'''simple docstring'''




import argparse

import torch

from diffusers import UNetaDConditionModel






def     snake_case__							(      _A: Union[str, Any]				,       _A: Optional[int]				,       _A: Union[str, Any]				,       _A: Union[str, Any]=None				,       _A: Tuple=None				,       _A: Dict=None			)   ->     List[Any]:

		'''simple docstring'''


		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():
						lowerCAmelCase        =  old_checkpoint[path]
						lowerCAmelCase        =  old_tensor.shape[0] // 3

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

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

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

						lowerCAmelCase        =  query.reshape(_A			)
						lowerCAmelCase        =  key.reshape(_A			)
						lowerCAmelCase        =  value.reshape(_A			)

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

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






def     snake_case__							(      _A: Optional[int]				,       _A: List[str]=0			)   ->     List[Any]:

		'''simple docstring'''


		lowerCAmelCase        =  []
		for old_item in old_list:
				lowerCAmelCase        =  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     snake_case__							(      _A: str				,       _A: Optional[Any]=1			)   ->     Dict:

		'''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     snake_case__							(      _A: Optional[int]				,       _A: Optional[Any]=0			)   ->     List[str]:

		'''simple docstring'''


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

		return mapping






def     snake_case__							(      _A: Optional[int]				,       _A: Dict=0			)   ->     Optional[int]:

		'''simple docstring'''


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

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

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

				lowerCAmelCase        =  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     snake_case__							(      _A: Optional[Any]				,       _A: Dict				,       _A: List[str]=None				,       _A: List[Any]=False			)   ->     str:

		'''simple docstring'''


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

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

		lowerCAmelCase        =  {}

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

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

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

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

		# Retrieves the keys for the input blocks only
		lowerCAmelCase        =  len({""".""".join(layer.split("""."""			)[:2]			) for layer in unet_state_dict if """input_blocks""" in layer}			)
		lowerCAmelCase        =  {
		    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
		lowerCAmelCase        =  len({""".""".join(layer.split("""."""			)[:2]			) for layer in unet_state_dict if """middle_block""" in layer}			)
		lowerCAmelCase        =  {
		    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
		lowerCAmelCase        =  len({""".""".join(layer.split("""."""			)[:2]			) for layer in unet_state_dict if """output_blocks""" in layer}			)
		lowerCAmelCase        =  {
		    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			):
				lowerCAmelCase        =  (i - 1) // (config["""layers_per_block"""] + 1)
				lowerCAmelCase        =  (i - 1) % (config["""layers_per_block"""] + 1)

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

				lowerCAmelCase        =  renew_resnet_paths(_A			)
				lowerCAmelCase        =  {"""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			)

				lowerCAmelCase        =  [key for key in resnets if """temopral_conv""" in key]
				lowerCAmelCase        =  renew_temp_conv_paths(_A			)
				lowerCAmelCase        =  {
				    """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			):
						lowerCAmelCase        =  renew_attention_paths(_A			)
						lowerCAmelCase        =  {"""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			):
						lowerCAmelCase        =  renew_attention_paths(_A			)
						lowerCAmelCase        =  {
						    """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			)

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

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

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

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

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

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

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

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

				if len(_A			) > 1:
						lowerCAmelCase        =  [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
						lowerCAmelCase        =  [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
						lowerCAmelCase        =  [key for key in output_blocks[i] if f"output_blocks.{i}.2" in key]

						lowerCAmelCase        =  renew_resnet_paths(_A			)
						lowerCAmelCase        =  renew_resnet_paths(_A			)

						lowerCAmelCase        =  {"""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			)

						lowerCAmelCase        =  [key for key in resnets if """temopral_conv""" in key]
						lowerCAmelCase        =  renew_temp_conv_paths(_A			)
						lowerCAmelCase        =  {
						    """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			)

