






"""simple docstring"""





from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings



@maybe_allow_in_graph
class       __A		(   nn.Module			):

					def __init__(			self  :      List[str]     ,		__snake_case  :      int     ,		__snake_case  :      int     ,		__snake_case  :      int     ,		__snake_case  :      Optional[Any]=0.0     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      str = "geglu"     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      bool = False     ,		__snake_case  :      bool = False     ,		__snake_case  :      bool = False     ,		__snake_case  :      bool = False     ,		__snake_case  :      bool = True     ,		__snake_case  :      str = "layer_norm"     ,		__snake_case  :      bool = False     ,		)			->							Dict:
											super().__init__()
											__magic_name__:	Union[str, Any]							=       only_cross_attention

											__magic_name__:	Union[str, Any]							=       (num_embeds_ada_norm is not None) and norm_type == """ada_norm_zero"""
											__magic_name__:	Any							=       (num_embeds_ada_norm is not None) and norm_type == """ada_norm"""

											if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
																	raise ValueError(
																	    F'`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'
																	    F' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'					)

											# Define 3 blocks. Each block has its own normalization layer.
											# 1. Self-Attn
											if self.use_ada_layer_norm:
																	__magic_name__:	List[str]							=       AdaLayerNorm(_UpperCAmelCase     ,		_UpperCAmelCase					)
											elif self.use_ada_layer_norm_zero:
																	__magic_name__:	Dict							=       AdaLayerNormZero(_UpperCAmelCase     ,		_UpperCAmelCase					)
											else:
																	__magic_name__:	Optional[Any]							=       nn.LayerNorm(_UpperCAmelCase     ,		elementwise_affine=_UpperCAmelCase					)
											__magic_name__:	Optional[int]							=       Attention(
											    query_dim=_UpperCAmelCase     ,		heads=_UpperCAmelCase     ,		dim_head=_UpperCAmelCase     ,		dropout=_UpperCAmelCase     ,		bias=_UpperCAmelCase     ,		cross_attention_dim=cross_attention_dim if only_cross_attention else None     ,		upcast_attention=_UpperCAmelCase     ,		)

											# 2. Cross-Attn
											if cross_attention_dim is not None or double_self_attention:
																	# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
																	# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
																	# the second cross attention block.
																	__magic_name__:	Union[str, Any]							=       (
																	    AdaLayerNorm(_UpperCAmelCase     ,		_UpperCAmelCase					)
																	    if self.use_ada_layer_norm
																	    else nn.LayerNorm(_UpperCAmelCase     ,		elementwise_affine=_UpperCAmelCase					)
																	)
																	__magic_name__:	Union[str, Any]							=       Attention(
																	    query_dim=_UpperCAmelCase     ,		cross_attention_dim=cross_attention_dim if not double_self_attention else None     ,		heads=_UpperCAmelCase     ,		dim_head=_UpperCAmelCase     ,		dropout=_UpperCAmelCase     ,		bias=_UpperCAmelCase     ,		upcast_attention=_UpperCAmelCase     ,		)  # is self-attn if encoder_hidden_states is none
											else:
																	__magic_name__:	str							=       None
																	__magic_name__:	Optional[Any]							=       None

											# 3. Feed-forward
											__magic_name__:	int							=       nn.LayerNorm(_UpperCAmelCase     ,		elementwise_affine=_UpperCAmelCase					)
											__magic_name__:	List[str]							=       FeedForward(_UpperCAmelCase     ,		dropout=_UpperCAmelCase     ,		activation_fn=_UpperCAmelCase     ,		final_dropout=_UpperCAmelCase					)

											# let chunk size default to None
											__magic_name__:	Tuple							=       None
											__magic_name__:	str							=       0

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      Optional[int]     ,		__snake_case  :      int					)			->							int:
											__magic_name__:	Optional[int]							=       chunk_size
											__magic_name__:	str							=       dim






					def   lowerCamelCase__   (			self  :      Optional[int]     ,		__snake_case  :      torch.FloatTensor     ,		__snake_case  :      Optional[torch.FloatTensor] = None     ,		__snake_case  :      Optional[torch.FloatTensor] = None     ,		__snake_case  :      Optional[torch.FloatTensor] = None     ,		__snake_case  :      Optional[torch.LongTensor] = None     ,		__snake_case  :      Dict[str, Any] = None     ,		__snake_case  :      Optional[torch.LongTensor] = None     ,		)			->							List[str]:


											if self.use_ada_layer_norm:
																	__magic_name__:	str							=       self.norma(_UpperCAmelCase     ,		_UpperCAmelCase					)
											elif self.use_ada_layer_norm_zero:
																	__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	Optional[int]							=       self.norma(
																	    _UpperCAmelCase     ,		_UpperCAmelCase     ,		_UpperCAmelCase     ,		hidden_dtype=hidden_states.dtype					)
											else:
																	__magic_name__:	Union[str, Any]							=       self.norma(_UpperCAmelCase					)

