


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()
__lowerCamelCase   	=  logging.get_logger(__name__)

__lowerCamelCase   	=  '''Hello world! cécé herlolip'''



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_      ,      a_     :  Dict				      =     os.path.split(__UpperCamelCase			)
     a_     :  str				      =     DataaVecTextModel.from_pretrained(
         __UpperCamelCase	,	checkpoint_file=__UpperCamelCase			)
     dataavec.eval()  # disable dropout
     a_     :  Tuple				      =     dataavec.models[0]
     a_     :  str				      =     dataavec_model.encoder.sentence_encoder
     a_     :  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=5_1_4	,	type_vocab_size=1	,	layer_norm_eps=1E-5	,	)
     if classification_head:
          a_     :  Any				      =     dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
     print("""Our BERT config:"""	,	__UpperCamelCase			)

     a_     :  Union[str, Any]				      =     DataaVecTextForSequenceClassification(__UpperCamelCase			) if classification_head else DataaVecTextForMaskedLM(__UpperCamelCase			)
     model.eval()

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

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

          # self attention
          a_     :  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)			)}'''
          )

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

          # self-attention output
          a_     :  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}'''
          a_     :  List[Any]				      =     dataavec_layer.self_attn.out_proj.weight
          a_     :  List[str]				      =     dataavec_layer.self_attn.out_proj.bias
          a_     :  str				      =     dataavec_layer.self_attn_layer_norm.weight
          a_     :  int				      =     dataavec_layer.self_attn_layer_norm.bias

          # intermediate
          a_     :  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}'''
          a_     :  Union[str, Any]				      =     dataavec_layer.fca.weight
          a_     :  List[str]				      =     dataavec_layer.fca.bias

          # output
          a_     :  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}'''
          a_     :  Optional[Any]				      =     dataavec_layer.fca.weight
          a_     :  int				      =     dataavec_layer.fca.bias
          a_     :  Dict				      =     dataavec_layer.final_layer_norm.weight
          a_     :  List[Any]				      =     dataavec_layer.final_layer_norm.bias
          # end of layer

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

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

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







from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar


class 	a__						(    lowerCAmelCase_     ):

       @abstractmethod
       def __lt__( self   :  T   ,				lowerCamelCase_   :  T     ):
            pass

       @abstractmethod
       def __gt__( self   :  T   ,				lowerCamelCase_   :  T     ):
            pass

       @abstractmethod
       def __eq__( self   :  T   ,				lowerCamelCase_   :  object     ):
            pass

__lowerCamelCase   	=  TypeVar('''T''', bound=Comparable)


class 	a__						(    Generic[T]     ):

       def __init__( self   :  Tuple     ):
            a_     :  list[T]				      =     []
            a_     :  int				      =     0

       def __repr__( self   :  Optional[Any]     ):
            return str(self.h     )

       def     UpperCAmelCase( self   :  Dict   ,				lowerCamelCase_   :  int     ):
            if child_idx > 0:
                 return (child_idx - 1) // 2
            return None

       def     UpperCAmelCase( self   :  Any   ,				lowerCamelCase_   :  int     ):
            a_     :  Tuple				      =     2 * parent_idx + 1
            if left_child_index < self.heap_size:
                 return left_child_index
            return None

       def     UpperCAmelCase( self   :  List[Any]   ,				lowerCamelCase_   :  int     ):
            a_     :  List[Any]				      =     2 * parent_idx + 2
            if right_child_index < self.heap_size:
                 return right_child_index
            return None

       def     UpperCAmelCase( self   :  Optional[int]   ,				lowerCamelCase_   :  int     ):
            if index < self.heap_size:
                 a_     :  int				      =     index
                 a_     :  Union[str, Any]				      =     self.left_child_idx(lowerCamelCase_     )
                 a_     :  Optional[int]				      =     self.right_child_idx(lowerCamelCase_     )
                 # check which child is larger than its parent
                 if left_child is not None and self.h[left_child] > self.h[violation]:
                      a_     :  str				      =     left_child
                 if right_child is not None and self.h[right_child] > self.h[violation]:
                      a_     :  List[Any]				      =     right_child
                 # if violation indeed exists
                 if violation != index:
                      # swap to fix the violation
                      a_      ,      a_     :  int				      =     self.h[index], self.h[violation]
                      # fix the subsequent violation recursively if any
                      self.max_heapify(lowerCamelCase_     )

       def     UpperCAmelCase( self   :  List[str]   ,				lowerCamelCase_   :  Iterable[T]     ):
            a_     :  Dict				      =     list(lowerCamelCase_     )
            a_     :  int				      =     len(self.h     )
            if self.heap_size > 1:
                 # max_heapify from right to left but exclude leaves (last level)
                 for i in range(self.heap_size // 2 - 1   ,				-1   ,				-1     ):
                      self.max_heapify(lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Tuple     ):
            if self.heap_size >= 2:
                 a_     :  int				      =     self.h[0]
                 a_     :  Dict				      =     self.h.pop(-1     )
                 self.heap_size -= 1
                 self.max_heapify(0     )
                 return me
            elif self.heap_size == 1:
                 self.heap_size -= 1
                 return self.h.pop(-1     )
            else:
                 raise Exception("""Empty heap"""     )

       def     UpperCAmelCase( self   :  List[str]   ,				lowerCamelCase_   :  T     ):
            self.h.append(lowerCamelCase_     )
            a_     :  Optional[int]				      =     (self.heap_size - 1) // 2
            self.heap_size += 1
            while idx >= 0:
                 self.max_heapify(lowerCamelCase_     )
                 a_     :  Union[str, Any]				      =     (idx - 1) // 2

       def     UpperCAmelCase( self   :  int     ):
            a_     :  Union[str, Any]				      =     self.heap_size
            for j in range(size - 1   ,				0   ,				-1     ):
                 a_      ,      a_     :  str				      =     self.h[j], self.h[0]
                 self.heap_size -= 1
                 self.max_heapify(0     )
            a_     :  Union[str, Any]				      =     size


if __name__ == "__main__":
       import doctest

       # run doc test
       doctest.testmod()

       # demo
       for unsorted in [
           [0],
           [2],
           [3, 5],
           [5, 3],
           [5, 5],
           [0, 0, 0, 0],
           [1, 1, 1, 1],
           [2, 2, 3, 5],
           [0, 2, 2, 3, 5],
           [2, 5, 3, 0, 2, 3, 0, 3],
           [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
           [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
           [-45, -2, -5],
       ]:
              print(f'''unsorted array: {unsorted}''')

              __lowerCamelCase   	=  Heap()
              heap.build_max_heap(unsorted)
              print(f'''after build heap: {heap}''')

              print(f'''max value: {heap.extract_max()}''')
              print(f'''after max value removed: {heap}''')

              heap.insert(100)
              print(f'''after new value 100 inserted: {heap}''')

              heap.heap_sort()
              print(f'''heap-sorted array: {heap}\n''')



