
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    int=1)      ->					int:






							'''simple docstring'''





							if n_shave_prefix_segments >= 0:
														return ".".join(path.split(".")[n_shave_prefix_segments:])
							else:
														return ".".join(path.split(".")[:n_shave_prefix_segments])




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    Optional[int]=0)      ->					Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      []
							for old_item in old_list:
														__UpperCamelCase			:     int         =      old_item.replace("in_layers.0"		,						"norm1")
														__UpperCamelCase			:     Union[str, Any]         =      new_item.replace("in_layers.2"		,						"conv1")

														__UpperCamelCase			:     int         =      new_item.replace("out_layers.0"		,						"norm2")
														__UpperCamelCase			:     int         =      new_item.replace("out_layers.3"		,						"conv2")

														__UpperCamelCase			:     int         =      new_item.replace("emb_layers.1"		,						"time_emb_proj")
														__UpperCamelCase			:     List[Any]         =      new_item.replace("skip_connection"		,						"conv_shortcut")

														__UpperCamelCase			:     List[Any]         =      shave_segments(_lowerCamelCase		,						n_shave_prefix_segments=_lowerCamelCase)

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

							return mapping




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str		,						_lowerCamelCase				:    int=0)      ->					Dict:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      []
							for old_item in old_list:
														__UpperCamelCase			:     Optional[Any]         =      old_item

														__UpperCamelCase			:     Any         =      new_item.replace("norm.weight"		,						"group_norm.weight")
														__UpperCamelCase			:     List[str]         =      new_item.replace("norm.bias"		,						"group_norm.bias")

														__UpperCamelCase			:     Any         =      new_item.replace("proj_out.weight"		,						"proj_attn.weight")
														__UpperCamelCase			:     int         =      new_item.replace("proj_out.bias"		,						"proj_attn.bias")

														__UpperCamelCase			:     Any         =      shave_segments(_lowerCamelCase		,						n_shave_prefix_segments=_lowerCamelCase)

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

							return mapping




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str		,						_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Dict=None		,						_lowerCamelCase				:    int=None		,						_lowerCamelCase				:    str=None)      ->					List[Any]:






							'''simple docstring'''





							assert isinstance(_lowerCamelCase		,						_lowerCamelCase), "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():
																					__UpperCamelCase			:     Union[str, Any]         =      old_checkpoint[path]
																					__UpperCamelCase			:     Optional[int]         =      old_tensor.shape[0] // 3

																					__UpperCamelCase			:     Optional[Any]         =      (-1, channels) if len(old_tensor.shape) == 3 else (-1)

																					__UpperCamelCase			:     Optional[int]         =      old_tensor.shape[0] // config["num_head_channels"] // 3

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

																					__UpperCamelCase			:     str         =      query.reshape(_lowerCamelCase)
																					__UpperCamelCase			:     Any         =      key.reshape(_lowerCamelCase)
																					__UpperCamelCase			:     int         =      value.reshape(_lowerCamelCase)

							for path in paths:
														__UpperCamelCase			:     Tuple         =      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
														__UpperCamelCase			:     int         =      new_path.replace("middle_block.0"		,						"mid_block.resnets.0")
														__UpperCamelCase			:     List[Any]         =      new_path.replace("middle_block.1"		,						"mid_block.attentions.0")
														__UpperCamelCase			:     Union[str, Any]         =      new_path.replace("middle_block.2"		,						"mid_block.resnets.1")

														if additional_replacements is not None:
																					for replacement in additional_replacements:
																												__UpperCamelCase			:     Any         =      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:
																					__UpperCamelCase			:     Tuple         =      old_checkpoint[path["old"]][:, :, 0]
														else:
																					__UpperCamelCase			:     int         =      old_checkpoint[path["old"]]




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str		,						_lowerCamelCase				:    Optional[Any])      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     str         =      {}

							__UpperCamelCase			:     Union[str, Any]         =      checkpoint["time_embed.0.weight"]
							__UpperCamelCase			:     Tuple         =      checkpoint["time_embed.0.bias"]
							__UpperCamelCase			:     str         =      checkpoint["time_embed.2.weight"]
							__UpperCamelCase			:     List[str]         =      checkpoint["time_embed.2.bias"]

