


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=1					):


		"""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       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=0					):


		"""simple docstring"""




		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(SCREAMING_SNAKE_CASE						,    n_shave_prefix_segments=SCREAMING_SNAKE_CASE					)

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

		return mapping
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=0					):


		"""simple docstring"""




		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(SCREAMING_SNAKE_CASE						,    n_shave_prefix_segments=SCREAMING_SNAKE_CASE					)

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

		return mapping
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None					):


		"""simple docstring"""




		assert isinstance(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					), "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(SCREAMING_SNAKE_CASE					)
						lowercase__						=				key.reshape(SCREAMING_SNAKE_CASE					)
						lowercase__						=				value.reshape(SCREAMING_SNAKE_CASE					)

		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       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		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(SCREAMING_SNAKE_CASE					)
		}

		# 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(SCREAMING_SNAKE_CASE					)
		}

		# 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(SCREAMING_SNAKE_CASE					)
		}

		for i in range(1						,    SCREAMING_SNAKE_CASE					):
				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(SCREAMING_SNAKE_CASE					)
				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(
				    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    additional_replacements=[meta_path, resnet_op]						,    config=SCREAMING_SNAKE_CASE					)

				if len(SCREAMING_SNAKE_CASE					):
						lowercase__						=				renew_attention_paths(SCREAMING_SNAKE_CASE					)
						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(
						    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    additional_replacements=[meta_path]						,    attention_paths_to_split=SCREAMING_SNAKE_CASE						,    config=SCREAMING_SNAKE_CASE						,    )

		lowercase__						=				middle_blocks[0]
		lowercase__						=				middle_blocks[1]
		lowercase__						=				middle_blocks[2]

		lowercase__						=				renew_resnet_paths(SCREAMING_SNAKE_CASE					)
		assign_to_checkpoint(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    config=SCREAMING_SNAKE_CASE					)

		lowercase__						=				renew_resnet_paths(SCREAMING_SNAKE_CASE					)
		assign_to_checkpoint(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    config=SCREAMING_SNAKE_CASE					)

		lowercase__						=				renew_attention_paths(SCREAMING_SNAKE_CASE					)
		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(
		    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    attention_paths_to_split=SCREAMING_SNAKE_CASE						,    config=SCREAMING_SNAKE_CASE					)

		for i in range(SCREAMING_SNAKE_CASE					):
				lowercase__						=				i // (config['''num_res_blocks'''] + 1)
				lowercase__						=				i % (config['''num_res_blocks'''] + 1)
				lowercase__						=				[shave_segments(SCREAMING_SNAKE_CASE						,    2					) for name in output_blocks[i]]
				lowercase__						=				{}

				for layer in output_block_layers:
						lowercase__					,		lowercase__						=				layer.split('''.'''					)[0], shave_segments(SCREAMING_SNAKE_CASE						,    1					)
						if layer_id in output_block_list:
								output_block_list[layer_id].append(SCREAMING_SNAKE_CASE					)
						else:
								lowercase__						=				[layer_name]

				if len(SCREAMING_SNAKE_CASE					) > 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(SCREAMING_SNAKE_CASE					)
						lowercase__						=				renew_resnet_paths(SCREAMING_SNAKE_CASE					)

						lowercase__						=				{'''old''': f'output_blocks.{i}.0', '''new''': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
						assign_to_checkpoint(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    additional_replacements=[meta_path]						,    config=SCREAMING_SNAKE_CASE					)

						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(SCREAMING_SNAKE_CASE					) == 2:
										lowercase__						=				[]

						if len(SCREAMING_SNAKE_CASE					):
								lowercase__						=				renew_attention_paths(SCREAMING_SNAKE_CASE					)
								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(
								    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    additional_replacements=[meta_path]						,    attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions					) else None						,    config=SCREAMING_SNAKE_CASE						,    )
				else:
						lowercase__						=				renew_resnet_paths(SCREAMING_SNAKE_CASE						,    n_shave_prefix_segments=1					)
						for path in resnet_0_paths:
								lowercase__						=				'''.'''.join(['''output_blocks''', str(SCREAMING_SNAKE_CASE					), path['''old''']]					)
								lowercase__						=				'''.'''.join(['''up_blocks''', str(SCREAMING_SNAKE_CASE					), '''resnets''', str(SCREAMING_SNAKE_CASE					), path['''new''']]					)

								lowercase__						=				checkpoint[old_path]

		return new_checkpoint


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(
			    '--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.')

			lowerCAmelCase	=							parser.parse_args()

			lowerCAmelCase	=							torch.load(args.checkpoint_path)

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

			lowerCAmelCase	=							convert_ldm_checkpoint(checkpoint, config)

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

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

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

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






import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


lowerCAmelCase	=							'\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n'


lowerCAmelCase	=							'\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'


lowerCAmelCase	=							R'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n    >>> metric = datasets.load_metric("competition_math")\n    >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'


@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION					,     _KWARGS_DESCRIPTION      )
class 	_a							(      datasets.Metric      ):

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       Tuple:





									"""simple docstring"""







									return datasets.MetricInfo(
									    description=_DESCRIPTION   ,						citation=_CITATION   ,						inputs_description=_KWARGS_DESCRIPTION   ,						features=datasets.Features(
									        {
									            '''predictions''': datasets.Value('''string'''							),
									            '''references''': datasets.Value('''string'''							),
									        }							)   ,						homepage='''https://github.com/hendrycks/math'''   ,						codebase_urls=['''https://github.com/hendrycks/math''']   ,						)


							def 				lowerCamelCase_			(							self:     Union[str, Any]   ,						UpperCamelCase_:     Any   ,						UpperCamelCase_:     int							)      ->       List[str]:





									"""simple docstring"""







									lowercase__						=				0.0
									for i, j in zip(UpperCamelCase_   ,						UpperCamelCase_							):
											n_correct += 1.0 if math_equivalence.is_equiv(UpperCamelCase_   ,						UpperCamelCase_							) else 0.0
									lowercase__						=				n_correct / len(UpperCamelCase_							)
									return {
									    "accuracy": accuracy,
									}


