





'''simple docstring'''






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


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






def      __A						(							a_						:  Path      ,a_						:  list	):
		lowerCAmelCase     :    Tuple						=   "\n".join(a_	)
		Path(a_	).open("w"	).writelines(a_	)






def      __A						(							a_						:  Any	):
		for split in ["train", "val", "test"]:
				_dump_articles(os.path.join(a_      ,f'''{split}.source'''	)      ,a_	)
				_dump_articles(os.path.join(a_      ,f'''{split}.target'''	)      ,a_	)
		return tmp_dir

class 		lowerCamelCase  (      _A       ):



	@parameterized.expand(
	    [
	        MBART_TINY,
	        MARIAN_TINY,
	        T5_TINY,
	        BART_TINY,
	        PEGASUS_XSUM,
	    ]		,      )
	@slow
	def   _lowerCamelCase    (   self		,      a_	):
			lowerCAmelCase     :    int						=   AutoTokenizer.from_pretrained(a_	)
			lowerCAmelCase     :    Dict						=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
			lowerCAmelCase     :    Any						=   max(len(tokenizer.encode(a_	)	) for a in ARTICLES	)
			lowerCAmelCase     :    Optional[int]						=   max(len(tokenizer.encode(a_	)	) for a in SUMMARIES	)
			lowerCAmelCase     :    List[str]						=   4
			lowerCAmelCase     :    List[str]						=   8
			assert max_len_target > max_src_len  # Will be truncated
			assert max_len_source > max_src_len  # Will be truncated
			lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
			lowerCAmelCase     :    List[Any]						=   SeqaSeqDataset(
			    a_		,      data_dir=a_		,      type_path="train"		,      max_source_length=a_		,      max_target_length=a_		,      src_lang=a_		,      tgt_lang=a_		,      )
			lowerCAmelCase     :    int						=   DataLoader(a_		,      batch_size=2		,      collate_fn=train_dataset.collate_fn	)
			for batch in dataloader:
					assert isinstance(a_		,      a_	)
					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     :    Union[str, Any]						=   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   _lowerCamelCase    (   self		,      a_	):
			lowerCAmelCase     :    str						=   AutoTokenizer.from_pretrained(a_	)
			lowerCAmelCase     :    str						=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
			lowerCAmelCase     :    Dict						=   max(len(tokenizer.encode(a_	)	) for a in ARTICLES	)
			lowerCAmelCase     :    Tuple						=   max(len(tokenizer.encode(a_	)	) for a in SUMMARIES	)
			lowerCAmelCase     :    Optional[Any]						=   4
			lowerCAmelCase     :    List[str]						=   LegacySeqaSeqDataset(
			    a_		,      data_dir=a_		,      type_path="train"		,      max_source_length=20		,      max_target_length=a_		,      )
			lowerCAmelCase     :    Dict						=   DataLoader(a_		,      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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"	)

			lowerCAmelCase     :    Optional[Any]						=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)	)
			lowerCAmelCase     :    Any						=   tmp_dir.joinpath("train.source"	).open().readlines()
			lowerCAmelCase     :    Tuple						=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)	)
			pack_data_dir(a_		,      a_		,      128		,      a_	)
			lowerCAmelCase     :    str						=   {x.name for x in tmp_dir.iterdir()}
			lowerCAmelCase     :    Any						=   {x.name for x in save_dir.iterdir()}
			lowerCAmelCase     :    Optional[int]						=   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(a_	) < len(a_	)
			assert len(a_	) == 1
			assert len(packed_examples[0]	) == sum(len(a_	) for x in orig_examples	)
			assert orig_paths == new_paths



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



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase	,		lowerCAmelCase	,		lowerCAmelCase     :    Optional[Any]						=   self._get_dataset(max_len=512	)
			lowerCAmelCase     :    Dict						=   2
			lowerCAmelCase     :    Union[str, Any]						=   ds.make_sortish_sampler(a_		,      shuffle=a_	)

			lowerCAmelCase     :    List[str]						=   DataLoader(a_		,      batch_size=a_		,      collate_fn=ds.collate_fn		,      num_workers=2	)
			lowerCAmelCase     :    Dict						=   DataLoader(a_		,      batch_size=a_		,      collate_fn=ds.collate_fn		,      num_workers=2		,      sampler=a_	)

			lowerCAmelCase     :    Optional[Any]						=   tokenizer.pad_token_id

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

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



	def   _lowerCamelCase    (   self		,      a_=1_000		,      a_=128	):
			if os.getenv("USE_REAL_DATA"		,      a_	):
					lowerCAmelCase     :    Optional[Any]						=   "examples/seq2seq/wmt_en_ro"
					lowerCAmelCase     :    str						=   max_len * 2 * 64
					if not Path(a_	).joinpath("train.len"	).exists():
							save_len_file(a_		,      a_	)
			else:
					lowerCAmelCase     :    List[Any]						=   "examples/seq2seq/test_data/wmt_en_ro"
					lowerCAmelCase     :    str						=   max_len * 4
					save_len_file(a_		,      a_	)

			lowerCAmelCase     :    Any						=   AutoTokenizer.from_pretrained(a_	)
			lowerCAmelCase     :    Optional[int]						=   SeqaSeqDataset(
			    a_		,      data_dir=a_		,      type_path="train"		,      max_source_length=a_		,      max_target_length=a_		,      n_obs=a_		,      )
			return ds, max_tokens, tokenizer



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase	,		lowerCAmelCase	,		lowerCAmelCase     :    str						=   self._get_dataset()
			lowerCAmelCase     :    Optional[Any]						=   set(DistributedSortishSampler(a_		,      256		,      num_replicas=2		,      rank=0		,      add_extra_examples=a_	)	)
			lowerCAmelCase     :    Union[str, Any]						=   set(DistributedSortishSampler(a_		,      256		,      num_replicas=2		,      rank=1		,      add_extra_examples=a_	)	)
			assert idsa.intersection(a_	) == set()





	@parameterized.expand(
	    [
	        MBART_TINY,
	        MARIAN_TINY,
	        T5_TINY,
	        BART_TINY,
	        PEGASUS_XSUM,
	    ]		,      )
	def   _lowerCamelCase    (   self		,      a_	):
			lowerCAmelCase     :    Union[str, Any]						=   AutoTokenizer.from_pretrained(a_		,      use_fast=a_	)
			if tok_name == MBART_TINY:
					lowerCAmelCase     :    str						=   SeqaSeqDataset(
					    a_		,      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     :    Any						=   train_dataset.dataset_kwargs
					assert "src_lang" in kwargs and "tgt_lang" in kwargs
			else:
					lowerCAmelCase     :    List[str]						=   SeqaSeqDataset(
					    a_		,      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     :    Union[str, Any]						=   train_dataset.dataset_kwargs
					assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
					assert len(a_	) == 1 if tok_name == BART_TINY else len(a_	) == 0










'''simple docstring'''






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


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

					parser.add_argument(
					    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
					)
					parser.add_argument(
					    """--original_config_file""",
					    type=str,
					    required=True,
					    help="""The YAML config file corresponding to the original architecture.""",
					)
					parser.add_argument(
					    """--num_in_channels""",
					    default=None,
					    type=int,
					    help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
					)
					parser.add_argument(
					    """--image_size""",
					    default=5_12,
					    type=int,
					    help=(
					        """The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
					        """ Base. Use 768 for Stable Diffusion v2."""
					    ),
					)
					parser.add_argument(
					    """--extract_ema""",
					    action="""store_true""",
					    help=(
					        """Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
					        """ or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
					        """ higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
					    ),
					)
					parser.add_argument(
					    """--upcast_attention""",
					    action="""store_true""",
					    help=(
					        """Whether the attention computation should always be upcasted. This is necessary when running stable"""
					        """ diffusion 2.1."""
					    ),
					)
					parser.add_argument(
					    """--from_safetensors""",
					    action="""store_true""",
					    help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
					)
					parser.add_argument(
					    """--to_safetensors""",
					    action="""store_true""",
					    help="""Whether to store pipeline in safetensors format or not.""",
					)
					parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
					parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")






					def      __A						(							a_						:  List[str]	):
							if string == "True":
									return True
							elif string == "False":
									return False
							else:
									raise ValueError(f'''could not parse string as bool {string}'''	)

					parser.add_argument(
					    """--use_linear_projection""", help="""Override for use linear projection""", required=False, type=parse_bool
					)

					parser.add_argument("""--cross_attention_dim""", help="""Override for cross attention_dim""", required=False, type=int)

					lowerCAmelCase       		=      parser.parse_args()

					lowerCAmelCase       		=      download_controlnet_from_original_ckpt(
					    checkpoint_path=args.checkpoint_path,
					    original_config_file=args.original_config_file,
					    image_size=args.image_size,
					    extract_ema=args.extract_ema,
					    num_in_channels=args.num_in_channels,
					    upcast_attention=args.upcast_attention,
					    from_safetensors=args.from_safetensors,
					    device=args.device,
					    use_linear_projection=args.use_linear_projection,
					    cross_attention_dim=args.cross_attention_dim,
					)

					controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



