

'''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 		__SCREAMING_SNAKE_CASE       ( nn.Module			):







				def __init__(					self    :		str		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		Any=0.0		,				UpperCAmelCase__    :		Optional[int] = None		,				UpperCAmelCase__    :		str = "geglu"		,				UpperCAmelCase__    :		Optional[int] = None		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		bool = True		,				UpperCAmelCase__    :		str = "layer_norm"		,				UpperCAmelCase__    :		bool = False		,				):
										'''simple docstring'''
										super().__init__()
										lowercase   :       int								=only_cross_attention

										lowercase   :       Tuple								=(num_embeds_ada_norm is not None) and norm_type == '''ada_norm_zero'''
										lowercase   :       Optional[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:
																lowercase   :       Union[str, Any]								=AdaLayerNorm(UpperCAmelCase__		,				UpperCAmelCase__      )
										elif self.use_ada_layer_norm_zero:
																lowercase   :       List[Any]								=AdaLayerNormZero(UpperCAmelCase__		,				UpperCAmelCase__      )
										else:
																lowercase   :       Tuple								=nn.LayerNorm(UpperCAmelCase__		,				elementwise_affine=UpperCAmelCase__      )
										lowercase   :       List[Any]								=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.
																lowercase   :       List[Any]								=(
																    AdaLayerNorm(UpperCAmelCase__		,				UpperCAmelCase__      )
																    if self.use_ada_layer_norm
																    else nn.LayerNorm(UpperCAmelCase__		,				elementwise_affine=UpperCAmelCase__      )
																)
																lowercase   :       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:
																lowercase   :       Union[str, Any]								=None
																lowercase   :       Optional[Any]								=None

										# 3. Feed-forward
										lowercase   :       Dict								=nn.LayerNorm(UpperCAmelCase__		,				elementwise_affine=UpperCAmelCase__      )
										lowercase   :       str								=FeedForward(UpperCAmelCase__		,				dropout=UpperCAmelCase__		,				activation_fn=UpperCAmelCase__		,				final_dropout=UpperCAmelCase__      )

										# let chunk size default to None
										lowercase   :       List[str]								=None
										lowercase   :       List[str]								=0







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										# Sets chunk feed-forward
										lowercase   :       Optional[Any]								=chunk_size
										lowercase   :       Optional[Any]								=dim







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		torch.FloatTensor		,				UpperCAmelCase__    :		Optional[torch.FloatTensor] = None		,				UpperCAmelCase__    :		Optional[torch.FloatTensor] = None		,				UpperCAmelCase__    :		Optional[torch.FloatTensor] = None		,				UpperCAmelCase__    :		Optional[torch.LongTensor] = None		,				UpperCAmelCase__    :		Dict[str, Any] = None		,				UpperCAmelCase__    :		Optional[torch.LongTensor] = None		,				):
										'''simple docstring'''
										# Notice that normalization is always applied before the real computation in the following blocks.
										# 1. Self-Attention
										if self.use_ada_layer_norm:
																lowercase   :       Dict								=self.norma(UpperCAmelCase__		,				UpperCAmelCase__      )
										elif self.use_ada_layer_norm_zero:
																lowercase		,  lowercase		,  lowercase		,  lowercase		,  lowercase   :       str								=self.norma(
																    UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				hidden_dtype=hidden_states.dtype      )
										else:
																lowercase   :       Tuple								=self.norma(UpperCAmelCase__      )

										lowercase   :       List[str]								=cross_attention_kwargs if cross_attention_kwargs is not None else {}

										lowercase   :       Optional[Any]								=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:
																lowercase   :       Optional[Any]								=gate_msa.unsqueeze(1      ) * attn_output
										lowercase   :       Any								=attn_output + hidden_states

										# 2. Cross-Attention
										if self.attna is not None:
																lowercase   :       str								=(
																    self.norma(UpperCAmelCase__		,				UpperCAmelCase__      ) if self.use_ada_layer_norm else self.norma(UpperCAmelCase__      )
																)

																lowercase   :       int								=self.attna(
																    UpperCAmelCase__		,				encoder_hidden_states=UpperCAmelCase__		,				attention_mask=UpperCAmelCase__		,				**UpperCAmelCase__		,				)
																lowercase   :       Tuple								=attn_output + hidden_states

