
'''simple docstring'''







import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


lowerCAmelCase_      =   [nn.LayerNorm]

lowerCAmelCase_      =   logging.get_logger(__name__)

lowerCAmelCase_      =   version.parse(version.parse(torch.__version__).base_version)

lowerCAmelCase_      =   parsed_torch_version_base >= version.parse("2.0")
lowerCAmelCase_      =   parsed_torch_version_base >= version.parse("1.12")
lowerCAmelCase_      =   parsed_torch_version_base >= version.parse("1.11")
lowerCAmelCase_      =   parsed_torch_version_base < version.parse("1.11")







def   lowerCAmelCase(  a__    :  Optional[Any]      , a__    :  Union[str, Any]      , a__    :  Union[str, Any]      , a__    :  Optional[Any]      , self    :  Optional[Any]      ):







       '''simple docstring'''






       from torch import _softmax_backward_data

       if is_torch_less_than_1_11:
              return _softmax_backward_data(a__      , a__      , parent.dim      , self      )
       else:
              return _softmax_backward_data(a__      , a__      , parent.dim      , self.dtype      )







def   lowerCAmelCase(  a__    :  Dict      , a__    :  Tuple      , a__    :  List[Any] = 0      ):







       '''simple docstring'''






       lowerCamelCase__				   =      index.to(layer.weight.device      )
       lowerCamelCase__				   =      layer.weight.index_select(a__      , a__      ).clone().detach()
       if layer.bias is not None:
              if dim == 1:
                     lowerCamelCase__				   =      layer.bias.clone().detach()
              else:
                     lowerCamelCase__				   =      layer.bias[index].clone().detach()
       lowerCamelCase__				   =      list(layer.weight.size()      )
       lowerCamelCase__				   =      len(a__      )
       lowerCamelCase__				   =      nn.Linear(new_size[1]      , new_size[0]      , bias=layer.bias is not None      ).to(layer.weight.device      )
       lowerCamelCase__				   =      False
       new_layer.weight.copy_(W.contiguous()      )
       lowerCamelCase__				   =      True
       if layer.bias is not None:
              lowerCamelCase__				   =      False
              new_layer.bias.copy_(b.contiguous()      )
              lowerCamelCase__				   =      True
       return new_layer







class 		snake_case_       (						nn.Module						):



   """simple docstring"""





   def __init__( self			,   UpperCamelCase			,   UpperCamelCase):
          super().__init__()
          lowerCamelCase__				   =      nf
          lowerCamelCase__				   =      nn.Parameter(torch.empty(_lowerCamelCase			,   _lowerCamelCase))
          lowerCamelCase__				   =      nn.Parameter(torch.zeros(_lowerCamelCase))
          nn.init.normal_(self.weight			,   std=0.0_2)







   def     __UpperCAmelCase      ( self			,   UpperCamelCase):
          lowerCamelCase__				   =      x.size()[:-1] + (self.nf,)
          lowerCamelCase__				   =      torch.addmm(self.bias			,   x.view(-1			,   x.size(-1))			,   self.weight)
          lowerCamelCase__				   =      x.view(_lowerCamelCase)
          return x







def   lowerCAmelCase(  a__    :  Optional[int]      , a__    :  str      , a__    :  List[Any] = 1      ):







       '''simple docstring'''






       lowerCamelCase__				   =      index.to(layer.weight.device      )
       lowerCamelCase__				   =      layer.weight.index_select(a__      , a__      ).clone().detach()
       if dim == 0:
              lowerCamelCase__				   =      layer.bias.clone().detach()
       else:
              lowerCamelCase__				   =      layer.bias[index].clone().detach()
       lowerCamelCase__				   =      list(layer.weight.size()      )
       lowerCamelCase__				   =      len(a__      )
       lowerCamelCase__				   =      ConvaD(new_size[1]      , new_size[0]      ).to(layer.weight.device      )
       lowerCamelCase__				   =      False
       new_layer.weight.copy_(W.contiguous()      )
       lowerCamelCase__				   =      True
       lowerCamelCase__				   =      False
       new_layer.bias.copy_(b.contiguous()      )
       lowerCamelCase__				   =      True
       return new_layer







def   lowerCAmelCase(  a__    :  Any      , a__    :  Dict      , a__    :  Dict = None      ):







       '''simple docstring'''






       if isinstance(a__      , nn.Linear      ):
              return prune_linear_layer(a__      , a__      , dim=0 if dim is None else dim      )
       elif isinstance(a__      , a__      ):
              return prune_convad_layer(a__      , a__      , dim=1 if dim is None else dim      )
       else:
              raise ValueError(f"""Can't prune layer of class {layer.__class__}"""      )







def   lowerCAmelCase(  a__    :  int      , a__    :  int      , a__    :  Dict      , *a__    :  Dict      ):







       '''simple docstring'''






       assert len(a__      ) > 0, f"""{input_tensors} has to be a tuple/list of tensors"""

       # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
       lowerCamelCase__				   =      len(inspect.signature(a__      ).parameters      )
       if num_args_in_forward_chunk_fn != len(a__      ):
              raise ValueError(
                  f"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(a__      )} input """
                  "tensors are given"      )

       if chunk_size > 0:
              lowerCamelCase__				   =      input_tensors[0].shape[chunk_dim]
              for input_tensor in input_tensors:
                     if input_tensor.shape[chunk_dim] != tensor_shape:
                            raise ValueError(
                                f"""All input tenors have to be of the same shape: {tensor_shape}, """
                                f"""found shape {input_tensor.shape[chunk_dim]}"""      )

              if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
                     raise ValueError(
                         f"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
                         f"""size {chunk_size}"""      )

              lowerCamelCase__				   =      input_tensors[0].shape[chunk_dim] // chunk_size

              # chunk input tensor into tuples
              lowerCamelCase__				   =      tuple(input_tensor.chunk(a__      , dim=a__      ) for input_tensor in input_tensors      )
              # apply forward fn to every tuple
              lowerCamelCase__				   =      tuple(forward_fn(*a__      ) for input_tensors_chunk in zip(*a__      )      )
              # concatenate output at same dimension
              return torch.cat(a__      , dim=a__      )

       return forward_fn(*a__      )







def   lowerCAmelCase(  a__    :  Optional[Any]      , a__    :  Optional[int]      , a__    :  Optional[Any]      , a__    :  Tuple      ):







       '''simple docstring'''






       lowerCamelCase__				   =      torch.ones(a__      , a__      )
       lowerCamelCase__				   =      set(a__      ) - already_pruned_heads  # Convert to set and remove already pruned heads
       for head in heads:
              # Compute how many pruned heads are before the head and move the index accordingly
              lowerCamelCase__				   =      head - sum(1 if h < head else 0 for h in already_pruned_heads      )
              lowerCamelCase__				   =      0
       lowerCamelCase__				   =      mask.view(-1      ).contiguous().eq(1      )
       lowerCamelCase__				   =      torch.arange(len(a__      )      )[mask].long()
       return heads, index







def   lowerCAmelCase(  *a__    :  List[str]      , a__    :  int = None      ):







       '''simple docstring'''






       return torch.meshgrid(*a__      , indexing=a__      )







def   lowerCAmelCase(  a__    :  Optional[int]      ):







       '''simple docstring'''






       return tensor.device, storage_ptr(a__      ), storage_size(a__      )



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__				:      Dict    		=     logging.get_logger(__name__)

a__				:      str    		=     '''Hello world! cécé herlolip'''







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				List[str]				      = os.path.split(a__   )
    SCREAMING_SNAKE_CASE				:				Any				      = DataaVecTextModel.from_pretrained(
        a__ ,						checkpoint_file=a__   )
    dataavec.eval()  # disable dropout
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec.models[0]
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_model.encoder.sentence_encoder
    SCREAMING_SNAKE_CASE				:				List[str]				      = 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:
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
    print('''Our BERT config:''' ,						a__   )

    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = DataaVecTextForSequenceClassification(a__   ) if classification_head else DataaVecTextForMaskedLM(a__   )
    model.eval()

    # Now let's copy all the weights.
    # Embeddings
    SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec_sent_encoder.embed_tokens.weight
    SCREAMING_SNAKE_CASE				:				int				      = dataavec_sent_encoder.embed_positions.weight
    SCREAMING_SNAKE_CASE				:				int				      = 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				:				Dict				      = dataavec_sent_encoder.layernorm_embedding.weight
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_sent_encoder.layernorm_embedding.bias

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

        # self attention
        SCREAMING_SNAKE_CASE				:				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)   )}"""
        )

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

        # self-attention output
        SCREAMING_SNAKE_CASE				:				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}"""
        SCREAMING_SNAKE_CASE				:				List[Any]				      = dataavec_layer.self_attn.out_proj.weight
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_layer.self_attn.out_proj.bias
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.self_attn_layer_norm.weight
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.self_attn_layer_norm.bias

        # intermediate
        SCREAMING_SNAKE_CASE				:				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}"""
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.fca.weight
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec_layer.fca.bias

        # output
        SCREAMING_SNAKE_CASE				:				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}"""
        SCREAMING_SNAKE_CASE				:				int				      = dataavec_layer.fca.weight
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.fca.bias
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.final_layer_norm.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_layer.final_layer_norm.bias
        # end of layer

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

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

    SCREAMING_SNAKE_CASE				:				List[str]				      = model(a__   )[0]
    if classification_head:
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(a__   )   )
    else:
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_model(a__   )[0]
    print(our_output.shape ,						their_output.shape   )
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.max(torch.abs(our_output - their_output   )   ).item()
    print(F"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.allclose(a__ ,						a__ ,						atol=1e-3   )
    print('''Do both models output the same tensors?''' ,						'''🔥''' if success else '''💩'''   )
    if not success:
        raise Exception('''Something went wRoNg'''   )

    pathlib.Path(a__   ).mkdir(parents=a__ ,						exist_ok=a__   )
    print(F"""Saving model to {pytorch_dump_folder_path}"""   )
    model.save_pretrained(a__   )


if __name__ == "__main__":
      a__				:      str    		=     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__				:      int    		=     parser.parse_args()
      convert_dataavec_checkpoint_to_pytorch(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )
