


'''simple docstring'''



import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
	import torch

	from ..models.auto.modeling_auto import (
	    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
	    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
	)

if is_tf_available() and is_tensorflow_probability_available():
	import tensorflow as tf
	import tensorflow_probability as tfp

	from ..models.auto.modeling_tf_auto import (
	    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
	    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
	)





class 							__UpperCamelCase       (	lowerCAmelCase_      ):




	def __call__(     self		,       __a=None		,       __a=None		,       **__a							):

						'''simple docstring'''





						requires_backends(self		,       'pandas'							)
						import pandas as pd

						if table is None:
											raise ValueError('Keyword argument `table` cannot be None.'							)
						elif query is None:
											if isinstance(__a		,       __a							) and table.get('query'							) is not None and table.get('table'							) is not None:
																__a    : Optional[int]      					=						[table]
											elif isinstance(__a		,       __a							) and len(__a							) > 0:
																if not all(isinstance(__a		,       __a							) for d in table							):
																					raise ValueError(
																					    f"""Keyword argument `table` should be a list of dict, but is {(type(__a							) for d in table)}"""							)

																if table[0].get('query'							) is not None and table[0].get('table'							) is not None:
																					__a    : Dict      					=						table
																else:
																					raise ValueError(
																					    'If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`'
																					    f""" and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys."""							)
											elif Dataset is not None and isinstance(__a		,       __a							) or isinstance(__a		,       types.GeneratorType							):
																return table
											else:
																raise ValueError(
																    'Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but '
																    f"""is {type(__a							)})"""							)
						else:
											__a    : List[Any]      					=						[{'table': table, 'query': query}]

						for tqa_pipeline_input in tqa_pipeline_inputs:
											if not isinstance(tqa_pipeline_input['table']		,       pd.DataFrame							):
																if tqa_pipeline_input["table"] is None:
																					raise ValueError('Table cannot be None.'							)

																__a    : int      					=						pd.DataFrame(tqa_pipeline_input['table']							)

						return tqa_pipeline_inputs





@add_end_docstrings(lowerCAmelCase_      )
class 							__UpperCamelCase       (	lowerCAmelCase_      ):
	A_			    =					"table,query"




	def __init__(     self		,       __a=TableQuestionAnsweringArgumentHandler()		,       *__a		,       **__a							):

						'''simple docstring'''





						super().__init__(*__a		,       **__a							)
						__a    : int      					=						args_parser

						self.check_model_type(
						    dict(
						        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
						        + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()							)
						    if self.framework == 'tf'
						    else dict(
						        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()							)							)

						__a    : int      					=						bool(getattr(self.model.config		,       'aggregation_labels'		,       __a							)							) and bool(
						    getattr(self.model.config		,       'num_aggregation_labels'		,       __a							)							)
						__a    : List[str]      					=						'tapas' if hasattr(self.model.config		,       'aggregation_labels'							) else None




	def    __UpperCAmelCase							(     self		,       **__a							):

						'''simple docstring'''





						return self.model(**__a							)




	def    __UpperCAmelCase							(     self		,       **__a							):

						'''simple docstring'''





						if self.framework == "pt":
											__a    : List[str]      					=						[]
											__a    : Dict      					=						[]
											__a    : List[str]      					=						None
											__a    : int      					=						inputs['input_ids'].shape[0]

											__a    : Any      					=						inputs['input_ids'].to(self.device							)
											__a    : List[Any]      					=						inputs['attention_mask'].to(self.device							)
											__a    : Union[str, Any]      					=						inputs['token_type_ids'].to(self.device							)
											__a    : Any      					=						None

											for index in range(__a							):
																# If sequences have already been processed, the token type IDs will be created according to the previous
																# answer.
																if prev_answers is not None:
																					__a    : str      					=						token_type_ids_example[:, 3]  # shape (seq_len,)
																					__a    : Optional[Any]      					=						np.zeros_like(prev_labels_example.cpu().numpy()							)  # shape (seq_len,)