							__UpperCamelCase			:     str         =      checkpoint["input_blocks.0.0.weight"]
							__UpperCamelCase			:     Tuple         =      checkpoint["input_blocks.0.0.bias"]

							__UpperCamelCase			:     int         =      checkpoint["out.0.weight"]
							__UpperCamelCase			:     Optional[int]         =      checkpoint["out.0.bias"]
							__UpperCamelCase			:     Union[str, Any]         =      checkpoint["out.2.weight"]
							__UpperCamelCase			:     str         =      checkpoint["out.2.bias"]

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

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

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

							for i in range(1		,						_lowerCamelCase):
														__UpperCamelCase			:     Optional[int]         =      (i - 1) // (config["num_res_blocks"] + 1)
														__UpperCamelCase			:     List[Any]         =      (i - 1) % (config["num_res_blocks"] + 1)

														__UpperCamelCase			:     Any         =      [key for key in input_blocks[i] if F'input_blocks.{i}.0' in key]
														__UpperCamelCase			:     Any         =      [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:
																					__UpperCamelCase			:     List[str]         =      checkpoint[
																					    F'input_blocks.{i}.0.op.weight'
																					]
																					__UpperCamelCase			:     Tuple         =      checkpoint[
																					    F'input_blocks.{i}.0.op.bias'
																					]
																					continue

														__UpperCamelCase			:     List[Any]         =      renew_resnet_paths(_lowerCamelCase)
														__UpperCamelCase			:     Optional[int]         =      {"old": F'input_blocks.{i}.0', "new": F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
														__UpperCamelCase			:     Optional[Any]         =      {"old": "resnets.2.op", "new": "downsamplers.0.op"}
														assign_to_checkpoint(
														    _lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						additional_replacements=[meta_path, resnet_op]		,						config=_lowerCamelCase)

														if len(_lowerCamelCase):
																					__UpperCamelCase			:     Tuple         =      renew_attention_paths(_lowerCamelCase)
																					__UpperCamelCase			:     str         =      {
																					    "old": F'input_blocks.{i}.1',
																					    "new": F'down_blocks.{block_id}.attentions.{layer_in_block_id}',
																					}
																					__UpperCamelCase			:     Tuple         =      {
																					    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(
																					    _lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						additional_replacements=[meta_path]		,						attention_paths_to_split=_lowerCamelCase		,						config=_lowerCamelCase		,						)

							__UpperCamelCase			:     List[str]         =      middle_blocks[0]
							__UpperCamelCase			:     List[Any]         =      middle_blocks[1]
							__UpperCamelCase			:     str         =      middle_blocks[2]

							__UpperCamelCase			:     Optional[int]         =      renew_resnet_paths(_lowerCamelCase)
							assign_to_checkpoint(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						config=_lowerCamelCase)

							__UpperCamelCase			:     Optional[int]         =      renew_resnet_paths(_lowerCamelCase)
							assign_to_checkpoint(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						config=_lowerCamelCase)

							__UpperCamelCase			:     str         =      renew_attention_paths(_lowerCamelCase)
							__UpperCamelCase			:     Optional[int]         =      {
							    "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(
							    _lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						attention_paths_to_split=_lowerCamelCase		,						config=_lowerCamelCase)

							for i in range(_lowerCamelCase):
														__UpperCamelCase			:     Dict         =      i // (config["num_res_blocks"] + 1)
														__UpperCamelCase			:     List[str]         =      i % (config["num_res_blocks"] + 1)
														__UpperCamelCase			:     Dict         =      [shave_segments(_lowerCamelCase		,						2) for name in output_blocks[i]]
														__UpperCamelCase			:     Optional[Any]         =      {}

														for layer in output_block_layers:
																					__UpperCamelCase     ,		__UpperCamelCase			:     Optional[Any]         =      layer.split(".")[0], shave_segments(_lowerCamelCase		,						1)
																					if layer_id in output_block_list:
																												output_block_list[layer_id].append(_lowerCamelCase)
																					else:
																												__UpperCamelCase			:     Any         =      [layer_name]

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

																					__UpperCamelCase			:     str         =      renew_resnet_paths(_lowerCamelCase)
																					__UpperCamelCase			:     str         =      renew_resnet_paths(_lowerCamelCase)

