





"""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:
						UpperCamelCase_	 =None

UpperCamelCase_	 =logging.get_logger(__name__)
UpperCamelCase_	 ={"""vocab_file""": """tokenizer.model""", """tokenizer_file""": """tokenizer.json"""}

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

# fmt: off
UpperCamelCase_	 ="""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 					_a		( _lowerCAmelCase							):
				UpperCamelCase								=       VOCAB_FILES_NAMES
				UpperCamelCase								=       LlamaTokenizer
				UpperCamelCase								=       '''left'''
				UpperCamelCase								=       ['''input_ids''', '''attention_mask''']






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




										'''simple docstring'''



										super().__init__(
										    vocab_file=lowerCAmelCase__,					tokenizer_file=lowerCAmelCase__,					clean_up_tokenization_spaces=lowerCAmelCase__,					unk_token=lowerCAmelCase__,					bos_token=lowerCAmelCase__,					eos_token=lowerCAmelCase__,					**lowerCAmelCase__,					)
										_UpperCamelCase     :   Dict      =       add_bos_token
										_UpperCamelCase     :   Any      =       add_eos_token
										self.update_post_processor()

										_UpperCamelCase     :   List[Any]      =       vocab_file
										_UpperCamelCase     :   List[Any]      =       False if not self.vocab_file else True






				def 							snake_case      (					self						:       Optional[Any]				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       self.bos_token
										_UpperCamelCase     :   List[Any]      =       self.bos_token_id

										_UpperCamelCase     :   str      =       self.eos_token
										_UpperCamelCase     :   Tuple      =       self.eos_token_id

										_UpperCamelCase     :   Optional[Any]      =       f"""{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"""
										_UpperCamelCase     :   int      =       f"""{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"""

										_UpperCamelCase     :   Union[str, Any]      =       []
										if self.add_bos_token:
																special_tokens.append((bos, bos_token_id)				)
										if self.add_eos_token:
																special_tokens.append((eos, eos_token_id)				)
										_UpperCamelCase     :   Any      =       processors.TemplateProcessing(
										    single=lowerCAmelCase__,					pair=lowerCAmelCase__,					special_tokens=lowerCAmelCase__				)






				@property
				def 							snake_case      (					self						:       int				)      -> Dict:




										'''simple docstring'''



										return self._add_eos_token






				@property
				def 							snake_case      (					self						:       Tuple				)      -> str:




										'''simple docstring'''



										return self._add_bos_token






				@add_eos_token.setter
				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       List[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       value
										self.update_post_processor()






				@add_bos_token.setter
				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       List[str]				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       value
										self.update_post_processor()






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       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(lowerCAmelCase__				):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
																return
										_UpperCamelCase     :   Optional[int]      =       os.path.join(
										    lowerCAmelCase__,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

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

										return (out_vocab_file,)






				def 							snake_case      (					self						:       Optional[Any],					lowerCAmelCase__						:       "Conversation"				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       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...)'''				)

										_UpperCamelCase     :   Optional[Any]      =       []
										if len(conversation.past_user_inputs				) > 0:
																if not conversation.past_user_inputs[0].startswith(lowerCAmelCase__				) or E_SYS not in conversation.past_user_inputs[0]:
																						_UpperCamelCase     :   Any      =       (
																						    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
																						)
										elif not dialogue[0][1].startswith(lowerCAmelCase__				) or E_SYS not in dialogue[0][1]:
																_UpperCamelCase     :   Tuple      =       (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=lowerCAmelCase__				)
										        + [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=lowerCAmelCase__				)
										return dialog_tokens






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_lowerCamelCase							:  Union[str, Any]     =				logging.get_logger(__name__)

_lowerCamelCase							:  Optional[int]     =				{
    "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}





class    __UpperCAmelCase	(			lowerCamelCase__		):
   UpperCamelCase  	=	"""m2m_100"""
   UpperCamelCase  	=	["""past_key_values"""]
   UpperCamelCase  	=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



   def __init__(				self				:	int,						__A				:	Optional[Any]=1_2_8_1_1_2,						__A				:	str=1_0_2_4,						__A				:	Tuple=1_2,						__A				:	Any=4_0_9_6,						__A				:	str=1_6,						__A				:	List[str]=1_2,						__A				:	int=4_0_9_6,						__A				:	List[Any]=1_6,						__A				:	Tuple=0.0_5,						__A				:	Tuple=0.0_5,						__A				:	List[Any]=True,						__A				:	int=True,						__A				:	str="relu",						__A				:	str=1_0_2_4,						__A				:	str=0.1,						__A				:	str=0.1,						__A				:	List[str]=0.0,						__A				:	List[str]=0.0_2,						__A				:	Any=2,						__A				:	List[str]=True,						__A				:	Tuple=1,						__A				:	Optional[Any]=0,						__A				:	Optional[Any]=2,						**__A				:	Optional[Any],						):
     UpperCAmelCase			:				str										=					vocab_size
     UpperCAmelCase			:				Optional[Any]										=					max_position_embeddings
     UpperCAmelCase			:				List[str]										=					d_model
     UpperCAmelCase			:				Any										=					encoder_ffn_dim
     UpperCAmelCase			:				Optional[int]										=					encoder_layers
     UpperCAmelCase			:				List[Any]										=					encoder_attention_heads
     UpperCAmelCase			:				str										=					decoder_ffn_dim
     UpperCAmelCase			:				Any										=					decoder_layers
     UpperCAmelCase			:				List[str]										=					decoder_attention_heads
     UpperCAmelCase			:				str										=					dropout
     UpperCAmelCase			:				int										=					attention_dropout
     UpperCAmelCase			:				Dict										=					activation_dropout
     UpperCAmelCase			:				Tuple										=					activation_function
     UpperCAmelCase			:				Any										=					init_std
     UpperCAmelCase			:				Union[str, Any]										=					encoder_layerdrop
     UpperCAmelCase			:				List[Any]										=					decoder_layerdrop
     UpperCAmelCase			:				Optional[Any]										=					use_cache
     UpperCAmelCase			:				str										=					encoder_layers
     UpperCAmelCase			:				List[Any]										=					scale_embedding  # scale factor will be sqrt(d_model) if True

     super().__init__(
         pad_token_id=__A,						bos_token_id=__A,						eos_token_id=__A,						is_encoder_decoder=__A,						decoder_start_token_id=__A,						**__A,						)






class    __UpperCAmelCase	(			lowerCamelCase__		):



   @property
   def 			__magic_name__       (				self				:	List[str]		):
     UpperCAmelCase			:				List[Any]										=					OrderedDict(
         [
             ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
             ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
         ]		)

     if self.use_past:
       UpperCAmelCase			:				List[str]										=					{0: '''batch'''}
       UpperCAmelCase			:				Any										=					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
     else:
       UpperCAmelCase			:				Any										=					{0: '''batch''', 1: '''decoder_sequence'''}
       UpperCAmelCase			:				List[Any]										=					{0: '''batch''', 1: '''decoder_sequence'''}

     if self.use_past:
       self.fill_with_past_key_values_(__A,						direction='''inputs'''		)
     return common_inputs



   def 			__magic_name__       (				self				:	Dict,						__A				:	PreTrainedTokenizer,						__A				:	int = -1,						__A				:	int = -1,						__A				:	bool = False,						__A				:	Optional[TensorType] = None,						):
     # Copied from OnnxConfig.generate_dummy_inputs
     # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     UpperCAmelCase			:				List[str]										=					compute_effective_axis_dimension(
         __A,						fixed_dimension=OnnxConfig.default_fixed_batch,						num_token_to_add=0		)

     # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
     UpperCAmelCase			:				Optional[int]										=					tokenizer.num_special_tokens_to_add(__A		)
     UpperCAmelCase			:				int										=					compute_effective_axis_dimension(
         __A,						fixed_dimension=OnnxConfig.default_fixed_sequence,						num_token_to_add=__A		)

     # Generate dummy inputs according to compute batch and sequence
     UpperCAmelCase			:				List[str]										=					[''' '''.join([tokenizer.unk_token]		) * seq_length] * batch_size
     UpperCAmelCase			:				Tuple										=					dict(tokenizer(__A,						return_tensors=__A		)		)
     return common_inputs



   def 			__magic_name__       (				self				:	Any,						__A				:	PreTrainedTokenizer,						__A				:	int = -1,						__A				:	int = -1,						__A				:	bool = False,						__A				:	Optional[TensorType] = None,						):
     UpperCAmelCase			:				int										=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         __A,						__A,						__A,						__A,						__A		)

     # Generate decoder inputs
     UpperCAmelCase			:				Union[str, Any]										=					seq_length if not self.use_past else 1
     UpperCAmelCase			:				Optional[int]										=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         __A,						__A,						__A,						__A,						__A		)
     UpperCAmelCase			:				Dict										=					{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
     UpperCAmelCase			:				Optional[int]										=					dict(**__A,						**__A		)

     if self.use_past:
       if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
       else:
         import torch
       UpperCAmelCase					,		UpperCAmelCase			:				List[str]										=					common_inputs['''input_ids'''].shape
       UpperCAmelCase			:				Dict										=					common_inputs['''decoder_input_ids'''].shape[1]
       UpperCAmelCase					,		UpperCAmelCase			:				Dict										=					self.num_attention_heads
       UpperCAmelCase			:				Union[str, Any]										=					(
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       UpperCAmelCase			:				str										=					decoder_seq_length + 3
       UpperCAmelCase			:				Union[str, Any]										=					(
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )

       UpperCAmelCase			:				Union[str, Any]										=					torch.cat(
           [common_inputs['''decoder_attention_mask'''], torch.ones(__A,						__A		)],						dim=1		)

       UpperCAmelCase			:				Any										=					[]
       # If the number of encoder and decoder layers are present in the model configuration, both are considered
       UpperCAmelCase					,		UpperCAmelCase			:				str										=					self.num_layers
       UpperCAmelCase			:				Any										=					min(__A,						__A		)
       UpperCAmelCase			:				Dict										=					max(__A,						__A		) - min_num_layers
       UpperCAmelCase			:				Tuple										=					'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

       for _ in range(__A		):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(__A		),
                 torch.zeros(__A		),
                 torch.zeros(__A		),
                 torch.zeros(__A		),
             )		)
       # TODO: test this.
       UpperCAmelCase			:				List[str]										=					encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
       for _ in range(__A,						__A		):
         common_inputs["past_key_values"].append((torch.zeros(__A		), torch.zeros(__A		))		)
     return common_inputs





   UpperCamelCase  	=	_generate_dummy_inputs_for_default_and_seqaseq_lm





