"""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()
__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     '''Hello world! cécé herlolip'''







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[Any]:
     snake_case_			,      snake_case_   			=   os.path.split(UpperCAmelCase   )
     snake_case_   			=   DataaVecTextModel.from_pretrained(
         UpperCAmelCase		,   checkpoint_file=UpperCAmelCase   )
     dataavec.eval()  # disable dropout
     snake_case_   			=   dataavec.models[0]
     snake_case_   			=   dataavec_model.encoder.sentence_encoder
     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=514		,   type_vocab_size=1		,   layer_norm_eps=1e-5		,   )
     if classification_head:
          snake_case_   			=   dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
     print('Our BERT config:'		,   UpperCAmelCase   )

     snake_case_   			=   DataaVecTextForSequenceClassification(UpperCAmelCase   ) if classification_head else DataaVecTextForMaskedLM(UpperCAmelCase   )
     model.eval()

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

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

          # self attention
          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)   )}'
          )

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

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

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

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

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

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

     snake_case_   			=   model(UpperCAmelCase   )[0]
     if classification_head:
          snake_case_   			=   dataavec.model.classification_heads['mnli'](dataavec.extract_features(UpperCAmelCase   )   )
     else:
          snake_case_   			=   dataavec_model(UpperCAmelCase   )[0]
     print(our_output.shape		,   their_output.shape   )
     snake_case_   			=   torch.max(torch.abs(our_output - their_output   )   ).item()
     print(f'max_absolute_diff = {max_absolute_diff}'   )  # ~ 1e-7
     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__":
    __UpperCamelCase										=     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.'''
    )
    __UpperCamelCase										=     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


__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     {
    '''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 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = "nystromformer"







       def __init__(					self,				lowerCAmelCase__=3_0000,				lowerCAmelCase__=768,				lowerCAmelCase__=12,				lowerCAmelCase__=12,				lowerCAmelCase__=3072,				lowerCAmelCase__="gelu_new",				lowerCAmelCase__=0.1,				lowerCAmelCase__=0.1,				lowerCAmelCase__=510,				lowerCAmelCase__=2,				lowerCAmelCase__=64,				lowerCAmelCase__=64,				lowerCAmelCase__=65,				lowerCAmelCase__=False,				lowerCAmelCase__=0.02,				lowerCAmelCase__=1e-5,				lowerCAmelCase__=1,				lowerCAmelCase__=0,				lowerCAmelCase__=2,				**lowerCAmelCase__,				) -> Optional[Any]:
            snake_case_   			=   vocab_size
            snake_case_   			=   max_position_embeddings
            snake_case_   			=   hidden_size
            snake_case_   			=   num_hidden_layers
            snake_case_   			=   num_attention_heads
            snake_case_   			=   intermediate_size
            snake_case_   			=   hidden_act
            snake_case_   			=   hidden_dropout_prob
            snake_case_   			=   attention_probs_dropout_prob
            snake_case_   			=   initializer_range
            snake_case_   			=   type_vocab_size
            snake_case_   			=   segment_means_seq_len
            snake_case_   			=   num_landmarks
            snake_case_   			=   conv_kernel_size
            snake_case_   			=   inv_coeff_init_option
            snake_case_   			=   layer_norm_eps
            super().__init__(pad_token_id=lowerCAmelCase__,				bos_token_id=lowerCAmelCase__,				eos_token_id=lowerCAmelCase__,				**lowerCAmelCase__)

