




import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.12.2"):
    raise Exception("requires fairseq >= 0.12.2")
if version.parse(fairseq.__version__) > version.parse("2"):
    raise Exception("requires fairseq < v2")

logging.set_verbosity_info()
snake_case_ : List[Any]    		=       logging.get_logger(__name__)

snake_case_ : Any    		=       "Hello, World!"
snake_case_ : Tuple    		=       "en_XX"


def    A						(__A   :    str   ,				__A   :    str   ,				__A   :    bool       )   ->      Dict:







     """simple docstring"""


     UpperCAmelCase_          =							Path('''data_bin'''       )
     UpperCAmelCase_          =							FairseqXmodModel.from_pretrained(
         model_name_or_path=str(Path(__A       ).parent       )   ,				checkpoint_file=Path(__A       ).name   ,				_name='''xmod_base'''   ,				arch='''xmod_base'''   ,				task='''multilingual_masked_lm'''   ,				data_name_or_path=str(__A       )   ,				bpe='''sentencepiece'''   ,				sentencepiece_model=str(Path(__A       ).parent / '''sentencepiece.bpe.model'''       )   ,				src_dict=str(data_dir / '''dict.txt'''       )   ,				)
     xmod.eval()  # disable dropout
     print(__A       )

     UpperCAmelCase_          =							xmod.model.encoder.sentence_encoder
     UpperCAmelCase_          =							XmodConfig(
         vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings   ,				hidden_size=xmod.cfg.model.encoder_embed_dim   ,				num_hidden_layers=xmod.cfg.model.encoder_layers   ,				num_attention_heads=xmod.cfg.model.encoder_attention_heads   ,				intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim   ,				max_position_embeddings=514   ,				type_vocab_size=1   ,				layer_norm_eps=1E-5   ,				pre_norm=xmod.cfg.model.encoder_normalize_before   ,				adapter_reduction_factor=getattr(xmod.cfg.model   ,				'''bottleneck'''   ,				2       )   ,				adapter_layer_norm=xmod.cfg.model.adapter_layer_norm   ,				adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm   ,				ln_before_adapter=xmod.cfg.model.ln_before_adapter   ,				languages=xmod.cfg.model.languages   ,				)
     if classification_head:
          UpperCAmelCase_          =							xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

     print('''Our X-MOD config:'''   ,				__A       )

     UpperCAmelCase_          =							XmodForSequenceClassification(__A       ) if classification_head else XmodForMaskedLM(__A       )
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     UpperCAmelCase_          =							xmod_sent_encoder.embed_tokens.weight
     UpperCAmelCase_          =							xmod_sent_encoder.embed_positions.weight
     UpperCAmelCase_          =							torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight       )  # just zero them out b/c xmod doesn't use them.

     UpperCAmelCase_          =							xmod_sent_encoder.layernorm_embedding.weight
     UpperCAmelCase_          =							xmod_sent_encoder.layernorm_embedding.bias

     for i in range(config.num_hidden_layers       ):
          # Encoder: start of layer
          UpperCAmelCase_          =							model.roberta.encoder.layer[i]
          UpperCAmelCase_          =							xmod_sent_encoder.layers[i]

          # self attention
          UpperCAmelCase_          =							layer.attention.self
          if not (
              xmod_layer.self_attn.k_proj.weight.data.shape
              == xmod_layer.self_attn.q_proj.weight.data.shape
              == xmod_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)       )
          ):
               raise AssertionError('''Dimensions of self-attention weights do not match.'''       )

          UpperCAmelCase_          =							xmod_layer.self_attn.q_proj.weight
          UpperCAmelCase_          =							xmod_layer.self_attn.q_proj.bias
          UpperCAmelCase_          =							xmod_layer.self_attn.k_proj.weight
          UpperCAmelCase_          =							xmod_layer.self_attn.k_proj.bias
          UpperCAmelCase_          =							xmod_layer.self_attn.v_proj.weight
          UpperCAmelCase_          =							xmod_layer.self_attn.v_proj.bias

          # self-attention output
          UpperCAmelCase_          =							layer.attention.output
          if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
               raise AssertionError('''Dimensions of self-attention output weights do not match.'''       )
          UpperCAmelCase_          =							xmod_layer.self_attn.out_proj.weight
          UpperCAmelCase_          =							xmod_layer.self_attn.out_proj.bias
          UpperCAmelCase_          =							xmod_layer.self_attn_layer_norm.weight
          UpperCAmelCase_          =							xmod_layer.self_attn_layer_norm.bias

          # intermediate
          UpperCAmelCase_          =							layer.intermediate
          if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError('''Dimensions of intermediate weights do not match.'''       )
          UpperCAmelCase_          =							xmod_layer.fca.weight
          UpperCAmelCase_          =							xmod_layer.fca.bias

          # output
          UpperCAmelCase_          =							layer.output
          if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError('''Dimensions of feed-forward weights do not match.'''       )
          UpperCAmelCase_          =							xmod_layer.fca.weight
          UpperCAmelCase_          =							xmod_layer.fca.bias
          UpperCAmelCase_          =							xmod_layer.final_layer_norm.weight
          UpperCAmelCase_          =							xmod_layer.final_layer_norm.bias
          if bert_output.adapter_layer_norm is not None:
               UpperCAmelCase_          =							xmod_layer.adapter_layer_norm.weight
               UpperCAmelCase_          =							xmod_layer.adapter_layer_norm.bias

          if sorted(bert_output.adapter_modules.keys()       ) != sorted(xmod_layer.adapter_modules.keys()       ):
               raise AssertionError('''Lists of language adapters do not match.'''       )
          for lang_code, adapter in xmod_layer.adapter_modules.items():
               UpperCAmelCase_          =							bert_output.adapter_modules[lang_code]
               UpperCAmelCase_          =							xmod_layer.adapter_modules[lang_code]
               UpperCAmelCase_          =							from_adapter.fca.weight
               UpperCAmelCase_          =							from_adapter.fca.bias
               UpperCAmelCase_          =							from_adapter.fca.weight
               UpperCAmelCase_          =							from_adapter.fca.bias

