






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__A  							=  logging.get_logger(__name__)

__A  							=  {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}



class 							_A (    UpperCamelCase							):
    """simple docstring"""

    lowerCamelCase  :       Union[str, Any]   =   'gpt2'
    lowerCamelCase  :       Any   =   ['past_key_values']
    lowerCamelCase  :       Tuple   =   {
        'hidden_size': 'n_embd',
        'max_position_embeddings': 'n_positions',
        'num_attention_heads': 'n_head',
        'num_hidden_layers': 'n_layer',
    }



    def __init__(							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    Dict=50257   ,    __SCREAMING_SNAKE_CASE			:    Dict=1024   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=768   ,    __SCREAMING_SNAKE_CASE			:    Tuple=12   ,    __SCREAMING_SNAKE_CASE			:    Tuple=12   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=None   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]="gelu_new"   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=0.1   ,    __SCREAMING_SNAKE_CASE			:    int=0.1   ,    __SCREAMING_SNAKE_CASE			:    Any=0.1   ,    __SCREAMING_SNAKE_CASE			:    Tuple=1e-5   ,    __SCREAMING_SNAKE_CASE			:    str=0.02   ,    __SCREAMING_SNAKE_CASE			:    int="cls_index"   ,    __SCREAMING_SNAKE_CASE			:    Dict=True   ,    __SCREAMING_SNAKE_CASE			:    str=None   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=True   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=0.1   ,    __SCREAMING_SNAKE_CASE			:    str=True   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=True   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=50256   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=50256   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=False   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=False   ,    **__SCREAMING_SNAKE_CASE			:    Dict   ,    )			->       Union[str, Any]:
        __UpperCAmelCase    							=vocab_size
        __UpperCAmelCase    							=n_positions
        __UpperCAmelCase    							=n_embd
        __UpperCAmelCase    							=n_layer
        __UpperCAmelCase    							=n_head
        __UpperCAmelCase    							=n_inner
        __UpperCAmelCase    							=activation_function
        __UpperCAmelCase    							=resid_pdrop
        __UpperCAmelCase    							=embd_pdrop
        __UpperCAmelCase    							=attn_pdrop
        __UpperCAmelCase    							=layer_norm_epsilon
        __UpperCAmelCase    							=initializer_range
        __UpperCAmelCase    							=summary_type
        __UpperCAmelCase    							=summary_use_proj
        __UpperCAmelCase    							=summary_activation
        __UpperCAmelCase    							=summary_first_dropout
        __UpperCAmelCase    							=summary_proj_to_labels
        __UpperCAmelCase    							=scale_attn_weights
        __UpperCAmelCase    							=use_cache
        __UpperCAmelCase    							=scale_attn_by_inverse_layer_idx
        __UpperCAmelCase    							=reorder_and_upcast_attn

        __UpperCAmelCase    							=bos_token_id
        __UpperCAmelCase    							=eos_token_id

        super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE   ,    eos_token_id=__SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			)




class 							_A (    UpperCamelCase							):
    """simple docstring"""

    def __init__(							self			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    List[str] = "default"   ,    __SCREAMING_SNAKE_CASE			:    Tuple = None   ,    __SCREAMING_SNAKE_CASE			:    Optional[int] = False   ,    )			->       str:
        super().__init__(__SCREAMING_SNAKE_CASE   ,    task=__SCREAMING_SNAKE_CASE   ,    patching_specs=__SCREAMING_SNAKE_CASE   ,    use_past=__SCREAMING_SNAKE_CASE			)
        if not getattr(self._config   ,    """pad_token_id"""   ,    __SCREAMING_SNAKE_CASE			):
            # TODO: how to do that better?
            __UpperCAmelCase    							=0



    @property
    def _a       (							self			:    List[str]			)			->       Mapping[str, Mapping[int, str]]:
        __UpperCAmelCase    							=OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}			)
        if self.use_past:
            self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE   ,    direction="""inputs"""			)
            __UpperCAmelCase    							={0: '''batch''', 1: '''past_sequence + sequence'''}
        else:
            __UpperCAmelCase    							={0: '''batch''', 1: '''sequence'''}

        return common_inputs



    @property
    def _a       (							self			:    Optional[Any]			)			->       int:
        return self._config.n_layer



    @property
    def _a       (							self			:    int			)			->       int:
        return self._config.n_head



    def _a       (							self			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    str = -1   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any] = -1   ,    __SCREAMING_SNAKE_CASE			:    Dict = False   ,    __SCREAMING_SNAKE_CASE			:    Tuple = None   ,    )			->       Mapping[str, Any]:
        __UpperCAmelCase    							=super(__SCREAMING_SNAKE_CASE   ,    self			).generate_dummy_inputs(
            __SCREAMING_SNAKE_CASE   ,    batch_size=__SCREAMING_SNAKE_CASE   ,    seq_length=__SCREAMING_SNAKE_CASE   ,    is_pair=__SCREAMING_SNAKE_CASE   ,    framework=__SCREAMING_SNAKE_CASE			)

        # We need to order the input in the way they appears in the forward()
        __UpperCAmelCase    							=OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}			)

        # Need to add the past_keys
        if self.use_past:
            if not is_torch_available():
                raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""			)
            else:
                import torch

                __UpperCAmelCase    							=common_inputs['''input_ids'''].shape
                # Not using the same length for past_key_values
                __UpperCAmelCase    							=seqlen + 2
                __UpperCAmelCase    							=(
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                __UpperCAmelCase    							=[
                    (torch.zeros(__SCREAMING_SNAKE_CASE			), torch.zeros(__SCREAMING_SNAKE_CASE			)) for _ in range(self.num_layers			)
                ]

        __UpperCAmelCase    							=common_inputs['''attention_mask''']
        if self.use_past:
            __UpperCAmelCase    							=ordered_inputs['''attention_mask'''].dtype
            __UpperCAmelCase    							=torch.cat(
                [ordered_inputs["""attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    dtype=__SCREAMING_SNAKE_CASE			)]   ,    dim=1			)

        return ordered_inputs



    @property
    def _a       (							self			:    Union[str, Any]			)			->       int:
        return 13












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  							=  logging.get_logger(__name__)

__A  							=  "Hello world! cécé herlolip"







def 			lowercase__  (				A_:	str     ,			A_:	str     ,			A_:	bool		)  ->							str:







       """simple docstring"""






       __UpperCAmelCase							,   __UpperCAmelCase    							=os.path.split(A_		)
       __UpperCAmelCase    							=DataaVecTextModel.from_pretrained(
           A_     ,			checkpoint_file=A_		)
       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=514     ,			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:"""     ,			A_		)

       __UpperCAmelCase    							=DataaVecTextForSequenceClassification(A_		) if classification_head else DataaVecTextForMaskedLM(A_		)
       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(A_		).unsqueeze(0		)  # batch of size 1

       __UpperCAmelCase    							=model(A_		)[0]
       if classification_head:
              __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(A_		)		)
       else:
              __UpperCAmelCase    							=dataavec_model(A_		)[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(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  							=  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  							=  parser.parse_args()
   convert_dataavec_checkpoint_to_pytorch(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )




