
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
      )


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


a__				:      Tuple    		=     [nn.LayerNorm]

a__				:      Tuple    		=     logging.get_logger(__name__)

a__				:      int    		=     version.parse(version.parse(torch.__version__).base_version)

a__				:      Dict    		=     parsed_torch_version_base >= version.parse('''2.0''')
a__				:      Any    		=     parsed_torch_version_base >= version.parse('''1.12''')
a__				:      Union[str, Any]    		=     parsed_torch_version_base >= version.parse('''1.11''')
a__				:      Union[str, Any]    		=     parsed_torch_version_base < version.parse('''1.11''')







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__ ,						self   ):



    """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 				UpperCAmelCase_(    a__ ,						a__ ,						a__ = 0   ):



    """simple docstring"""



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






class     a_       (			nn.Module							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->int:
           super().__init__()
           SCREAMING_SNAKE_CASE				:				List[str]				      = nf
           SCREAMING_SNAKE_CASE				:				List[Any]				      = nn.Parameter(torch.empty(_lowerCamelCase				,       _lowerCamelCase   )   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = nn.Parameter(torch.zeros(_lowerCamelCase   )   )
           nn.init.normal_(self.weight				,       std=0.0_2   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Tuple:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = x.size()[:-1] + (self.nf,)
           SCREAMING_SNAKE_CASE				:				int				      = torch.addmm(self.bias				,       x.view(-1				,       x.size(-1   )   )				,       self.weight   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = x.view(_lowerCamelCase   )
           return x







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ = 1   ):



    """simple docstring"""



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







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ = 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 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						*a__   ):



    """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
    SCREAMING_SNAKE_CASE				:				str				      = 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:
        SCREAMING_SNAKE_CASE				:				Dict				      = 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}"""   )

        SCREAMING_SNAKE_CASE				:				Any				      = input_tensors[0].shape[chunk_dim] // chunk_size

        # chunk input tensor into tuples
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = tuple(input_tensor.chunk(a__ ,						dim=a__   ) for input_tensor in input_tensors   )
        # apply forward fn to every tuple
        SCREAMING_SNAKE_CASE				:				Tuple				      = 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 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[int]				      = torch.ones(a__ ,						a__   )
    SCREAMING_SNAKE_CASE				:				str				      = 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
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = head - sum(1 if h < head else 0 for h in already_pruned_heads   )
        SCREAMING_SNAKE_CASE				:				str				      = 0
    SCREAMING_SNAKE_CASE				:				List[str]				      = mask.view(-1   ).contiguous().eq(1   )
    SCREAMING_SNAKE_CASE				:				torch.LongTensor				      = torch.arange(len(a__   )   )[mask].long()
    return heads, index







def 				UpperCAmelCase_(    *a__ ,						a__ = None   ):



    """simple docstring"""



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







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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