



import os
import re
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase							=       logging.get_logger(__name__)
UpperCAmelCase							=       {"""vocab_file""": """spiece.model"""}

UpperCAmelCase							=       {
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
}

UpperCAmelCase							=       {
    """google/fnet-base""": 512,
    """google/fnet-large""": 512,
}

UpperCAmelCase							=       """▁"""

class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   Tuple       =       VOCAB_FILES_NAMES
							UpperCamelCase__					:   List[str]       =       PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__					:   List[Any]       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__					:   List[str]       =       ["""input_ids""", """token_type_ids"""]


							def __init__(				self	,	a_	,	a_=False	,	a_=True	,	a_=True	,	a_="<unk>"	,	a_="[SEP]"	,	a_="<pad>"	,	a_="[CLS]"	,	a_="[MASK]"	,	a_ = None	,	**a_	,	):
											# 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.
											a__					   =		(
											    AddedToken(a_	,	lstrip=a_	,	rstrip=a_	,	normalized=a_		)
											    if isinstance(a_	,	a_		)
											    else mask_token
											)

											a__					   =		{} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    do_lower_case=a_	,	remove_space=a_	,	keep_accents=a_	,	unk_token=a_	,	sep_token=a_	,	pad_token=a_	,	cls_token=a_	,	mask_token=a_	,	sp_model_kwargs=self.sp_model_kwargs	,	**a_	,	)

											a__					   =		do_lower_case
											a__					   =		remove_space
											a__					   =		keep_accents
											a__					   =		vocab_file

											a__					   =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(a_		)


							@property
							def _a   (				self		):
											return len(self.sp_model		)


							def _a   (				self		):
											a__					   =		{self.convert_ids_to_tokens(a_		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab


							def __getstate__(				self		):
											a__					   =		self.__dict__.copy()
											a__					   =		None
											return state


							def __setstate__(				self	,	a_		):
											a__					   =		d

											# for backward compatibility
											if not hasattr(self	,	"""sp_model_kwargs"""		):
															a__					   =		{}

											a__					   =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(self.vocab_file		)


							def _a   (				self	,	a_		):
											if self.remove_space:
															a__					   =		""" """.join(inputs.strip().split()		)
											else:
															a__					   =		inputs
											a__					   =		outputs.replace("""``"""	,	"""\""""		).replace("""''"""	,	"""\""""		)

											if not self.keep_accents:
															a__					   =		unicodedata.normalize("""NFKD"""	,	a_		)
															a__					   =		"""""".join([c for c in outputs if not unicodedata.combining(a_		)]		)
											if self.do_lower_case:
															a__					   =		outputs.lower()

											return outputs


							def _a   (				self	,	a_		):
											a__					   =		self.preprocess_text(a_		)
											a__					   =		self.sp_model.encode(a_	,	out_type=a_		)
											a__					   =		[]
											for piece in pieces:
															if len(a_		) > 1 and piece[-1] == str(""","""		) and piece[-2].isdigit():
																			a__					   =		self.sp_model.EncodeAsPieces(piece[:-1].replace(a_	,	""""""		)		)
																			if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																							if len(cur_pieces[0]		) == 1:
																											a__					   =		cur_pieces[1:]
																							else:
																											a__					   =		cur_pieces[0][1:]
																			cur_pieces.append(piece[-1]		)
																			new_pieces.extend(a_		)
															else:
																			new_pieces.append(a_		)

											return new_pieces


							def _a   (				self	,	a_		):
											return self.sp_model.PieceToId(a_		)


							def _a   (				self	,	a_		):
											return self.sp_model.IdToPiece(a_		)


							def _a   (				self	,	a_		):
											a__					   =		[]
											a__					   =		""""""
											a__					   =		False
											for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																			if not prev_is_special:
																							out_string += " "
																			out_string += self.sp_model.decode(a_		) + token
																			a__					   =		True
																			a__					   =		[]
															else:
																			current_sub_tokens.append(a_		)
																			a__					   =		False
											out_string += self.sp_model.decode(a_		)
											return out_string.strip()


							def _a   (				self	,	a_	,	a_ = False	,	a_ = None	,	a_ = True	,	**a_	,	):
											a__					   =		kwargs.pop("""use_source_tokenizer"""	,	a_		)

											a__					   =		self.convert_ids_to_tokens(a_	,	skip_special_tokens=a_		)

