


"""simple docstring"""







import argparse

import torch

from diffusers import UNetaDConditionModel






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase=None	,			_UpperCamelCase=None	,			_UpperCamelCase=None ):





						"""simple docstring"""

						assert isinstance(_UpperCamelCase	,			_UpperCamelCase ), "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():
																		UpperCAmelCase__		=    old_checkpoint[path]
																		UpperCAmelCase__		=    old_tensor.shape[0] // 3

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

																		UpperCAmelCase__		=    old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

																		UpperCAmelCase__		=    query.reshape(_UpperCamelCase )
																		UpperCAmelCase__		=    key.reshape(_UpperCamelCase )
																		UpperCAmelCase__		=    value.reshape(_UpperCamelCase )

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

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=0 ):





						"""simple docstring"""

						UpperCAmelCase__		=    []
						for old_item in old_list:
												UpperCAmelCase__		=    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 					a__ (       _UpperCamelCase	,			_UpperCamelCase=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__ (       _UpperCamelCase	,			_UpperCamelCase=0 ):





						"""simple docstring"""

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

						return mapping






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=0 ):





						"""simple docstring"""

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

												UpperCAmelCase__		=    new_item.replace('''out_layers.0'''	,			'''norm2''' )
												UpperCAmelCase__		=    new_item.replace('''out_layers.3'''	,			'''conv2''' )

												UpperCAmelCase__		=    new_item.replace('''emb_layers.1'''	,			'''time_emb_proj''' )
												UpperCAmelCase__		=    new_item.replace('''skip_connection'''	,			'''conv_shortcut''' )

												UpperCAmelCase__		=    shave_segments(_UpperCamelCase	,			n_shave_prefix_segments=_UpperCamelCase )

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

						return mapping






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase=None	,			_UpperCamelCase=False ):





						"""simple docstring"""

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

						UpperCAmelCase__		=    '''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''' ):
																								UpperCAmelCase__		=    '''model_ema.''' + ''''''.join(key.split('''.''' )[1:] )
																								UpperCAmelCase__		=    checkpoint.pop(_UpperCamelCase )
						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:
																		UpperCAmelCase__		=    checkpoint.pop(_UpperCamelCase )

						UpperCAmelCase__		=    {}

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

						UpperCAmelCase__		=    unet_state_dict['''input_blocks.0.0.weight''']
						UpperCAmelCase__		=    unet_state_dict['''input_blocks.0.0.bias''']

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

						UpperCAmelCase__		=    unet_state_dict['''out.0.weight''']
						UpperCAmelCase__		=    unet_state_dict['''out.0.bias''']
						UpperCAmelCase__		=    unet_state_dict['''out.2.weight''']
						UpperCAmelCase__		=    unet_state_dict['''out.2.bias''']

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

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

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

						for i in range(1	,			_UpperCamelCase ):
												UpperCAmelCase__		=    (i - 1) // (config['''layers_per_block'''] + 1)
												UpperCAmelCase__		=    (i - 1) % (config['''layers_per_block'''] + 1)

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

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

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

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

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

						UpperCAmelCase__		=    middle_blocks[0]
						UpperCAmelCase__		=    [key for key in resnet_a if '''temopral_conv''' in key]
						UpperCAmelCase__		=    middle_blocks[1]
						UpperCAmelCase__		=    middle_blocks[2]
						UpperCAmelCase__		=    middle_blocks[3]
						UpperCAmelCase__		=    [key for key in resnet_a if '''temopral_conv''' in key]

						UpperCAmelCase__		=    renew_resnet_paths(_UpperCamelCase )
						UpperCAmelCase__		=    {'''old''': '''middle_block.0''', '''new''': '''mid_block.resnets.0'''}
						assign_to_checkpoint(
						    _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			config=_UpperCamelCase	,			additional_replacements=[meta_path] )

						UpperCAmelCase__		=    renew_temp_conv_paths(_UpperCamelCase )
						UpperCAmelCase__		=    {'''old''': '''middle_block.0.temopral_conv''', '''new''': '''mid_block.temp_convs.0'''}
						assign_to_checkpoint(
						    _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			config=_UpperCamelCase	,			additional_replacements=[meta_path] )

						UpperCAmelCase__		=    renew_resnet_paths(_UpperCamelCase )
						UpperCAmelCase__		=    {'''old''': '''middle_block.3''', '''new''': '''mid_block.resnets.1'''}
						assign_to_checkpoint(
						    _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			config=_UpperCamelCase	,			additional_replacements=[meta_path] )

						UpperCAmelCase__		=    renew_temp_conv_paths(_UpperCamelCase )
						UpperCAmelCase__		=    {'''old''': '''middle_block.3.temopral_conv''', '''new''': '''mid_block.temp_convs.1'''}
						assign_to_checkpoint(
						    _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			config=_UpperCamelCase	,			additional_replacements=[meta_path] )

						UpperCAmelCase__		=    renew_attention_paths(_UpperCamelCase )
						UpperCAmelCase__		=    {'''old''': '''middle_block.1''', '''new''': '''mid_block.attentions.0'''}
						assign_to_checkpoint(
						    _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			additional_replacements=[meta_path]	,			config=_UpperCamelCase )

						UpperCAmelCase__		=    renew_attention_paths(_UpperCamelCase )
						UpperCAmelCase__		=    {'''old''': '''middle_block.2''', '''new''': '''mid_block.temp_attentions.0'''}
						assign_to_checkpoint(
						    _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			additional_replacements=[meta_path]	,			config=_UpperCamelCase )

						for i in range(_UpperCamelCase ):
												UpperCAmelCase__		=    i // (config['''layers_per_block'''] + 1)
												UpperCAmelCase__		=    i % (config['''layers_per_block'''] + 1)
												UpperCAmelCase__		=    [shave_segments(_UpperCamelCase	,			2 ) for name in output_blocks[i]]
												UpperCAmelCase__		=    {}

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

												if len(_UpperCamelCase ) > 1:
																		UpperCAmelCase__		=    [key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
																		UpperCAmelCase__		=    [key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
																		UpperCAmelCase__		=    [key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

																		UpperCAmelCase__		=    renew_resnet_paths(_UpperCamelCase )
																		UpperCAmelCase__		=    renew_resnet_paths(_UpperCamelCase )

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

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

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

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

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

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

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

						return new_checkpoint


if __name__ == "__main__":
						__a			 =   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.")
						__a			 =   parser.parse_args()

						__a			 =   torch.load(args.checkpoint_path, map_location="cpu")
						__a			 =   UNetaDConditionModel()

						__a			 =   convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

						__a			 =   set(unet.state_dict().keys()) - set(converted_ckpt.keys())
						__a			 =   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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__a			 =   logging.get_logger(__name__)

__a			 =   {
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}
class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      """pegasus_x"""
							SCREAMING_SNAKE_CASE										=      ["""past_key_values"""]
							SCREAMING_SNAKE_CASE										=      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






							def __init__(						self :     int		, lowerCamelCase_ :     Any=96103		, lowerCamelCase_ :     Union[str, Any]=16384		, lowerCamelCase_ :     Any=16		, lowerCamelCase_ :     str=4096		, lowerCamelCase_ :     Tuple=16		, lowerCamelCase_ :     Optional[int]=16		, lowerCamelCase_ :     Tuple=4096		, lowerCamelCase_ :     Any=16		, lowerCamelCase_ :     List[str]=0.0		, lowerCamelCase_ :     int=0.0		, lowerCamelCase_ :     List[Any]=True		, lowerCamelCase_ :     str=True		, lowerCamelCase_ :     int="gelu"		, lowerCamelCase_ :     Optional[Any]=1024		, lowerCamelCase_ :     Optional[int]=0.1		, lowerCamelCase_ :     str=0.0		, lowerCamelCase_ :     Optional[Any]=0.0		, lowerCamelCase_ :     List[str]=0.02		, lowerCamelCase_ :     Optional[Any]=0		, lowerCamelCase_ :     List[str]=True		, lowerCamelCase_ :     List[Any]=0		, lowerCamelCase_ :     str=1		, lowerCamelCase_ :     Optional[Any]=1		, lowerCamelCase_ :     Tuple=32		, lowerCamelCase_ :     Dict=512		, lowerCamelCase_ :     List[Any]=True		, **lowerCamelCase_ :     int		, ):
													UpperCAmelCase__		=    vocab_size
													UpperCAmelCase__		=    max_position_embeddings
													UpperCAmelCase__		=    d_model
													UpperCAmelCase__		=    encoder_ffn_dim
													UpperCAmelCase__		=    encoder_layers
													UpperCAmelCase__		=    encoder_attention_heads
													UpperCAmelCase__		=    decoder_ffn_dim
													UpperCAmelCase__		=    decoder_layers
													UpperCAmelCase__		=    decoder_attention_heads
													UpperCAmelCase__		=    dropout
													UpperCAmelCase__		=    attention_dropout
													UpperCAmelCase__		=    activation_dropout
													UpperCAmelCase__		=    activation_function
													UpperCAmelCase__		=    init_std
													UpperCAmelCase__		=    encoder_layerdrop
													UpperCAmelCase__		=    decoder_layerdrop
													UpperCAmelCase__		=    use_cache
													UpperCAmelCase__		=    encoder_layers
													UpperCAmelCase__		=    scale_embedding  # scale factor will be sqrt(d_model) if True

													UpperCAmelCase__		=    num_global_tokens
													UpperCAmelCase__		=    block_size
													UpperCAmelCase__		=    stagger_local_blocks

													super().__init__(
													    pad_token_id=lowerCamelCase_		, eos_token_id=lowerCamelCase_		, is_encoder_decoder=lowerCamelCase_		, decoder_start_token_id=lowerCamelCase_		, forced_eos_token_id=lowerCamelCase_		, **lowerCamelCase_		, )






							@property
							def 					lowerCAmelCase     (						self :     Optional[int]				):
													return self.encoder_attention_heads






							@property
							def 					lowerCAmelCase     (						self :     Optional[int]				):
													return self.d_model