

'''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()
lowerCamelCase				:   int      				=  logging.get_logger(__name__)

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


def  _lowerCAmelCase      (		_UpperCamelCase		:  str							,				_UpperCamelCase		:  str							,				_UpperCamelCase		:  bool       )   ->    Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=os.path.split(_UpperCamelCase       )
     _SCREAMING_SNAKE_CASE												=DataaVecTextModel.from_pretrained(
         _UpperCamelCase							,				checkpoint_file=_UpperCamelCase       )
     dataavec.eval()  # disable dropout
     _SCREAMING_SNAKE_CASE												=dataavec.models[0]
     _SCREAMING_SNAKE_CASE												=dataavec_model.encoder.sentence_encoder
     _SCREAMING_SNAKE_CASE												=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_14							,				type_vocab_size=1							,				layer_norm_eps=1E-5							,				)
     if classification_head:
          _SCREAMING_SNAKE_CASE												=dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
     print('Our BERT config:'							,				_UpperCamelCase       )

     _SCREAMING_SNAKE_CASE												=DataaVecTextForSequenceClassification(_UpperCamelCase       ) if classification_head else DataaVecTextForMaskedLM(_UpperCamelCase       )
     model.eval()

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

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

          # self attention
          _SCREAMING_SNAKE_CASE												=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)       )}"
          )

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

          # self-attention output
          _SCREAMING_SNAKE_CASE												=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}"
          _SCREAMING_SNAKE_CASE												=dataavec_layer.self_attn.out_proj.weight
          _SCREAMING_SNAKE_CASE												=dataavec_layer.self_attn.out_proj.bias
          _SCREAMING_SNAKE_CASE												=dataavec_layer.self_attn_layer_norm.weight
          _SCREAMING_SNAKE_CASE												=dataavec_layer.self_attn_layer_norm.bias

          # intermediate
          _SCREAMING_SNAKE_CASE												=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}"
          _SCREAMING_SNAKE_CASE												=dataavec_layer.fca.weight
          _SCREAMING_SNAKE_CASE												=dataavec_layer.fca.bias

          # output
          _SCREAMING_SNAKE_CASE												=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}"
          _SCREAMING_SNAKE_CASE												=dataavec_layer.fca.weight
          _SCREAMING_SNAKE_CASE												=dataavec_layer.fca.bias
          _SCREAMING_SNAKE_CASE												=dataavec_layer.final_layer_norm.weight
          _SCREAMING_SNAKE_CASE												=dataavec_layer.final_layer_norm.bias
          # end of layer

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

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

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






'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase				:   Dict      				=  logging.get_logger(__name__)

lowerCamelCase				:   Dict      				=  {
    "uw-madison/nystromformer-512": "https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}







class 							A__					( A__						):
  A__					=       'nystromformer'





  def __init__(	self   :			List[str]							,      _a   :			Dict=3_0000							,      _a   :			Dict=768							,      _a   :			Union[str, Any]=12							,      _a   :			Optional[int]=12							,      _a   :			Union[str, Any]=3072							,      _a   :			Optional[Any]="gelu_new"							,      _a   :			Dict=0.1							,      _a   :			Dict=0.1							,      _a   :			Union[str, Any]=510							,      _a   :			Any=2							,      _a   :			List[Any]=64							,      _a   :			int=64							,      _a   :			Optional[Any]=65							,      _a   :			str=False							,      _a   :			List[Any]=0.02							,      _a   :			List[str]=1e-5							,      _a   :			Optional[Any]=1							,      _a   :			Optional[Any]=0							,      _a   :			List[Any]=2							,      **_a   :			List[Any]							,      )			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=vocab_size
       _SCREAMING_SNAKE_CASE												=max_position_embeddings
       _SCREAMING_SNAKE_CASE												=hidden_size
       _SCREAMING_SNAKE_CASE												=num_hidden_layers
       _SCREAMING_SNAKE_CASE												=num_attention_heads
       _SCREAMING_SNAKE_CASE												=intermediate_size
       _SCREAMING_SNAKE_CASE												=hidden_act
       _SCREAMING_SNAKE_CASE												=hidden_dropout_prob
       _SCREAMING_SNAKE_CASE												=attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE												=initializer_range
       _SCREAMING_SNAKE_CASE												=type_vocab_size
       _SCREAMING_SNAKE_CASE												=segment_means_seq_len
       _SCREAMING_SNAKE_CASE												=num_landmarks
       _SCREAMING_SNAKE_CASE												=conv_kernel_size
       _SCREAMING_SNAKE_CASE												=inv_coeff_init_option
       _SCREAMING_SNAKE_CASE												=layer_norm_eps
       super().__init__(pad_token_id=_a							,      bos_token_id=_a							,      eos_token_id=_a							,      **_a				)