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

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

																												# Clear attentions as they have been attributed above.
																												if len(_lowerCamelCase) == 2:
																																			__UpperCamelCase			:     int         =      []

																					if len(_lowerCamelCase):
																												__UpperCamelCase			:     Any         =      renew_attention_paths(_lowerCamelCase)
																												__UpperCamelCase			:     Optional[Any]         =      {
																												    "old": F'output_blocks.{i}.1',
																												    "new": F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
																												}
																												__UpperCamelCase			:     str         =      {
																												    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(
																												    _lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						additional_replacements=[meta_path]		,						attention_paths_to_split=to_split if any("qkv" in key for key in attentions) else None		,						config=_lowerCamelCase		,						)
														else:
																					__UpperCamelCase			:     Union[str, Any]         =      renew_resnet_paths(_lowerCamelCase		,						n_shave_prefix_segments=1)
																					for path in resnet_0_paths:
																												__UpperCamelCase			:     Optional[int]         =      ".".join(["output_blocks", str(_lowerCamelCase), path["old"]])
																												__UpperCamelCase			:     str         =      ".".join(["up_blocks", str(_lowerCamelCase), "resnets", str(_lowerCamelCase), path["new"]])

																												__UpperCamelCase			:     Optional[int]         =      checkpoint[old_path]

							return new_checkpoint


if __name__ == "__main__":
		lowercase						:      Union[str, Any]									= 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						:      Union[str, Any]									= parser.parse_args()

		lowercase						:      Tuple									= torch.load(args.checkpoint_path)

		with open(args.config_file) as f:
				lowercase						:      Union[str, Any]									= json.loads(f.read())

		lowercase						:      Any									= convert_ldm_checkpoint(checkpoint, config)

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

		lowercase						:      Union[str, Any]									= UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

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

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

import re
import string

import numpy as np

import datasets


lowercase						:      List[str]									= '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'

lowercase						:      List[str]									= '\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results["exact_match"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    33.3\n\n'

lowercase						:      List[str]									= '\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,		_KWARGS_DESCRIPTION)
class 				lowerCamelCase__							(       datasets.Metric):




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:Dict		)	->				Optional[Any]:
								return datasets.MetricInfo(
								    description=_DESCRIPTION  ,     citation=_CITATION  ,     inputs_description=_KWARGS_DESCRIPTION  ,     features=datasets.Features(
								        {
								            "predictions": datasets.Value("string"  ,     id="sequence"		),
								            "references": datasets.Value("string"  ,     id="sequence"		),
								        }		)  ,     reference_urls=[]  ,     )







	def      _lowerCamelCase       (      self	:int  ,     a	:Optional[Any]  ,     a	:Dict  ,     a	:Optional[int]=None  ,     a	:int=False  ,     a	:Tuple=False  ,     a	:Optional[int]=False  ,     )	->				Any:
								if regexes_to_ignore is not None:
															for s in regexes_to_ignore:
																						__UpperCamelCase			:     List[Any]         =      np.array([re.sub(a  ,     ""  ,     a		) for x in predictions]		)
																						__UpperCamelCase			:     Optional[Any]         =      np.array([re.sub(a  ,     ""  ,     a		) for x in references]		)
								else:
															__UpperCamelCase			:     Optional[int]         =      np.asarray(a		)
															__UpperCamelCase			:     List[str]         =      np.asarray(a		)

								if ignore_case:
															__UpperCamelCase			:     Optional[int]         =      np.char.lower(a		)
															__UpperCamelCase			:     str         =      np.char.lower(a		)

								if ignore_punctuation:
															__UpperCamelCase			:     Tuple         =      string.punctuation.maketrans(""  ,     ""  ,     string.punctuation		)
															__UpperCamelCase			:     int         =      np.char.translate(a  ,     table=a		)
															__UpperCamelCase			:     str         =      np.char.translate(a  ,     table=a		)

								if ignore_numbers:
															__UpperCamelCase			:     List[str]         =      string.digits.maketrans(""  ,     ""  ,     string.digits		)
															__UpperCamelCase			:     Tuple         =      np.char.translate(a  ,     table=a		)
															__UpperCamelCase			:     Union[str, Any]         =      np.char.translate(a  ,     table=a		)

								__UpperCamelCase			:     List[Any]         =      predictions == references

								return {"exact_match": np.mean(a		) * 1_0_0}