
"""simple docstring"""


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     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Any:
	_lowerCAmelCase						,							_lowerCAmelCase								=os.path.split(__UpperCamelCase       )
	_lowerCAmelCase								=DataaVecTextModel.from_pretrained(
	    __UpperCamelCase    , checkpoint_file=__UpperCamelCase       )
	dataavec.eval()  # disable dropout
	_lowerCAmelCase								=dataavec.models[0]
	_lowerCAmelCase								=dataavec_model.encoder.sentence_encoder
	_lowerCAmelCase								=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								=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
	print("""Our BERT config:"""    , __UpperCamelCase       )

	_lowerCAmelCase								=DataaVecTextForSequenceClassification(__UpperCamelCase       ) if classification_head else DataaVecTextForMaskedLM(__UpperCamelCase       )
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	_lowerCAmelCase								=dataavec_sent_encoder.embed_tokens.weight
	_lowerCAmelCase								=dataavec_sent_encoder.embed_positions.weight
	_lowerCAmelCase								=torch.zeros_like(
	    model.dataavec_text.embeddings.token_type_embeddings.weight       )  # just zero them out b/c data2vec doesn't use them.
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.weight
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.bias

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

		# self attention
		_lowerCAmelCase								=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								=dataavec_layer.self_attn.q_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.bias

		# self-attention output
		_lowerCAmelCase								=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								=dataavec_layer.self_attn.out_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.bias

		# intermediate
		_lowerCAmelCase								=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								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias

		# output
		_lowerCAmelCase								=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								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.bias
		# end of layer

	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.bias
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.bias
	else:
		# LM Head
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.bias

	# Let's check that we get the same results.
	_lowerCAmelCase								=dataavec.encode(__UpperCamelCase       ).unsqueeze(0       )  # batch of size 1

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

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


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
			)





"""simple docstring"""


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_xlm_roberta import XLMRobertaTokenizer
else:
			__A       						=       None


__A       						=       logging.get_logger(__name__)

__A       						=       {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__A       						=       {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'
        ),
    },
}

__A       						=       {
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}







class 		lowerCamelCase__					(					__magic_name__				):





			'''simple docstring'''
			lowerCamelCase  					=		VOCAB_FILES_NAMES
			lowerCamelCase  					=		PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase  					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase  					=		['''input_ids''', '''attention_mask''']
			lowerCamelCase  					=		XLMRobertaTokenizer



			def __init__(							self     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase="<s>"     ,     __UpperCAmelCase="</s>"     ,     __UpperCAmelCase="</s>"     ,     __UpperCAmelCase="<s>"     ,     __UpperCAmelCase="<unk>"     ,     __UpperCAmelCase="<pad>"     ,     __UpperCAmelCase="<mask>"     ,     **__UpperCAmelCase     ,     ) ->     str:
				# Mask token behave like a normal word, i.e. include the space before it
				_lowerCAmelCase								=AddedToken(__UpperCAmelCase     ,     lstrip=__UpperCAmelCase     ,     rstrip=__UpperCAmelCase  ) if isinstance(__UpperCAmelCase     ,     __UpperCAmelCase  ) else mask_token

				super().__init__(
				    __UpperCAmelCase     ,     tokenizer_file=__UpperCAmelCase     ,     bos_token=__UpperCAmelCase     ,     eos_token=__UpperCAmelCase     ,     sep_token=__UpperCAmelCase     ,     cls_token=__UpperCAmelCase     ,     unk_token=__UpperCAmelCase     ,     pad_token=__UpperCAmelCase     ,     mask_token=__UpperCAmelCase     ,     **__UpperCAmelCase     ,     )

				_lowerCAmelCase								=vocab_file
				_lowerCAmelCase								=False if not self.vocab_file else True



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     List[int]:

				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				_lowerCAmelCase								=[self.cls_token_id]
				_lowerCAmelCase								=[self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     List[int]:
				_lowerCAmelCase								=[self.sep_token_id]
				_lowerCAmelCase								=[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 + sep + token_ids_a + sep  ) * [0]



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     Tuple[str]:
				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(__UpperCAmelCase  ):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''  )
					return
				_lowerCAmelCase								=os.path.join(
				    __UpperCAmelCase     ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

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

				return (out_vocab_file,)