          # end of layer

     if xmod_sent_encoder.layer_norm is not None:
          UpperCAmelCase_          =							xmod_sent_encoder.layer_norm.weight
          UpperCAmelCase_          =							xmod_sent_encoder.layer_norm.bias

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

     # Let's check that we get the same results.
     UpperCAmelCase_          =							xmod.encode(__A       ).unsqueeze(0       )  # batch of size 1
     model.roberta.set_default_language(__A       )

     UpperCAmelCase_          =							model(__A       )[0]
     if classification_head:
          UpperCAmelCase_          =							xmod.model.classification_heads['''mnli'''](xmod.extract_features(__A       )       )
     else:
          UpperCAmelCase_          =							xmod.model(__A   ,				lang_id=[SAMPLE_LANGUAGE]       )[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'''       )

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


if __name__ == "__main__":
    snake_case_ : Union[str, Any]    		=       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--xmod_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."
    )
    snake_case_ : Optional[Any]    		=       parser.parse_args()
    convert_xmod_checkpoint_to_pytorch(
        args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )












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


snake_case_ : List[str]    		=       logging.get_logger(__name__)


snake_case_ : Tuple    		=       {
    "Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
    "Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
    "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
    "Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
    "Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
    "Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
    "Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
    "Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
    "Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
    "Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
    "Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
    "Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}







class __snake_case			(			a		):
   UpperCAmelCase__  :    str          =  '''codegen'''
   UpperCAmelCase__  :    int          =  {
       '''max_position_embeddings''': '''n_positions''',
       '''hidden_size''': '''n_embd''',
       '''num_attention_heads''': '''n_head''',
       '''num_hidden_layers''': '''n_layer''',
   }







   def __init__(      self		:						Union[str, Any]					,						_snake_case		:						Union[str, Any]=50400					,						_snake_case		:						Optional[int]=2048					,						_snake_case		:						Union[str, Any]=2048					,						_snake_case		:						List[str]=4096					,						_snake_case		:						Any=28					,						_snake_case		:						List[str]=16					,						_snake_case		:						int=64					,						_snake_case		:						Tuple=None					,						_snake_case		:						Dict="gelu_new"					,						_snake_case		:						Union[str, Any]=0.0					,						_snake_case		:						Optional[Any]=0.0					,						_snake_case		:						List[Any]=0.0					,						_snake_case		:						List[Any]=1e-5					,						_snake_case		:						List[str]=0.0_2					,						_snake_case		:						Optional[Any]=True					,						_snake_case		:						int=50256					,						_snake_case		:						Tuple=50256					,						_snake_case		:						int=False					,						**_snake_case		:						Any					,						):

        """simple docstring"""


        UpperCAmelCase_          =							vocab_size
        UpperCAmelCase_          =							n_ctx
        UpperCAmelCase_          =							n_positions
        UpperCAmelCase_          =							n_embd
        UpperCAmelCase_          =							n_layer
        UpperCAmelCase_          =							n_head
        UpperCAmelCase_          =							n_inner
        UpperCAmelCase_          =							rotary_dim
        UpperCAmelCase_          =							activation_function
        UpperCAmelCase_          =							resid_pdrop
        UpperCAmelCase_          =							embd_pdrop
        UpperCAmelCase_          =							attn_pdrop
        UpperCAmelCase_          =							layer_norm_epsilon
        UpperCAmelCase_          =							initializer_range
        UpperCAmelCase_          =							use_cache

        UpperCAmelCase_          =							bos_token_id
        UpperCAmelCase_          =							eos_token_id

        super().__init__(
            bos_token_id=_snake_case					,						eos_token_id=_snake_case					,						tie_word_embeddings=_snake_case					,						**_snake_case)







class __snake_case			(			a		):







   def __init__(      self		:						Tuple					,						_snake_case		:						PretrainedConfig					,						_snake_case		:						str = "default"					,						_snake_case		:						List[PatchingSpec] = None					,						_snake_case		:						bool = False					,						):

        """simple docstring"""


        super().__init__(_snake_case					,						task=_snake_case					,						patching_specs=_snake_case					,						use_past=_snake_case)
        if not getattr(self._config					,						'''pad_token_id'''					,						_snake_case):
             # TODO: how to do that better?
             UpperCAmelCase_          =							0







   @property
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}})
        if self.use_past:
             self.fill_with_past_key_values_(_snake_case					,						direction='''inputs''')
             UpperCAmelCase_          =							{0: '''batch''', 1: '''past_sequence + sequence'''}
        else:
             UpperCAmelCase_          =							{0: '''batch''', 1: '''sequence'''}

        return common_inputs







   @property
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        return self._config.n_layer







   @property
   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        return self._config.n_head







   def lowerCamelCase						(      self		:						Optional[int]					,						_snake_case		:						PreTrainedTokenizer					,						_snake_case		:						int = -1					,						_snake_case		:						int = -1					,						_snake_case		:						bool = False					,						_snake_case		:						Optional[TensorType] = None					,						):

        """simple docstring"""


        UpperCAmelCase_          =							super(_snake_case					,						self).generate_dummy_inputs(
            _snake_case					,						batch_size=_snake_case					,						seq_length=_snake_case					,						is_pair=_snake_case					,						framework=_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_     ,       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(_snake_case), torch.zeros(_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(_snake_case					,						_snake_case					,						dtype=_snake_case)]					,						dim=1)

        return ordered_inputs







   @property
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        return 13