											__magic_name__:	Optional[int]							=       cross_attention_kwargs if cross_attention_kwargs is not None else {}

											__magic_name__:	str							=       self.attna(
											    _UpperCAmelCase     ,		encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None     ,		attention_mask=_UpperCAmelCase     ,		**_UpperCAmelCase     ,		)
											if self.use_ada_layer_norm_zero:
																	__magic_name__:	Tuple							=       gate_msa.unsqueeze(1					) * attn_output
											__magic_name__:	List[str]							=       attn_output + hidden_states

											# 2. Cross-Attention
											if self.attna is not None:
																	__magic_name__:	List[Any]							=       (
																	    self.norma(_UpperCAmelCase     ,		_UpperCAmelCase					) if self.use_ada_layer_norm else self.norma(_UpperCAmelCase					)
																	)

																	__magic_name__:	Tuple							=       self.attna(
																	    _UpperCAmelCase     ,		encoder_hidden_states=_UpperCAmelCase     ,		attention_mask=_UpperCAmelCase     ,		**_UpperCAmelCase     ,		)
																	__magic_name__:	Optional[Any]							=       attn_output + hidden_states

											# 3. Feed-forward
											__magic_name__:	Optional[int]							=       self.norma(_UpperCAmelCase					)

											if self.use_ada_layer_norm_zero:
																	__magic_name__:	Union[str, Any]							=       norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

											if self._chunk_size is not None:
																	# "feed_forward_chunk_size" can be used to save memory
																	if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
																							raise ValueError(
																							    F'`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'					)

																	__magic_name__:	Dict							=       norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
																	__magic_name__:	Optional[Any]							=       torch.cat(
																	    [self.ff(_UpperCAmelCase					) for hid_slice in norm_hidden_states.chunk(_UpperCAmelCase     ,		dim=self._chunk_dim					)]     ,		dim=self._chunk_dim     ,		)
											else:
																	__magic_name__:	List[Any]							=       self.ff(_UpperCAmelCase					)

											if self.use_ada_layer_norm_zero:
																	__magic_name__:	List[Any]							=       gate_mlp.unsqueeze(1					) * ff_output

											__magic_name__:	Dict							=       ff_output + hidden_states

											return hidden_states



class       __A		(   nn.Module			):

					def __init__(			self  :      Dict     ,		__snake_case  :      int     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      int = 4     ,		__snake_case  :      float = 0.0     ,		__snake_case  :      str = "geglu"     ,		__snake_case  :      bool = False     ,		)			->							Union[str, Any]:
											super().__init__()
											__magic_name__:	Optional[int]							=       int(dim * mult					)
											__magic_name__:	Dict							=       dim_out if dim_out is not None else dim

											if activation_fn == "gelu":
																	__magic_name__:	str							=       GELU(_UpperCAmelCase     ,		_UpperCAmelCase					)
											if activation_fn == "gelu-approximate":
																	__magic_name__:	List[str]							=       GELU(_UpperCAmelCase     ,		_UpperCAmelCase     ,		approximate="""tanh"""					)
											elif activation_fn == "geglu":
																	__magic_name__:	int							=       GEGLU(_UpperCAmelCase     ,		_UpperCAmelCase					)
											elif activation_fn == "geglu-approximate":
																	__magic_name__:	Optional[Any]							=       ApproximateGELU(_UpperCAmelCase     ,		_UpperCAmelCase					)

											__magic_name__:	int							=       nn.ModuleList([]					)
											# project in
											self.net.append(_UpperCAmelCase					)
											# project dropout
											self.net.append(nn.Dropout(_UpperCAmelCase					)					)
											# project out
											self.net.append(nn.Linear(_UpperCAmelCase     ,		_UpperCAmelCase					)					)
											# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
											if final_dropout:
																	self.net.append(nn.Dropout(_UpperCAmelCase					)					)






