

import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
			raise Exception('requires fairseq >= 1.0.0a')

logging.set_verbosity_info()
a_						     =    logging.get_logger(__name__)

a_						     =    'Hello world! cécé herlolip'

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      str				     =       FairseqRobertaModel.from_pretrained(A__)
	roberta.eval()  # disable dropout
	SCREAMING_SNAKE_CASE   :      Tuple				     =       roberta.model.encoder.sentence_encoder
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       XLMRobertaConfig(
	    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings							,			hidden_size=roberta.cfg.model.encoder_embed_dim							,			num_hidden_layers=roberta.cfg.model.encoder_layers							,			num_attention_heads=roberta.cfg.model.encoder_attention_heads							,			intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim							,			max_position_embeddings=514							,			type_vocab_size=1							,			layer_norm_eps=1E-5							,			)
	if classification_head:
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

	print("Our RoBERTa config:"							,			A__)

	SCREAMING_SNAKE_CASE   :      List[str]				     =       XLMRobertaXLForSequenceClassification(A__) if classification_head else XLMRobertaXLForMaskedLM(A__)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	SCREAMING_SNAKE_CASE   :      int				     =       roberta_sent_encoder.embed_tokens.weight
	SCREAMING_SNAKE_CASE   :      Tuple				     =       roberta_sent_encoder.embed_positions.weight
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight)  # just zero them out b/c RoBERTa doesn't use them.

	SCREAMING_SNAKE_CASE   :      int				     =       roberta_sent_encoder.layer_norm.weight
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       roberta_sent_encoder.layer_norm.bias

	for i in range(config.num_hidden_layers):
		# Encoder: start of layer
		SCREAMING_SNAKE_CASE   :      str				     =       model.roberta.encoder.layer[i]
		SCREAMING_SNAKE_CASE   :      int				     =       roberta_sent_encoder.layers[i]

		SCREAMING_SNAKE_CASE   :      Dict				     =       layer.attention
		SCREAMING_SNAKE_CASE   :      Tuple				     =       roberta_layer.self_attn_layer_norm.weight
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       roberta_layer.self_attn_layer_norm.bias

		# self attention
		SCREAMING_SNAKE_CASE   :      Tuple				     =       layer.attention.self
		assert (
		    roberta_layer.self_attn.k_proj.weight.data.shape
		    == roberta_layer.self_attn.q_proj.weight.data.shape
		    == roberta_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size))
		)

		SCREAMING_SNAKE_CASE   :      Tuple				     =       roberta_layer.self_attn.q_proj.weight
		SCREAMING_SNAKE_CASE   :      Dict				     =       roberta_layer.self_attn.q_proj.bias
		SCREAMING_SNAKE_CASE   :      Any				     =       roberta_layer.self_attn.k_proj.weight
		SCREAMING_SNAKE_CASE   :      str				     =       roberta_layer.self_attn.k_proj.bias
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       roberta_layer.self_attn.v_proj.weight
		SCREAMING_SNAKE_CASE   :      Dict				     =       roberta_layer.self_attn.v_proj.bias

		# self-attention output
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       layer.attention.output
		assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       roberta_layer.self_attn.out_proj.weight
		SCREAMING_SNAKE_CASE   :      Dict				     =       roberta_layer.self_attn.out_proj.bias

		# this one is final layer norm
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       roberta_layer.final_layer_norm.weight
		SCREAMING_SNAKE_CASE   :      Dict				     =       roberta_layer.final_layer_norm.bias

		# intermediate
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		SCREAMING_SNAKE_CASE   :      str				     =       roberta_layer.fca.weight
		SCREAMING_SNAKE_CASE   :      str				     =       roberta_layer.fca.bias

		# output
		SCREAMING_SNAKE_CASE   :      Any				     =       layer.output
		assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       roberta_layer.fca.weight
		SCREAMING_SNAKE_CASE   :      int				     =       roberta_layer.fca.bias
		# end of layer

	if classification_head:
		SCREAMING_SNAKE_CASE   :      Any				     =       roberta.model.classification_heads["mnli"].dense.weight
		SCREAMING_SNAKE_CASE   :      int				     =       roberta.model.classification_heads["mnli"].dense.bias
		SCREAMING_SNAKE_CASE   :      str				     =       roberta.model.classification_heads["mnli"].out_proj.weight
		SCREAMING_SNAKE_CASE   :      Any				     =       roberta.model.classification_heads["mnli"].out_proj.bias
	else:
		# LM Head
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       roberta.model.encoder.lm_head.dense.weight
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       roberta.model.encoder.lm_head.dense.bias
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       roberta.model.encoder.lm_head.layer_norm.weight
		SCREAMING_SNAKE_CASE   :      List[str]				     =       roberta.model.encoder.lm_head.layer_norm.bias
		SCREAMING_SNAKE_CASE   :      Any				     =       roberta.model.encoder.lm_head.weight
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       roberta.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	SCREAMING_SNAKE_CASE   :      Any				     =       roberta.encode(A__).unsqueeze(0)  # batch of size 1

	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       model(A__)[0]
	if classification_head:
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       roberta.model.classification_heads["mnli"](roberta.extract_features(A__))
	else:
		SCREAMING_SNAKE_CASE   :      int				     =       roberta.model(A__)[0]
	print(our_output.shape							,			their_output.shape)
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.max(torch.abs(our_output - their_output)).item()
	print(f"max_absolute_diff = {max_absolute_diff}")  # ~ 1e-7
	SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.allclose(A__							,			A__							,			atol=1E-3)
	print("Do both models output the same tensors?"							,			"🔥" if success else "💩")
	if not success:
		raise Exception("Something went wRoNg")

	pathlib.Path(A__).mkdir(parents=A__							,			exist_ok=A__)
	print(f"Saving model to {pytorch_dump_folder_path}")
	model.save_pretrained(A__)


if __name__ == "__main__":
			a_						     =    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--roberta_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.'
			)
			a_						     =    parser.parse_args()
			convert_xlm_roberta_xl_checkpoint_to_pytorch(
			    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)





from collections.abc import Iterable
from typing import Any






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Tuple      ,      a							: int | None = None	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       value
				SCREAMING_SNAKE_CASE   :      Node | None				     =       None  # Added in order to delete a node easier
				SCREAMING_SNAKE_CASE   :      Node | None				     =       None
				SCREAMING_SNAKE_CASE   :      Node | None				     =       None






			def __repr__(			self							: Optional[Any]	)							->       str:

				"""simple docstring"""

				from pprint import pformat

				if self.left is None and self.right is None:
					return str(self.value	)
				return pformat({F"{self.value}": (self.left, self.right)}      ,      indent=1	)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Optional[int]      ,      a							: Node | None = None	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       root
			def __str__(			self							: Any	)							->       str:

				"""simple docstring"""

				return str(self.root	)
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Node      ,      a							: Node | None	)							->       None:

				"""simple docstring"""

				if new_children is not None:  # reset its kids
					SCREAMING_SNAKE_CASE   :      Dict				     =       node.parent
				if node.parent is not None:  # reset its parent
					if self.is_right(a	):  # If it is the right children
						SCREAMING_SNAKE_CASE   :      Dict				     =       new_children
					else:
						SCREAMING_SNAKE_CASE   :      Any				     =       new_children
				else:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       new_children
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: Node	)							->       bool:

				"""simple docstring"""

				if node.parent and node.parent.right:
					return node == node.parent.right
				return False
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       bool:

				"""simple docstring"""

				return self.root is None
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       Node(a	)  # create a new Node
				if self.empty():  # if Tree is empty
					SCREAMING_SNAKE_CASE   :      Tuple				     =       new_node  # set its root
				else:  # Tree is not empty
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.root  # from root
					if parent_node is None:
						return
					while True:  # While we don't get to a leaf
						if value < parent_node.value:  # We go left
							if parent_node.left is None:
								SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       new_node  # We insert the new node in a leaf
								break
							else:
								SCREAMING_SNAKE_CASE   :      Any				     =       parent_node.left
						else:
							if parent_node.right is None:
								SCREAMING_SNAKE_CASE   :      str				     =       new_node
								break
							else:
								SCREAMING_SNAKE_CASE   :      Optional[int]				     =       parent_node.right
					SCREAMING_SNAKE_CASE   :      int				     =       parent_node
			def 							__UpperCamelCase (			self							: Any      ,      *a							: str	)							->       None:

