
"""simple docstring"""






import argparse

import torch

from diffusers import UNetaDConditionModel



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__=None ,      __magic_name__=None ,      __magic_name__=None			)	->Optional[Any]:
			assert isinstance(_A ,      _A			), "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(_A			)
									__lowercase						      =					key.reshape(_A			)
									__lowercase						      =					value.reshape(_A			)

			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

						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
						__lowercase						      =					old_checkpoint[path["old"]]
						__lowercase						      =					["proj_attn.weight"]
						__lowercase						      =					["proj_out.weight", "proj_in.weight"]
						if any(k in new_path for k in names			):
									__lowercase						      =					weight[:, :, 0]
						elif any(k in new_path for k in names_a			) and len(weight.shape			) > 2 and ".attentions." not in new_path:
									__lowercase						      =					weight[:, :, 0]
						else:
									__lowercase						      =					weight



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

						#         new_item = new_item.replace('norm.weight', 'group_norm.weight')
						#         new_item = new_item.replace('norm.bias', 'group_norm.bias')

						#         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
						#         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

						#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

			return mapping



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__=1			)	->List[str]:







			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			)	->Optional[int]:
			__lowercase						      =					[]
			for old_item in old_list:
						mapping.append({"old": old_item, "new": old_item}			)

			return mapping



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__=0			)	->Tuple:
			__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(_A ,      n_shave_prefix_segments=_A			)

						if "temopral_conv" not in old_item:
									mapping.append({"old": old_item, "new": new_item}			)

			return mapping



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__=None ,      __magic_name__=False			)	->Any:
			__lowercase						      =					{}
			__lowercase						      =					list(checkpoint.keys()			)

			__lowercase						      =					"model.diffusion_model."

			# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
			if sum(k.startswith("model_ema"			) for k in keys			) > 1_0_0 and extract_ema:
						print(F'''Checkpoint {path} has both EMA and non-EMA weights.'''			)
						print(
						    "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
						    " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."			)
						for key in keys:
									if key.startswith("model.diffusion_model"			):
												__lowercase						      =					"model_ema." + "".join(key.split("."			)[1:]			)
												__lowercase						      =					checkpoint.pop(_A			)
			else:
						if sum(k.startswith("model_ema"			) for k in keys			) > 1_0_0:
									print(
									    "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
									    " weights (usually better for inference), please make sure to add the `--extract_ema` flag."			)

						for key in keys:
									__lowercase						      =					checkpoint.pop(_A			)

			__lowercase						      =					{}

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

			if config["class_embed_type"] is None:
						# No parameters to port
						...
			elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
						__lowercase						      =					unet_state_dict["label_emb.0.0.weight"]
						__lowercase						      =					unet_state_dict["label_emb.0.0.bias"]
						__lowercase						      =					unet_state_dict["label_emb.0.2.weight"]
						__lowercase						      =					unet_state_dict["label_emb.0.2.bias"]
			else:
						raise NotImplementedError(F'''Not implemented `class_embed_type`: {config['class_embed_type']}'''			)

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

			__lowercase						      =					[v for v in unet_state_dict if v.startswith("input_blocks.0.1"			)]
			__lowercase						      =					renew_attention_paths(_A			)
			__lowercase						      =					{"old": "input_blocks.0.1", "new": "transformer_in"}
			assign_to_checkpoint(_A ,      _A ,      _A ,      additional_replacements=[meta_path] ,      config=_A			)

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

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

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

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

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

						__lowercase						      =					[
						    key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key and F'''input_blocks.{i}.0.op''' not in key
						]
						__lowercase						      =					[key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]
						__lowercase						      =					[key for key in input_blocks[i] if F'''input_blocks.{i}.2''' in key]

						if F'''input_blocks.{i}.op.weight''' in unet_state_dict:
									__lowercase						      =					unet_state_dict.pop(
									    F'''input_blocks.{i}.op.weight'''			)
									__lowercase						      =					unet_state_dict.pop(
									    F'''input_blocks.{i}.op.bias'''			)