																					__a    : List[str]      					=						token_type_ids[index]  # shape (seq_len, 7)
																					for i in range(model_labels.shape[0]							):
																										__a    : Tuple      					=						token_type_ids_example[:, 0].tolist()[i]
																										__a    : int      					=						token_type_ids_example[:, 1].tolist()[i] - 1
																										__a    : Any      					=						token_type_ids_example[:, 2].tolist()[i] - 1

																										if row_id >= 0 and col_id >= 0 and segment_id == 1:
																															__a    : int      					=						int(prev_answers[(col_id, row_id)]							)

																					__a    : Dict      					=						torch.from_numpy(__a							).type(torch.long							).to(self.device							)

																__a    : Union[str, Any]      					=						input_ids[index]
																__a    : Optional[int]      					=						attention_mask[index]  # shape (seq_len,)
																__a    : Optional[Any]      					=						token_type_ids[index]  # shape (seq_len, 7)
																__a    : List[Any]      					=						self.model(
																    input_ids=input_ids_example.unsqueeze(0							)		,       attention_mask=attention_mask_example.unsqueeze(0							)		,       token_type_ids=token_type_ids_example.unsqueeze(0							)		,       )
																__a    : List[str]      					=						outputs.logits

																if self.aggregate:
																					all_aggregations.append(outputs.logits_aggregation							)

																all_logits.append(__a							)

																__a    : List[str]      					=						torch.distributions.Bernoulli(logits=__a							)
																__a    : int      					=						dist_per_token.probs * attention_mask_example.type(torch.floataa							).to(
																    dist_per_token.probs.device							)

																__a    : Dict      					=						collections.defaultdict(__a							)
																for i, p in enumerate(probabilities.squeeze().tolist()							):
																					__a    : Optional[int]      					=						token_type_ids_example[:, 0].tolist()[i]
																					__a    : Union[str, Any]      					=						token_type_ids_example[:, 1].tolist()[i] - 1
																					__a    : List[str]      					=						token_type_ids_example[:, 2].tolist()[i] - 1
																					if col >= 0 and row >= 0 and segment_id == 1:
																										coords_to_probs[(col, row)].append(__a							)

																__a    : str      					=						{key: np.array(coords_to_probs[key]							).mean() > 0.5 for key in coords_to_probs}

											__a    : Tuple      					=						torch.cat(tuple(__a							)		,       0							)

											return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(__a							)		,       0							))
						else:
											__a    : Any      					=						[]
											__a    : str      					=						[]
											__a    : int      					=						None
											__a    : int      					=						inputs['input_ids'].shape[0]

											__a    : Optional[int]      					=						inputs['input_ids']
											__a    : List[str]      					=						inputs['attention_mask']
											__a    : str      					=						inputs['token_type_ids'].numpy()
											__a    : Dict      					=						None

											for index in range(__a							):
																# If sequences have already been processed, the token type IDs will be created according to the previous
																# answer.
																if prev_answers is not None:
																					__a    : Any      					=						token_type_ids_example[:, 3]  # shape (seq_len,)
																					__a    : Tuple      					=						np.zeros_like(__a		,       dtype=np.intaa							)  # shape (seq_len,)

																					__a    : str      					=						token_type_ids[index]  # shape (seq_len, 7)
																					for i in range(model_labels.shape[0]							):
																										__a    : List[str]      					=						token_type_ids_example[:, 0].tolist()[i]
																										__a    : str      					=						token_type_ids_example[:, 1].tolist()[i] - 1
																										__a    : int      					=						token_type_ids_example[:, 2].tolist()[i] - 1

																										if row_id >= 0 and col_id >= 0 and segment_id == 1:
																															__a    : Optional[Any]      					=						int(prev_answers[(col_id, row_id)]							)

