



'''simple docstring'''




import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
						raise Exception('''requires fairseq >= 1.0.0a''')

logging.set_verbosity_info()
lowerCamelCase							:int       = logging.get_logger(__name__)

lowerCamelCase							:Union[str, Any]       = '''Hello world! cécé herlolip'''



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						int     =					FairseqRobertaModel.from_pretrained(lowerCamelCase__   )
			roberta.eval()  # disable dropout
			A_     :						Dict     =					roberta.model.encoder.sentence_encoder
			A_     :						Any     =					XLMRobertaConfig(
			    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings						,				hidden_size=roberta.cfg.model.encoder_embed_dim						,				num_hidden_layers=roberta.cfg.model.encoder_layers						,				num_attention_heads=roberta.cfg.model.encoder_attention_heads						,				intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim						,				max_position_embeddings=5_14						,				type_vocab_size=1						,				layer_norm_eps=1E-5						,				)
			if classification_head:
						A_     :						Any     =					roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

			print("""Our RoBERTa config:"""						,				lowerCamelCase__   )

			A_     :						Union[str, Any]     =					XLMRobertaXLForSequenceClassification(lowerCamelCase__   ) if classification_head else XLMRobertaXLForMaskedLM(lowerCamelCase__   )
			model.eval()

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

			A_     :						List[Any]     =					roberta_sent_encoder.layer_norm.weight
			A_     :						Dict     =					roberta_sent_encoder.layer_norm.bias

			for i in range(config.num_hidden_layers   ):
						# Encoder: start of layer
						A_     :						BertLayer     =					model.roberta.encoder.layer[i]
						A_     :						TransformerSentenceEncoderLayer     =					roberta_sent_encoder.layers[i]

						A_     :						RobertaAttention     =					layer.attention
						A_     :						Dict     =					roberta_layer.self_attn_layer_norm.weight
						A_     :						Tuple     =					roberta_layer.self_attn_layer_norm.bias

						# self attention
						A_     :						BertSelfAttention     =					layer.attention.self
						assert (
						    roberta_layer.self_attn.k_proj.weight.data.shape
						    == roberta_layer.self_attn.q_proj.weight.data.shape
						    == roberta_layer.self_attn.v_proj.weight.data.shape
						    == torch.Size((config.hidden_size, config.hidden_size)   )
						)

						A_     :						Dict     =					roberta_layer.self_attn.q_proj.weight
						A_     :						Any     =					roberta_layer.self_attn.q_proj.bias
						A_     :						Optional[Any]     =					roberta_layer.self_attn.k_proj.weight
						A_     :						Any     =					roberta_layer.self_attn.k_proj.bias
						A_     :						Any     =					roberta_layer.self_attn.v_proj.weight
						A_     :						str     =					roberta_layer.self_attn.v_proj.bias

						# self-attention output
						A_     :						BertSelfOutput     =					layer.attention.output
						assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
						A_     :						Any     =					roberta_layer.self_attn.out_proj.weight
						A_     :						Union[str, Any]     =					roberta_layer.self_attn.out_proj.bias

						# this one is final layer norm
						A_     :						Tuple     =					roberta_layer.final_layer_norm.weight
						A_     :						int     =					roberta_layer.final_layer_norm.bias

						# intermediate
						A_     :						BertIntermediate     =					layer.intermediate
						assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
						A_     :						List[Any]     =					roberta_layer.fca.weight
						A_     :						Dict     =					roberta_layer.fca.bias

						# output
						A_     :						BertOutput     =					layer.output
						assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
						A_     :						int     =					roberta_layer.fca.weight
						A_     :						Optional[Any]     =					roberta_layer.fca.bias
						# end of layer

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

			# Let's check that we get the same results.
			A_     :						torch.Tensor     =					roberta.encode(lowerCamelCase__   ).unsqueeze(0   )  # batch of size 1

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

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


if __name__ == "__main__":
						lowerCamelCase							:str       = argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--roberta_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.'''
						)
						lowerCamelCase							:Tuple       = parser.parse_args()
						convert_xlm_roberta_xl_checkpoint_to_pytorch(
						    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)




'''simple docstring'''




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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
						from ...tokenization_utils_base import TextInput
from ...utils import logging


lowerCamelCase							:Tuple       = logging.get_logger(__name__)

lowerCamelCase							:Optional[int]       = {'''vocab_file''': '''spiece.model'''}

lowerCamelCase							:int       = {
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase							:Tuple       = {
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}

lowerCamelCase							:str       = '''▁'''







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Any		      = VOCAB_FILES_NAMES
	__SCREAMING_SNAKE_CASE       :		Optional[Any]		      = PRETRAINED_VOCAB_FILES_MAP
	__SCREAMING_SNAKE_CASE       :		str		      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = ['input_ids', 'attention_mask']


	def __init__(self	,				lowercase	,				lowercase="</s>"	,				lowercase="<unk>"	,				lowercase="<pad>"	,				lowercase=100	,				lowercase=None	,				lowercase = None	,				lowercase=True	,				**lowercase	,				):
				# Add extra_ids to the special token list
				if extra_ids > 0 and additional_special_tokens is None:
							A_     :						Any     =					[F'<extra_id_{i}>' for i in range(lowercase							)]
				elif extra_ids > 0 and additional_special_tokens is not None:
							# Check that we have the right number of extra_id special tokens
							A_     :						Tuple     =					len(set(filter(lambda lowercase							: bool("""extra_id""" in str(lowercase							)							)	,				lowercase							)							)							)
							if extra_tokens != extra_ids:
										raise ValueError(
										    F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
										    """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
										    """ tokens"""							)
				if legacy:
							logger.warning_once(
							    F'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
							    """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""							)

				A_     :						str     =					legacy
				A_     :						Dict     =					{} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    eos_token=lowercase	,				unk_token=lowercase	,				pad_token=lowercase	,				extra_ids=lowercase	,				additional_special_tokens=lowercase	,				sp_model_kwargs=self.sp_model_kwargs	,				legacy=lowercase	,				**lowercase	,				)

				A_     :						List[str]     =					vocab_file
				A_     :						Tuple     =					extra_ids

				A_     :						Tuple     =					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(lowercase							)


	@staticmethod
	def    _a     (lowercase	,				lowercase	,				lowercase							):
				if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
							A_     :						Union[str, Any]     =					TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
							if init_max_model_length is not None and init_max_model_length != max_model_length:
										return init_max_model_length
							elif init_max_model_length is None:
										warnings.warn(
										    """This tokenizer was incorrectly instantiated with a model max length of"""
										    F' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
										    """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
										    """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
										    F' {pretrained_model_name_or_path} automatically truncating your input to'
										    F' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
										    F' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
										    """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
										    """ instantiate this tokenizer with `model_max_length` set to your preferred value."""	,				lowercase	,				)

				return max_model_length


	@property
	def    _a     (self							):
				return self.sp_model.get_piece_size() + self._extra_ids


	def    _a     (self							):
				A_     :						Union[str, Any]     =					{self.convert_ids_to_tokens(lowercase							): i for i in range(self.vocab_size							)}
				vocab.update(self.added_tokens_encoder							)
				return vocab


	def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = False							):
				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=lowercase	,				token_ids_a=lowercase	,				already_has_special_tokens=lowercase							)

				# normal case: some special tokens
				if token_ids_a is None:
							return ([0] * len(lowercase							)) + [1]
				return ([0] * len(lowercase							)) + [1] + ([0] * len(lowercase							)) + [1]


	def    _a     (self							):
				return list(
				    set(filter(lambda lowercase							: bool(re.search(R"""<extra_id_\d+>"""	,				lowercase							)							) is not None	,				self.additional_special_tokens							)							)							)


	def    _a     (self							):
				return [self._convert_token_to_id(lowercase							) for token in self.get_sentinel_tokens()]


	def    _a     (self	,				lowercase							):
				if len(lowercase							) > 0 and token_ids[-1] == self.eos_token_id:
							warnings.warn(
							    F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
							    """ eos tokens being added."""							)
							return token_ids
				else:
							return token_ids + [self.eos_token_id]


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						Dict     =					[self.eos_token_id]

				if token_ids_a is None:
							return len(token_ids_a + eos							) * [0]
				return len(token_ids_a + eos + token_ids_a + eos							) * [0]


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						Optional[Any]     =					self._add_eos_if_not_present(lowercase							)
				if token_ids_a is None:
							return token_ids_a
				else:
							A_     :						List[Any]     =					self._add_eos_if_not_present(lowercase							)
							return token_ids_a + token_ids_a


	def __getstate__(self							):
				A_     :						int     =					self.__dict__.copy()
				A_     :						Tuple     =					None
				return state


	def __setstate__(self	,				lowercase							):
				A_     :						Tuple     =					d

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

				A_     :						Any     =					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(self.vocab_file							)


	def    _a     (self	,				lowercase	,				**lowercase							):
				# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
				# the beginning of the text
				if not self.legacy:
							A_     :						Tuple     =					SPIECE_UNDERLINE + text.replace(lowercase	,				""" """							)
				return super().tokenize(lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase	,				**lowercase							):
				if not self.legacy:
							A_     :						Dict     =					text.startswith(lowercase							)
							if is_first:
										A_     :						str     =					text[1:]

				A_     :						Optional[int]     =					self.sp_model.encode(lowercase	,				out_type=lowercase							)

				if not self.legacy and not is_first and not text.startswith(""" """							) and tokens[0].startswith(lowercase							):
							A_     :						Optional[int]     =					([tokens[0][1:]] if len(tokens[0]							) > 1 else []) + tokens[1:]
				return tokens


	def    _a     (self	,				lowercase							):
				if token.startswith("""<extra_id_"""							):
							A_     :						Union[str, Any]     =					re.match(R"""<extra_id_(\d+)>"""	,				lowercase							)
							A_     :						str     =					int(match.group(1							)							)
							return self.vocab_size - num - 1
				return self.sp_model.piece_to_id(lowercase							)


	def    _a     (self	,				lowercase							):
				if index < self.sp_model.get_piece_size():
							A_     :						List[Any]     =					self.sp_model.IdToPiece(lowercase							)
				else:
							A_     :						Dict     =					F'<extra_id_{self.vocab_size - 1 - index}>'
				return token


	def    _a     (self	,				lowercase							):
				A_     :						Union[str, Any]     =					[]
				A_     :						int     =					""""""
				A_     :						Any     =					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(lowercase							) + token
										A_     :						Dict     =					True
										A_     :						Union[str, Any]     =					[]
							else:
										current_sub_tokens.append(lowercase							)
										A_     :						Optional[Any]     =					False
				out_string += self.sp_model.decode(lowercase							)
				return out_string.strip()


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

				if os.path.abspath(self.vocab_file							) != os.path.abspath(lowercase							) and os.path.isfile(self.vocab_file							):
							copyfile(self.vocab_file	,				lowercase							)
				elif not os.path.isfile(self.vocab_file							):
							with open(lowercase	,				"""wb"""							) as fi:
										A_     :						Union[str, Any]     =					self.sp_model.serialized_model_proto()
										fi.write(lowercase							)

				return (out_vocab_file,)