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

						__lowercase						      =					[key for key in resnets if "temopral_conv" in key]
						__lowercase						      =					renew_temp_conv_paths(_A			)
						__lowercase						      =					{
						    "old": F'''input_blocks.{i}.0.temopral_conv''',
						    "new": F'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
						}
						assign_to_checkpoint(
						    _A ,      _A ,      _A ,      additional_replacements=[meta_path] ,      config=_A			)

						if len(_A			):
									__lowercase						      =					renew_attention_paths(_A			)
									__lowercase						      =					{"old": F'''input_blocks.{i}.1''', "new": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
									assign_to_checkpoint(
									    _A ,      _A ,      _A ,      additional_replacements=[meta_path] ,      config=_A			)

						if len(_A			):
									__lowercase						      =					renew_attention_paths(_A			)
									__lowercase						      =					{
									    "old": F'''input_blocks.{i}.2''',
									    "new": F'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
									}
									assign_to_checkpoint(
									    _A ,      _A ,      _A ,      additional_replacements=[meta_path] ,      config=_A			)

			__lowercase						      =					middle_blocks[0]
			__lowercase						      =					[key for key in resnet_a if "temopral_conv" in key]
			__lowercase						      =					middle_blocks[1]
			__lowercase						      =					middle_blocks[2]
			__lowercase						      =					middle_blocks[3]
			__lowercase						      =					[key for key in resnet_a if "temopral_conv" in key]

			__lowercase						      =					renew_resnet_paths(_A			)
			__lowercase						      =					{"old": "middle_block.0", "new": "mid_block.resnets.0"}
			assign_to_checkpoint(
			    _A ,      _A ,      _A ,      config=_A ,      additional_replacements=[meta_path]			)

			__lowercase						      =					renew_temp_conv_paths(_A			)
			__lowercase						      =					{"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
			assign_to_checkpoint(
			    _A ,      _A ,      _A ,      config=_A ,      additional_replacements=[meta_path]			)

			__lowercase						      =					renew_resnet_paths(_A			)
			__lowercase						      =					{"old": "middle_block.3", "new": "mid_block.resnets.1"}
			assign_to_checkpoint(
			    _A ,      _A ,      _A ,      config=_A ,      additional_replacements=[meta_path]			)

			__lowercase						      =					renew_temp_conv_paths(_A			)
			__lowercase						      =					{"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
			assign_to_checkpoint(
			    _A ,      _A ,      _A ,      config=_A ,      additional_replacements=[meta_path]			)

			__lowercase						      =					renew_attention_paths(_A			)
			__lowercase						      =					{"old": "middle_block.1", "new": "mid_block.attentions.0"}
			assign_to_checkpoint(
			    _A ,      _A ,      _A ,      additional_replacements=[meta_path] ,      config=_A			)

			__lowercase						      =					renew_attention_paths(_A			)
			__lowercase						      =					{"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
			assign_to_checkpoint(
			    _A ,      _A ,      _A ,      additional_replacements=[meta_path] ,      config=_A			)

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

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

						if len(_A			) > 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						      =					[key for key in output_blocks[i] if F'''output_blocks.{i}.2''' in key]

									__lowercase						      =					renew_resnet_paths(_A			)
									__lowercase						      =					renew_resnet_paths(_A			)

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

									__lowercase						      =					[key for key in resnets if "temopral_conv" in key]
									__lowercase						      =					renew_temp_conv_paths(_A			)
									__lowercase						      =					{
									    "old": F'''output_blocks.{i}.0.temopral_conv''',
									    "new": F'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
									}
									assign_to_checkpoint(
									    _A ,      _A ,      _A ,      additional_replacements=[meta_path] ,      config=_A			)

									__lowercase						      =					{k: sorted(_A			) for k, v in output_block_list.items()}
									if ["conv.bias", "conv.weight"] in output_block_list.values():
												__lowercase						      =					list(output_block_list.values()			).index(["conv.bias", "conv.weight"]			)
												__lowercase						      =					unet_state_dict[
												    F'''output_blocks.{i}.{index}.conv.weight'''
												]
												__lowercase						      =					unet_state_dict[
												    F'''output_blocks.{i}.{index}.conv.bias'''
												]

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

									if len(_A			):
												__lowercase						      =					renew_attention_paths(_A			)
												__lowercase						      =					{
												    "old": F'''output_blocks.{i}.1''',
												    "new": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
												}
												assign_to_checkpoint(
												    _A ,      _A ,      _A ,      additional_replacements=[meta_path] ,      config=_A			)

									if len(_A			):
												__lowercase						      =					renew_attention_paths(_A			)
												__lowercase						      =					{
												    "old": F'''output_blocks.{i}.2''',
												    "new": F'''up_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
												}
												assign_to_checkpoint(
												    _A ,      _A ,      _A ,      additional_replacements=[meta_path] ,      config=_A			)
						else:
									__lowercase						      =					renew_resnet_paths(_A ,      n_shave_prefix_segments=1			)
									for path in resnet_0_paths:
												__lowercase						      =					".".join(["output_blocks", str(_A			), path["old"]]			)
												__lowercase						      =					".".join(["up_blocks", str(_A			), "resnets", str(_A			), path["new"]]			)
												__lowercase						      =					unet_state_dict[old_path]

									__lowercase						      =					[l for l in output_block_layers if "temopral_conv" in l]
									for path in temopral_conv_paths:
												__lowercase						      =					path.split("temopral_conv."			)[-1]
												__lowercase						      =					".".join(["output_blocks", str(_A			), str(_A			), "temopral_conv", pruned_path]			)
												__lowercase						      =					".".join(["up_blocks", str(_A			), "temp_convs", str(_A			), pruned_path]			)
												__lowercase						      =					unet_state_dict[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('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
					_lowercase							 =							parser.parse_args()

					_lowercase							 =							torch.load(args.checkpoint_path, map_location='''cpu''')
					_lowercase							 =							UNetaDConditionModel()

					_lowercase							 =							convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

					_lowercase							 =							set(unet.state_dict().keys()) - set(converted_ckpt.keys())
					_lowercase							 =							set(converted_ckpt.keys()) - set(unet.state_dict().keys())

					assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

					# load state_dict
					unet.load_state_dict(converted_ckpt)

					unet.save_pretrained(args.dump_path)

					# -- finish converting the unet --







"""simple docstring"""






import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


_lowercase							 =							'''\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
'''

_lowercase							 =							'''
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
'''

_lowercase							 =							'''
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer \'id\' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - \'id\': id of the question-answer pair (see above),
            - \'answers\': a list of Dict {\'text\': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    \'exact\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
    \'total\': Number of score considered
    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer
    \'HasAns_total\': Number of score considered
    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer
    \'NoAns_total\': Number of score considered
    \'best_exact\': Best exact match (with varying threshold)
    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match
    \'best_f1\': Best F1 (with varying threshold)
    \'best_f1_thresh\': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_v2_metric = datasets.load_metric("squad_v2")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}
'''

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



							'''simple docstring'''







							def      SCREAMING_SNAKE_CASE	(    self					) ->       Tuple:




										'''simple docstring'''
										return datasets.MetricInfo(
										    description=_DESCRIPTION     ,						citation=_CITATION     ,						inputs_description=_KWARGS_DESCRIPTION     ,						features=datasets.Features(
										        {
										            "predictions": {
										                "id": datasets.Value("string"					),
										                "prediction_text": datasets.Value("string"					),
										                "no_answer_probability": datasets.Value("float32"					),
										            },
										            "references": {
										                "id": datasets.Value("string"					),
										                "answers": datasets.features.Sequence(
										                    {"text": datasets.Value("string"					), "answer_start": datasets.Value("int32"					)}					),
										            },
										        }					)     ,						codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]     ,						reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]     ,						)





							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase=1.0					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					{p["id"]: p["no_answer_probability"] for p in predictions}
										__lowercase						      =					[{"paragraphs": [{"qas": references}]}]
										__lowercase						      =					{p["id"]: p["prediction_text"] for p in predictions}

										__lowercase						      =					make_qid_to_has_ans(_lowerCamelCase					)  # maps qid to True/False
										__lowercase						      =					[k for k, v in qid_to_has_ans.items() if v]
										__lowercase						      =					[k for k, v in qid_to_has_ans.items() if not v]

										__lowercase      ,				__lowercase						      =					get_raw_scores(_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					apply_no_ans_threshold(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					apply_no_ans_threshold(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					make_eval_dict(_lowerCamelCase     ,						_lowerCamelCase					)

										if has_ans_qids:
													__lowercase						      =					make_eval_dict(_lowerCamelCase     ,						_lowerCamelCase     ,						qid_list=_lowerCamelCase					)
													merge_eval(_lowerCamelCase     ,						_lowerCamelCase     ,						"HasAns"					)
										if no_ans_qids:
													__lowercase						      =					make_eval_dict(_lowerCamelCase     ,						_lowerCamelCase     ,						qid_list=_lowerCamelCase					)
													merge_eval(_lowerCamelCase     ,						_lowerCamelCase     ,						"NoAns"					)
										find_all_best_thresh(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
										return dict(_lowerCamelCase					)