										# 3. Feed-forward
										lowercase   :       List[str]								=self.norma(UpperCAmelCase__      )

										if self.use_ada_layer_norm_zero:
																lowercase   :       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`.'''      )

																lowercase   :       Any								=norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
																lowercase   :       List[str]								=torch.cat(
																    [self.ff(UpperCAmelCase__      ) for hid_slice in norm_hidden_states.chunk(UpperCAmelCase__		,				dim=self._chunk_dim      )]		,				dim=self._chunk_dim		,				)
										else:
																lowercase   :       str								=self.ff(UpperCAmelCase__      )

										if self.use_ada_layer_norm_zero:
																lowercase   :       int								=gate_mlp.unsqueeze(1      ) * ff_output

										lowercase   :       Any								=ff_output + hidden_states

										return hidden_states





class 		__SCREAMING_SNAKE_CASE       ( nn.Module			):







				def __init__(					self    :		Optional[int]		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		Optional[int] = None		,				UpperCAmelCase__    :		int = 4		,				UpperCAmelCase__    :		float = 0.0		,				UpperCAmelCase__    :		str = "geglu"		,				UpperCAmelCase__    :		bool = False		,				):
										'''simple docstring'''
										super().__init__()
										lowercase   :       List[Any]								=int(dim * mult      )
										lowercase   :       Optional[int]								=dim_out if dim_out is not None else dim

										if activation_fn == "gelu":
																lowercase   :       List[Any]								=GELU(UpperCAmelCase__		,				UpperCAmelCase__      )
										if activation_fn == "gelu-approximate":
																lowercase   :       int								=GELU(UpperCAmelCase__		,				UpperCAmelCase__		,				approximate='''tanh'''      )
										elif activation_fn == "geglu":
																lowercase   :       Optional[int]								=GEGLU(UpperCAmelCase__		,				UpperCAmelCase__      )
										elif activation_fn == "geglu-approximate":
																lowercase   :       Tuple								=ApproximateGELU(UpperCAmelCase__		,				UpperCAmelCase__      )

										lowercase   :       Optional[Any]								=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    :		int		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										for module in self.net:
																lowercase   :       List[Any]								=module(UpperCAmelCase__      )
										return hidden_states





class 		__SCREAMING_SNAKE_CASE       ( nn.Module			):







				def __init__(					self    :		List[str]		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		str = "none"      ):
										'''simple docstring'''
										super().__init__()
										lowercase   :       str								=nn.Linear(UpperCAmelCase__		,				UpperCAmelCase__      )
										lowercase   :       Union[str, Any]								=approximate







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										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    :		List[Any]		,				UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=self.proj(UpperCAmelCase__      )
										lowercase   :       Optional[Any]								=self.gelu(UpperCAmelCase__      )
										return hidden_states





class 		__SCREAMING_SNAKE_CASE       ( nn.Module			):







				def __init__(					self    :		int		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										super().__init__()
										lowercase   :       List[Any]								=nn.Linear(UpperCAmelCase__		,				dim_out * 2      )







				def    lowerCamelCase_  (					self    :		int		,				UpperCAmelCase__    :		List[Any]      ):
										'''simple docstring'''
										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    :		List[str]		,				UpperCAmelCase__    :		List[str]      ):
										'''simple docstring'''
										lowercase		,  lowercase   :       Union[str, Any]								=self.proj(UpperCAmelCase__      ).chunk(2		,				dim=-1      )
										return hidden_states * self.gelu(UpperCAmelCase__      )





class 		__SCREAMING_SNAKE_CASE       ( nn.Module			):







				def __init__(					self    :		Dict		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										super().__init__()
										lowercase   :       Tuple								=nn.Linear(UpperCAmelCase__		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Tuple		,				UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Any								=self.proj(UpperCAmelCase__      )
										return x * torch.sigmoid(1.7_02 * x      )





class 		__SCREAMING_SNAKE_CASE       ( nn.Module			):







				def __init__(					self    :		List[Any]		,				UpperCAmelCase__    :		Dict		,				UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										super().__init__()
										lowercase   :       Tuple								=nn.Embedding(UpperCAmelCase__		,				UpperCAmelCase__      )
										lowercase   :       Dict								=nn.SiLU()
										lowercase   :       int								=nn.Linear(UpperCAmelCase__		,				embedding_dim * 2      )
										lowercase   :       List[Any]								=nn.LayerNorm(UpperCAmelCase__		,				elementwise_affine=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Any		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=self.linear(self.silu(self.emb(UpperCAmelCase__      )      )      )
										lowercase		,  lowercase   :       Any								=torch.chunk(UpperCAmelCase__		,				2      )
										lowercase   :       Tuple								=self.norm(UpperCAmelCase__      ) * (1 + scale) + shift
										return x





class 		__SCREAMING_SNAKE_CASE       ( nn.Module			):







				def __init__(					self    :		int		,				UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		List[str]      ):
										'''simple docstring'''
										super().__init__()

										lowercase   :       int								=CombinedTimestepLabelEmbeddings(UpperCAmelCase__		,				UpperCAmelCase__      )

