



'''simple docstring'''



from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

_lowercase       :						Tuple						=TypeVar("T")
_lowercase       :						str						=TypeVar("U")






class 			_SCREAMING_SNAKE_CASE				(Generic[T, U]      ):



			def __init__(     self		:						Union[str, Any]    ,			__UpperCamelCase		:						T | None    ,			__UpperCamelCase		:						U | None			)      ->      Dict:
							"""simple docstring"""





							snake_case__      :   Optional[int]      =    key
							snake_case__      :   Any      =    val
							snake_case__      :   int      =    0
							snake_case__      :   DoubleLinkedListNode[T, U] | None      =    None
							snake_case__      :   DoubleLinkedListNode[T, U] | None      =    None







			def __repr__(     self		:						Union[str, Any]			)      ->      str:
							"""simple docstring"""





							return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
							    self.key    ,			self.val    ,			self.freq    ,			self.next is not None    ,			self.prev is not None			)






class 			_SCREAMING_SNAKE_CASE				(Generic[T, U]      ):



			def __init__(     self		:						List[str]			)      ->      None:
							"""simple docstring"""





							snake_case__      :   DoubleLinkedListNode[T, U]      =    DoubleLinkedListNode(__UpperCamelCase    ,			__UpperCamelCase			)
							snake_case__      :   DoubleLinkedListNode[T, U]      =    DoubleLinkedListNode(__UpperCamelCase    ,			__UpperCamelCase			)
							snake_case__  ,      snake_case__      :   Tuple      =    self.rear, self.head



			def __repr__(     self		:						str			)      ->      str:
							"""simple docstring"""





							snake_case__      :   Union[str, Any]      =    ['''DoubleLinkedList''']
							snake_case__      :   Union[str, Any]      =    self.head
							while node.next is not None:
											rep.append(str(__UpperCamelCase			)			)
											snake_case__      :   List[Any]      =    node.next
							rep.append(str(self.rear			)			)
							return ",\n    ".join(__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						DoubleLinkedListNode[T, U]			)      ->      None:
							"""simple docstring"""





							snake_case__      :   Tuple      =    self.rear.prev

							# All nodes other than self.head are guaranteed to have non-None previous
							assert previous is not None

							snake_case__      :   Dict      =    node
							snake_case__      :   str      =    previous
							snake_case__      :   List[Any]      =    node
							snake_case__      :   Optional[int]      =    self.rear
							node.freq += 1
							self._position_node(__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						Optional[int]    ,			__UpperCamelCase		:						DoubleLinkedListNode[T, U]			)      ->      None:
							"""simple docstring"""





							while node.prev is not None and node.prev.freq > node.freq:
											# swap node with previous node
											snake_case__      :   str      =    node.prev

											snake_case__      :   Dict      =    previous_node.prev
											snake_case__      :   Union[str, Any]      =    node.prev
											snake_case__      :   Tuple      =    previous_node
											snake_case__      :   List[str]      =    node







			def 				lowerCAmelCase (     self		:						Any    ,			__UpperCamelCase		:						DoubleLinkedListNode[T, U]			)      ->      DoubleLinkedListNode[T, U] | None:
							"""simple docstring"""





							if node.prev is None or node.next is None:
											return None

							snake_case__      :   Any      =    node.next
							snake_case__      :   Any      =    node.prev
							snake_case__      :   Optional[int]      =    None
							snake_case__      :   Any      =    None
							return node






class 			_SCREAMING_SNAKE_CASE				(Generic[T, U]      ):
			A__								=  {}



			def __init__(     self		:						List[Any]    ,			__UpperCamelCase		:						int			)      ->      Optional[int]:
							"""simple docstring"""





							snake_case__      :   DoubleLinkedList[T, U]      =    DoubleLinkedList()
							snake_case__      :   Tuple      =    capacity
							snake_case__      :   str      =    0
							snake_case__      :   List[str]      =    0
							snake_case__      :   Optional[Any]      =    0
							snake_case__      :   dict[T, DoubleLinkedListNode[T, U]]      =    {}



			def __repr__(     self		:						Union[str, Any]			)      ->      str:
							"""simple docstring"""





							return (
							    F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
							    F'''capacity={self.capacity}, current_size={self.num_keys})'''
							)



			def __contains__(     self		:						Tuple    ,			__UpperCamelCase		:						T			)      ->      bool:
							"""simple docstring"""





							return key in self.cache



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						T			)      ->      U | None:
							"""simple docstring"""





							if key in self.cache:
											self.hits += 1
											snake_case__      :   DoubleLinkedListNode[T, U]      =    self.cache[key]
											snake_case__      :   Optional[int]      =    self.list.remove(self.cache[key]			)
											assert node == value_node