																					__a    : Union[str, Any]      					=						model_labels

																__a    : str      					=						input_ids[index]
																__a    : Any      					=						attention_mask[index]  # shape (seq_len,)
																__a    : List[str]      					=						token_type_ids[index]  # shape (seq_len, 7)
																__a    : Dict      					=						self.model(
																    input_ids=np.expand_dims(__a		,       axis=0							)		,       attention_mask=np.expand_dims(__a		,       axis=0							)		,       token_type_ids=np.expand_dims(__a		,       axis=0							)		,       )
																__a    : Dict      					=						outputs.logits

																if self.aggregate:
																					all_aggregations.append(outputs.logits_aggregation							)

																all_logits.append(__a							)

																__a    : Tuple      					=						tfp.distributions.Bernoulli(logits=__a							)
																__a    : int      					=						dist_per_token.probs_parameter() * tf.cast(__a		,       tf.floataa							)

																__a    : Optional[Any]      					=						collections.defaultdict(__a							)
																__a    : List[str]      					=						token_type_ids_example
																for i, p in enumerate(tf.squeeze(__a							).numpy().tolist()							):
																					__a    : List[str]      					=						token_type_ids_example[:, 0].tolist()[i]
																					__a    : Optional[int]      					=						token_type_ids_example[:, 1].tolist()[i] - 1
																					__a    : Tuple      					=						token_type_ids_example[:, 2].tolist()[i] - 1
																					if col >= 0 and row >= 0 and segment_id == 1:
																										coords_to_probs[(col, row)].append(__a							)

																__a    : List[str]      					=						{key: np.array(coords_to_probs[key]							).mean() > 0.5 for key in coords_to_probs}

											__a    : List[Any]      					=						tf.concat(tuple(__a							)		,       0							)

											return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(__a							)		,       0							))




	def __call__(     self		,       *__a		,       **__a							):

						'''simple docstring'''





						__a    : str      					=						self._args_parser(*__a		,       **__a							)

						__a    : Dict      					=						super().__call__(__a		,       **__a							)
						if len(__a							) == 1:
											return results[0]
						return results




	def    __UpperCAmelCase							(     self		,       __a=None		,       __a=None		,       __a=None		,       **__a							):

						'''simple docstring'''





						__a    : List[Any]      					=						{}
						if padding is not None:
											__a    : Dict      					=						padding
						if truncation is not None:
											__a    : Dict      					=						truncation

						__a    : List[Any]      					=						{}
						if sequential is not None:
											__a    : Dict      					=						sequential
						return preprocess_params, forward_params, {}




	def    __UpperCAmelCase							(     self		,       __a		,       __a=None		,       __a=True		,       __a=None							):

						'''simple docstring'''





						if truncation is None:
											if self.type == "tapas":
																__a    : List[str]      					=						'drop_rows_to_fit'
											else:
																__a    : Union[str, Any]      					=						'do_not_truncate'

						__a					,       __a    : Optional[Any]      					=						pipeline_input['table'], pipeline_input['query']
						if table.empty:
											raise ValueError('table is empty'							)
						if query is None or query == "":
											raise ValueError('query is empty'							)
						__a    : List[Any]      					=						self.tokenizer(__a		,       __a		,       return_tensors=self.framework		,       truncation=__a		,       padding=__a							)
						__a    : List[Any]      					=						table
						return inputs




	def    __UpperCAmelCase							(     self		,       __a		,       __a=False							):

						'''simple docstring'''





						__a    : Tuple      					=						model_inputs.pop('table'							)

						if self.type == "tapas":
											if sequential:
																__a    : Optional[int]      					=						self.sequential_inference(**__a							)
											else:
																__a    : List[Any]      					=						self.batch_inference(**__a							)
						else:
											__a    : Optional[Any]      					=						self.model.generate(**__a							)
						__a    : int      					=						{'model_inputs': model_inputs, 'table': table, 'outputs': outputs}
						return model_outputs





	def    __UpperCAmelCase							(     self		,       __a							):

						'''simple docstring'''





