



"""simple docstring"""







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

UpperCAmelCase:  str			     =				logging.get_logger(__name__)

UpperCAmelCase:  Tuple			     =				{"""vocab_file""": """tokenizer.model"""}

UpperCAmelCase:  int			     =				{
    """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""",
    },
}
UpperCAmelCase:  Union[str, Any]			     =				{
    """hf-internal-testing/llama-tokenizer""": 2_048,
}
UpperCAmelCase:  List[Any]			     =				"""▁"""

UpperCAmelCase     , UpperCAmelCase:  int			     =				"""[INST]""", """[/INST]"""
UpperCAmelCase     , UpperCAmelCase:  Union[str, Any]			     =				"""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
UpperCAmelCase:  Optional[int]			     =				"""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   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_							:       int						       =  PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_							:       List[Any]						       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_							:       Dict						       =  ["input_ids", "attention_mask"]






	def __init__(     self				,UpperCAmelCase_				,UpperCAmelCase_="<unk>"				,UpperCAmelCase_="<s>"				,UpperCAmelCase_="</s>"				,UpperCAmelCase_=None				,UpperCAmelCase_ = None				,UpperCAmelCase_=True				,UpperCAmelCase_=False				,UpperCAmelCase_=False				,UpperCAmelCase_=True				,**UpperCAmelCase_				,):
			_lowercase				: List[str]     =   {} if sp_model_kwargs is None else sp_model_kwargs
			_lowercase				: List[Any]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else bos_token
			_lowercase				: Union[str, Any]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else eos_token
			_lowercase				: Optional[int]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else unk_token
			_lowercase				: Optional[Any]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else pad_token
			super().__init__(
			    bos_token=UpperCAmelCase_				,eos_token=UpperCAmelCase_				,unk_token=UpperCAmelCase_				,pad_token=UpperCAmelCase_				,add_bos_token=UpperCAmelCase_				,add_eos_token=UpperCAmelCase_				,sp_model_kwargs=self.sp_model_kwargs				,clean_up_tokenization_spaces=UpperCAmelCase_				,legacy=UpperCAmelCase_				,**UpperCAmelCase_				,)
			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"""   )
			_lowercase				: Tuple     =   legacy
			_lowercase				: Optional[int]     =   vocab_file
			_lowercase				: Any     =   add_bos_token
			_lowercase				: Union[str, Any]     =   add_eos_token
			_lowercase				: Union[str, Any]     =   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
			self.sp_model.Load(UpperCAmelCase_   )






	def __getstate__(     self   ):
			_lowercase				: Union[str, Any]     =   self.__dict__.copy()
			_lowercase				: Optional[int]     =   None
			_lowercase				: Optional[Any]     =   self.sp_model.serialized_model_proto()
			return state






	def __setstate__(     self				,UpperCAmelCase_   ):
			_lowercase				: List[Any]     =   d
			_lowercase				: List[Any]     =   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
			self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )






	@property
	def    lowerCamelCase__					(     self   ):
			return self.sp_model.get_piece_size()






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






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,**UpperCAmelCase_   ):
			# 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:
					_lowercase				: str     =   SPIECE_UNDERLINE + text.replace(UpperCAmelCase_				,""" """   )
			return super().tokenize(UpperCAmelCase_				,**UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			if not self.legacy:
					_lowercase				: Any     =   text.startswith(UpperCAmelCase_   )
					if is_first:
							_lowercase				: str     =   text[1:]

			_lowercase				: Dict     =   self.sp_model.encode(UpperCAmelCase_				,out_type=UpperCAmelCase_   )

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






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			return self.sp_model.piece_to_id(UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: List[str]     =   self.sp_model.IdToPiece(UpperCAmelCase_   )
			return token






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: List[Any]     =   []
			_lowercase				: Dict     =   """"""
			_lowercase				: List[Any]     =   False
			for i, token in enumerate(UpperCAmelCase_   ):
					# 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(UpperCAmelCase_   ) + token
							_lowercase				: Optional[Any]     =   True
							_lowercase				: str     =   []
					else:
							current_sub_tokens.append(UpperCAmelCase_   )
							_lowercase				: Optional[Any]     =   False
			out_string += self.sp_model.decode(UpperCAmelCase_   )
			return out_string






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None   ):
			if not os.path.isdir(UpperCAmelCase_   ):
					logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
					return
			_lowercase				: str     =   os.path.join(
			    UpperCAmelCase_				,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

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

			return (out_vocab_file,)






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=None   ):
			_lowercase				: Any     =   [self.bos_token_id] if self.add_bos_token else []
			_lowercase				: List[str]     =   [self.eos_token_id] if self.add_eos_token else []

