



'''simple docstring'''






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

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


def  snake_case_     (					_lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						bool  )      ->   Dict:
							UpperCAmelCase :       Optional[Any] 							=		Path('''data_bin'''  )
							UpperCAmelCase :       Any 							=		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 :       Dict 							=		xmod.model.encoder.sentence_encoder
							UpperCAmelCase :       Dict 							=		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 :       Dict 							=		xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

							UpperCAmelCase :       Dict 							=		XmodForSequenceClassification(__A  ) if classification_head else XmodForMaskedLM(__A  )
							model.eval()

							# Now let's copy all the weights.
							# Embeddings
							UpperCAmelCase :       Optional[int] 							=		xmod_sent_encoder.embed_tokens.weight
							UpperCAmelCase :       Union[str, Any] 							=		xmod_sent_encoder.embed_positions.weight
							UpperCAmelCase :       Optional[int] 							=		torch.zeros_like(
							    model.roberta.embeddings.token_type_embeddings.weight  )  # just zero them out b/c xmod doesn't use them.

							UpperCAmelCase :       Union[str, Any] 							=		xmod_sent_encoder.layernorm_embedding.weight
							UpperCAmelCase :       Tuple 							=		xmod_sent_encoder.layernorm_embedding.bias

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

														# self attention
														UpperCAmelCase :       Optional[int] 							=		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 :       Optional[Any] 							=		xmod_layer.self_attn.q_proj.weight
														UpperCAmelCase :       Optional[int] 							=		xmod_layer.self_attn.q_proj.bias
														UpperCAmelCase :       List[str] 							=		xmod_layer.self_attn.k_proj.weight
														UpperCAmelCase :       Dict 							=		xmod_layer.self_attn.k_proj.bias
														UpperCAmelCase :       List[Any] 							=		xmod_layer.self_attn.v_proj.weight
														UpperCAmelCase :       Dict 							=		xmod_layer.self_attn.v_proj.bias

														# self-attention output
														UpperCAmelCase :       Dict 							=		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 :       Optional[Any] 							=		xmod_layer.self_attn.out_proj.weight
														UpperCAmelCase :       Optional[Any] 							=		xmod_layer.self_attn.out_proj.bias
														UpperCAmelCase :       Optional[int] 							=		xmod_layer.self_attn_layer_norm.weight
														UpperCAmelCase :       str 							=		xmod_layer.self_attn_layer_norm.bias

														# intermediate
														UpperCAmelCase :       Optional[int] 							=		layer.intermediate
														if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError('''Dimensions of intermediate weights do not match.'''  )
														UpperCAmelCase :       Optional[int] 							=		xmod_layer.fca.weight
														UpperCAmelCase :       Tuple 							=		xmod_layer.fca.bias

														# output
														UpperCAmelCase :       List[str] 							=		layer.output
														if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError('''Dimensions of feed-forward weights do not match.'''  )
														UpperCAmelCase :       Optional[Any] 							=		xmod_layer.fca.weight
														UpperCAmelCase :       int 							=		xmod_layer.fca.bias
														UpperCAmelCase :       str 							=		xmod_layer.final_layer_norm.weight
														UpperCAmelCase :       List[Any] 							=		xmod_layer.final_layer_norm.bias
														if bert_output.adapter_layer_norm is not None:
																					UpperCAmelCase :       Dict 							=		xmod_layer.adapter_layer_norm.weight
																					UpperCAmelCase :       List[Any] 							=		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 :       Dict 							=		bert_output.adapter_modules[lang_code]
																					UpperCAmelCase :       Union[str, Any] 							=		xmod_layer.adapter_modules[lang_code]
																					UpperCAmelCase :       Tuple 							=		from_adapter.fca.weight
																					UpperCAmelCase :       str 							=		from_adapter.fca.bias
																					UpperCAmelCase :       Any 							=		from_adapter.fca.weight
																					UpperCAmelCase :       Any 							=		from_adapter.fca.bias

														# end of layer

							if xmod_sent_encoder.layer_norm is not None:
														UpperCAmelCase :       str 							=		xmod_sent_encoder.layer_norm.weight
														UpperCAmelCase :       Optional[int] 							=		xmod_sent_encoder.layer_norm.bias

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

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

							UpperCAmelCase :       Any 							=		model(__A  )[0]
							if classification_head:
														UpperCAmelCase :       Optional[int] 							=		xmod.model.classification_heads['''mnli'''](xmod.extract_features(__A  )  )
							else:
														UpperCAmelCase :       Optional[Any] 							=		xmod.model(__A   ,      lang_id=[SAMPLE_LANGUAGE]  )[0]
							print(our_output.shape   ,      their_output.shape  )
							UpperCAmelCase :       List[Any] 							=		torch.max(torch.abs(our_output - their_output  )  ).item()
							print(f"""max_absolute_diff = {max_absolute_diff}"""  )  # ~ 1e-7
							UpperCAmelCase :       str 							=		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__":
						UpperCamelCase__:   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."
						)
						UpperCamelCase__:   Optional[Any]								=			parser.parse_args()
						convert_xmod_checkpoint_to_pytorch(
						    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)











from typing import TYPE_CHECKING

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


snake_case_ : Dict    		=       {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Tuple    		=       ["MBartTokenizer"]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : str    		=       ["MBartTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[Any]    		=       [
        "MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MBartForCausalLM",
        "MBartForConditionalGeneration",
        "MBartForQuestionAnswering",
        "MBartForSequenceClassification",
        "MBartModel",
        "MBartPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Any    		=       [
        "TFMBartForConditionalGeneration",
        "TFMBartModel",
        "TFMBartPreTrainedModel",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[str]    		=       [
        "FlaxMBartForConditionalGeneration",
        "FlaxMBartForQuestionAnswering",
        "FlaxMBartForSequenceClassification",
        "FlaxMBartModel",
        "FlaxMBartPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart import MBartTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart_fast import MBartTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mbart import (
            MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
            MBartForCausalLM,
            MBartForConditionalGeneration,
            MBartForQuestionAnswering,
            MBartForSequenceClassification,
            MBartModel,
            MBartPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mbart import (
            FlaxMBartForConditionalGeneration,
            FlaxMBartForQuestionAnswering,
            FlaxMBartForSequenceClassification,
            FlaxMBartModel,
            FlaxMBartPreTrainedModel,
        )

else:
    import sys

    snake_case_ : Any    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






