
"""simple docstring"""






import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__=1      )						->Optional[Any]:
			if n_shave_prefix_segments >= 0:
						return ".".join(path.split("."      )[n_shave_prefix_segments:]      )
			else:
						return ".".join(path.split("."      )[:n_shave_prefix_segments]      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__=0      )						->Any:
			__lowercase       =       []
			for old_item in old_list:
						__lowercase       =       old_item.replace("in_layers.0"			,   "norm1"      )
						__lowercase       =       new_item.replace("in_layers.2"			,   "conv1"      )

						__lowercase       =       new_item.replace("out_layers.0"			,   "norm2"      )
						__lowercase       =       new_item.replace("out_layers.3"			,   "conv2"      )

						__lowercase       =       new_item.replace("emb_layers.1"			,   "time_emb_proj"      )
						__lowercase       =       new_item.replace("skip_connection"			,   "conv_shortcut"      )

						__lowercase       =       shave_segments(__magic_name__			,   n_shave_prefix_segments=__magic_name__      )

						mapping.append({"old": old_item, "new": new_item}      )

			return mapping



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__=0      )						->Union[str, Any]:
			__lowercase       =       []
			for old_item in old_list:
						__lowercase       =       old_item

						__lowercase       =       new_item.replace("norm.weight"			,   "group_norm.weight"      )
						__lowercase       =       new_item.replace("norm.bias"			,   "group_norm.bias"      )

						__lowercase       =       new_item.replace("proj_out.weight"			,   "proj_attn.weight"      )
						__lowercase       =       new_item.replace("proj_out.bias"			,   "proj_attn.bias"      )

						__lowercase       =       shave_segments(__magic_name__			,   n_shave_prefix_segments=__magic_name__      )

						mapping.append({"old": old_item, "new": new_item}      )

			return mapping



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__=None			,   __magic_name__=None			,   __magic_name__=None      )						->int:
			assert isinstance(__magic_name__			,   __magic_name__      ), "Paths should be a list of dicts containing 'old' and 'new' keys."

			# Splits the attention layers into three variables.
			if attention_paths_to_split is not None:
						for path, path_map in attention_paths_to_split.items():
									__lowercase       =       old_checkpoint[path]
									__lowercase       =       old_tensor.shape[0] // 3

									__lowercase       =       (-1, channels) if len(old_tensor.shape      ) == 3 else (-1)

									__lowercase       =       old_tensor.shape[0] // config["num_head_channels"] // 3

									__lowercase       =       old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]      )
									__lowercase   ,					__lowercase   ,					__lowercase       =       old_tensor.split(channels // num_heads			,   dim=1      )

									__lowercase       =       query.reshape(__magic_name__      )
									__lowercase       =       key.reshape(__magic_name__      )
									__lowercase       =       value.reshape(__magic_name__      )

			for path in paths:
						__lowercase       =       path["new"]

						# These have already been assigned
						if attention_paths_to_split is not None and new_path in attention_paths_to_split:
									continue

						# Global renaming happens here
						__lowercase       =       new_path.replace("middle_block.0"			,   "mid_block.resnets.0"      )
						__lowercase       =       new_path.replace("middle_block.1"			,   "mid_block.attentions.0"      )
						__lowercase       =       new_path.replace("middle_block.2"			,   "mid_block.resnets.1"      )

						if additional_replacements is not None:
									for replacement in additional_replacements:
												__lowercase       =       new_path.replace(replacement["old"]			,   replacement["new"]      )

        # proj_attn.weight has to be converted from conv 1D to linear
						if "proj_attn.weight" in new_path:
									__lowercase       =       old_checkpoint[path["old"]][:, :, 0]
						else:
									__lowercase       =       old_checkpoint[path["old"]]



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Tuple:
			__lowercase       =       {}

			__lowercase       =       checkpoint["time_embed.0.weight"]
			__lowercase       =       checkpoint["time_embed.0.bias"]
			__lowercase       =       checkpoint["time_embed.2.weight"]
			__lowercase       =       checkpoint["time_embed.2.bias"]

