



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

import sentencepiece as spm

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


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

A__     				=	logging.get_logger(__name__)

A__     				=	{"""vocab_file""": """tokenizer.model"""}

A__     				=	{
    """vocab_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model""",
    },
    """tokenizer_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json""",
    },
}
A__     				=	{
    """hf-internal-testing/llama-tokenizer""": 20_48,
}
A__     				=	"""▁"""

A__				,      A__     				=	"""[INST]""", """[/INST]"""
A__				,      A__     				=	"""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
A__     				=	"""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  __lowerCAmelCase			(					lowerCamelCase__	):
				__lowerCamelCase			=    VOCAB_FILES_NAMES
				__lowerCamelCase			=    PRETRAINED_VOCAB_FILES_MAP
				__lowerCamelCase			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCamelCase			=    ['''input_ids''', '''attention_mask''']




				def __init__(							self						,    _snake_case						,    _snake_case="<unk>"						,    _snake_case="<s>"						,    _snake_case="</s>"						,    _snake_case=None						,    _snake_case = None						,    _snake_case=True						,    _snake_case=False						,    _snake_case=False						,    _snake_case=True						,    **_snake_case						,    ):



									"""simple docstring"""





									_lowerCAmelCase      =   {} if sp_model_kwargs is None else sp_model_kwargs
									_lowerCAmelCase      =   AddedToken(_snake_case						,    lstrip=_snake_case						,    rstrip=_snake_case		) if isinstance(_snake_case						,    _snake_case		) else bos_token
									_lowerCAmelCase      =   AddedToken(_snake_case						,    lstrip=_snake_case						,    rstrip=_snake_case		) if isinstance(_snake_case						,    _snake_case		) else eos_token
									_lowerCAmelCase      =   AddedToken(_snake_case						,    lstrip=_snake_case						,    rstrip=_snake_case		) if isinstance(_snake_case						,    _snake_case		) else unk_token
									_lowerCAmelCase      =   AddedToken(_snake_case						,    lstrip=_snake_case						,    rstrip=_snake_case		) if isinstance(_snake_case						,    _snake_case		) else pad_token
									super().__init__(
									    bos_token=_snake_case						,    eos_token=_snake_case						,    unk_token=_snake_case						,    pad_token=_snake_case						,    add_bos_token=_snake_case						,    add_eos_token=_snake_case						,    sp_model_kwargs=self.sp_model_kwargs						,    clean_up_tokenization_spaces=_snake_case						,    legacy=_snake_case						,    **_snake_case						,    )
									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"""		)
									_lowerCAmelCase      =   legacy
									_lowerCAmelCase      =   vocab_file
									_lowerCAmelCase      =   add_bos_token
									_lowerCAmelCase      =   add_eos_token
									_lowerCAmelCase      =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(_snake_case		)




				def __getstate__(							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   self.__dict__.copy()
									_lowerCAmelCase      =   None
									_lowerCAmelCase      =   self.sp_model.serialized_model_proto()
									return state




				def __setstate__(							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   d
									_lowerCAmelCase      =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)




				@property
				def 							snake_case       (							self		):



									"""simple docstring"""





									return self.sp_model.get_piece_size()




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   {self.convert_ids_to_tokens(_snake_case		): i for i in range(self.vocab_size		)}
									vocab.update(self.added_tokens_encoder		)
									return vocab




				def 							snake_case       (							self						,    _snake_case						,    **_snake_case		):



									"""simple docstring"""





									if not self.legacy:
														_lowerCAmelCase      =   SPIECE_UNDERLINE + text.replace(_snake_case						,    """ """		)
									return super().tokenize(_snake_case						,    **_snake_case		)




				def 							snake_case       (							self						,    _snake_case		):



									"""simple docstring"""





									if not self.legacy:
														_lowerCAmelCase      =   text.startswith(_snake_case		)
														if is_first:
																			_lowerCAmelCase      =   text[1:]

									_lowerCAmelCase      =   self.sp_model.encode(_snake_case						,    out_type=_snake_case		)

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




				def 							snake_case       (							self						,    _snake_case		):



									"""simple docstring"""





									return self.sp_model.piece_to_id(_snake_case		)




				def 							snake_case       (							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   self.sp_model.IdToPiece(_snake_case		)
									return token




				def 							snake_case       (							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   []
									_lowerCAmelCase      =   """"""
									_lowerCAmelCase      =   False
									for i, token in enumerate(_snake_case		):
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																			if not prev_is_special and i != 0:
																								out_string += " "
																			out_string += self.sp_model.decode(_snake_case		) + token
																			_lowerCAmelCase      =   True
																			_lowerCAmelCase      =   []
														else:
																			current_sub_tokens.append(_snake_case		)
																			_lowerCAmelCase      =   False
									out_string += self.sp_model.decode(_snake_case		)
									return out_string




				def 							snake_case       (							self						,    _snake_case						,    _snake_case = None		):



									"""simple docstring"""





									if not os.path.isdir(_snake_case		):
														logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
														return
									_lowerCAmelCase      =   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		) and os.path.isfile(self.vocab_file		):
														copyfile(self.vocab_file						,    _snake_case		)
									elif not os.path.isfile(self.vocab_file		):
														with open(_snake_case						,    """wb"""		) as fi:
																			_lowerCAmelCase      =   self.sp_model.serialized_model_proto()
																			fi.write(_snake_case		)

									return (out_vocab_file,)




				def 							snake_case       (							self						,    _snake_case						,    _snake_case=None		):



									"""simple docstring"""





									_lowerCAmelCase      =   [self.bos_token_id] if self.add_bos_token else []
									_lowerCAmelCase      =   [self.eos_token_id] if self.add_eos_token else []

									_lowerCAmelCase      =   bos_token_id + token_ids_a + eos_token_id

									if token_ids_a is not None:
														_lowerCAmelCase      =   output + bos_token_id + token_ids_a + eos_token_id

									return output




				def 							snake_case       (							self						,    _snake_case						,    _snake_case = None						,    _snake_case = False		):



									"""simple docstring"""





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

									_lowerCAmelCase      =   [1] if self.add_bos_token else []
									_lowerCAmelCase      =   [1] if self.add_eos_token else []

									if token_ids_a is None:
														return bos_token_id + ([0] * len(_snake_case		)) + eos_token_id
									return (
									    bos_token_id
									    + ([0] * len(_snake_case		))
									    + eos_token_id
									    + bos_token_id
									    + ([0] * len(_snake_case		))
									    + eos_token_id
									)




				def 							snake_case       (							self						,    _snake_case						,    _snake_case = None		):



									"""simple docstring"""





									_lowerCAmelCase      =   [self.bos_token_id] if self.add_bos_token else []
									_lowerCAmelCase      =   [self.eos_token_id] if self.add_eos_token else []

									_lowerCAmelCase      =   [0] * len(bos_token_id + token_ids_a + eos_token_id		)

									if token_ids_a is not None:
														output += [1] * len(bos_token_id + token_ids_a + eos_token_id		)

									return output




				def 							snake_case       (							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   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...)"""		)

									_lowerCAmelCase      =   []
									if len(conversation.past_user_inputs		) > 0:
														if not conversation.past_user_inputs[0].startswith(_snake_case		) or E_SYS not in conversation.past_user_inputs[0]:
																			_lowerCAmelCase      =   (
																			    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
																			)
									elif not dialogue[0][1].startswith(_snake_case		) or E_SYS not in dialogue[0][1]:
														_lowerCAmelCase      =   (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=_snake_case		)
									        + [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=_snake_case		)
									return dialog_tokens








import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


A__     				=	torch.cuda.is_available()
A__     				=	[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
A__     				=	["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
A__     				=	{
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1e-0_8,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}


def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   """\n""".join(snake_case      )
					Path(snake_case      ).open("""w"""      ).writelines(snake_case      )


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(snake_case     ,						F'{split}.source'      )     ,						snake_case      )
										_dump_articles(os.path.join(snake_case     ,						F'{split}.target'      )     ,						snake_case      )
					return tmp_dir





class  __lowerCAmelCase			(					lowerCamelCase__	):




				@classmethod
				def 							snake_case       (							cls		):



									"""simple docstring"""





									return cls




				@require_torch_multi_gpu
				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   {
									    """no_teacher""": True,
									    """freeze_encoder""": True,
									    """gpus""": 2,
									    """overwrite_output_dir""": True,
									    """sortish_sampler""": True,
									}
									self._test_distiller_cli_fork(_snake_case						,    check_contents=_snake_case		)




				def 							snake_case       (							self						,    _snake_case						,    _snake_case=True		):



									"""simple docstring"""





									_lowerCAmelCase      =   {
									    """label_smoothing""": 0.0,
									    """early_stopping_patience""": -1,
									    """train_batch_size""": 1,
									    """eval_batch_size""": 2,
									    """max_epochs""": 2,
									    """alpha_mlm""": 0.2,
									    """alpha_ce""": 0.8,
									    """do_predict""": True,
									    """model_name_or_path""": """sshleifer/tinier_bart""",
									    """teacher""": CHEAP_ARGS["""model_name_or_path"""],
									    """val_check_interval""": 0.5,
									}
									default_updates.update(_snake_case		)
									_lowerCAmelCase      =   CHEAP_ARGS.copy()
									_lowerCAmelCase      =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
									_lowerCAmelCase      =   self.get_auto_remove_tmp_dir()
									args_d.update(data_dir=_snake_case						,    output_dir=_snake_case						,    **_snake_case		)

									def convert(_snake_case						,    _snake_case		):
														if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
																			return ""
														if v is False or v is None:
																			return ""
														if v is True:  # or len(str(v))==0:
																			return F'--{k}'
														return F'--{k}={v}'

									_lowerCAmelCase      =   [x for x in (convert(_snake_case						,    _snake_case		) for k, v in args_d.items()) if len(_snake_case		)]
									_lowerCAmelCase      =   [sys.executable, F'{self.test_file_dir}/distillation.py'] + cli_args
									execute_subprocess_async(_snake_case						,    env=self.get_env()		)

									_lowerCAmelCase      =   os.listdir(_snake_case		)
									_lowerCAmelCase      =   {os.path.basename(_snake_case		) for p in contents}
									_lowerCAmelCase      =   [p for p in contents if p.endswith("""ckpt"""		)]
									assert len(_snake_case		) > 0

									self.assertIn("""test_generations.txt"""						,    _snake_case		)
									self.assertIn("""test_results.txt"""						,    _snake_case		)

									# get the following from the module, (we don't have access to `model` here)
									_lowerCAmelCase      =   os.path.join(_snake_case						,    """metrics.json"""		)
									_lowerCAmelCase      =   """rouge2"""

									_lowerCAmelCase      =   load_json(_snake_case		)
									# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
									print(_snake_case		)
									_lowerCAmelCase      =   metrics["""val"""][-1]
									self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]						,    0.01		)
									self.assertIsInstance(last_step_stats[F'val_avg_{val_metric}']						,    _snake_case		)
									self.assertEqual(len(metrics["""test"""]		)						,    1		)
									_lowerCAmelCase      =   int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1		)
									self.assertEqual(len(metrics["""val"""]		)						,    _snake_case		)