						__a    : Dict      					=						model_outputs['model_inputs']
						__a    : Optional[int]      					=						model_outputs['table']
						__a    : str      					=						model_outputs['outputs']
						if self.type == "tapas":
											if self.aggregate:
																__a					,       __a    : int      					=						outputs[:2]
																__a    : List[str]      					=						self.tokenizer.convert_logits_to_predictions(__a		,       __a		,       __a							)
																__a					,       __a    : Tuple      					=						predictions
																__a    : Tuple      					=						{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(__a							)}

																__a    : List[Any]      					=						self.model.config.no_aggregation_label_index
																__a    : Optional[Any]      					=						{
																    i: aggregators[i] + ' > ' for i, pred in enumerate(__a							) if pred != no_agg_label_index
																}
											else:
																__a    : Union[str, Any]      					=						outputs[0]
																__a    : str      					=						self.tokenizer.convert_logits_to_predictions(__a		,       __a							)
																__a    : Tuple      					=						predictions[0]
																__a    : Tuple      					=						{}
																__a    : Dict      					=						{}
											__a    : Any      					=						[]
											for index, coordinates in enumerate(__a							):
																__a    : Optional[int]      					=						[table.iat[coordinate] for coordinate in coordinates]
																__a    : Optional[int]      					=						aggregators.get(__a		,       ''							)
																__a    : str      					=						aggregators_prefix.get(__a		,       ''							)
																__a    : Optional[int]      					=						{
																    'answer': aggregator_prefix + ', '.join(__a							),
																    'coordinates': coordinates,
																    'cells': [table.iat[coordinate] for coordinate in coordinates],
																}
																if aggregator:
																					__a    : int      					=						aggregator

																answers.append(__a							)
											if len(__a							) == 0:
																raise PipelineException('Empty answer'							)
						else:
											__a    : Tuple      					=						[{'answer': answer} for answer in self.tokenizer.batch_decode(__a		,       skip_special_tokens=__a							)]

						return answers if len(__a							) > 1 else answers[0]







'''simple docstring'''



import argparse

import torch

from diffusers import UNetaDConditionModel






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Tuple=None		,   _SCREAMING_SNAKE_CASE   :     Any=None		,   _SCREAMING_SNAKE_CASE   :     List[Any]=None     ):
					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():
															__a    : Union[str, Any]      					=						old_checkpoint[path]
															__a    : List[str]      					=						old_tensor.shape[0] // 3

															__a    : Dict      					=						(-1, channels) if len(old_tensor.shape     ) == 3 else (-1)

															__a    : List[str]      					=						old_tensor.shape[0] // config['num_head_channels'] // 3

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

															__a    : Optional[int]      					=						query.reshape(_SCREAMING_SNAKE_CASE     )
															__a    : Tuple      					=						key.reshape(_SCREAMING_SNAKE_CASE     )
															__a    : List[str]      					=						value.reshape(_SCREAMING_SNAKE_CASE     )

					for path in paths:
										__a    : 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

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

        # proj_attn.weight has to be converted from conv 1D to linear
										__a    : Any      					=						old_checkpoint[path['old']]
										__a    : Tuple      					=						['proj_attn.weight']
										__a    : Tuple      					=						['proj_out.weight', 'proj_in.weight']
										if any(k in new_path for k in names     ):
															__a    : Tuple      					=						weight[:, :, 0]
										elif any(k in new_path for k in names_a     ) and len(weight.shape     ) > 2 and ".attentions." not in new_path:
															__a    : int      					=						weight[:, :, 0]
										else:
															__a    : Union[str, Any]      					=						weight






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[int]		,   _SCREAMING_SNAKE_CASE   :     str=0     ):
					__a    : Tuple      					=						[]
					for old_item in old_list:
										__a    : Dict      					=						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							(_SCREAMING_SNAKE_CASE   :     Optional[Any]		,   _SCREAMING_SNAKE_CASE   :     Optional[int]=1     ):
					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							(_SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     str=0     ):
					__a    : Optional[Any]      					=						[]
					for old_item in old_list:
										mapping.append({'old': old_item, 'new': old_item}     )

					return mapping






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]=0     ):
					__a    : Tuple      					=						[]
					for old_item in old_list:
										__a    : Optional[int]      					=						old_item.replace('in_layers.0'		,   'norm1'     )
										__a    : Optional[Any]      					=						new_item.replace('in_layers.2'		,   'conv1'     )