											# To avoid mixing byte-level and unicode for byte-level BPT
											# we need to build string separately for added tokens and byte-level tokens
											# cf. https://github.com/huggingface/transformers/issues/1133
											a__					   =		[]
											a__					   =		[]
											for token in filtered_tokens:
															if skip_special_tokens and token in self.all_special_ids:
																			continue
															if token in self.added_tokens_encoder:
																			if current_sub_text:
																							sub_texts.append(self.convert_tokens_to_string(a_		)		)
																							a__					   =		[]
																			sub_texts.append(a_		)
															else:
																			current_sub_text.append(a_		)
											if current_sub_text:
															sub_texts.append(self.convert_tokens_to_string(a_		)		)

											# Mimic the behavior of the Rust tokenizer:
											# No space after <unk>
											if spaces_between_special_tokens:
															a__					   =		re.sub(r"""(<unk>) """	,	r"""\1"""	,	""" """.join(a_		)		)
											else:
															a__					   =		"""""".join(a_		)

											a__					   =		(
											    clean_up_tokenization_spaces
											    if clean_up_tokenization_spaces is not None
											    else self.clean_up_tokenization_spaces
											)
											if clean_up_tokenization_spaces:
															a__					   =		self.clean_up_tokenization(a_		)
															return clean_text
											else:
															return text


							def _a   (				self	,	a_	,	a_ = None		):
											a__					   =		[self.sep_token_id]
											a__					   =		[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 _a   (				self	,	a_	,	a_ = None	,	a_ = False		):

											if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=a_	,	token_ids_a=a_	,	already_has_special_tokens=a_		)

											if token_ids_a is not None:
															return [1] + ([0] * len(a_		)) + [1] + ([0] * len(a_		)) + [1]
											return [1] + ([0] * len(a_		)) + [1]


							def _a   (				self	,	a_	,	a_ = None		):
											a__					   =		[self.sep_token_id]
											a__					   =		[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 _a   (				self	,	a_	,	a_ = None		):
											if not os.path.isdir(a_		):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
															return
											a__					   =		os.path.join(
											    a_	,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

											if os.path.abspath(self.vocab_file		) != os.path.abspath(a_		) and os.path.isfile(self.vocab_file		):
															copyfile(self.vocab_file	,	a_		)
											elif not os.path.isfile(self.vocab_file		):
															with open(a_	,	"""wb"""		) as fi:
																			a__					   =		self.sp_model.serialized_model_proto()
																			fi.write(a_		)

											return (out_vocab_file,)






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()
UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       """Hello world! cécé herlolip"""

def       A_   (						__a  :					str			,		__a  :					str			,		__a  :					bool		):

				"""simple docstring"""
				a__   ,      a__					   =		os.path.split(__a		)
				a__					   =		DataaVecTextModel.from_pretrained(
				    __a			,		checkpoint_file=__a		)
				dataavec.eval()  # disable dropout
				a__					   =		dataavec.models[0]
				a__					   =		dataavec_model.encoder.sentence_encoder
				a__					   =		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:
								a__					   =		dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
				print("""Our BERT config:"""			,		__a		)

				a__					   =		DataaVecTextForSequenceClassification(__a		) if classification_head else DataaVecTextForMaskedLM(__a		)
				model.eval()

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

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

								# self attention
								a__					   =		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)		)}'''
								)

								a__					   =		dataavec_layer.self_attn.q_proj.weight
								a__					   =		dataavec_layer.self_attn.q_proj.bias
								a__					   =		dataavec_layer.self_attn.k_proj.weight
								a__					   =		dataavec_layer.self_attn.k_proj.bias
								a__					   =		dataavec_layer.self_attn.v_proj.weight
								a__					   =		dataavec_layer.self_attn.v_proj.bias

								# self-attention output
								a__					   =		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}'''
								a__					   =		dataavec_layer.self_attn.out_proj.weight
								a__					   =		dataavec_layer.self_attn.out_proj.bias
								a__					   =		dataavec_layer.self_attn_layer_norm.weight
								a__					   =		dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								a__					   =		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}'''
								a__					   =		dataavec_layer.fca.weight
								a__					   =		dataavec_layer.fca.bias

								# output
								a__					   =		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}'''
								a__					   =		dataavec_layer.fca.weight
								a__					   =		dataavec_layer.fca.bias
								a__					   =		dataavec_layer.final_layer_norm.weight
								a__					   =		dataavec_layer.final_layer_norm.bias
								# end of layer

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

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

				a__					   =		model(__a		)[0]
				if classification_head:
								a__					   =		dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__a		)		)
				else:
								a__					   =		dataavec_model(__a		)[0]
				print(our_output.shape			,		their_output.shape		)
				a__					   =		torch.max(torch.abs(our_output - their_output		)		).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
				a__					   =		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"""		)

				pathlib.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							=       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."""
	)
	UpperCAmelCase							=       parser.parse_args()
	convert_dataavec_checkpoint_to_pytorch(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)

