
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__    (    __lowerCAmelCase  :  str   ,							__lowerCAmelCase  :  str   ,							__lowerCAmelCase  :  bool    ):




			"""simple docstring"""


			lowerCAmelCase_     ,  lowerCAmelCase_      =     os.path.split(__lowerCAmelCase    )
			lowerCAmelCase_      =     DataaVecTextModel.from_pretrained(
			    __lowerCAmelCase   ,							checkpoint_file=__lowerCAmelCase    )
			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:"   ,							__lowerCAmelCase    )

			lowerCAmelCase_      =     DataaVecTextForSequenceClassification(__lowerCAmelCase    ) if classification_head else DataaVecTextForMaskedLM(__lowerCAmelCase    )
			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(__lowerCAmelCase    ).unsqueeze(0    )  # batch of size 1

			lowerCAmelCase_      =     model(__lowerCAmelCase    )[0]
			if classification_head:
						lowerCAmelCase_      =     dataavec.model.classification_heads["mnli"](dataavec.extract_features(__lowerCAmelCase    )    )
			else:
						lowerCAmelCase_      =     dataavec_model(__lowerCAmelCase    )[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(__lowerCAmelCase   ,							__lowerCAmelCase   ,							atol=1e-3    )
			print("Do both models output the same tensors?"   ,							"🔥" if success else "💩"    )
			if not success:
						raise Exception("Something went wRoNg"    )

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


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
			)





# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint





class      _lowerCAmelCase :




				def __init__( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				str:
							lowerCAmelCase_      =     key
							lowerCAmelCase_      =     freq




				def __str__( self					)    ->				Any:
							return f"""Node(key={self.key}, freq={self.freq})"""






def        lowerCamelCase__    (    __lowerCAmelCase  :  Optional[int]   ,							__lowerCAmelCase  :  Dict   ,							__lowerCAmelCase  :  Optional[Any]   ,							__lowerCAmelCase  :  Optional[int]   ,							__lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  List[Any]    ):




			"""simple docstring"""


			if i > j or i < 0 or j > len(__lowerCAmelCase    ) - 1:
						return

			lowerCAmelCase_      =     root[i][j]
			if parent == -1:  # root does not have a parent
						print(F"""{key[node]} is the root of the binary search tree."""    )
			elif is_left:
						print(F"""{key[node]} is the left child of key {parent}."""    )
			else:
						print(F"""{key[node]} is the right child of key {parent}."""    )

			print_binary_search_tree(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							node - 1   ,							key[node]   ,							__lowerCAmelCase    )
			print_binary_search_tree(__lowerCAmelCase   ,							__lowerCAmelCase   ,							node + 1   ,							__lowerCAmelCase   ,							key[node]   ,							__lowerCAmelCase    )






def        lowerCamelCase__    (    __lowerCAmelCase  :  Union[str, Any]    ):




			"""simple docstring"""


			nodes.sort(key=lambda __lowerCAmelCase    : node.key    )

			lowerCAmelCase_      =     len(__lowerCAmelCase    )

			lowerCAmelCase_      =     [nodes[i].key for i in range(__lowerCAmelCase    )]
			lowerCAmelCase_      =     [nodes[i].freq for i in range(__lowerCAmelCase    )]

			# This 2D array stores the overall tree cost (which's as minimized as possible);
			# for a single key, cost is equal to frequency of the key.
			lowerCAmelCase_      =     [[freqs[i] if i == j else 0 for j in range(__lowerCAmelCase    )] for i in range(__lowerCAmelCase    )]
			# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
			# array
			lowerCAmelCase_      =     [[freqs[i] if i == j else 0 for j in range(__lowerCAmelCase    )] for i in range(__lowerCAmelCase    )]
			# stores tree roots that will be used later for constructing binary search tree
			lowerCAmelCase_      =     [[i if i == j else 0 for j in range(__lowerCAmelCase    )] for i in range(__lowerCAmelCase    )]

			for interval_length in range(2   ,							n + 1    ):
						for i in range(n - interval_length + 1    ):
									lowerCAmelCase_      =     i + interval_length - 1

									lowerCAmelCase_      =     sys.maxsize  # set the value to "infinity"
									lowerCAmelCase_      =     total[i][j - 1] + freqs[j]

									# Apply Knuth's optimization
									# Loop without optimization: for r in range(i, j + 1):
									for r in range(root[i][j - 1]   ,							root[i + 1][j] + 1    ):  # r is a temporal root
												lowerCAmelCase_      =     dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
												lowerCAmelCase_      =     dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
												lowerCAmelCase_      =     left + total[i][j] + right

												if dp[i][j] > cost:
															lowerCAmelCase_      =     cost
															lowerCAmelCase_      =     r

			print("Binary search tree nodes:"    )
			for node in nodes:
						print(__lowerCAmelCase    )

			print(F"""\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."""    )
			print_binary_search_tree(__lowerCAmelCase   ,							__lowerCAmelCase   ,							0   ,							n - 1   ,							-1   ,							__lowerCAmelCase    )






def        lowerCamelCase__    (    ):




			"""simple docstring"""


			lowerCAmelCase_      =     [Node(__lowerCAmelCase   ,							randint(1   ,							50    )    ) for i in range(10   ,							0   ,							-1    )]
			find_optimal_binary_search_tree(__lowerCAmelCase    )


if __name__ == "__main__":
			main()