			_lowercase				: Union[str, Any]     =   bos_token_id + token_ids_a + eos_token_id

			if token_ids_a is not None:
					_lowercase				: int     =   output + bos_token_id + token_ids_a + eos_token_id

			return output






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

			_lowercase				: Tuple     =   [1] if self.add_bos_token else []
			_lowercase				: Dict     =   [1] if self.add_eos_token else []

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






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None   ):
			_lowercase				: Union[str, Any]     =   [self.bos_token_id] if self.add_bos_token else []
			_lowercase				: str     =   [self.eos_token_id] if self.add_eos_token else []

			_lowercase				: Any     =   [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    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: Tuple     =   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...)"""   )

			_lowercase				: List[int]     =   []
			if len(conversation.past_user_inputs   ) > 0:
					if not conversation.past_user_inputs[0].startswith(UpperCAmelCase_   ) or E_SYS not in conversation.past_user_inputs[0]:
							_lowercase				: Optional[int]     =   (
							    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
							)
			elif not dialogue[0][1].startswith(UpperCAmelCase_   ) or E_SYS not in dialogue[0][1]:
					_lowercase				: List[str]     =   (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=UpperCAmelCase_   )
			        + [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=UpperCAmelCase_   )
			return dialog_tokens










"""simple docstring"""




import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       CpmAntConfig,
       CpmAntForCausalLM,
       CpmAntModel,
       CpmAntTokenizer,
   )

@require_torch
class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_=2   , snake_case_=8   , snake_case_=True   , snake_case_=False   , snake_case_=False   , snake_case_=False   , snake_case_=False   , snake_case_=9_9   , snake_case_=3_2   , snake_case_=3   , snake_case_=4   , snake_case_=3_7   , snake_case_=3_2   , snake_case_=1_2_8   , snake_case_=8   , snake_case_=8   , snake_case_=8   , snake_case_=1.0   , snake_case_=True   , ):

        """simple docstring"""

        A_    :			List[Any]									=    parent
        A_    :			Union[str, Any]									=    batch_size
        A_    :			Dict									=    seq_length
        A_    :			Tuple									=    is_training
        A_    :			Union[str, Any]									=    use_token_type_ids
        A_    :			Optional[Any]									=    use_input_mask
        A_    :			str									=    use_labels
        A_    :			Optional[int]									=    use_mc_token_ids
        A_    :			str									=    vocab_size
        A_    :			Any									=    hidden_size
        A_    :			List[Any]									=    num_hidden_layers
        A_    :			str									=    num_attention_heads
        A_    :			Optional[Any]									=    intermediate_size
        A_    :			List[str]									=    num_buckets
        A_    :			Tuple									=    max_distance
        A_    :			Optional[int]									=    prompt_length
        A_    :			List[str]									=    prompt_types
        A_    :			Dict									=    segment_types
        A_    :			List[str]									=    init_std
        A_    :			Union[str, Any]									=    return_dict

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Dict									=    {}
        A_    :			Any									=    ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size    ).type(torch.intaa    )
        A_    :			Union[str, Any]									=    False

        A_    :			Optional[int]									=    self.get_config()

        return (config, input_ids)

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return CpmAntConfig(
            vocab_size=self.vocab_size   , hidden_size=self.hidden_size   , num_hidden_layers=self.num_hidden_layers   , num_attention_heads=self.num_attention_heads   , dim_ff=self.intermediate_size   , position_bias_num_buckets=self.num_buckets   , position_bias_max_distance=self.max_distance   , prompt_types=self.prompt_types   , prompt_length=self.prompt_length   , segment_types=self.segment_types   , use_cache=snake_case_   , init_std=self.init_std   , return_dict=self.return_dict   , )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , *snake_case_    ):

        """simple docstring"""

        A_    :			Tuple									=    CpmAntModel(config=snake_case_    )
        model.to(snake_case_    )
        model.eval()

        A_    :			Union[str, Any]									=    model(**snake_case_    ).last_hidden_state

        self.parent.assertEqual(hidden_states.shape   , (self.batch_size, self.seq_length, config.hidden_size)    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , *snake_case_    ):

        """simple docstring"""

        A_    :			Dict									=    CpmAntForCausalLM(snake_case_    )
        model.to(snake_case_    )
        A_    :			str									=    input_ids['input_ids'].to(snake_case_    )
        model.eval()