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

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

						if len(_A			):
								lowerCAmelCase        =  renew_attention_paths(_A			)
								lowerCAmelCase        =  {
								    """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			):
								lowerCAmelCase        =  renew_attention_paths(_A			)
								lowerCAmelCase        =  {
								    """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:
						lowerCAmelCase        =  renew_resnet_paths(_A				,       n_shave_prefix_segments=1			)
						for path in resnet_0_paths:
								lowerCAmelCase        =  """.""".join(["""output_blocks""", str(_A			), path["""old"""]]			)
								lowerCAmelCase        =  """.""".join(["""up_blocks""", str(_A			), """resnets""", str(_A			), path["""new"""]]			)
								lowerCAmelCase        =  unet_state_dict[old_path]

						lowerCAmelCase        =  [l for l in output_block_layers if """temopral_conv""" in l]
						for path in temopral_conv_paths:
								lowerCAmelCase        =  path.split("""temopral_conv."""			)[-1]
								lowerCAmelCase        =  """.""".join(["""output_blocks""", str(_A			), str(_A			), """temopral_conv""", pruned_path]			)
								lowerCAmelCase        =  """.""".join(["""up_blocks""", str(_A			), """temp_convs""", str(_A			), pruned_path]			)
								lowerCAmelCase        =  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'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowercase				   =  logging.get_logger(__name__)

__lowercase				   =  {
    '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
    '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
    '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
    '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
}






class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			UpperCAmelCase_      :					int								=	'''flaubert'''
			UpperCAmelCase_      :					Optional[Any]								=	{
			    '''hidden_size''': '''emb_dim''',
			    '''num_attention_heads''': '''n_heads''',
			    '''num_hidden_layers''': '''n_layers''',
			    '''n_words''': '''vocab_size''',  # For backward compatibility
			}



			def __init__(       self	,   __lowerCAmelCase=False	,   __lowerCAmelCase=0.0	,   __lowerCAmelCase=30145	,   __lowerCAmelCase=2048	,   __lowerCAmelCase=12	,   __lowerCAmelCase=16	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=True	,   __lowerCAmelCase=False	,   __lowerCAmelCase=False	,   __lowerCAmelCase=False	,   __lowerCAmelCase=1	,   __lowerCAmelCase=True	,   __lowerCAmelCase=512	,   __lowerCAmelCase=2048**-0.5	,   __lowerCAmelCase=1E-1_2	,   __lowerCAmelCase=0.02	,   __lowerCAmelCase=0	,   __lowerCAmelCase=1	,   __lowerCAmelCase=2	,   __lowerCAmelCase=3	,   __lowerCAmelCase=5	,   __lowerCAmelCase=True	,   __lowerCAmelCase="first"	,   __lowerCAmelCase=True	,   __lowerCAmelCase=None	,   __lowerCAmelCase=True	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=5	,   __lowerCAmelCase=5	,   __lowerCAmelCase=0	,   __lowerCAmelCase=0	,   __lowerCAmelCase=2	,   __lowerCAmelCase=0	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  pre_norm
					lowerCAmelCase        =  layerdrop
					lowerCAmelCase        =  vocab_size
					lowerCAmelCase        =  emb_dim
					lowerCAmelCase        =  n_layers
					lowerCAmelCase        =  n_heads
					lowerCAmelCase        =  dropout
					lowerCAmelCase        =  attention_dropout
					lowerCAmelCase        =  gelu_activation
					lowerCAmelCase        =  sinusoidal_embeddings
					lowerCAmelCase        =  causal
					lowerCAmelCase        =  asm
					lowerCAmelCase        =  n_langs
					lowerCAmelCase        =  use_lang_emb
					lowerCAmelCase        =  layer_norm_eps
					lowerCAmelCase        =  bos_index
					lowerCAmelCase        =  eos_index
					lowerCAmelCase        =  pad_index
					lowerCAmelCase        =  unk_index
					lowerCAmelCase        =  mask_index
					lowerCAmelCase        =  is_encoder
					lowerCAmelCase        =  max_position_embeddings
					lowerCAmelCase        =  embed_init_std
					lowerCAmelCase        =  init_std
					lowerCAmelCase        =  summary_type
					lowerCAmelCase        =  summary_use_proj
					lowerCAmelCase        =  summary_activation
					lowerCAmelCase        =  summary_proj_to_labels
					lowerCAmelCase        =  summary_first_dropout
					lowerCAmelCase        =  start_n_top
					lowerCAmelCase        =  end_n_top
					lowerCAmelCase        =  mask_token_id
					lowerCAmelCase        =  lang_id

					if "n_words" in kwargs:
							lowerCAmelCase        =  kwargs["""n_words"""]

					super().__init__(pad_token_id=__lowerCAmelCase	,   bos_token_id=__lowerCAmelCase	,   **__lowerCAmelCase)







class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			@property
			def 							a_	(       self):




					"""simple docstring"""


					if self.task == "multiple-choice":
							lowerCAmelCase        =  {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							lowerCAmelCase        =  {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					    ])