										__a    : str      					=						new_item.replace('out_layers.0'		,   'norm2'     )
										__a    : str      					=						new_item.replace('out_layers.3'		,   'conv2'     )

										__a    : List[Any]      					=						new_item.replace('emb_layers.1'		,   'time_emb_proj'     )
										__a    : List[Any]      					=						new_item.replace('skip_connection'		,   'conv_shortcut'     )

										__a    : int      					=						shave_segments(_SCREAMING_SNAKE_CASE		,   n_shave_prefix_segments=_SCREAMING_SNAKE_CASE     )

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

					return mapping






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     str		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     Any=None		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]=False     ):
					__a    : Dict      					=						{}
					__a    : Tuple      					=						list(checkpoint.keys()     )

					__a    : Optional[int]      					=						'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     ) > 100 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'     ):
																				__a    : int      					=						'model_ema.' + ''.join(key.split('.'     )[1:]     )
																				__a    : Optional[int]      					=						checkpoint.pop(_SCREAMING_SNAKE_CASE     )
					else:
										if sum(k.startswith('model_ema'     ) for k in keys     ) > 100:
															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:
															__a    : Optional[Any]      					=						checkpoint.pop(_SCREAMING_SNAKE_CASE     )

					__a    : Union[str, Any]      					=						{}

					__a    : Optional[Any]      					=						unet_state_dict['time_embed.0.weight']
					__a    : Union[str, Any]      					=						unet_state_dict['time_embed.0.bias']
					__a    : Any      					=						unet_state_dict['time_embed.2.weight']
					__a    : Dict      					=						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":
										__a    : List[Any]      					=						unet_state_dict['label_emb.0.0.weight']
										__a    : Union[str, Any]      					=						unet_state_dict['label_emb.0.0.bias']
										__a    : Optional[Any]      					=						unet_state_dict['label_emb.0.2.weight']
										__a    : Union[str, Any]      					=						unet_state_dict['label_emb.0.2.bias']
					else:
										raise NotImplementedError(F"""Not implemented `class_embed_type`: {config["class_embed_type"]}"""     )

					__a    : str      					=						unet_state_dict['input_blocks.0.0.weight']
					__a    : Tuple      					=						unet_state_dict['input_blocks.0.0.bias']

					__a    : Optional[int]      					=						[v for v in unet_state_dict if v.startswith('input_blocks.0.1'     )]
					__a    : List[str]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
					__a    : List[str]      					=						{'old': 'input_blocks.0.1', 'new': 'transformer_in'}
					assign_to_checkpoint(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )

					__a    : Any      					=						unet_state_dict['out.0.weight']
					__a    : Union[str, Any]      					=						unet_state_dict['out.0.bias']
					__a    : Optional[int]      					=						unet_state_dict['out.2.weight']
					__a    : Union[str, Any]      					=						unet_state_dict['out.2.bias']

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

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

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

					for i in range(1		,   _SCREAMING_SNAKE_CASE     ):
										__a    : List[str]      					=						(i - 1) // (config['layers_per_block'] + 1)
										__a    : Tuple      					=						(i - 1) % (config['layers_per_block'] + 1)

										__a    : Optional[Any]      					=						[
										    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
										]
										__a    : List[str]      					=						[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
										__a    : Tuple      					=						[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:
															__a    : Optional[Any]      					=						unet_state_dict.pop(
															    F"""input_blocks.{i}.op.weight"""     )
															__a    : Optional[int]      					=						unet_state_dict.pop(
															    F"""input_blocks.{i}.op.bias"""     )

										__a    : str      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
										__a    : Tuple      					=						{'old': F"""input_blocks.{i}.0""", 'new': F"""down_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     )

