





"""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()
lowerCamelCase_	:		Optional[int]         =   logging.get_logger(__name__)

lowerCamelCase_	:		List[Any]         =   'Hello world! cécé herlolip'





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_		,			A_    :			Optional[Any]									=    os.path.split(_UpperCAmelCase   )
  A_    :			Optional[int]									=    DataaVecTextModel.from_pretrained(
      _UpperCAmelCase					,   checkpoint_file=_UpperCAmelCase   )
  dataavec.eval()  # disable dropout
  A_    :			int									=    dataavec.models[0]
  A_    :			int									=    dataavec_model.encoder.sentence_encoder
  A_    :			List[str]									=    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_    :			List[Any]									=    dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
  print('Our BERT config:'					,   _UpperCAmelCase   )

  A_    :			Union[str, Any]									=    DataaVecTextForSequenceClassification(_UpperCAmelCase   ) if classification_head else DataaVecTextForMaskedLM(_UpperCAmelCase   )
  model.eval()

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

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

    # self attention
    A_    :			BertSelfAttention									=    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_    :			Union[str, Any]									=    dataavec_layer.self_attn.q_proj.weight
    A_    :			int									=    dataavec_layer.self_attn.q_proj.bias
    A_    :			Any									=    dataavec_layer.self_attn.k_proj.weight
    A_    :			Optional[int]									=    dataavec_layer.self_attn.k_proj.bias
    A_    :			Any									=    dataavec_layer.self_attn.v_proj.weight
    A_    :			Optional[Any]									=    dataavec_layer.self_attn.v_proj.bias

    # self-attention output
    A_    :			BertSelfOutput									=    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_    :			Dict									=    dataavec_layer.self_attn.out_proj.weight
    A_    :			Any									=    dataavec_layer.self_attn.out_proj.bias
    A_    :			Any									=    dataavec_layer.self_attn_layer_norm.weight
    A_    :			Optional[Any]									=    dataavec_layer.self_attn_layer_norm.bias

    # intermediate
    A_    :			BertIntermediate									=    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_    :			Optional[Any]									=    dataavec_layer.fca.weight
    A_    :			Any									=    dataavec_layer.fca.bias

    # output
    A_    :			BertOutput									=    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_    :			Optional[Any]									=    dataavec_layer.fca.weight
    A_    :			Union[str, Any]									=    dataavec_layer.fca.bias
    A_    :			Optional[int]									=    dataavec_layer.final_layer_norm.weight
    A_    :			Tuple									=    dataavec_layer.final_layer_norm.bias
    # end of layer

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

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

  A_    :			Dict									=    model(_UpperCAmelCase   )[0]
  if classification_head:
    A_    :			List[Any]									=    dataavec.model.classification_heads['mnli'](dataavec.extract_features(_UpperCAmelCase   )   )
  else:
    A_    :			Optional[Any]									=    dataavec_model(_UpperCAmelCase   )[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_    :			List[Any]									=    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__":
   lowerCamelCase_	:		str         =   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.'
   )
   lowerCamelCase_	:		Any         =   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 TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation

require_version('''tokenizers>=0.13.3''')

if is_sentencepiece_available():
					from .tokenization_llama import LlamaTokenizer
else:
					__lowerCAmelCase							:  List[str] 				=		None

__lowerCAmelCase							:  Optional[int] 				=		logging.get_logger(__name__)
__lowerCAmelCase							:  int 				=		{'''vocab_file''': '''tokenizer.model''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCAmelCase				,__lowerCAmelCase							:  Any 				=		'''[INST]''', '''[/INST]'''
__lowerCAmelCase				,__lowerCAmelCase							:  Tuple 				=		'''<<SYS>>\n''', '''\n<</SYS>>\n\n'''

