




'''simple docstring'''





import argparse

import torch

from diffusers import UNetaDConditionModel


def   UpperCamelCase_(			snake_case							: Any							,       snake_case							: Union[str, Any]							,       snake_case							: str							,       snake_case							: List[str]=None							,       snake_case							: Union[str, Any]=None							,       snake_case							: Union[str, Any]=None  ):

							'''simple docstring'''






							assert isinstance(snake_case							,       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():
																					snake_case_			      =   old_checkpoint[path]
																					snake_case_			      =   old_tensor.shape[0] // 3

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

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

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

																					snake_case_			      =   query.reshape(snake_case  )
																					snake_case_			      =   key.reshape(snake_case  )
																					snake_case_			      =   value.reshape(snake_case  )

							for path in paths:
														snake_case_			      =   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:
																												snake_case_			      =   new_path.replace(replacement["old"]							,       replacement["new"]  )

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


def   UpperCamelCase_(			snake_case							: Dict							,       snake_case							: Tuple=0  ):

							'''simple docstring'''






							snake_case_			      =   []
							for old_item in old_list:
														snake_case_			      =   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   UpperCamelCase_(			snake_case							: Any							,       snake_case							: Tuple=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   UpperCamelCase_(			snake_case							: str							,       snake_case							: Dict=0  ):

							'''simple docstring'''






							snake_case_			      =   []
							for old_item in old_list:
														mapping.append({"old": old_item, "new": old_item}  )

							return mapping


def   UpperCamelCase_(			snake_case							: Optional[Any]							,       snake_case							: Optional[Any]=0  ):

							'''simple docstring'''






							snake_case_			      =   []
							for old_item in old_list:
														snake_case_			      =   old_item.replace("in_layers.0"							,       "norm1"  )
														snake_case_			      =   new_item.replace("in_layers.2"							,       "conv1"  )

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

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

														snake_case_			      =   shave_segments(snake_case							,       n_shave_prefix_segments=snake_case  )

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

							return mapping


def   UpperCamelCase_(			snake_case							: Tuple							,       snake_case							: Tuple							,       snake_case							: List[str]=None							,       snake_case							: Optional[int]=False  ):

							'''simple docstring'''






							snake_case_			      =   {}
							snake_case_			      =   list(checkpoint.keys()  )

							snake_case_			      =   "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"  ):
																												snake_case_			      =   "model_ema." + "".join(key.split("."  )[1:]  )
																												snake_case_			      =   checkpoint.pop(snake_case  )
							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:
																					snake_case_			      =   checkpoint.pop(snake_case  )

							snake_case_			      =   {}

							snake_case_			      =   unet_state_dict["time_embed.0.weight"]
							snake_case_			      =   unet_state_dict["time_embed.0.bias"]
							snake_case_			      =   unet_state_dict["time_embed.2.weight"]
							snake_case_			      =   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":
														snake_case_			      =   unet_state_dict["label_emb.0.0.weight"]
														snake_case_			      =   unet_state_dict["label_emb.0.0.bias"]
														snake_case_			      =   unet_state_dict["label_emb.0.2.weight"]
														snake_case_			      =   unet_state_dict["label_emb.0.2.bias"]
							else:
														raise NotImplementedError(f'Not implemented `class_embed_type`: {config["class_embed_type"]}'  )

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

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

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

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

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

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

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

														snake_case_			      =   [
														    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
														]
														snake_case_			      =   [key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]
														snake_case_			      =   [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:
																					snake_case_			      =   unet_state_dict.pop(
																					    f'input_blocks.{i}.op.weight'  )
																					snake_case_			      =   unet_state_dict.pop(
																					    f'input_blocks.{i}.op.bias'  )

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

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

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

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

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

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

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

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

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

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

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

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

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

														if len(snake_case  ) > 1:
																					snake_case_			      =   [key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
																					snake_case_			      =   [key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]
																					snake_case_			      =   [key for key in output_blocks[i] if f'output_blocks.{i}.2' in key]

																					snake_case_			      =   renew_resnet_paths(snake_case  )
																					snake_case_			      =   renew_resnet_paths(snake_case  )

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

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

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

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

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

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

																					snake_case_			      =   [l for l in output_block_layers if "temopral_conv" in l]
																					for path in temopral_conv_paths:
																												snake_case_			      =   path.split("temopral_conv."  )[-1]
																												snake_case_			      =   ".".join(["output_blocks", str(snake_case  ), str(snake_case  ), "temopral_conv", pruned_path]  )
																												snake_case_			      =   ".".join(["up_blocks", str(snake_case  ), "temp_convs", str(snake_case  ), pruned_path]  )
																												snake_case_			      =   unet_state_dict[old_path]

							return new_checkpoint


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE							:     Any   	=				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.")
						_SCREAMING_SNAKE_CASE							:     Optional[int]   	=				parser.parse_args()

						_SCREAMING_SNAKE_CASE							:     Optional[Any]   	=				torch.load(args.checkpoint_path, map_location="cpu")
						_SCREAMING_SNAKE_CASE							:     Any   	=				UNetaDConditionModel()

						_SCREAMING_SNAKE_CASE							:     List[Any]   	=				convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

						_SCREAMING_SNAKE_CASE							:     str   	=				set(unet.state_dict().keys()) - set(converted_ckpt.keys())
						_SCREAMING_SNAKE_CASE							:     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 --










'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class 				_snake_case	(			unittest.TestCase							):




			@slow
			def      lowerCAmelCase__       (      self							)       ->       List[Any]:

										'''simple docstring'''




										snake_case_			      =   CamembertModel.from_pretrained("camembert-base"							)
										model.to(a__							)

										snake_case_			      =   torch.tensor(
										    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]       ,							device=a__       ,							dtype=torch.long       ,							)  # J'aime le camembert !
										with torch.no_grad():
																	snake_case_			      =   model(a__							)["last_hidden_state"]
										snake_case_			      =   torch.Size((1, 10, 768)							)
										self.assertEqual(output.shape       ,							a__							)
										# compare the actual values for a slice.
										snake_case_			      =   torch.tensor(
										    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]       ,							device=a__       ,							dtype=torch.float       ,							)
										# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
										# camembert.eval()
										# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

										self.assertTrue(torch.allclose(output[:, :3, :3]       ,							a__       ,							atol=1e-4							)							)




