

'''simple docstring'''





from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder





@dataclass
class 		__SCREAMING_SNAKE_CASE       ( __a			):
				lowerCamelCase_							=						42





class 		__SCREAMING_SNAKE_CASE       ( __a	,       __a			):
				lowerCamelCase_							=						True







				@register_to_config
				def __init__(					self    :		Union[str, Any]		,				UpperCAmelCase__    :		int = 3		,				UpperCAmelCase__    :		int = 3		,				UpperCAmelCase__    :		Tuple[str] = ("DownEncoderBlock2D",)		,				UpperCAmelCase__    :		Tuple[str] = ("UpDecoderBlock2D",)		,				UpperCAmelCase__    :		Tuple[int] = (64,)		,				UpperCAmelCase__    :		int = 1		,				UpperCAmelCase__    :		str = "silu"		,				UpperCAmelCase__    :		int = 4		,				UpperCAmelCase__    :		int = 32		,				UpperCAmelCase__    :		int = 32		,				UpperCAmelCase__    :		float = 0.1_82_15		,				):
										'''simple docstring'''
										super().__init__()

										# pass init params to Encoder
										lowercase   :       Dict								=Encoder(
										    in_channels=snake_case__		,				out_channels=snake_case__		,				down_block_types=snake_case__		,				block_out_channels=snake_case__		,				layers_per_block=snake_case__		,				act_fn=snake_case__		,				norm_num_groups=snake_case__		,				double_z=snake_case__		,				)

										# pass init params to Decoder
										lowercase   :       Union[str, Any]								=Decoder(
										    in_channels=snake_case__		,				out_channels=snake_case__		,				up_block_types=snake_case__		,				block_out_channels=snake_case__		,				layers_per_block=snake_case__		,				norm_num_groups=snake_case__		,				act_fn=snake_case__		,				)

										lowercase   :       int								=nn.Convad(2 * latent_channels		,				2 * latent_channels		,				1      )
										lowercase   :       Optional[int]								=nn.Convad(snake_case__		,				snake_case__		,				1      )

										lowercase   :       int								=False
										lowercase   :       Tuple								=False

										# only relevant if vae tiling is enabled
										lowercase   :       List[Any]								=self.config.sample_size
										lowercase   :       Tuple								=(
										    self.config.sample_size[0]
										    if isinstance(self.config.sample_size		,				(list, tuple)      )
										    else self.config.sample_size
										)
										lowercase   :       Dict								=int(sample_size / (2 ** (len(self.config.block_out_channels      ) - 1))      )
										lowercase   :       Any								=0.25







				def    lowerCamelCase_  (					self    :		int		,				UpperCAmelCase__    :		Dict		,				UpperCAmelCase__    :		Optional[int]=False      ):
										'''simple docstring'''
										if isinstance(snake_case__		,				(Encoder, Decoder)      ):
																lowercase   :       str								=value







				def    lowerCamelCase_  (					self    :		Any		,				UpperCAmelCase__    :		bool = True      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=use_tiling







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										self.enable_tiling(snake_case__      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=True







				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=False







				@property
				# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       Tuple								={}

										def fn_recursive_add_processors(UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		torch.nn.Module		,				UpperCAmelCase__    :		Dict[str, AttentionProcessor]      ):
																if hasattr(snake_case__		,				'''set_processor'''      ):
																						lowercase   :       str								=module.processor

																for sub_name, child in module.named_children():
																						fn_recursive_add_processors(F'''{name}.{sub_name}'''		,				snake_case__		,				snake_case__      )

																return processors

										for name, module in self.named_children():
																fn_recursive_add_processors(snake_case__		,				snake_case__		,				snake_case__      )

										return processors







				def    lowerCamelCase_  (					self    :		List[Any]		,				UpperCAmelCase__    :		Union[AttentionProcessor, Dict[str, AttentionProcessor]]      ):
										'''simple docstring'''
										lowercase   :       List[str]								=len(self.attn_processors.keys()      )