# fmt: off
__lowerCAmelCase							:  List[str] 				=		'''You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don\'t know the answer to a question, please don\'t share false information.'''
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	snake_case__							:		List[str]         =		VOCAB_FILES_NAMES
	snake_case__							:		Optional[Any]         =		LlamaTokenizer
	snake_case__							:		Union[str, Any]         =		'left'
	snake_case__							:		Any         =		['input_ids', 'attention_mask']


	def __init__(  self :Optional[Any]       ,    __magic_name__ :Optional[Any]=None       ,    __magic_name__ :Tuple=None       ,    __magic_name__ :List[Any]=False       ,    __magic_name__ :List[str]="<unk>"       ,    __magic_name__ :List[str]="<s>"       ,    __magic_name__ :List[str]="</s>"       ,    __magic_name__ :Any=True       ,    __magic_name__ :Union[str, Any]=False       ,    **__magic_name__ :Optional[int]       ,    )			->   List[Any]:

			'''simple docstring'''






			super().__init__(
			    vocab_file=__magic_name__       ,    tokenizer_file=__magic_name__       ,    clean_up_tokenization_spaces=__magic_name__       ,    unk_token=__magic_name__       ,    bos_token=__magic_name__       ,    eos_token=__magic_name__       ,    **__magic_name__       ,    )
			a__   		=     add_bos_token
			a__   		=     add_eos_token
			self.update_post_processor()

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


	def    _UpperCamelCase							(  self :List[str]			)			->   Optional[int]:

			'''simple docstring'''






			a__   		=     self.bos_token
			a__   		=     self.bos_token_id

			a__   		=     self.eos_token
			a__   		=     self.eos_token_id

			a__   		=     F"{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"
			a__   		=     F"{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"

			a__   		=     []
			if self.add_bos_token:
					special_tokens.append((bos, bos_token_id)			)
			if self.add_eos_token:
					special_tokens.append((eos, eos_token_id)			)
			a__   		=     processors.TemplateProcessing(
			    single=__magic_name__       ,    pair=__magic_name__       ,    special_tokens=__magic_name__			)


	@property
	def    _UpperCamelCase							(  self :Optional[int]			)			->   Any:

			'''simple docstring'''






			return self._add_eos_token


	@property
	def    _UpperCamelCase							(  self :Optional[int]			)			->   Optional[Any]:

			'''simple docstring'''






			return self._add_bos_token


	@add_eos_token.setter
	def    _UpperCamelCase							(  self :int       ,    __magic_name__ :int			)			->   int:

			'''simple docstring'''






			a__   		=     value
			self.update_post_processor()


	@add_bos_token.setter
	def    _UpperCamelCase							(  self :Any       ,    __magic_name__ :str			)			->   Optional[int]:

			'''simple docstring'''






			a__   		=     value
			self.update_post_processor()


	def    _UpperCamelCase							(  self :Dict       ,    __magic_name__ :str       ,    __magic_name__ :Optional[str] = None			)			->   Tuple[str]:

			'''simple docstring'''






			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(__magic_name__			):
					logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
					return
			a__   		=     os.path.join(
			    __magic_name__       ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

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

			return (out_vocab_file,)






	def    _UpperCamelCase							(  self :int       ,    __magic_name__ :"Conversation"			)			->   List[Any]:

			'''simple docstring'''






			a__   		=     list(conversation.iter_texts()			)
			if not all([is_user for is_user, msg in dialogue[::2]]			) or not all(
			    [not is_user for is_user, msg in dialogue[1::2]]			):
					raise ValueError(
					    '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''			)

			a__   		=     []
			if len(conversation.past_user_inputs			) > 0:
					if not conversation.past_user_inputs[0].startswith(__magic_name__			) or E_SYS not in conversation.past_user_inputs[0]:
							a__   		=     (
							    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
							)
			elif not dialogue[0][1].startswith(__magic_name__			) or E_SYS not in dialogue[0][1]:
					a__   		=     (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

			dialog_tokens += sum(
			    [
			        [self.bos_token_id]
			        + self.encode(
			            F"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} "       ,    add_special_tokens=__magic_name__			)
			        + [self.eos_token_id]
			        for prompt, answer in zip(dialogue[::2]       ,    dialogue[1::2]			)
			    ]       ,    []       ,    )
			if not (dialogue[-1][0]):
					raise ValueError(F"Last message must be from user, got {dialogue[-1]['role']}"			)
			dialog_tokens += [self.bos_token_id] + self.encode(
			    F"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"       ,    add_special_tokens=__magic_name__			)
			return dialog_tokens