											# node is guaranteed not None because it is in self.cache
											assert node is not None
											self.list.add(__UpperCamelCase			)
											return node.val
							self.miss += 1
							return None



			def 				lowerCAmelCase (     self		:						List[Any]    ,			__UpperCamelCase		:						T    ,			__UpperCamelCase		:						U			)      ->      None:
							"""simple docstring"""





							if key not in self.cache:
											if self.num_keys >= self.capacity:
															# delete first node when over capacity
															snake_case__      :   Any      =    self.list.head.next

															# guaranteed to have a non-None first node when num_keys > 0
															# explain to type checker via assertions
															assert first_node is not None
															assert first_node.key is not None
															assert self.list.remove(__UpperCamelCase			) is not None
															# first_node guaranteed to be in list

															del self.cache[first_node.key]
															self.num_keys -= 1
											snake_case__      :   List[Any]      =    DoubleLinkedListNode(__UpperCamelCase    ,			__UpperCamelCase			)
											self.list.add(self.cache[key]			)
											self.num_keys += 1

							else:
											snake_case__      :   Any      =    self.list.remove(self.cache[key]			)
											assert node is not None  # node guaranteed to be in list
											snake_case__      :   List[str]      =    value
											self.list.add(__UpperCamelCase			)







			@classmethod
			def 				lowerCAmelCase (     cls		:						type[LFUCache[T, U]]    ,			__UpperCamelCase		:						int = 128			)      ->      Callable[[Callable[[T], U]], Callable[..., U]]:
							"""simple docstring"""





							def cache_decorator_inner(__UpperCamelCase		:						Callable[[T], U]			) -> Callable[..., U]:
											def cache_decorator_wrapper(*__UpperCamelCase		:						T			) -> U:
															if func not in cls.decorator_function_to_instance_map:
																			snake_case__      :   int      =    LFUCache(__UpperCamelCase			)

															snake_case__      :   str      =    cls.decorator_function_to_instance_map[func].get(args[0]			)
															if result is None:
																			snake_case__      :   List[str]      =    func(*__UpperCamelCase			)
																			cls.decorator_function_to_instance_map[func].put(args[0]    ,			__UpperCamelCase			)
															return result

											def cache_info() -> LFUCache[T, U]:
															return cls.decorator_function_to_instance_map[func]

											setattr(__UpperCamelCase    ,			'''cache_info'''    ,			__UpperCamelCase			)  # noqa: B010

											return cache_decorator_wrapper

							return cache_decorator_inner


if __name__ == "__main__":
				import doctest

				doctest.testmod()









'''simple docstring'''



import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
				raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
_lowercase       :						Tuple						=logging.get_logger(__name__)

_lowercase       :						Optional[int]						="Hello world! cécé herlolip"


def 							__UpperCAmelCase				(				UpperCamelCase__				:str   ,				UpperCamelCase__				:str   ,				UpperCamelCase__				:bool			)		->						int:
				snake_case__  ,      snake_case__      :   int      =    os.path.split(UpperCamelCase__			)
				snake_case__      :   List[Any]      =    DataaVecTextModel.from_pretrained(
				    UpperCamelCase__   ,				checkpoint_file=UpperCamelCase__			)
				dataavec.eval()  # disable dropout
				snake_case__      :   Dict      =    dataavec.models[0]
				snake_case__      :   int      =    dataavec_model.encoder.sentence_encoder
				snake_case__      :   List[Any]      =    DataaVecTextConfig(
				    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings   ,				hidden_size=dataavec_model.args.encoder_embed_dim   ,				num_hidden_layers=dataavec_model.args.encoder_layers   ,				num_attention_heads=dataavec_model.args.encoder_attention_heads   ,				intermediate_size=dataavec_model.args.encoder_ffn_embed_dim   ,				max_position_embeddings=514   ,				type_vocab_size=1   ,				layer_norm_eps=1e-5   ,				)
				if classification_head:
								snake_case__      :   int      =    dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
				print('''Our BERT config:'''   ,				UpperCamelCase__			)

				snake_case__      :   Optional[int]      =    DataaVecTextForSequenceClassification(UpperCamelCase__			) if classification_head else DataaVecTextForMaskedLM(UpperCamelCase__			)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				snake_case__      :   Optional[Any]      =    dataavec_sent_encoder.embed_tokens.weight
				snake_case__      :   Union[str, Any]      =    dataavec_sent_encoder.embed_positions.weight
				snake_case__      :   Any      =    torch.zeros_like(
				    model.dataavec_text.embeddings.token_type_embeddings.weight			)  # just zero them out b/c data2vec doesn't use them.
				snake_case__      :   Tuple      =    dataavec_sent_encoder.layernorm_embedding.weight
				snake_case__      :   Dict      =    dataavec_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers			):
								# Encoder: start of layer
								snake_case__      :   BertLayer      =    model.dataavec_text.encoder.layer[i]
								snake_case__      :   TransformerSentenceEncoderLayer      =    dataavec_sent_encoder.layers[i]

								# self attention
								snake_case__      :   BertSelfAttention      =    layer.attention.self
								assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
								)
								assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
								)
								assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
								)

								snake_case__      :   List[Any]      =    dataavec_layer.self_attn.q_proj.weight
								snake_case__      :   List[str]      =    dataavec_layer.self_attn.q_proj.bias
								snake_case__      :   str      =    dataavec_layer.self_attn.k_proj.weight
								snake_case__      :   List[Any]      =    dataavec_layer.self_attn.k_proj.bias
								snake_case__      :   Optional[Any]      =    dataavec_layer.self_attn.v_proj.weight
								snake_case__      :   int      =    dataavec_layer.self_attn.v_proj.bias

								# self-attention output
								snake_case__      :   BertSelfOutput      =    layer.attention.output
								assert (
								    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
								), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
								snake_case__      :   Optional[int]      =    dataavec_layer.self_attn.out_proj.weight
								snake_case__      :   str      =    dataavec_layer.self_attn.out_proj.bias
								snake_case__      :   int      =    dataavec_layer.self_attn_layer_norm.weight
								snake_case__      :   List[Any]      =    dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								snake_case__      :   BertIntermediate      =    layer.intermediate
								assert (
								    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								snake_case__      :   List[str]      =    dataavec_layer.fca.weight
								snake_case__      :   str      =    dataavec_layer.fca.bias

								# output
								snake_case__      :   BertOutput      =    layer.output
								assert (
								    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								snake_case__      :   List[str]      =    dataavec_layer.fca.weight
								snake_case__      :   List[Any]      =    dataavec_layer.fca.bias
								snake_case__      :   str      =    dataavec_layer.final_layer_norm.weight
								snake_case__      :   List[Any]      =    dataavec_layer.final_layer_norm.bias
								# end of layer

				if classification_head:
								snake_case__      :   Any      =    dataavec.model.classification_heads['''mnli'''].dense.weight
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].dense.bias
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].out_proj.weight
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].out_proj.bias
				else:
								# LM Head
								snake_case__      :   str      =    dataavec_model.encoder.lm_head.dense.weight
								snake_case__      :   str      =    dataavec_model.encoder.lm_head.dense.bias
								snake_case__      :   Union[str, Any]      =    dataavec_model.encoder.lm_head.layer_norm.weight
								snake_case__      :   Tuple      =    dataavec_model.encoder.lm_head.layer_norm.bias
								snake_case__      :   Tuple      =    dataavec_model.encoder.lm_head.weight
								snake_case__      :   List[str]      =    dataavec_model.encoder.lm_head.bias

				# Let's check that we get the same results.
				snake_case__      :   torch.Tensor      =    dataavec.encode(UpperCamelCase__			).unsqueeze(0			)  # batch of size 1

				snake_case__      :   str      =    model(UpperCamelCase__			)[0]
				if classification_head:
								snake_case__      :   List[str]      =    dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(UpperCamelCase__			)			)
				else:
								snake_case__      :   Tuple      =    dataavec_model(UpperCamelCase__			)[0]
				print(our_output.shape   ,				their_output.shape			)
				snake_case__      :   Dict      =    torch.max(torch.abs(our_output - their_output			)			).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
				snake_case__      :   Tuple      =    torch.allclose(UpperCamelCase__   ,				UpperCamelCase__   ,				atol=1e-3			)
				print('''Do both models output the same tensors?'''   ,				'''🔥''' if success else '''💩'''			)
				if not success:
								raise Exception('''Something went wRoNg'''			)

				pathlib.Path(UpperCamelCase__			).mkdir(parents=UpperCamelCase__   ,				exist_ok=UpperCamelCase__			)
				print(F'''Saving model to {pytorch_dump_folder_path}'''			)
				model.save_pretrained(UpperCamelCase__			)


if __name__ == "__main__":
				_lowercase       :						Any						=argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				parser.add_argument(
				    "--classification_head", action="store_true", help="Whether to convert a final classification head."
				)
				_lowercase       :						Tuple						=parser.parse_args()
				convert_dataavec_checkpoint_to_pytorch(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)




