






"""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  :						int					       =							logging.get_logger(__name__)

a  :						Union[str, Any]					       =							"Hello world! cécé herlolip"




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

 UpperCAmelCase__ 						=				DataaVecTextForSequenceClassification(_lowerCamelCase			) if classification_head else DataaVecTextForMaskedLM(_lowerCamelCase			)
 model.eval()

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

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

  # self attention
  UpperCAmelCase__ 						=				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)			)}'''
  )

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

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

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

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

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

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

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










from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
__lowercase							:List[Any]							=	TypeVar("T", bound="Enum")



@unique
class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    80
							snake_case_							=    70
							snake_case_							=    60
							snake_case_							=    50
							snake_case_							=    40
							snake_case_							=    30
							snake_case_							=    20
							snake_case_							=    10







@unique
class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    24
							snake_case_							=    21
							snake_case_							=    18
							snake_case_							=    15
							snake_case_							=    12
							snake_case_							=    9
							snake_case_							=    6
							snake_case_							=    3
							snake_case_							=    2
							snake_case_							=    1
							snake_case_							=    -1
							snake_case_							=    -2
							snake_case_							=    -3
							snake_case_							=    -6
							snake_case_							=    -9
							snake_case_							=    -12
							snake_case_							=    -15
							snake_case_							=    -18
							snake_case_							=    -21
							snake_case_							=    -24

							@classmethod
							def   A_	(   cls					:  type[T]     )						->dict:
													return {unit.name: unit.value for unit in cls if unit.value > 0}






							@classmethod
							def   A_	(   cls					:  type[T]     )						->dict:
													return {unit.name: unit.value for unit in cls if unit.value < 0}







def    UpperCAmelCase				(  _lowerCamelCase   :	float  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					str =       SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
						for name_prefix, value_prefix in prefixes.items():
												SCREAMING_SNAKE_CASE__  :					str =       value / (10**value_prefix)
												if numerical_part > 1:
																		return f"""{numerical_part!s} {name_prefix}"""
						return str(_lowerCamelCase  )







def    UpperCAmelCase				(  _lowerCamelCase   :	float  ):





						'''simple docstring'''


						for prefix in BinaryUnit:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       value / (2**prefix.value)
												if numerical_part > 1:
																		return f"""{numerical_part!s} {prefix.name}"""
						return str(_lowerCamelCase  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()