					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      Optional[Any]					)			->							Tuple:


											for module in self.net:
																	__magic_name__:	Tuple							=       module(_UpperCAmelCase					)
											return hidden_states



class       __A		(   nn.Module			):

					def __init__(			self  :      Tuple     ,		__snake_case  :      int     ,		__snake_case  :      int     ,		__snake_case  :      str = "none"					)			->							int:
											super().__init__()
											__magic_name__:	str							=       nn.Linear(_UpperCAmelCase     ,		_UpperCAmelCase					)
											__magic_name__:	Tuple							=       approximate

					def   lowerCamelCase__   (			self  :      int     ,		__snake_case  :      List[str]					)			->							List[Any]:


											if gate.device.type != "mps":
																	return F.gelu(_UpperCAmelCase     ,		approximate=self.approximate					)
											# mps: gelu is not implemented for float16
											return F.gelu(gate.to(dtype=torch.floataa					)     ,		approximate=self.approximate					).to(dtype=gate.dtype					)






					def   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      List[str]					)			->							Any:
											__magic_name__:	int							=       self.proj(_UpperCAmelCase					)
											__magic_name__:	Optional[Any]							=       self.gelu(_UpperCAmelCase					)
											return hidden_states



class       __A		(   nn.Module			):

					def __init__(			self  :      Optional[Any]     ,		__snake_case  :      int     ,		__snake_case  :      int					)			->							Union[str, Any]:
											super().__init__()
											__magic_name__:	List[Any]							=       nn.Linear(_UpperCAmelCase     ,		dim_out * 2					)

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      Any					)			->							Optional[int]:


											if gate.device.type != "mps":
																	return F.gelu(_UpperCAmelCase					)
											# mps: gelu is not implemented for float16
											return F.gelu(gate.to(dtype=torch.floataa					)					).to(dtype=gate.dtype					)






					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      Optional[Any]					)			->							List[Any]:
											__magic_name__,			__magic_name__:	Optional[int]							=       self.proj(_UpperCAmelCase					).chunk(2     ,		dim=-1					)
											return hidden_states * self.gelu(_UpperCAmelCase					)



class       __A		(   nn.Module			):

					def __init__(			self  :      Dict     ,		__snake_case  :      int     ,		__snake_case  :      int					)			->							str:
											super().__init__()
											__magic_name__:	Optional[Any]							=       nn.Linear(_UpperCAmelCase     ,		_UpperCAmelCase					)






					def   lowerCamelCase__   (			self  :      int     ,		__snake_case  :      int					)			->							Optional[int]:
											__magic_name__:	List[str]							=       self.proj(_UpperCAmelCase					)
											return x * torch.sigmoid(1.702 * x					)



class       __A		(   nn.Module			):

					def __init__(			self  :      Optional[Any]     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      Union[str, Any]					)			->							str:
											super().__init__()
											__magic_name__:	Any							=       nn.Embedding(_UpperCAmelCase     ,		_UpperCAmelCase					)
											__magic_name__:	Tuple							=       nn.SiLU()
											__magic_name__:	Dict							=       nn.Linear(_UpperCAmelCase     ,		embedding_dim * 2					)
											__magic_name__:	Tuple							=       nn.LayerNorm(_UpperCAmelCase     ,		elementwise_affine=_UpperCAmelCase					)






					def   lowerCamelCase__   (			self  :      Tuple     ,		__snake_case  :      List[Any]     ,		__snake_case  :      Dict					)			->							Optional[Any]:
											__magic_name__:	Tuple							=       self.linear(self.silu(self.emb(_UpperCAmelCase					)					)					)
											__magic_name__,			__magic_name__:	Optional[Any]							=       torch.chunk(_UpperCAmelCase     ,		2					)
											__magic_name__:	Dict							=       self.norm(_UpperCAmelCase					) * (1 + scale) + shift
											return x



class       __A		(   nn.Module			):

					def __init__(			self  :      List[Any]     ,		__snake_case  :      List[Any]     ,		__snake_case  :      Dict					)			->							List[str]:
											super().__init__()

											__magic_name__:	Union[str, Any]							=       CombinedTimestepLabelEmbeddings(_UpperCAmelCase     ,		_UpperCAmelCase					)

											__magic_name__:	Optional[int]							=       nn.SiLU()
											__magic_name__:	str							=       nn.Linear(_UpperCAmelCase     ,		6 * embedding_dim     ,		bias=_UpperCAmelCase					)
											__magic_name__:	Union[str, Any]							=       nn.LayerNorm(_UpperCAmelCase     ,		elementwise_affine=_UpperCAmelCase     ,		eps=1E-6					)






					def   lowerCamelCase__   (			self  :      int     ,		__snake_case  :      int     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      int     ,		__snake_case  :      int=None					)			->							Any:
											__magic_name__:	Dict							=       self.linear(self.silu(self.emb(_UpperCAmelCase     ,		_UpperCAmelCase     ,		hidden_dtype=_UpperCAmelCase					)					)					)
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	List[Any]							=       emb.chunk(6     ,		dim=1					)
											__magic_name__:	str							=       self.norm(_UpperCAmelCase					) * (1 + scale_msa[:, None]) + shift_msa[:, None]
											return x, gate_msa, shift_mlp, scale_mlp, gate_mlp



class       __A		(   nn.Module			):

					def __init__(			self  :      int     ,		__snake_case  :      int     ,		__snake_case  :      int     ,		__snake_case  :      int     ,		__snake_case  :      Optional[str] = None     ,		__snake_case  :      float = 1E-5					)			->							Optional[int]:
											super().__init__()
											__magic_name__:	Optional[Any]							=       num_groups
											__magic_name__:	List[str]							=       eps

											if act_fn is None:
																	__magic_name__:	Optional[int]							=       None
											else:
																	__magic_name__:	List[str]							=       get_activation(_UpperCAmelCase					)

											__magic_name__:	int							=       nn.Linear(_UpperCAmelCase     ,		out_dim * 2					)






					def   lowerCamelCase__   (			self  :      Any     ,		__snake_case  :      List[str]     ,		__snake_case  :      List[Any]					)			->							int:


											if self.act:
																	__magic_name__:	Optional[int]							=       self.act(_UpperCAmelCase					)
											__magic_name__:	List[Any]							=       self.linear(_UpperCAmelCase					)
											__magic_name__:	Optional[Any]							=       emb[:, :, None, None]
											__magic_name__,			__magic_name__:	Any							=       emb.chunk(2     ,		dim=1					)

											__magic_name__:	Optional[Any]							=       F.group_norm(_UpperCAmelCase     ,		self.num_groups     ,		eps=self.eps					)
											__magic_name__:	Any							=       x * (1 + scale) + shift
											return x







import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""simple docstring"""




		lowercase__	     =						[
		    """encoder.version""",
		    """decoder.version""",
		    """model.encoder.version""",
		    """model.decoder.version""",
		    """_float_tensor""",
		    """decoder.output_projection.weight""",
		]
		for k in ignore_keys:
				state_dict.pop(__magic_name__       ,       __magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				int	)  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__     ,      lowercase__	     =						emb.weight.shape
		lowercase__	     =						nn.Linear(__magic_name__       ,       __magic_name__       ,       bias=__magic_name__	)
		lowercase__	     =						emb.weight.data
		return lin_layer






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str="facebook/mbart-large-en-ro"       ,       __magic_name__	:				Any=False       ,       __magic_name__	:				int=False	)  ->    Optional[int]:
		"""simple docstring"""




		lowercase__	     =						torch.load(__magic_name__       ,       map_location="""cpu"""	)["""model"""]
		remove_ignore_keys_(__magic_name__	)
		lowercase__	     =						state_dict["""encoder.embed_tokens.weight"""].shape[0]

		lowercase__	     =						MBartConfig.from_pretrained(__magic_name__       ,       vocab_size=__magic_name__	)
		if mbart_aa and finetuned:
				lowercase__	     =						"""relu"""

		lowercase__	     =						state_dict["""decoder.embed_tokens.weight"""]
		lowercase__	     =						MBartForConditionalGeneration(__magic_name__	)
		model.model.load_state_dict(__magic_name__	)

		if finetuned:
				lowercase__	     =						make_linear_from_emb(model.model.shared	)

		return model


if __name__ == "__main__":
	A			:						Optional[Any]        =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
	)
	parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument(
	    '--hf_config',
	    default='facebook/mbart-large-cc25',
	    type=str,
	    help='Which huggingface architecture to use: mbart-large',
	)
	parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
	parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
	A			:						Any        =  parser.parse_args()
	A			:						str        =  convert_fairseq_mbart_checkpoint_from_disk(
	    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
	)
	model.save_pretrained(args.pytorch_dump_folder_path)