			__lowercase       =       checkpoint["input_blocks.0.0.weight"]
			__lowercase       =       checkpoint["input_blocks.0.0.bias"]

			__lowercase       =       checkpoint["out.0.weight"]
			__lowercase       =       checkpoint["out.0.bias"]
			__lowercase       =       checkpoint["out.2.weight"]
			__lowercase       =       checkpoint["out.2.bias"]

			# Retrieves the keys for the input blocks only
			__lowercase       =       len({".".join(layer.split("."      )[:2]      ) for layer in checkpoint if "input_blocks" in layer}      )
			__lowercase       =       {
			    layer_id: [key for key in checkpoint if F'''input_blocks.{layer_id}''' in key]
			    for layer_id in range(__magic_name__      )
			}

			# Retrieves the keys for the middle blocks only
			__lowercase       =       len({".".join(layer.split("."      )[:2]      ) for layer in checkpoint if "middle_block" in layer}      )
			__lowercase       =       {
			    layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
			    for layer_id in range(__magic_name__      )
			}

			# Retrieves the keys for the output blocks only
			__lowercase       =       len({".".join(layer.split("."      )[:2]      ) for layer in checkpoint if "output_blocks" in layer}      )
			__lowercase       =       {
			    layer_id: [key for key in checkpoint if F'''output_blocks.{layer_id}''' in key]
			    for layer_id in range(__magic_name__      )
			}

			for i in range(1			,   __magic_name__      ):
						__lowercase       =       (i - 1) // (config["num_res_blocks"] + 1)
						__lowercase       =       (i - 1) % (config["num_res_blocks"] + 1)

						__lowercase       =       [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
						__lowercase       =       [key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]

						if F'''input_blocks.{i}.0.op.weight''' in checkpoint:
									__lowercase       =       checkpoint[
									    F'''input_blocks.{i}.0.op.weight'''
									]
									__lowercase       =       checkpoint[
									    F'''input_blocks.{i}.0.op.bias'''
									]
									continue

						__lowercase       =       renew_resnet_paths(__magic_name__      )
						__lowercase       =       {"old": F'''input_blocks.{i}.0''', "new": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
						__lowercase       =       {"old": "resnets.2.op", "new": "downsamplers.0.op"}
						assign_to_checkpoint(
						    __magic_name__			,   __magic_name__			,   __magic_name__			,   additional_replacements=[meta_path, resnet_op]			,   config=__magic_name__      )

						if len(__magic_name__      ):
									__lowercase       =       renew_attention_paths(__magic_name__      )
									__lowercase       =       {
									    "old": F'''input_blocks.{i}.1''',
									    "new": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
									}
									__lowercase       =       {
									    F'''input_blocks.{i}.1.qkv.bias''': {
									        "key": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
									        "query": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
									        "value": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
									    },
									    F'''input_blocks.{i}.1.qkv.weight''': {
									        "key": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
									        "query": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
									        "value": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
									    },
									}
									assign_to_checkpoint(
									    __magic_name__			,   __magic_name__			,   __magic_name__			,   additional_replacements=[meta_path]			,   attention_paths_to_split=__magic_name__			,   config=__magic_name__			,   )

			__lowercase       =       middle_blocks[0]
			__lowercase       =       middle_blocks[1]
			__lowercase       =       middle_blocks[2]

			__lowercase       =       renew_resnet_paths(__magic_name__      )
			assign_to_checkpoint(__magic_name__			,   __magic_name__			,   __magic_name__			,   config=__magic_name__      )

			__lowercase       =       renew_resnet_paths(__magic_name__      )
			assign_to_checkpoint(__magic_name__			,   __magic_name__			,   __magic_name__			,   config=__magic_name__      )