										if isinstance(snake_case__		,				snake_case__      ) and len(snake_case__      ) != count:
																raise ValueError(
																    F'''A dict of processors was passed, but the number of processors {len(snake_case__      )} does not match the'''
																    F''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''      )

										def fn_recursive_attn_processor(UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		torch.nn.Module		,				UpperCAmelCase__    :		List[Any]      ):
																if hasattr(snake_case__		,				'''set_processor'''      ):
																						if not isinstance(snake_case__		,				snake_case__      ):
																												module.set_processor(snake_case__      )
																						else:
																												module.set_processor(processor.pop(F'''{name}.processor'''      )      )

																for sub_name, child in module.named_children():
																						fn_recursive_attn_processor(F'''{name}.{sub_name}'''		,				snake_case__		,				snake_case__      )

										for name, module in self.named_children():
																fn_recursive_attn_processor(snake_case__		,				snake_case__		,				snake_case__      )







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										self.set_attn_processor(AttnProcessor()      )







				@apply_forward_hook
				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				UpperCAmelCase__    :		torch.FloatTensor		,				UpperCAmelCase__    :		bool = True      ):
										'''simple docstring'''
										if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
																return self.tiled_encode(snake_case__		,				return_dict=snake_case__      )

										if self.use_slicing and x.shape[0] > 1:
																lowercase   :       Optional[Any]								=[self.encoder(snake_case__      ) for x_slice in x.split(1      )]
																lowercase   :       List[str]								=torch.cat(snake_case__      )
										else:
																lowercase   :       List[Any]								=self.encoder(snake_case__      )

										lowercase   :       Optional[int]								=self.quant_conv(snake_case__      )
										lowercase   :       int								=DiagonalGaussianDistribution(snake_case__      )

										if not return_dict:
																return (posterior,)

										return AutoencoderKLOutput(latent_dist=snake_case__      )







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		torch.FloatTensor		,				UpperCAmelCase__    :		bool = True      ):
										'''simple docstring'''
										if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
																return self.tiled_decode(snake_case__		,				return_dict=snake_case__      )

										lowercase   :       int								=self.post_quant_conv(snake_case__      )
										lowercase   :       Dict								=self.decoder(snake_case__      )

										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=snake_case__      )







				@apply_forward_hook
				def    lowerCamelCase_  (					self    :		Any		,				UpperCAmelCase__    :		torch.FloatTensor		,				UpperCAmelCase__    :		bool = True      ):
										'''simple docstring'''
										if self.use_slicing and z.shape[0] > 1:
																lowercase   :       str								=[self._decode(snake_case__      ).sample for z_slice in z.split(1      )]
																lowercase   :       Union[str, Any]								=torch.cat(snake_case__      )
										else:
																lowercase   :       Union[str, Any]								=self._decode(snake_case__      ).sample

										if not return_dict:
																return (decoded,)

										return DecoderOutput(sample=snake_case__      )







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		Dict		,				UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										lowercase   :       Tuple								=min(a.shape[2]		,				b.shape[2]		,				snake_case__      )
										for y in range(snake_case__      ):
																lowercase   :       Any								=a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
										return b







				def    lowerCamelCase_  (					self    :		int		,				UpperCAmelCase__    :		Any		,				UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       Dict								=min(a.shape[3]		,				b.shape[3]		,				snake_case__      )
										for x in range(snake_case__      ):
																lowercase   :       Any								=a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
										return b







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		torch.FloatTensor		,				UpperCAmelCase__    :		bool = True      ):
										'''simple docstring'''
										lowercase   :       int								=int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)      )
										lowercase   :       Union[str, Any]								=int(self.tile_latent_min_size * self.tile_overlap_factor      )
										lowercase   :       List[str]								=self.tile_latent_min_size - blend_extent

