


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 typing import Any
def 			__SCREAMING_SNAKE_CASE							(					*lowercase__	:list[list[int]]					)							->	list[list[int]]:
						if all(_check_not_integer(lowercase__					) for m in matrix_s					):
												for i in matrix_s[1:]:
																		_verify_matrix_sizes(matrix_s[0]   ,    lowercase__					)
												return [[sum(lowercase__					) for t in zip(*lowercase__					)] for m in zip(*lowercase__					)]
						raise TypeError('Expected a matrix, got int/list instead'					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:list[list[int]]					)							->	list[list[int]]:
						if (
						    _check_not_integer(lowercase__					)
						    and _check_not_integer(lowercase__					)
						    and _verify_matrix_sizes(lowercase__   ,    lowercase__					)
						):
												return [[i - j for i, j in zip(*lowercase__					)] for m in zip(lowercase__   ,    lowercase__					)]
						raise TypeError('Expected a matrix, got int/list instead'					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:int | float					)							->	list[list[float]]:
						return [[x * n for x in row] for row in matrix]
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:list[list[int]]					)							->	list[list[int]]:
						if _check_not_integer(lowercase__					) and _check_not_integer(lowercase__					):
												__magic_name__		,			__magic_name__					:				str				=   _verify_matrix_sizes(lowercase__   ,    lowercase__					)

						if cols[0] != rows[1]:
												__magic_name__					:				Union[str, Any]				=   (
												    'Cannot multiply matrix of dimensions '
												    f"({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})"
												)
												raise ValueError(lowercase__					)
						return [
						    [sum(m * n for m, n in zip(lowercase__   ,    lowercase__					)					) for j in zip(*lowercase__					)] for i in matrix_a
						]
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:int					)							->	list[list[int]]:
						__magic_name__					:				Tuple				=   int(lowercase__					)
						return [[int(row == column					) for column in range(lowercase__					)] for row in range(lowercase__					)]
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:bool = True					)							->	list[list[int]] | map[list[int]]:
						if _check_not_integer(lowercase__					):
												if return_map:
																		return map(lowercase__   ,    zip(*lowercase__					)					)
												else:
																		return list(map(lowercase__   ,    zip(*lowercase__					)					)					)
						raise TypeError('Expected a matrix, got int/list instead'					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:int   ,    lowercase__	:int					)							->	list[list[int]]:
						__magic_name__					:				int				=   matrix[:row] + matrix[row + 1 :]
						return [row[:column] + row[column + 1 :] for row in minor]
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]					)							->	Any:
						if len(lowercase__					) == 1:
												return matrix[0][0]

						return sum(
						    x * determinant(minor(lowercase__   ,    0   ,    lowercase__					)					) * (-1) ** i
						    for i, x in enumerate(matrix[0]					)					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]					)							->	list[list[float]] | None:
						__magic_name__					:				Tuple				=   determinant(lowercase__					)
						if det == 0:
												return None

						__magic_name__					:				Optional[int]				=   [
						    [determinant(minor(lowercase__   ,    lowercase__   ,    lowercase__					)					) for j in range(len(lowercase__					)					)]
						    for i in range(len(lowercase__					)					)
						]

						__magic_name__					:				Optional[Any]				=   [
						    [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]					)]
						    for row in range(len(lowercase__					)					)
						]
						__magic_name__					:				Optional[Any]				=   list(transpose(lowercase__					)					)
						return scalar_multiply(lowercase__   ,    1 / det					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]					)							->	bool:
						return not isinstance(lowercase__   ,    lowercase__					) and not isinstance(matrix[0]   ,    lowercase__					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]					)							->	tuple[int, int]:
						return len(lowercase__					), len(matrix[0]					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:list[list[int]]					)							->	tuple[tuple[int, int], tuple[int, int]]:
						__magic_name__					:				str				=   _shape(lowercase__					) + _shape(lowercase__					)
						if shape[0] != shape[3] or shape[1] != shape[2]:
												__magic_name__					:				Optional[Any]				=   (
												    'operands could not be broadcast together with shape '
												    f"({shape[0], shape[1]}), ({shape[2], shape[3]})"
												)
												raise ValueError(lowercase__					)
						return (shape[0], shape[2]), (shape[1], shape[3])
def 			__SCREAMING_SNAKE_CASE							(					)							->	None:
						__magic_name__					:				Dict				=   [[1_2, 1_0], [3, 9]]
						__magic_name__					:				Optional[int]				=   [[3, 4], [7, 4]]
						__magic_name__					:				Optional[Any]				=   [[1_1, 1_2, 1_3, 1_4], [2_1, 2_2, 2_3, 2_4], [3_1, 3_2, 3_3, 3_4], [4_1, 4_2, 4_3, 4_4]]
						__magic_name__					:				List[str]				=   [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
						print(f"Add Operation, {add(lowercase__   ,    lowercase__					) = } \n"					)
						print(
						    f"Multiply Operation, {multiply(lowercase__   ,    lowercase__					) = } \n"   ,    )
						print(f"Identity: {identity(5					)}\n"					)
						print(f"Minor of {matrix_c} = {minor(lowercase__   ,    1   ,    2					)} \n"					)
						print(f"Determinant of {matrix_b} = {determinant(lowercase__					)} \n"					)
						print(f"Inverse of {matrix_d} = {inverse(lowercase__					)}\n"					)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()


