase__          =			v.replace(lowerCAmelCase_       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			v
     lowerCAmelCase__          =			{v: unet_state_dict[k] for k, v in mapping.items()}
     return new_state_dict


# ================#
# VAE Conversion #
# ================#

UpperCamelCase								=					[
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
     # down_blocks have two resnets
     for j in range(2):
          UpperCamelCase								=					F"""encoder.down_blocks.{i}.resnets.{j}."""
          UpperCamelCase								=					F"""encoder.down.{i}.block.{j}."""
          vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

     if i < 3:
          UpperCamelCase								=					F"""down_blocks.{i}.downsamplers.0."""
          UpperCamelCase								=					F"""down.{i}.downsample."""
          vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

          UpperCamelCase								=					F"""up_blocks.{i}.upsamplers.0."""
          UpperCamelCase								=					F"""up.{3-i}.upsample."""
          vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

     # up_blocks have three resnets
     # also, up blocks in hf are numbered in reverse from sd
     for j in range(3):
          UpperCamelCase								=					F"""decoder.up_blocks.{i}.resnets.{j}."""
          UpperCamelCase								=					F"""decoder.up.{3-i}.block.{j}."""
          vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
     UpperCamelCase								=					F"""mid_block.resnets.{i}."""
     UpperCamelCase								=					F"""mid.block_{i+1}."""
     vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


UpperCamelCase								=					[
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]



def 							_A       (						lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     return w.reshape(*w.shape       ,    1       ,    1      )



def 							_A       (						lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     lowerCAmelCase__          =			{k: k for k in vae_state_dict.keys()}
     for k, v in mapping.items():
          for sd_part, hf_part in vae_conversion_map:
               lowerCAmelCase__          =			v.replace(lowerCAmelCase_       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			v
     for k, v in mapping.items():
          if "attentions" in k:
               for sd_part, hf_part in vae_conversion_map_attn:
                    lowerCAmelCase__          =			v.replace(lowerCAmelCase_       ,    lowerCAmelCase_      )
               lowerCAmelCase__          =			v
     lowerCAmelCase__          =			{v: vae_state_dict[k] for k, v in mapping.items()}
     lowerCAmelCase__          =			["q", "k", "v", "proj_out"]
     for k, v in new_state_dict.items():
          for weight_name in weights_to_convert:
               if F'mid.attn_1.{weight_name}.weight' in k:
                    print(F'Reshaping {k} for SD format'      )
                    lowerCAmelCase__          =			reshape_weight_for_sd(lowerCAmelCase_      )
     return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


UpperCamelCase								=					[
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
UpperCamelCase								=					{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
UpperCamelCase								=					re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
UpperCamelCase								=					{'q': 0, 'k': 1, 'v': 2}



def 							_A       (						lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     lowerCAmelCase__          =			{}
     lowerCAmelCase__          =			{}
     lowerCAmelCase__          =			{}
     for k, v in text_enc_dict.items():
          if (
              k.endswith(".self_attn.q_proj.weight"      )
              or k.endswith(".self_attn.k_proj.weight"      )
              or k.endswith(".self_attn.v_proj.weight"      )
          ):
               lowerCAmelCase__          =			k[: -len(".q_proj.weight"      )]
               lowerCAmelCase__          =			k[-len("q_proj.weight"      )]
               if k_pre not in capture_qkv_weight:
                    lowerCAmelCase__          =			[None, None, None]
               lowerCAmelCase__          =			v
               continue

          if (
              k.endswith(".self_attn.q_proj.bias"      )
              or k.endswith(".self_attn.k_proj.bias"      )
              or k.endswith(".self_attn.v_proj.bias"      )
          ):
               lowerCAmelCase__          =			k[: -len(".q_proj.bias"      )]
               lowerCAmelCase__          =			k[-len("q_proj.bias"      )]
               if k_pre not in capture_qkv_bias:
                    lowerCAmelCase__          =			[None, None, None]
               lowerCAmelCase__          =			v
               continue

          lowerCAmelCase__          =			textenc_pattern.sub(lambda lowerCAmelCase_      : protected[re.escape(m.group(0      )      )]       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			v

     for k_pre, tensors in capture_qkv_weight.items():
          if None in tensors:
               raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"      )
          lowerCAmelCase__          =			textenc_pattern.sub(lambda lowerCAmelCase_      : protected[re.escape(m.group(0      )      )]       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			torch.cat(lowerCAmelCase_      )

     for k_pre, tensors in capture_qkv_bias.items():
          if None in tensors:
               raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"      )
          lowerCAmelCase__          =			textenc_pattern.sub(lambda lowerCAmelCase_      : protected[re.escape(m.group(0      )      )]       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			torch.cat(lowerCAmelCase_      )

     return new_state_dict



def 							_A       (						lowerCAmelCase_					: Any      ):
     """simple docstring"""

     return text_enc_dict


if __name__ == "__main__":
     UpperCamelCase								=					argparse.ArgumentParser()

     parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
     parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
     parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
     parser.add_argument(
         '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
     )

     UpperCamelCase								=					parser.parse_args()

     assert args.model_path is not None, "Must provide a model path!"

     assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

     # Path for safetensors
     UpperCamelCase								=					osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
     UpperCamelCase								=					osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
     UpperCamelCase								=					osp.join(args.model_path, 'text_encoder', 'model.safetensors')

     # Load models from safetensors if it exists, if it doesn't pytorch
     if osp.exists(unet_path):
          UpperCamelCase								=					load_file(unet_path, device='cpu')
     else:
          UpperCamelCase								=					osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
          UpperCamelCase								=					torch.load(unet_path, map_location='cpu')

     if osp.exists(vae_path):
          UpperCamelCase								=					load_file(vae_path, device='cpu')
     else:
          UpperCamelCase								=					osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
          UpperCamelCase								=					torch.load(vae_path, map_location='cpu')

     if osp.exists(text_enc_path):
          UpperCamelCase								=					load_file(text_enc_path, device='cpu')
     else:
          UpperCamelCase								=					osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
          UpperCamelCase								=					torch.load(text_enc_path, map_location='cpu')

     # Convert the UNet model
     UpperCamelCase								=					convert_unet_state_dict(unet_state_dict)
     UpperCamelCase								=					{'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

     # Convert the VAE model
     UpperCamelCase								=					convert_vae_state_dict(vae_state_dict)
     UpperCamelCase								=					{'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

     # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
     UpperCamelCase								=					'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

     if is_vaa_model:
          # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
          UpperCamelCase								=					{'transformer.' + k: v for k, v in text_enc_dict.items()}
          UpperCamelCase								=					convert_text_enc_state_dict_vaa(text_enc_dict)
          UpperCamelCase								=					{'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
     else:
          UpperCamelCase								=					convert_text_enc_state_dict(text_enc_dict)
          UpperCamelCase								=					{'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

     # Put together new checkpoint
     UpperCamelCase								=					{**unet_state_dict, **vae_state_dict, **text_enc_dict}
     if args.half:
          UpperCamelCase								=					{k: v.half() for k, v in state_dict.items()}

     if args.use_safetensors:
          save_file(state_dict, args.checkpoint_path)
     else:
          UpperCamelCase								=					{'state_dict': state_dict}
          torch.save(state_dict, args.checkpoint_path)