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







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		Dict		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		Any		,				UpperCAmelCase__    :		str=None      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=self.linear(self.silu(self.emb(UpperCAmelCase__		,				UpperCAmelCase__		,				hidden_dtype=UpperCAmelCase__      )      )      )
										lowercase		,  lowercase		,  lowercase		,  lowercase		,  lowercase		,  lowercase   :       int								=emb.chunk(6		,				dim=1      )
										lowercase   :       Dict								=self.norm(UpperCAmelCase__      ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
										return x, gate_msa, shift_mlp, scale_mlp, gate_mlp





class 		__SCREAMING_SNAKE_CASE       ( nn.Module			):







				def __init__(					self    :		Any		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		Optional[str] = None		,				UpperCAmelCase__    :		float = 1E-5      ):
										'''simple docstring'''
										super().__init__()
										lowercase   :       Tuple								=num_groups
										lowercase   :       List[Any]								=eps

										if act_fn is None:
																lowercase   :       Tuple								=None
										else:
																lowercase   :       Optional[int]								=get_activation(UpperCAmelCase__      )

										lowercase   :       Optional[Any]								=nn.Linear(UpperCAmelCase__		,				out_dim * 2      )







				def    lowerCamelCase_  (					self    :		Optional[Any]		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		Optional[Any]      ):
										'''simple docstring'''
										if self.act:
																lowercase   :       List[Any]								=self.act(UpperCAmelCase__      )
										lowercase   :       Dict								=self.linear(UpperCAmelCase__      )
										lowercase   :       int								=emb[:, :, None, None]
										lowercase		,  lowercase   :       List[Any]								=emb.chunk(2		,				dim=1      )

										lowercase   :       str								=F.group_norm(UpperCAmelCase__		,				self.num_groups		,				eps=self.eps      )
										lowercase   :       int								=x * (1 + scale) + shift
										return x








'''simple docstring'''





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							UpperCamelCase_							=					argparse.ArgumentParser(
							    description=(
							        """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
							        """ Distillation"""
							    )
							)
							parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
							parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
							parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
							parser.add_argument("""--vocab_transform""", action="""store_true""")
							UpperCamelCase_							=					parser.parse_args()

							if args.model_type == "roberta":
														UpperCamelCase_							=					RobertaForMaskedLM.from_pretrained(args.model_name)
														UpperCamelCase_							=					"""roberta"""
							elif args.model_type == "gpt2":
														UpperCamelCase_							=					GPTaLMHeadModel.from_pretrained(args.model_name)
														UpperCamelCase_							=					"""transformer"""

							UpperCamelCase_							=					model.state_dict()
							UpperCamelCase_							=					{}

							# Embeddings #
							if args.model_type == "gpt2":
														for param_name in ["wte.weight", "wpe.weight"]:
																					UpperCamelCase_							=					state_dict[f'''{prefix}.{param_name}''']
							else:
														for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																					UpperCamelCase_							=					f'''{prefix}.embeddings.{w}.weight'''
																					UpperCamelCase_							=					state_dict[param_name]
														for w in ["weight", "bias"]:
																					UpperCamelCase_							=					f'''{prefix}.embeddings.LayerNorm.{w}'''
																					UpperCamelCase_							=					state_dict[param_name]

    # Transformer Blocks #
							UpperCamelCase_							=					0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														if args.model_type == "gpt2":
																					for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																												for w in ["weight", "bias"]:
																																			UpperCamelCase_							=					state_dict[
																																			    f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
																																			]
																					UpperCamelCase_							=					state_dict[f'''{prefix}.h.{teacher_idx}.attn.bias''']
														else:
																					for layer in [
																					    "attention.self.query",
																					    "attention.self.key",
																					    "attention.self.value",
																					    "attention.output.dense",
																					    "attention.output.LayerNorm",
																					    "intermediate.dense",
																					    "output.dense",
																					    "output.LayerNorm",
																					]:
																												for w in ["weight", "bias"]:
																																			UpperCamelCase_							=					state_dict[
																																			    f'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
																																			]
														std_idx += 1

							# Language Modeling Head ###s
							if args.model_type == "roberta":
														for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
																					UpperCamelCase_							=					state_dict[f'''{layer}''']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												UpperCamelCase_							=					state_dict[f'''lm_head.dense.{w}''']
																												UpperCamelCase_							=					state_dict[f'''lm_head.layer_norm.{w}''']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					UpperCamelCase_							=					state_dict[f'''{prefix}.ln_f.{w}''']
														UpperCamelCase_							=					state_dict["""lm_head.weight"""]

							print(f'''N layers selected for distillation: {std_idx}''')
							print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

							print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
							torch.save(compressed_sd, args.dump_checkpoint)