        A_    :			int									=    model(**snake_case_    )
        self.parent.assertEqual(
            model_output.logits.shape   , (self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)   , )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_		,			A_    :			Optional[Any]									=    self.prepare_config_and_inputs()
        return config, inputs_dict


@require_torch
class 					_UpperCAmelCase    (  UpperCAmelCase__     ,  UpperCAmelCase__     ,  unittest.TestCase						):



      '''simple docstring'''
      lowercase_ :					Any             =  (CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
      lowercase_ :					int             =  (
          {"""feature-extraction""": CpmAntModel, """text-generation""": CpmAntForCausalLM} if is_torch_available() else {}
      )

      lowercase_ :					Any             =  False
      lowercase_ :					List[Any]             =  False
      lowercase_ :					Dict             =  False
      lowercase_ :					List[Any]             =  False
      lowercase_ :					Union[str, Any]             =  False

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Dict									=    CpmAntModelTester(self    )
        A_    :			Optional[int]									=    ConfigTester(self   , config_class=snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        self.config_tester.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        unittest.skip('CPMAnt doesn\'t support input_embeds.'    )(self.test_inputs_embeds    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        unittest.skip(
            'CPMAnt doesn\'t support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model.'    )(self.test_retain_grad_hidden_states_attentions    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_		,			A_    :			Optional[Any]									=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_cpmant_model(snake_case_   , snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_		,			A_    :			List[Any]									=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(snake_case_   , snake_case_    )


@require_torch
class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      @tooslow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    '今天天气真好！'
        A_    :			Tuple									=    'openbmb/cpm-ant-10b'
        A_    :			Tuple									=    CpmAntModel.from_pretrained(snake_case_    )
        A_    :			Dict									=    CpmAntTokenizer.from_pretrained(snake_case_    )
        A_    :			str									=    tokenizer(snake_case_   , return_tensors='pt'    )
        A_    :			Union[str, Any]									=    model(**snake_case_    ).last_hidden_state

        A_    :			Optional[int]									=    torch.tensor(
            [[[6.17_08, 5.92_44, 1.08_35], [6.52_07, 6.28_93, -11.33_24], [-1.01_07, -0.05_76, -5.95_77]]]   , )
        self.assertTrue(torch.allclose(hidden_states[:, :3, :3]   , snake_case_   , atol=1E-2    )    )


@require_torch
class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      @tooslow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    '今天天气真好！'
        A_    :			Union[str, Any]									=    'openbmb/cpm-ant-10b'
        A_    :			List[str]									=    CpmAntForCausalLM.from_pretrained(snake_case_    )
        A_    :			Union[str, Any]									=    CpmAntTokenizer.from_pretrained(snake_case_    )
        A_    :			int									=    tokenizer(snake_case_   , return_tensors='pt'    )
        A_    :			Tuple									=    model(**snake_case_    ).logits

        A_    :			Union[str, Any]									=    torch.tensor(
            [[[-6.42_67, -6.40_83, -6.39_58], [-5.88_02, -5.94_47, -5.78_11], [-5.38_96, -5.48_20, -5.42_95]]]   , )
        self.assertTrue(torch.allclose(hidden_states[:, :3, :3]   , snake_case_   , atol=1E-2    )    )

      @tooslow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    'openbmb/cpm-ant-10b'
        A_    :			Optional[int]									=    CpmAntForCausalLM.from_pretrained(snake_case_    )
        A_    :			Optional[Any]									=    CpmAntTokenizer.from_pretrained(snake_case_    )
        A_    :			List[str]									=    '今天天气不错，'
        A_    :			Dict									=    '今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的'
        A_    :			str									=    tokenizer(snake_case_   , return_tensors='pt'    )
        A_    :			Dict									=    model.generate(**snake_case_    )
        A_    :			Union[str, Any]									=    tokenizer.batch_decode(snake_case_    )
        self.assertEqual(snake_case_   , snake_case_    )

      @tooslow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    'openbmb/cpm-ant-10b'
        A_    :			List[Any]									=    CpmAntForCausalLM.from_pretrained(snake_case_    )
        A_    :			Optional[Any]									=    CpmAntTokenizer.from_pretrained(snake_case_    )
        A_    :			int									=    ['今天天气不错，', '新年快乐，万事如意！']
        A_    :			List[Any]									=    [
            '今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的',
            '新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的',
        ]
        A_    :			Tuple									=    tokenizer(snake_case_   , return_tensors='pt'   , padding=snake_case_    )
        A_    :			int									=    model.generate(**snake_case_    )
        A_    :			Union[str, Any]									=    tokenizer.batch_decode(snake_case_    )
        self.assertEqual(snake_case_   , snake_case_    )