			__lowercase       =       renew_attention_paths(__magic_name__      )
			__lowercase       =       {
			    "middle_block.1.qkv.bias": {
			        "key": "mid_block.attentions.0.key.bias",
			        "query": "mid_block.attentions.0.query.bias",
			        "value": "mid_block.attentions.0.value.bias",
			    },
			    "middle_block.1.qkv.weight": {
			        "key": "mid_block.attentions.0.key.weight",
			        "query": "mid_block.attentions.0.query.weight",
			        "value": "mid_block.attentions.0.value.weight",
			    },
			}
			assign_to_checkpoint(
			    __magic_name__			,   __magic_name__			,   __magic_name__			,   attention_paths_to_split=__magic_name__			,   config=__magic_name__      )

			for i in range(__magic_name__      ):
						__lowercase       =       i // (config["num_res_blocks"] + 1)
						__lowercase       =       i % (config["num_res_blocks"] + 1)
						__lowercase       =       [shave_segments(__magic_name__			,   2      ) for name in output_blocks[i]]
						__lowercase       =       {}

						for layer in output_block_layers:
									__lowercase   ,					__lowercase       =       layer.split("."      )[0], shave_segments(__magic_name__			,   1      )
									if layer_id in output_block_list:
												output_block_list[layer_id].append(__magic_name__      )
									else:
												__lowercase       =       [layer_name]

						if len(__magic_name__      ) > 1:
									__lowercase       =       [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
									__lowercase       =       [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

									__lowercase       =       renew_resnet_paths(__magic_name__      )
									__lowercase       =       renew_resnet_paths(__magic_name__      )

									__lowercase       =       {"old": F'''output_blocks.{i}.0''', "new": F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
									assign_to_checkpoint(__magic_name__			,   __magic_name__			,   __magic_name__			,   additional_replacements=[meta_path]			,   config=__magic_name__      )

									if ["conv.weight", "conv.bias"] in output_block_list.values():
												__lowercase       =       list(output_block_list.values()      ).index(["conv.weight", "conv.bias"]      )
												__lowercase       =       checkpoint[
												    F'''output_blocks.{i}.{index}.conv.weight'''
												]
												__lowercase       =       checkpoint[
												    F'''output_blocks.{i}.{index}.conv.bias'''
												]

												# Clear attentions as they have been attributed above.
												if len(__magic_name__      ) == 2:
															__lowercase       =       []

									if len(__magic_name__      ):
												__lowercase       =       renew_attention_paths(__magic_name__      )
												__lowercase       =       {
												    "old": F'''output_blocks.{i}.1''',
												    "new": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
												}
												__lowercase       =       {
												    F'''output_blocks.{i}.1.qkv.bias''': {
												        "key": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
												        "query": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
												        "value": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
												    },
												    F'''output_blocks.{i}.1.qkv.weight''': {
												        "key": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
												        "query": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
												        "value": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
												    },
												}
												assign_to_checkpoint(
												    __magic_name__			,   __magic_name__			,   __magic_name__			,   additional_replacements=[meta_path]			,   attention_paths_to_split=to_split if any("qkv" in key for key in attentions      ) else None			,   config=__magic_name__			,   )
						else:
									__lowercase       =       renew_resnet_paths(__magic_name__			,   n_shave_prefix_segments=1      )
									for path in resnet_0_paths:
												__lowercase       =       ".".join(["output_blocks", str(__magic_name__      ), path["old"]]      )
												__lowercase       =       ".".join(["up_blocks", str(__magic_name__      ), "resnets", str(__magic_name__      ), path["new"]]      )

												__lowercase       =       checkpoint[old_path]

			return new_checkpoint


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

					parser.add_argument(
					    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
					)

					parser.add_argument(
					    '''--config_file''',
					    default=None,
					    type=str,
					    required=True,
					    help='''The config json file corresponding to the architecture.''',
					)

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

					_lowercase							=				parser.parse_args()

					_lowercase							=				torch.load(args.checkpoint_path)

					with open(args.config_file) as f:
										_lowercase							=				json.loads(f.read())