										# Split the image into 512x512 tiles and encode them separately.
										lowercase   :       str								=[]
										for i in range(0		,				x.shape[2]		,				snake_case__      ):
																lowercase   :       str								=[]
																for j in range(0		,				x.shape[3]		,				snake_case__      ):
																						lowercase   :       Union[str, Any]								=x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																						lowercase   :       int								=self.encoder(snake_case__      )
																						lowercase   :       Tuple								=self.quant_conv(snake_case__      )
																						row.append(snake_case__      )
																rows.append(snake_case__      )
										lowercase   :       int								=[]
										for i, row in enumerate(snake_case__      ):
																lowercase   :       Optional[int]								=[]
																for j, tile in enumerate(snake_case__      ):
																						# blend the above tile and the left tile
																						# to the current tile and add the current tile to the result row
																						if i > 0:
																												lowercase   :       List[str]								=self.blend_v(rows[i - 1][j]		,				snake_case__		,				snake_case__      )
																						if j > 0:
																												lowercase   :       Optional[Any]								=self.blend_h(row[j - 1]		,				snake_case__		,				snake_case__      )
																						result_row.append(tile[:, :, :row_limit, :row_limit]      )
																result_rows.append(torch.cat(snake_case__		,				dim=3      )      )

										lowercase   :       str								=torch.cat(snake_case__		,				dim=2      )
										lowercase   :       Optional[Any]								=DiagonalGaussianDistribution(snake_case__      )

										if not return_dict:
																return (posterior,)

										return AutoencoderKLOutput(latent_dist=snake_case__      )







				def    lowerCamelCase_  (					self    :		Any		,				UpperCAmelCase__    :		torch.FloatTensor		,				UpperCAmelCase__    :		bool = True      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)      )
										lowercase   :       List[Any]								=int(self.tile_sample_min_size * self.tile_overlap_factor      )
										lowercase   :       str								=self.tile_sample_min_size - blend_extent

										# Split z into overlapping 64x64 tiles and decode them separately.
										# The tiles have an overlap to avoid seams between tiles.
										lowercase   :       Any								=[]
										for i in range(0		,				z.shape[2]		,				snake_case__      ):
																lowercase   :       str								=[]
																for j in range(0		,				z.shape[3]		,				snake_case__      ):
																						lowercase   :       Optional[Any]								=z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																						lowercase   :       Any								=self.post_quant_conv(snake_case__      )
																						lowercase   :       List[str]								=self.decoder(snake_case__      )
																						row.append(snake_case__      )
																rows.append(snake_case__      )
										lowercase   :       Optional[int]								=[]
										for i, row in enumerate(snake_case__      ):
																lowercase   :       Any								=[]
																for j, tile in enumerate(snake_case__      ):
																						# blend the above tile and the left tile
																						# to the current tile and add the current tile to the result row
																						if i > 0:
																												lowercase   :       List[Any]								=self.blend_v(rows[i - 1][j]		,				snake_case__		,				snake_case__      )
																						if j > 0:
																												lowercase   :       Optional[int]								=self.blend_h(row[j - 1]		,				snake_case__		,				snake_case__      )
																						result_row.append(tile[:, :, :row_limit, :row_limit]      )
																result_rows.append(torch.cat(snake_case__		,				dim=3      )      )

										lowercase   :       Optional[Any]								=torch.cat(snake_case__		,				dim=2      )
										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=snake_case__      )







				def    lowerCamelCase_  (					self    :		int		,				UpperCAmelCase__    :		torch.FloatTensor		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		bool = True		,				UpperCAmelCase__    :		Optional[torch.Generator] = None		,				):
										'''simple docstring'''
										lowercase   :       Dict								=sample
										lowercase   :       int								=self.encode(snake_case__      ).latent_dist
										if sample_posterior:
																lowercase   :       List[Any]								=posterior.sample(generator=snake_case__      )
										else:
																lowercase   :       Dict								=posterior.mode()
										lowercase   :       Union[str, Any]								=self.decode(snake_case__      ).sample

										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=snake_case__      )



'''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)





