




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()
SCREAMING_SNAKE_CASE__	:				Tuple  =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__	:				Optional[Any]  =       'Hello world! cécé herlolip'

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			Tuple:
       lowerCamelCase			,				lowerCamelCase       :				str      			= os.path.split(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[str]      			= DataaVecTextModel.from_pretrained(
           _SCREAMING_SNAKE_CASE			,checkpoint_file=_SCREAMING_SNAKE_CASE   )
       dataavec.eval()  # disable dropout
       lowerCamelCase       :				Union[str, Any]      			= dataavec.models[0]
       lowerCamelCase       :				Dict      			= dataavec_model.encoder.sentence_encoder
       lowerCamelCase       :				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:
              lowerCamelCase       :				List[str]      			= dataavec.model.classification_heads["mnli"].out_proj.weight.shape[0]
       print("Our BERT config:"			,_SCREAMING_SNAKE_CASE   )

       lowerCamelCase       :				int      			= DataaVecTextForSequenceClassification(_SCREAMING_SNAKE_CASE   ) if classification_head else DataaVecTextForMaskedLM(_SCREAMING_SNAKE_CASE   )
       model.eval()

       # Now let's copy all the weights.
       # Embeddings
       lowerCamelCase       :				int      			= dataavec_sent_encoder.embed_tokens.weight
       lowerCamelCase       :				List[str]      			= dataavec_sent_encoder.embed_positions.weight
       lowerCamelCase       :				List[Any]      			= torch.zeros_like(
           model.dataavec_text.embeddings.token_type_embeddings.weight   )  # just zero them out b/c data2vec doesn't use them.
       lowerCamelCase       :				str      			= dataavec_sent_encoder.layernorm_embedding.weight
       lowerCamelCase       :				Tuple      			= dataavec_sent_encoder.layernorm_embedding.bias

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

              # self attention
              lowerCamelCase       :				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)   )}'''
              )

              lowerCamelCase       :				int      			= dataavec_layer.self_attn.q_proj.weight
              lowerCamelCase       :				int      			= dataavec_layer.self_attn.q_proj.bias
              lowerCamelCase       :				int      			= dataavec_layer.self_attn.k_proj.weight
              lowerCamelCase       :				str      			= dataavec_layer.self_attn.k_proj.bias
              lowerCamelCase       :				int      			= dataavec_layer.self_attn.v_proj.weight
              lowerCamelCase       :				List[Any]      			= dataavec_layer.self_attn.v_proj.bias

              # self-attention output
              lowerCamelCase       :				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}'''
              lowerCamelCase       :				Dict      			= dataavec_layer.self_attn.out_proj.weight
              lowerCamelCase       :				Optional[Any]      			= dataavec_layer.self_attn.out_proj.bias
              lowerCamelCase       :				str      			= dataavec_layer.self_attn_layer_norm.weight
              lowerCamelCase       :				Optional[int]      			= dataavec_layer.self_attn_layer_norm.bias

              # intermediate
              lowerCamelCase       :				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}'''
              lowerCamelCase       :				Optional[Any]      			= dataavec_layer.fca.weight
              lowerCamelCase       :				Tuple      			= dataavec_layer.fca.bias

              # output
              lowerCamelCase       :				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}'''
              lowerCamelCase       :				str      			= dataavec_layer.fca.weight
              lowerCamelCase       :				Optional[int]      			= dataavec_layer.fca.bias
              lowerCamelCase       :				List[Any]      			= dataavec_layer.final_layer_norm.weight
              lowerCamelCase       :				List[str]      			= dataavec_layer.final_layer_norm.bias
              # end of layer

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

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

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

       pathlib.Path(_SCREAMING_SNAKE_CASE   ).mkdir(parents=_SCREAMING_SNAKE_CASE			,exist_ok=_SCREAMING_SNAKE_CASE   )
       print(f'''Saving model to {pytorch_dump_folder_path}'''   )
       model.save_pretrained(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__	:				List[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.'
       )
       SCREAMING_SNAKE_CASE__	:				int  =       parser.parse_args()
       convert_dataavec_checkpoint_to_pytorch(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__	:				Optional[int]  =       {
    'configuration_gptsan_japanese': ['GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTSanJapaneseConfig'],
    'tokenization_gptsan_japanese': ['GPTSanJapaneseTokenizer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__	:				Optional[int]  =       [
           'GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
           'GPTSanJapaneseForConditionalGeneration',
           'GPTSanJapaneseModel',
           'GPTSanJapanesePreTrainedModel',
       ]
       SCREAMING_SNAKE_CASE__	:				List[str]  =       [
           'GPTSanJapaneseTokenizer',
       ]


if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gptsan_japanese import (
                  GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTSanJapaneseForConditionalGeneration,
                  GPTSanJapaneseModel,
                  GPTSanJapanesePreTrainedModel,
              )
              from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
       import sys

       SCREAMING_SNAKE_CASE__	:				Optional[Any]  =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