				"""simple docstring"""

				for value in values:
					self.__insert(a	)
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: List[Any]	)							->       Node | None:

				"""simple docstring"""

				if self.empty():
					raise IndexError("Warning: Tree is empty! please use another."	)
				else:
					SCREAMING_SNAKE_CASE   :      str				     =       self.root
					# use lazy evaluation here to avoid NoneType Attribute error
					while node is not None and node.value is not value:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       node.left if value < node.value else node.right
					return node
			def 							__UpperCamelCase (			self							: int      ,      a							: Node | None = None	)							->       Node | None:

				"""simple docstring"""

				if node is None:
					if self.root is None:
						return None
					SCREAMING_SNAKE_CASE   :      Tuple				     =       self.root

				if not self.empty():
					while node.right is not None:
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       node.right
				return node
			def 							__UpperCamelCase (			self							: int      ,      a							: Node | None = None	)							->       Node | None:

				"""simple docstring"""

				if node is None:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.root
				if self.root is None:
					return None
				if not self.empty():
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.root
					while node.left is not None:
						SCREAMING_SNAKE_CASE   :      str				     =       node.left
				return node
			def 							__UpperCamelCase (			self							: Dict      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       self.search(a	)  # Look for the node with that label
				if node is not None:
					if node.left is None and node.right is None:  # If it has no children
						self.__reassign_nodes(a      ,      a	)
					elif node.left is None:  # Has only right children
						self.__reassign_nodes(a      ,      node.right	)
					elif node.right is None:  # Has only left children
						self.__reassign_nodes(a      ,      node.left	)
					else:
						SCREAMING_SNAKE_CASE   :      Tuple				     =       self.get_max(
						    node.left	)  # Gets the max value of the left branch
						self.remove(tmp_node.value	)  # type: ignore
						SCREAMING_SNAKE_CASE   :      str				     =       (
						    tmp_node.value  # type: ignore
						)  # Assigns the value to the node to delete and keep tree structure
			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: Node | None	)							->       Iterable:

				"""simple docstring"""

				if node is not None:
					yield node  # Preorder Traversal
					yield from self.preorder_traverse(node.left	)
					yield from self.preorder_traverse(node.right	)
			def 							__UpperCamelCase (			self							: str      ,      a							: Union[str, Any]=None	)							->       Any:

				"""simple docstring"""

				if traversal_function is None:
					return self.preorder_traverse(self.root	)
				else:
					return traversal_function(self.root	)
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: list      ,      a							: Node | None	)							->       None:

				"""simple docstring"""

				if node:
					self.inorder(a      ,      node.left	)
					arr.append(node.value	)
					self.inorder(a      ,      node.right	)






			def 							__UpperCamelCase (			self							: int      ,      a							: int      ,      a							: Node	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      list[int]				     =       []
				self.inorder(a      ,      a	)  # append all values to list using inorder traversal
				return arr[k - 1]

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      int				     =       []
	if curr_node is not None:
		SCREAMING_SNAKE_CASE   :      List[str]				     =       postorder(curr_node.left) + postorder(curr_node.right) + [curr_node]
	return node_list

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      int				     =       (8, 3, 6, 1, 10, 14, 13, 4, 7)
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       BinarySearchTree()
	for i in testlist:
		t.insert(_a)

	# Prints all the elements of the list in order traversal
	print(_a)

	if t.search(6) is not None:
		print("The value 6 exists")
	else:
		print("The value 6 doesn't exist")

	if t.search(-1) is not None:
		print("The value -1 exists")
	else:
		print("The value -1 doesn't exist")

	if not t.empty():
		print("Max Value: "							,			t.get_max().value)  # type: ignore
		print("Min Value: "							,			t.get_min().value)  # type: ignore

	for i in testlist:
		t.remove(_a)
		print(_a)


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)