					_lowercase							=				convert_ldm_checkpoint(checkpoint, config)

					if "ldm" in config:
										del config["ldm"]

					_lowercase							=				UNetaDModel(**config)
					model.load_state_dict(converted_checkpoint)

					try:
										_lowercase							=				DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
										_lowercase							=				VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

										_lowercase							=				LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
										pipe.save_pretrained(args.dump_path)
					except:  # noqa: E722
										model.save_pretrained(args.dump_path)







"""simple docstring"""






import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


_lowercase							=				[
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.de'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.en'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.fr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.frr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.it'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.simple'''},
    {'''dataset''': '''snli''', '''config_name''': '''plain_text'''},
    {'''dataset''': '''eli5''', '''config_name''': '''LFQA_reddit'''},
    {'''dataset''': '''wiki40b''', '''config_name''': '''en'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.compressed'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.no_index'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.multiset.no_index'''},
    {'''dataset''': '''natural_questions''', '''config_name''': '''default'''},
]



def      lowerCAmelCase__			(		__magic_name__=True      )						->int:
			if with_config:
						return [
						    {
						        "testcase_name": d["dataset"] + "/" + d["config_name"],
						        "dataset": d["dataset"],
						        "config_name": d["config_name"],
						    }
						    for d in DATASETS_ON_HF_GCP
						]
			else:
						return [
						    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
						]

@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__a       )       )
class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			int          =   None
							_lowerCamelCase					:			int          =   None
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							Optional[Any]:




										'''simple docstring'''
										with TemporaryDirectory() as tmp_dir:
													__lowercase       =       dataset_module_factory(_lowerCamelCase  ,    cache_dir=_lowerCamelCase       )

													__lowercase       =       import_main_class(dataset_module.module_path  ,    dataset=_lowerCamelCase       )

													__lowercase       =       builder_cls(
													    cache_dir=_lowerCamelCase  ,    config_name=_lowerCamelCase  ,    hash=dataset_module.hash  ,    )

													__lowercase       =       "/".join(
													    [
													        HF_GCP_BASE_URL,
													        builder_instance._relative_data_dir(with_hash=_lowerCamelCase       ).replace(os.sep  ,    "/"       ),
													        config.DATASET_INFO_FILENAME,
													    ]       )
													__lowercase       =       cached_path(_lowerCamelCase  ,    cache_dir=_lowerCamelCase       )
													self.assertTrue(os.path.exists(_lowerCamelCase       )       )





@pytest.mark.integration
def      lowerCAmelCase__			(		__magic_name__      )						->str:
			__lowercase       =       tmp_path_factory.mktemp("test_hf_gcp"      ) / "test_wikipedia_simple"
			__lowercase       =       dataset_module_factory("wikipedia"			,   cache_dir=__magic_name__      )
			__lowercase       =       import_main_class(dataset_module.module_path      )
			__lowercase       =       builder_cls(
			    cache_dir=__magic_name__			,   config_name="20220301.frr"			,   hash=dataset_module.hash			,   )
			# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
			__lowercase       =       None
			builder_instance.download_and_prepare()
			__lowercase       =       builder_instance.as_dataset()
			assert ds







@pytest.mark.integration
def      lowerCAmelCase__			(		__magic_name__      )						->Any:
			__lowercase       =       dataset_module_factory("wikipedia"			,   cache_dir=__magic_name__      )
			__lowercase       =       import_main_class(dataset_module.module_path			,   dataset=__magic_name__      )
			__lowercase       =       builder_cls(
			    cache_dir=__magic_name__			,   config_name="20220301.frr"			,   hash=dataset_module.hash			,   )
			__lowercase       =       builder_instance.as_streaming_dataset()
			assert ds
			assert isinstance(__magic_name__			,   __magic_name__      )
			assert "train" in ds
			assert isinstance(ds["train"]			,   __magic_name__      )
			assert next(iter(ds["train"]      )      )





