
"""simple docstring"""


import numpy as np







class 						lowerCamelCase__   :





			'''simple docstring'''



			def __init__(				self	,	__UpperCAmelCase=5	,	__UpperCAmelCase=5    )					->	Any:
				_lowerCAmelCase								=depth
				_lowerCAmelCase								=0
				_lowerCAmelCase								=None
				_lowerCAmelCase								=None
				_lowerCAmelCase								=min_leaf_size
				_lowerCAmelCase								=None



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase	,	__UpperCAmelCase    )					->	Optional[int]:
				if labels.ndim != 1:
					print("""Error: Input labels must be one dimensional"""    )

				return np.mean((labels - prediction) ** 2    )



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase	,	__UpperCAmelCase    )					->	Tuple:
				if x.ndim != 1:
					print("""Error: Input data set must be one dimensional"""    )
					return
				if len(__UpperCAmelCase    ) != len(__UpperCAmelCase    ):
					print("""Error: X and y have different lengths"""    )
					return
				if y.ndim != 1:
					print("""Error: Data set labels must be one dimensional"""    )
					return

				if len(__UpperCAmelCase    ) < 2 * self.min_leaf_size:
					_lowerCAmelCase								=np.mean(__UpperCAmelCase    )
					return

				if self.depth == 1:
					_lowerCAmelCase								=np.mean(__UpperCAmelCase    )
					return

				_lowerCAmelCase								=0
				_lowerCAmelCase								=self.mean_squared_error(__UpperCAmelCase	,	np.mean(__UpperCAmelCase    )    ) * 2
				for i in range(len(__UpperCAmelCase    )    ):
					if len(x[:i]    ) < self.min_leaf_size:
						continue
					elif len(x[i:]    ) < self.min_leaf_size:
						continue
					else:
						_lowerCAmelCase								=self.mean_squared_error(x[:i]	,	np.mean(y[:i]    )    )
						_lowerCAmelCase								=self.mean_squared_error(x[i:]	,	np.mean(y[i:]    )    )
						_lowerCAmelCase								=error_left + error_right
						if error < min_error:
							_lowerCAmelCase								=i
							_lowerCAmelCase								=error

				if best_split != 0:
					_lowerCAmelCase								=x[:best_split]
					_lowerCAmelCase								=y[:best_split]
					_lowerCAmelCase								=x[best_split:]
					_lowerCAmelCase								=y[best_split:]

					_lowerCAmelCase								=x[best_split]
					_lowerCAmelCase								=DecisionTree(
					    depth=self.depth - 1	,	min_leaf_size=self.min_leaf_size    )
					_lowerCAmelCase								=DecisionTree(
					    depth=self.depth - 1	,	min_leaf_size=self.min_leaf_size    )
					self.left.train(__UpperCAmelCase	,	__UpperCAmelCase    )
					self.right.train(__UpperCAmelCase	,	__UpperCAmelCase    )
				else:
					_lowerCAmelCase								=np.mean(__UpperCAmelCase    )

				return



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase    )					->	List[Any]:
				if self.prediction is not None:
					return self.prediction
				elif self.left or self.right is not None:
					if x >= self.decision_boundary:
						return self.right.predict(__UpperCAmelCase    )
					else:
						return self.left.predict(__UpperCAmelCase    )
				else:
					print("""Error: Decision tree not yet trained"""    )
					return None







class 						lowerCamelCase__   :





			'''simple docstring'''



			@staticmethod
			def    _lowerCAmelCase						(				__UpperCAmelCase	,	__UpperCAmelCase    )					->	str:
				_lowerCAmelCase								=float(0    )
				for label in labels:
					squared_error_sum += (label - prediction) ** 2

				return float(squared_error_sum / labels.size    )







def        _lowerCamelCase()							->					Union[str, Any]:
	_lowerCAmelCase								=np.arange(-1.0       ,					1.0       ,					0.0_05			)
	_lowerCAmelCase								=np.sin(__UpperCamelCase			)

	_lowerCAmelCase								=DecisionTree(depth=10       ,					min_leaf_size=10			)
	tree.train(__UpperCamelCase       ,					__UpperCamelCase			)

	_lowerCAmelCase								=(np.random.rand(10			) * 2) - 1
	_lowerCAmelCase								=np.array([tree.predict(__UpperCamelCase			) for x in test_cases]			)
	_lowerCAmelCase								=np.mean((predictions - test_cases) ** 2			)

	print("""Test values: """ + str(__UpperCamelCase			)			)
	print("""Predictions: """ + str(__UpperCamelCase			)			)
	print("""Average error: """ + str(__UpperCamelCase			)			)


if __name__ == "__main__":
			main()
			import doctest

			doctest.testmod(name='mean_squarred_error', verbose=True)





"""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()
__A					=			logging.get_logger(__name__)

__A					=			'Hello world! cécé herlolip'



def        _lowerCamelCase(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			)							->					List[Any]:
	_lowerCAmelCase		,				_lowerCAmelCase								=os.path.split(__UpperCamelCase			)
	_lowerCAmelCase								=DataaVecTextModel.from_pretrained(
	    __UpperCamelCase       ,					checkpoint_file=__UpperCamelCase			)
	dataavec.eval()  # disable dropout
	_lowerCAmelCase								=dataavec.models[0]
	_lowerCAmelCase								=dataavec_model.encoder.sentence_encoder
	_lowerCAmelCase								=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:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
	print("""Our BERT config:"""       ,					__UpperCamelCase			)

	_lowerCAmelCase								=DataaVecTextForSequenceClassification(__UpperCamelCase			) if classification_head else DataaVecTextForMaskedLM(__UpperCamelCase			)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	_lowerCAmelCase								=dataavec_sent_encoder.embed_tokens.weight
	_lowerCAmelCase								=dataavec_sent_encoder.embed_positions.weight
	_lowerCAmelCase								=torch.zeros_like(
	    model.dataavec_text.embeddings.token_type_embeddings.weight			)  # just zero them out b/c data2vec doesn't use them.
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.weight
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.bias

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

		# self attention
		_lowerCAmelCase								=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)			)}'''
		)

		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.bias

		# self-attention output
		_lowerCAmelCase								=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}'''
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.bias

		# intermediate
		_lowerCAmelCase								=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}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias

		# output
		_lowerCAmelCase								=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}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.bias
		# end of layer

	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.bias
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.bias
	else:
		# LM Head
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.bias

	# Let's check that we get the same results.
	_lowerCAmelCase								=dataavec.encode(__UpperCamelCase			).unsqueeze(0			)  # batch of size 1

	_lowerCAmelCase								=model(__UpperCamelCase			)[0]
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__UpperCamelCase			)			)
	else:
		_lowerCAmelCase								=dataavec_model(__UpperCamelCase			)[0]
	print(our_output.shape       ,					their_output.shape			)
	_lowerCAmelCase								=torch.max(torch.abs(our_output - their_output			)			).item()
	print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
	_lowerCAmelCase								=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__":
			__A					=			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.'
			)
			__A					=			parser.parse_args()
			convert_dataavec_checkpoint_to_pytorch(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)



