import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_albert import AlbertTokenizer
else:
						A__           =							None

A__           =							logging.get_logger(__name__)
A__           =							{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

A__           =							{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
    },
}

A__           =							{
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

A__           =							"""▁"""
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     VOCAB_FILES_NAMES
							_UpperCAmelCase						    =     PRETRAINED_VOCAB_FILES_MAP
							_UpperCAmelCase						    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_UpperCAmelCase						    =     AlbertTokenizer







							def __init__(				self     :    int	,					__snake_case     :    Optional[Any]=None	,					__snake_case     :    Dict=None	,					__snake_case     :    Optional[int]=True	,					__snake_case     :    str=True	,					__snake_case     :    Any=False	,					__snake_case     :    Union[str, Any]="[CLS]"	,					__snake_case     :    str="[SEP]"	,					__snake_case     :    Union[str, Any]="<unk>"	,					__snake_case     :    Union[str, Any]="[SEP]"	,					__snake_case     :    List[Any]="<pad>"	,					__snake_case     :    Optional[Any]="[CLS]"	,					__snake_case     :    Optional[Any]="[MASK]"	,					**__snake_case     :    List[Any]	,					):
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										lowerCamelCase						:Optional[Any]						  =     (
										    AddedToken(__snake_case	,					lstrip=__snake_case	,					rstrip=__snake_case	,					normalized=__snake_case		)
										    if isinstance(__snake_case	,					__snake_case		)
										    else mask_token
										)

										super().__init__(
										    __snake_case	,					tokenizer_file=__snake_case	,					do_lower_case=__snake_case	,					remove_space=__snake_case	,					keep_accents=__snake_case	,					bos_token=__snake_case	,					eos_token=__snake_case	,					unk_token=__snake_case	,					sep_token=__snake_case	,					pad_token=__snake_case	,					cls_token=__snake_case	,					mask_token=__snake_case	,					**__snake_case	,					)

										lowerCamelCase						:List[Any]						  =     do_lower_case
										lowerCamelCase						:str						  =     remove_space
										lowerCamelCase						:Dict						  =     keep_accents
										lowerCamelCase						:Union[str, Any]						  =     vocab_file
										lowerCamelCase						:str						  =     False if not self.vocab_file else True







							def 				snake_case  (				self     :    List[Any]	,					__snake_case     :    List[int]	,					__snake_case     :    Optional[List[int]] = None		):
										lowerCamelCase						:List[str]						  =     [self.sep_token_id]
										lowerCamelCase						:int						  =     [self.cls_token_id]
										if token_ids_a is None:
													return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep







							def 				snake_case  (				self     :    Tuple	,					__snake_case     :    List[int]	,					__snake_case     :    Optional[List[int]] = None		):
										lowerCamelCase						:List[Any]						  =     [self.sep_token_id]
										lowerCamelCase						:Any						  =     [self.cls_token_id]

										if token_ids_a is None:
													return len(cls + token_ids_a + sep		) * [0]
										return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







							def 				snake_case  (				self     :    Optional[int]	,					__snake_case     :    str	,					__snake_case     :    Optional[str] = None		):
										if not self.can_save_slow_tokenizer:
													raise ValueError(
													    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
													    '''tokenizer.'''		)

										if not os.path.isdir(__snake_case		):
													logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
													return
										lowerCamelCase						:Optional[Any]						  =     os.path.join(
										    __snake_case	,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

										if os.path.abspath(self.vocab_file		) != os.path.abspath(__snake_case		):
													copyfile(self.vocab_file	,					__snake_case		)

										return (out_vocab_file,)




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()
A__           =							logging.get_logger(__name__)

A__           =							"""Hello, World!"""
A__           =							"""en_XX"""





def  _lowerCamelCase					(   a_				: str						,			a_				: str						,			a_				: bool):
			lowerCamelCase						:int						  =     Path('''data_bin''')
			lowerCamelCase						:Union[str, 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_)

			lowerCamelCase						:Any						  =     xmod.model.encoder.sentence_encoder
			lowerCamelCase						:List[str]						  =     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=5_14						,			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:
						lowerCamelCase						:Dict						  =     xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

			lowerCamelCase						:List[Any]						  =     XmodForSequenceClassification(a_) if classification_head else XmodForMaskedLM(a_)
			model.eval()

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

			lowerCamelCase						:List[Any]						  =     xmod_sent_encoder.layernorm_embedding.weight
			lowerCamelCase						:Optional[int]						  =     xmod_sent_encoder.layernorm_embedding.bias

			for i in range(config.num_hidden_layers):
						# Encoder: start of layer
						lowerCamelCase						:Union[str, Any]						  =     model.roberta.encoder.layer[i]
						lowerCamelCase						:List[str]						  =     xmod_sent_encoder.layers[i]

						# self attention
						lowerCamelCase						: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.''')

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

						# self-attention output
						lowerCamelCase						:Optional[int]						  =     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.''')
						lowerCamelCase						:List[Any]						  =     xmod_layer.self_attn.out_proj.weight
						lowerCamelCase						:Union[str, Any]						  =     xmod_layer.self_attn.out_proj.bias
						lowerCamelCase						:str						  =     xmod_layer.self_attn_layer_norm.weight
						lowerCamelCase						:List[Any]						  =     xmod_layer.self_attn_layer_norm.bias

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

						# output
						lowerCamelCase						: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.''')
						lowerCamelCase						:str						  =     xmod_layer.fca.weight
						lowerCamelCase						:int						  =     xmod_layer.fca.bias
						lowerCamelCase						:List[Any]						  =     xmod_layer.final_layer_norm.weight
						lowerCamelCase						:List[str]						  =     xmod_layer.final_layer_norm.bias
						if bert_output.adapter_layer_norm is not None:
									lowerCamelCase						:List[str]						  =     xmod_layer.adapter_layer_norm.weight
									lowerCamelCase						:int						  =     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():
									lowerCamelCase						:Optional[int]						  =     bert_output.adapter_modules[lang_code]
									lowerCamelCase						:Dict						  =     xmod_layer.adapter_modules[lang_code]
									lowerCamelCase						:List[Any]						  =     from_adapter.fca.weight
									lowerCamelCase						:List[Any]						  =     from_adapter.fca.bias
									lowerCamelCase						:Dict						  =     from_adapter.fca.weight
									lowerCamelCase						:Optional[Any]						  =     from_adapter.fca.bias

						# end of layer

			if xmod_sent_encoder.layer_norm is not None:
						lowerCamelCase						:Dict						  =     xmod_sent_encoder.layer_norm.weight
						lowerCamelCase						:List[Any]						  =     xmod_sent_encoder.layer_norm.bias

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

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

			lowerCamelCase						:Any						  =     model(a_)[0]
			if classification_head:
						lowerCamelCase						:Dict						  =     xmod.model.classification_heads['''mnli'''](xmod.extract_features(a_))
			else:
						lowerCamelCase						:int						  =     xmod.model(a_						,			lang_id=[SAMPLE_LANGUAGE])[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						: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__":
			A__           =							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."""
			)
			A__           =							parser.parse_args()
			convert_xmod_checkpoint_to_pytorch(
			    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)