										__a    : Optional[int]      					=						[key for key in resnets if 'temopral_conv' in key]
										__a    : Tuple      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
										__a    : List[Any]      					=						{
										    'old': F"""input_blocks.{i}.0.temopral_conv""",
										    'new': F"""down_blocks.{block_id}.temp_convs.{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 len(_SCREAMING_SNAKE_CASE     ):
															__a    : List[Any]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
															__a    : Optional[Any]      					=						{'old': F"""input_blocks.{i}.1""", 'new': F"""down_blocks.{block_id}.attentions.{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 len(_SCREAMING_SNAKE_CASE     ):
															__a    : List[str]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
															__a    : str      					=						{
															    'old': F"""input_blocks.{i}.2""",
															    'new': F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
															}
															assign_to_checkpoint(
															    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )

					__a    : Union[str, Any]      					=						middle_blocks[0]
					__a    : str      					=						[key for key in resnet_a if 'temopral_conv' in key]
					__a    : Dict      					=						middle_blocks[1]
					__a    : str      					=						middle_blocks[2]
					__a    : List[str]      					=						middle_blocks[3]
					__a    : Any      					=						[key for key in resnet_a if 'temopral_conv' in key]

					__a    : Optional[int]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
					__a    : List[Any]      					=						{'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
					assign_to_checkpoint(
					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )

					__a    : Optional[int]      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
					__a    : List[Any]      					=						{'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
					assign_to_checkpoint(
					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )

					__a    : Optional[int]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
					__a    : Dict      					=						{'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
					assign_to_checkpoint(
					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )

					__a    : Dict      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
					__a    : Optional[int]      					=						{'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
					assign_to_checkpoint(
					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )

					__a    : List[Any]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
					__a    : int      					=						{'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
					assign_to_checkpoint(
					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )

					__a    : str      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
					__a    : Optional[Any]      					=						{'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
					assign_to_checkpoint(
					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )

					for i in range(_SCREAMING_SNAKE_CASE     ):
										__a    : List[str]      					=						i // (config['layers_per_block'] + 1)
										__a    : Optional[int]      					=						i % (config['layers_per_block'] + 1)
										__a    : Any      					=						[shave_segments(_SCREAMING_SNAKE_CASE		,   2     ) for name in output_blocks[i]]
										__a    : Optional[Any]      					=						{}

										for layer in output_block_layers:
															__a					,       __a    : Optional[int]      					=						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:
																				__a    : int      					=						[layer_name]

										if len(_SCREAMING_SNAKE_CASE     ) > 1:
															__a    : Any      					=						[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
															__a    : List[Any]      					=						[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
															__a    : Optional[Any]      					=						[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

															__a    : Optional[int]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
															__a    : List[Any]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )

															__a    : Dict      					=						{'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     )

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

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

																				# Clear attentions as they have been attributed above.
																				if len(_SCREAMING_SNAKE_CASE     ) == 2:
																									__a    : Optional[Any]      					=						[]

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

															__a    : Union[str, Any]      					=						[l for l in output_block_layers if 'temopral_conv' in l]
															for path in temopral_conv_paths:
																				__a    : Any      					=						path.split('temopral_conv.'     )[-1]
																				__a    : int      					=						'.'.join(['output_blocks', str(_SCREAMING_SNAKE_CASE     ), str(_SCREAMING_SNAKE_CASE     ), 'temopral_conv', pruned_path]     )
																				__a    : str      					=						'.'.join(['up_blocks', str(_SCREAMING_SNAKE_CASE     ), 'temp_convs', str(_SCREAMING_SNAKE_CASE     ), pruned_path]     )
																				__a    : Optional[Any]      					=						unet_state_dict[old_path]

					return new_checkpoint


if __name__ == "__main__":
	__lowercase			:		Optional[int] =			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			:		str =			parser.parse_args()

	__lowercase			:		Dict =			torch.load(args.checkpoint_path, map_location='cpu')
	__lowercase			:		Union[str, Any] =			UNetaDConditionModel()

	__lowercase			:		Optional[Any] =			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

	__lowercase			:		Optional[int] =			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
	__lowercase			:		List[str] =			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 --



